Aboutorab, H, Hussain, OK, Saberi, M & Hussain, FK 2022, 'A reinforcement learning-based framework for disruption risk identification in supply chains', Future Generation Computer Systems, vol. 126, pp. 110-122.
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Risk management is one of the critical activities which needs to be done well to ensure supply chain activities operate smoothly. The first step in risk management is risk identification, in which the risk manager identifies the risk events of interest for further analysis. The timely identification of risk events in the risk identification step is crucial for the risk manager to be proactive in managing the supply chain risks in its operations. Undertaking this step manually, however, is tedious and time-consuming. With the increased sophistication and capability of advanced computing algorithms, various eminent supply chain researchers have called for the use of artificial intelligence techniques to increase efficiency and efficacy when performing their tasks. In this paper, we demonstrate how reinforcement learning, which is one of the recent artificial intelligence techniques, can assist risk managers to proactively identify the risks to their operations. We explain the working of our proposed Reinforcement Learning-based approach for Proactive Risk Identification (RL-PRI) and its various steps. We then show the performance accuracy of RL-PRI in identifying the risk events of interest by comparing its output with the risk events which are manually identified by professional risk managers.
Adhikari, S, Thapa, S, Naseem, U, Singh, P, Huo, H, Bharathy, G & Prasad, M 2022, 'Exploiting linguistic information from Nepali transcripts for early detection of Alzheimer's disease using natural language processing and machine learning techniques', International Journal of Human-Computer Studies, vol. 160, pp. 102761-102761.
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Adhikari, S, Thapa, S, Naseem, U, Singh, P, Huo, H, Bharathy, G & Prasad, M 2022, 'Exploiting linguistic information from Nepali transcripts for early detection of Alzheimer's disease using natural language processing and machine learning techniques.', Int. J. Hum. Comput. Stud., vol. 160, pp. 102761-102761.
Alderighi, T, Malomo, L, Auzinger, T, Bickel, B, Cignoni, P & Pietroni, N 2022, 'State of the Art in Computational Mould Design.', Comput. Graph. Forum, vol. 41, no. 6, pp. 435-452.
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AbstractMoulding refers to a set of manufacturing techniques in which a mould, usually a cavity or a solid frame, is used to shape a liquid or pliable material into an object of the desired shape. The popularity of moulding comes from its effectiveness, scalability and versatility in terms of employed materials. Its relevance as a fabrication process is demonstrated by the extensive literature covering different aspects related to mould design, from material flow simulation to the automation of mould geometry design. In this state‐of‐the‐art report, we provide an extensive review of the automatic methods for the design of moulds, focusing on contributions from a geometric perspective. We classify existing mould design methods based on their computational approach and the nature of their target moulding process. We summarize the relationships between computational approaches and moulding techniques, highlighting their strengths and limitations. Finally, we discuss potential future research directions.
Almansor, EH, Hussain, FK & Hussain, OK 2022, 'Measuring chatbot quality of service to predict human-machine hand-over using a character deep learning model', International Journal of Web and Grid Services, vol. 18, no. 4, pp. 479-479.
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Alsolbi, I, Wu, M, Zhang, Y, Joshi, S, Sharma, M, Tafavogh, S, Sinha, A & Prasad, M 2022, 'Different approaches of bibliometric analysis for data analytics applications in non-profit organisations', Journal of Smart Environments and Green Computing, vol. 2, no. 3, pp. 90-104.
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Aim: Profitable companies that used data analytics have a double gain in cost reduction, demand prediction, and decision-making. However, using data analysis in non-profit organisations (NPOs) can help understand and identify more patterns of donors, volunteers, and anticipated future cash, gifts, and grants. This article presents a bibliometric study of 2673 to discover the use of data analytics in different NPOs and understand its contribution. Methods: We characterise the associations between data analysis techniques and NPOs using, Bibliometrics R tool, a co-term analysis and scientific evolutionary pathways analysis, as well as identify the research topic changes in this field throughout time. Results: The findings revealed three key conclusions may be drawn from the findings: (1) In the sphere of NPOs, robust and conventional statistical methods-based data analysis procedures are dominantly common at all times; (2) Healthcare and public affairs are two crucial sectors that involve data analytics to support decision-making and problem-solving; (3) Artificial Intelligence (AI) based data analytics is a recently emerging trending, especially in the healthcare-related sector; however, it is still at an immature stage, and more efforts are needed to nourish its development. Conclusion: The research findings can leverage future research and add value to the existing literature on the subject of data analytics.
Alsufyani, N & Gill, AQ 2022, 'Digitalisation performance assessment: A systematic review', Technology in Society, vol. 68, pp. 101894-101894.
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Organisations are showing a keen interest in digitalisation. However, they are uncertain about how to determine the impact of digitalisation on organisation performance outcomes. This places decision-makers in a challenging position to assess the feasibility and intended performance outcomes of digitalisation. This paper aims to address this important research need and provides the performance indicators, measures, metrics and scales based on a systematic review of 30 selected papers. The results from this review were synthesised using the “adaptive enterprise architecture”, and “results and determinants” frameworks as theoretical lenses. This work will benefit researchers and practitioners interested in studying the impact of digitalisation on organisational performance.
Altulyan, M, Yao, L, Wang, X, Huang, C, Kanhere, SS & Sheng, QZ 2022, 'A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions', The Computer Journal, vol. 65, no. 8, pp. 2098-2132.
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Abstract Recommendation is a critical tool for developing and promoting the benefits of the Internet of Things (IoT). In recent years, recommender systems have attracted considerable attention in many IoT-related fields such as smart health, smart home, smart tourism and smart marketing. However, traditional recommender system approaches fail to exploit ever-growing, dynamic and heterogeneous IoT data in building recommender systems for the IoT (RSIoT). This article aims to provide a comprehensive review of state-of-the-art RSIoT, including the related techniques, applications and a discussion on the limitations of applying recommendation systems to IoT. Finally, we propose a reference framework for comparing existing studies to guide future research and practices.
Alzoubi, YI & Gill, AQ 2022, 'Can Agile Enterprise Architecture be Implemented Successfully in Distributed Agile Development? Empirical Findings', Global Journal of Flexible Systems Management, vol. 23, no. 2, pp. 221-235.
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A potential solution to the high failure rate in distributed agile development and enhance the success of projects is through implementing agile enterprise architecture, though the success is still to be established. The present paper empirically investigates the gap, by defining the role and commitment of implementing agile enterprise architecture on distributed agile development. The data were collected by interviewing 12 key team members and observing four team meetings over 2 months and analyzing using thematic analysis. The present study suggests that implementing agile enterprise architecture is possible in distributed agile development and may have a positive impact on project success. However, many questions demand further investigation.
Alzoubi, YI, Gill, A & Mishra, A 2022, 'A systematic review of the purposes of Blockchain and fog computing integration: classification and open issues', Journal of Cloud Computing, vol. 11, no. 1, p. 80.
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AbstractThe fog computing concept was proposed to help cloud computing for the data processing of Internet of Things (IoT) applications. However, fog computing faces several challenges such as security, privacy, and storage. One way to address these challenges is to integrate blockchain with fog computing. There are several applications of blockchain-fog computing integration that have been proposed, recently, due to their lucrative benefits such as enhancing security and privacy. There is a need to systematically review and synthesize the literature on this topic of blockchain-fog computing integration. The purposes of integrating blockchain and fog computing were determined using a systematic literature review approach and tailored search criteria established from the research questions. In this research, 181 relevant papers were found and reviewed. The results showed that the authors proposed the combination of blockchain and fog computing for several purposes such as security, privacy, access control, and trust management. A lack of standards and laws may make it difficult for blockchain and fog computing to be integrated in the future, particularly in light of newly developed technologies like quantum computing and artificial intelligence. The findings of this paper serve as a resource for researchers and practitioners of blockchain-fog computing integration for future research and designs.
Anwar, A, Kanwal, S, Tahir, M, Saqib, M, Uzair, M, Rahmani, MKI & Ullah, H 2022, 'Image Aesthetic Assessment: A Comparative Study of Hand-Crafted & Deep Learning Models', IEEE Access, vol. 10, pp. 101770-101789.
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Automatic image aesthetics assessment is a computer vision problem dealing with categorizing images into different aesthetic levels. The categorization is usually done by analyzing an input image and computing some measure of the degree to which the image adheres to the fundamental principles of photography such as balance, rhythm, harmony, contrast, unity, look, feel, tone, and texture. Due to its diverse applications in many areas, automatic image aesthetic assessment has gained significant research attention in recent years. This article presents a comparative study of different automatic image aesthetics assessment techniques from the year 2005 to 2021. A number of conventional hand-crafted as well as modern deep learning-based approaches are reviewed and analyzed for their performance on various publicly available datasets. Additionally, critical aspects of different features and models have also been discussed to analyze their performance and limitations in different situations. The comparative analysis reveals that deep learning based approaches excel hand-crafted based techniques in image aesthetic assessment.
Anwar, MJ, Gill, AQ, Fitzgibbon, AD & Gull, I 2022, 'PESTLE+ risk analysis model to assess pandemic preparedness of digital ecosystems.', Secur. Priv., vol. 5, no. 1.
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AbstractCOVID‐19 pandemic has affected every country in many ways. Its substantial economic impacts are causing businesses to fade, pushing many nations into an economic downturn. This exposes organizations worldwide to unique risks which cannot be foreseen with conventional methods of risk analysis. This research is part of a broader action design research project conducted in collaboration with industry partner to answer an important research question: How to extend PESTLE risk analysis model to assess pandemic preparedness? In this context, the health factor is added to extend the traditional PESTLE risk analysis model. Furthermore, the interdependence between PESTLE factors has also been investigated, which has not been discussed before. The contribution of this research is the novel PESTLE+ risk analysis model that will help individuals and businesses to improve their understanding of the health crisis, such as the COVID‐19, adjust accordingly and eventually endure the ongoing crisis, which is driving most businesses into liquidation.
Aung, TWW, Wan, Y, Huo, H & Sui, Y 2022, 'Multi-triage: A multi-task learning framework for bug triage', Journal of Systems and Software, vol. 184, pp. 111133-111133.
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Assigning developers and allocating issue types are two important tasks in the bug triage process. Existing approaches tackle these two tasks separately, which is time-consuming due to repetition of effort and negating the values of correlated information between tasks. In this paper, a multi-triage model is proposed that resolves both tasks simultaneously via multi-task learning (MTL). First, both tasks can be regarded as a classification problem, based on historical issue reports. Second, performances on both tasks can be improved by jointly interpreting the representations of the issue report information. To do so, a text encoder and abstract syntax tree (AST) encoder are used to extract the feature representation of bug descriptions and code snippets accordingly. Finally, due to the disproportionate ratio of class labels in training datasets, the contextual data augmentation approach is introduced to generate syntactic issue reports to balance the class labels. Experiments were conducted on eleven open-source projects to demonstrate the effectiveness of this model compared with state-of-the-art methods.
Azadi, M, Emrouznejad, A, Ramezani, F & Hussain, FK 2022, 'Efficiency Measurement of Cloud Service Providers Using Network Data Envelopment Analysis', IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 348-355.
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IEEE An increasing number of organizations and businesses around the world use cloud computing services to improve their performance in the competitive marketplace. However, one of the biggest challenges in using cloud computing services is performance measurement and the selection of the best cloud service providers (CSPs) based on quality of service (QoS) requirements (Duan, 2017). To address this shortcoming in this article we propose a network data envelopment analysis (DEA) method in measuring the efficiency of CSPs. When network dimensions are taken into consideration, a more comprehensive analysis is enabled where divisional efficiency is reflected in overall efficiency estimates. This helps managers and decision makers in organizations to make accurate decisions in selecting cloud services. In the current study, variable returns to scale (VRS), the non-oriented network slacks-based measure (SBM) model and input-oriented and output-oriented SBM models are applied to measure the performance of 18 CSPs. The obtained results show the superiority of the network DEA model and they also demonstrate that the proposed model can evaluate and rank CSPs much better than compared to traditional DEA models.
Azadi, M, Moghaddas, Z, Farzipoor Saen, R & Hussain, FK 2022, 'Financing manufacturers for investing in Industry 4.0 technologies: internal financing vs. External financing', International Journal of Production Research, pp. 1-17.
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Supply chain finance (SCF) as a crucial approach plays a key role in improving commitment, trust, financial flows, and profitability in a supply chain (SC). Many industrial organisations finance their SC through two resources: internal financing (buyer) and external financing (bank). The main objective of this paper is to develop an advanced data envelopment analysis (DEA) model for measuring the sustainability of financing resources of Industry 4.0 technologies. To do so, for the first time a non-radial DEA model in the presence of both zero inputs and ratio data is proposed. In this paper, the sustainability factors, including economic, environmental, and social factors are incorporated into the proposed approach. The developed DEA model, for the first time, is applied in SCF. The results show the most sustainable financial resource for investing in Industry 4.0 technologies. Also, the inputs and outputs’ inefficiencies are determined.
Bagherimehrab, M, Sanders, YR, Berry, DW, Brennen, GK & Sanders, BC 2022, 'Nearly Optimal Quantum Algorithm for Generating the Ground State of a Free Quantum Field Theory', PRX Quantum, vol. 3, no. 2, p. 020364.
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We devise a quasilinear quantum algorithm for generating an approximation for the ground state of a quantum field theory (QFT). Our quantum algorithm delivers a superquadratic speedup over the state-of-the-art quantum algorithm for ground-state generation, overcomes the ground-state-generation bottleneck of the prior approach and is optimal up to a polylogarithmic factor. Specifically, we establish two quantum algorithms - Fourier-based and wavelet-based - to generate the ground state of a free massive scalar bosonic QFT with gate complexity quasilinear in the number of discretized QFT modes. The Fourier-based algorithm is limited to translationally invariant QFTs. Numerical simulations show that the wavelet-based algorithm successfully yields the ground state for a QFT with broken translational invariance. Furthermore, the cost of preparing particle excitations in the wavelet approach is independent of the energy scale. Our algorithms require a routine for generating one-dimensional Gaussian (1DG) states. We replace the standard method for 1DG-state generation, which requires the quantum computer to perform lots of costly arithmetic, with a novel method based on inequality testing that significantly reduces the need for arithmetic. Our method for 1DG-state generation is generic and could be extended to preparing states whose amplitudes can be computed on the fly by a quantum computer.
Bashir, MR, Gill, AQ & Beydoun, G 2022, 'A Reference Architecture for IoT-Enabled Smart Buildings.', SN Comput. Sci., vol. 3, pp. 493-493.
Bashir, MR, Gill, AQ & Beydoun, G 2022, 'A Reference Architecture for IoT-Enabled Smart Buildings', SN Computer Science, vol. 3, no. 6, p. 493.
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AbstractThe management and analytics of big data generated from IoT sensors deployed in smart buildings pose a real challenge in today’s world. Hence, there is a clear need for an IoT focused Integrated Big Data Management and Analytics framework to enable the near real-time autonomous control and management of smart buildings. The focus of this paper is on the development and evaluation of the reference architecture required to support such a framework. The applicability of the reference architecture is evaluated by taking into account various example scenarios for a smart building involving the management and analysis of near real-time IoT data from 1000 sensors. The results demonstrate that the reference architecture can guide the complex integration and orchestration of real-time IoT data management, analytics, and autonomous control of smart buildings, and that the architecture can be scaled up to address challenges for other smart environments.
Benedict, G & Gill, AQ 2022, 'A regulatory control framework for decentrally governed DLT systems: Action design research', Information & Management, vol. 59, no. 7, pp. 103555-103555.
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Benedict, G & Gill, AQ 2022, 'A regulatory control framework for decentrally governed DLT systems: Action design research.', Inf. Manag., vol. 59, pp. 103555-103555.
Bersenev, EY, Berseneva, АP, Prysyazhnyuk, A, McGregor, C, Berseneva, IА, Funtova, II & Chernikova, AG 2022, 'Cybernetic Approach to Health Assessment', CARDIOMETRY, no. 23, pp. 31-40.
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The exploration of orbital space served as a prerequisite for the creation of a new direction of medical science in relation to the very extreme conditions of life of spacecraft crews. Space medicine, relying on the most modern research methods and approaches, thanks to the development of new medical devices and the use of unique data analysis algorithms, has made a significant contribution to the development of telemedicine, medical cybernetics, and prenosological principles for assessing the state of human health. The review reflects the main stages in the development of medical cybernetics and prenosological diagnostics based on the assessment of the regulatory components of the cardiovascular system. Discussed the aspects of the application of the method of mathematical analysis of the heart rhythm in relation to the assessment and forecast of the working capacity of cosmonauts, at the simulating model of microgravity and confinement. Shown the useful methodically apply for the healthcare of manufacture teams at the plants, passenger bus driver’s employments. As the part of appliance of the new advance tools of children and adolescents public health during the educating process at schools. The created system for analyzing the current functional state of human health and mathematical models that make it possible to predict its negative changes make it possible to predetermine the vector of development of medicine in the future. The foundations of knowledge gained over the period of more than 70 years of scientific activity of Professor R.M. Bavsky are reflected in promising areas of cardiology research using computer technologies - such as Cardiometry technologies.
Bi, S, Cui, J, Ni, W, Jiang, Y, Yu, S & Wang, X 2022, 'Three-Dimensional Cooperative Positioning for Internet of Things Provenance', IEEE Internet of Things Journal, vol. 9, no. 20, pp. 19945-19958.
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A large number of Internet of Things (IoT) devices have been interconnected for information collection and exchange. The data are only meaningful if it is captured at the expected location (i.e., the IoT devices or sensors are not removed accidentally or intentionally). This article presents a new algorithm, which cooperatively locates multiple IoT devices deployed in a 3-D space based on pairwise Euclidean distance measurements. When the distance measurement noises are negligible, a new feasibility problem of rank-3 variables is formulated. We solve the problem using the difference-of-convex (DC) programming to preserve the rank-3 constraints, rather than relaxing the constraints, using semidefinite relaxation (SDR). When the distance measurements are corrupted by additive noises and nonlight-of-sight (NLOS) propagation, a maximum-likelihood estimation (MLE) problem is formulated and transformed to a DC program solved with the rank-3 constraints preserved. Simulation results indicate that the proposed approach can achieve satisfactory accuracy results with a low complexity and strong robustness to the irregular topology, poor connectivity, and measurement errors, as compared to existing SDR-based alternatives.
Bin Sawad, A, Narayan, B, Alnefaie, A, Maqbool, A, Mckie, I, Smith, J, Yuksel, B, Puthal, D, Prasad, M & Kocaballi, AB 2022, 'A Systematic Review on Healthcare Artificial Intelligent Conversational Agents for Chronic Conditions', Sensors, vol. 22, no. 7, pp. 2625-2625.
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This paper reviews different types of conversational agents used in health care for chronic conditions, examining their underlying communication technology, evaluation measures, and AI methods. A systematic search was performed in February 2021 on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library. Studies were included if they focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases, involved conversational agents, and tested the system with human users. The search retrieved 1087 articles. Twenty-six studies met the inclusion criteria. Out of 26 conversational agents (CAs), 16 were chatbots, seven were embodied conversational agents (ECA), one was a conversational agent in a robot, and another was a relational agent. One agent was not specified. Based on this review, the overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users’ feedback shows helpfulness, satisfaction, and ease of use in more than half of included studies. Although many users in the studies appear to feel more comfortable with CAs, there is still a lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions due to the insufficient reporting of technical implementation details.
Binh, NTM, Ngoc, NH, Binh, HTT, Van, NK & Yu, S 2022, 'A family system based evolutionary algorithm for obstacle-evasion minimal exposure path problem in Internet of Things', Expert Systems with Applications, vol. 200, pp. 116943-116943.
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Barrier coverage in wireless sensor networks (WSNs) is a well-known model for military security applications in IoTs, in which sensors are deployed to detect every movement over the predefined border. The fundamental sub-problem of barrier coverage in WSNs is the minimal exposure path (MEP) problem. The MEP refers to the worst-case coverage path where an intruder can move through the sensing field with the lowest capability to be detected. Knowledge about MEP is useful for network designers to identify the worst coverage in WSNs. Most prior research focused on this problem with the assumption that the WSN has an ideal deployment environment without obstacles, causing existing gaps between theoretical and practical WSNs systems. To overcome this drawback, we investigate a systematic and generic MEP problem under real-world environment networks by presenting obstacles called Obstacle-Evasion-MEP (hereinafter OE-MEP). We propose an algorithm to create several types of arbitrary-shaped obstacles inside the deployment area of WSNs. The OE-MEP problem is an NP-Hard with high dimension, non-differentiation, non-linearity, and constraints. Based upon its characteristics, we then devise an elite algorithm namely Family System based Evolutionary Algorithm (FEA) with our newly-proposed concepts of Family System, tailored to efficiently solve the OE-MEP. We also build an extension to a custom-made simulation environment to integrate a variety of network topologies as well as obstacles. Experimental results on numerous instances indicate that the proposed algorithm is suitable for the converted OE-MEP problem and performs better in solution accuracy than existing approaches.
Braytee, A, Naji, M & Kennedy, PJ 2022, 'Unsupervised Domain-Adaptation-Based Tensor Feature Learning With Structure Preservation', IEEE Transactions on Artificial Intelligence, vol. 3, no. 3, pp. 370-380.
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Domain adaptation (DA) is widely used in computer vision and pattern recognition applications. It is an effective process where a model is trained on objects from the source domain to predict the categories of the objects in the target domain. The aim of feature extraction in domain adaptation is to learn the best representation of the data in a certain domain and use it in other domains. However, the main challenge here is the difference between the data distributions of the source and target domains. Also, in computer vision, the data are represented as tensor objects such as 3-D images and video sequences. Most of the existing methods in DA apply vectorization to the data, which leads to information loss due to failure to preserve the natural tensor structure in a low-dimensional space. Thus, in this article, we propose unsupervised DA-based tensor feature learning (UDA-TFL) as a novel adapted feature extraction method that aims to avoid vectorization during transfer knowledge simultaneously; retain the structure of the tensor objects; reduce the data discrepancy between source and target domains; and represent the original tensor object in a lower dimensional space that is resistant to noise. Therefore, multilinear projections are determined to learn the tensor subspace without vectorizing the original tensor objects via an alternating optimization strategy. We integrate maximum mean discrepancy in the objective function to reduce the difference between source and target distributions. Extensive experiments are conducted on 39 cross-domain datasets from different fields, including images and videos. The promising results indicate that UDA-TFL significantly outperforms the state-of-the-art.
Brunese, L, Martino, P, Mischi, M, Prasad, M & Santone, A 2022, 'Editorial: Radiomics in prostate cancer imaging', Frontiers in Oncology, vol. 12, p. 1010901.
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Bui, HT, Hussain, OK, Prior, D, Hussain, FK & Saberi, M 2022, 'Proof by Earnestness (PoE) to determine the authenticity of subjective information in blockchains - application in supply chain risk management', Knowledge-Based Systems, vol. 250, pp. 108972-108972.
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Blockchain is being used in various global supply chains with its ever-increasing maturity and popularity. However, in the presence of subjective information that does not have a digital footprint, blockchain application is a grey area. This is due to the difficulty in confirming the authenticity or legitimacy of information before achieving consensus on it using existing mechanisms such as Proof of Work (PoW), Proof of Authority (PoA) or Proof of Stake (PoS). In this paper, we attempt to address this issue. Specifically, we propose the Proof by Earnestness (PoE) consensus mechanism that determines the subjective information's truthfulness before further processing and formalising in blockchains. We consider supply chain risk management (SCRM) as our application area due to the vast amount of available subjective information.
Bukhari, A, Hussain, FK & Hussain, OK 2022, 'Fog node discovery and selection: A Systematic literature review', Future Generation Computer Systems, vol. 135, pp. 114-128.
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Cai, B, Li, X, Kong, W, Yuan, J & Yu, S 2022, 'A Reliable and Lightweight Trust Inference Model for Service Recommendation in SIoT', IEEE Internet of Things Journal, vol. 9, no. 13, pp. 10988-11003.
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In the era of Internet of Things (IoT), millions of heterogeneous IoT devices generate an explosion of data and services waiting to be discovered. The convergence of IoT with social networks (SIoT) interconnects multiple IoT applications and alleviates the common data sparsity and cold start problems in traditional recommendation systems. However, the social trust relationships may also be very sparse, which affects the accuracy of trust-based recommendation systems. Meanwhile, mobile devices have limited resources and are more vulnerable to malicious attacks in the IoT environment. In order to complete the trust relationship and further improve the trust-based recommendation performance, we propose a reliable and lightweight trust inference model for service recommendation in SIoT, called TIRec. Firstly, we obtain a comprehensive weighted centrality metric (LGWC) considering both local and global contexts. Based on this, we propose a corresponding lightweight trust path selection algorithm. Then, we present a reliable trust inference calculation algorithm consist of trust propagation and aggregation strategy, which can efficiently resist two common malicious attacks. Finally, we incorporate the rating, direct trust and indirect trust together into the matrix factorization model, and integrate the influence of truster and trustee to obtain the synthetic model for rating predication. To the best of our knowledge, this paper is the first to integrate trust inference algorithm into the trust-based recommendation systems. Extensive experiments are conducted on three real-world datasets, and the results show that our TIRec model performs better than other advanced recommendation models in both “all users” view and “cold start users” view.
Cao, Y, Wang, Z, Wang, J, Wei, Y & Yu, S 2022, 'Chitosan-bridged synthesis of 2D/2D hierarchical nanostructure towards promoting the fire safety and mechanical property of epoxy resin', Composites Part A: Applied Science and Manufacturing, vol. 158, pp. 106958-106958.
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Cao, Y, Yu, S, Wang, Z & Wang, J 2022, 'Interfacial gluing strategy derived closely-coupled ternary LDH@P-RGO nanohybrid towards constructing fire-safe epoxy resin', Polymer Degradation and Stability, vol. 205, pp. 110130-110130.
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Cetindamar, D, Shdifat, B & Erfani, E 2022, 'Understanding Big Data Analytics Capability and Sustainable Supply Chains', Information Systems Management, vol. 39, no. 1, pp. 19-33.
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This paper presents the knowledge available in the literature regarding big data analytics capability (BDAC) and sustainable supply chain performance (SSCP). A detailed analysis of systematic literature reviews points out the lack of studies bridging these two separate streams of work. The paper puts forward a research agenda for researchers interested in understanding the impact of big data on sustainability.
Chalmers, T, Hickey, BA, Newton, P, Lin, C-T, Sibbritt, D, McLachlan, CS, Clifton-Bligh, R, Morley, J & Lal, S 2022, 'Stress Watch: The Use of Heart Rate and Heart Rate Variability to Detect Stress: A Pilot Study Using Smart Watch Wearables', Sensors, vol. 22, no. 1, pp. 151-151.
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Stress is an inherent part of the normal human experience. Although, for the most part, this stress response is advantageous, chronic, heightened, or inappropriate stress responses can have deleterious effects on the human body. It has been suggested that individuals who experience repeated or prolonged stress exhibit blunted biological stress responses when compared to the general population. Thus, when assessing whether a ubiquitous stress response exists, it is important to stratify based on resting levels in the absence of stress. Research has shown that stress that causes symptomatic responses requires early intervention in order to mitigate possible associated mental health decline and personal risks. Given this, real-time monitoring of stress may provide immediate biofeedback to the individual and allow for early self-intervention. This study aimed to determine if the change in heart rate variability could predict, in two different cohorts, the quality of response to acute stress when exposed to an acute stressor and, in turn, contribute to the development of a physiological algorithm for stress which could be utilized in future smartwatch technologies. This study also aimed to assess whether baseline stress levels may affect the changes seen in heart rate variability at baseline and following stress tasks. A total of 30 student doctor participants and 30 participants from the general population were recruited for the study. The Trier Stress Test was utilized to induce stress, with resting and stress phase ECGs recorded, as well as inter-second heart rate (recorded using a FitBit). Although the present study failed to identify ubiquitous patterns of HRV and HR changes during stress, it did identify novel changes in these parameters between resting and stress states. This study has shown that the utilization of HRV as a measure of stress should be calculated with consideration of resting (baseline) anxiety and stress states in order to ens...
Chalmers, T, Hickey, BA, Newton, P, Lin, C-T, Sibbritt, D, McLachlan, CS, Clifton-Bligh, R, Morley, JW & Lal, S 2022, 'Associations between Sleep Quality and Heart Rate Variability: Implications for a Biological Model of Stress Detection Using Wearable Technology', International Journal of Environmental Research and Public Health, vol. 19, no. 9, pp. 5770-5770.
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Introduction: The autonomic nervous system plays a vital role in the modulation of many vital bodily functions, one of which is sleep and wakefulness. Many studies have investigated the link between autonomic dysfunction and sleep cycles; however, few studies have investigated the links between short-term sleep health, as determined by the Pittsburgh Quality of Sleep Index (PSQI), such as subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction, and autonomic functioning in healthy individuals. Aim: In this cross-sectional study, the aim was to investigate the links between short-term sleep quality and duration, and heart rate variability in 60 healthy individuals, in order to provide useful information about the effects of stress and sleep on heart rate variability (HRV) indices, which in turn could be integrated into biological models for wearable devices. Methods: Sleep parameters were collected from participants on commencement of the study, and HRV was derived using an electrocardiogram (ECG) during a resting and stress task (Trier Stress Test). Result: Low-frequency to high-frequency (LF:HF) ratio was significantly higher during the stress task than during the baseline resting phase, and very-low-frequency and high-frequency HRV were inversely related to impaired sleep during stress tasks. Conclusion: Given the ubiquitous nature of wearable technologies for monitoring health states, in particular HRV, it is important to consider the impacts of sleep states when using these technologies to interpret data. Very-low-frequency HRV during the stress task was found to be inversely related to three negative sleep indices: sleep quality, daytime dysfunction, and global sleep score.
Che, X, Zuo, H, Lu, J & Chen, D 2022, 'Fuzzy Multioutput Transfer Learning for Regression', IEEE Transactions on Fuzzy Systems, vol. 30, no. 7, pp. 2438-2451.
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Multi-output regression aims to predict multiple continuous outputs simultaneously using the common set of input variables. The significant challenge arises from modeling relevance between inputs and outputs. Moreover, the shortage of labeled multi-output data and the divergence of data are other factors that impede the development of multi-output regression problems. The recent emergence of transfer learning techniques, which have the ability of leveraging previously acquired knowl- edge from a similar domain, provide a solution to the above issues. In this paper, a novel fuzzy transfer learning method is proposed to tackle the multi-output regression problems in ho- mogeneous and heterogeneous scenarios. By considering output- input dependencies and inter-output correlations, fuzzy rules are extracted to reflect the shared characteristics of different outputs and capture their uniqueness. For a homogeneous scenario, fuzzy rules are first accumulated in a related domain (called the source domain), which has a sufficient amount of training data. Based on different transform strategies, the fuzzy rules are then transferred to improve the new but similar regression tasks in the current domain (called the target domain), where only a few data have multiple responses. On this basis, we handle a more complex heterogeneous scenario by learning a latent input space to reduce the disagreement of variables between domains. The experiment results on thirteen real-world datasets with multiple outputs illustrate the effectiveness of our method. The impact of core coefficients on performance is also analyzed.
Chen, XC, Hellmann, A & Sood, S 2022, 'A framework for analyst economic incentives and cognitive biases: Origination of the walk-down in earnings forecasts', Journal of Behavioral and Experimental Finance, vol. 36, pp. 100759-100759.
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Chen, Y, Su, Y, Zhang, M, Chai, H, Wei, Y & Yu, S 2022, 'FedTor: An Anonymous Framework of Federated Learning in Internet of Things', IEEE Internet of Things Journal, vol. 9, no. 19, pp. 18620-18631.
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With a large number of devices and a wealth of user data sets, the Internet of Things (IoT) has become a great host for federated learning (FL). At the same time, the massive amount of user data in IoT results in desperate demand for privacy preserving. The onion router (Tor) is a promising method to solve the privacy issue in IoT-based FL by user anonymity. However, IoT devices' resource is too limited to execute the cryptographic operations in Tor. Moreover, network traffics in Tor can be easily controlled by malicious routers with a fake high self-reported bandwidth. In this article, taking advantage of the Tor, we will introduce an anonymous FL framework in IoT called FedTor. To decrease the cryptographic cost in conventional Tor, we propose a lightweight shared key generation scheme for resource-limited IoT devices. Furthermore, we use the difference between the self-reported bandwidth and the bandwidth observed from others to measure the reputation of onion routers. A reputation-based router selection (RBRS) scheme is then brought up to defend traffic control from malicious routers. We conducted extensive simulations to compare FedTor with related works. The results show that the RBRS scheme can decrease the malicious rate of onion routers and the lightweight shared key has a cost advantage over other schemes.
Chen, Y, Sun, X, Wei, W, Dong, Y & Liang, CJ 2022, 'A Prediction and Visual Analysis Method for Graduation Destination of Undergraduates Based on LambdaMART Model', International Journal of Information and Communication Technology Education, vol. 18, no. 2, pp. 1-19.
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Predicting graduation destination can help students determine their learning goals in advance, help faculty optimize curriculum and provide career guidance for students. In this paper, the authors first propose a prediction algorithm for graduation destination of undergraduates based on LambdaMART, called PGDU_LM, which uses Spearman correlation coefficient to analyze the correlation between subjects and graduate destinations and extract characteristic subjects, and uses LambdaMART ranking model to calculate students' propensity scores in different graduate destinations. Second, a visual analysis method for students' course grades and graduation destinations is designed to support users to analyze student data from multiple dimensions. Finally, a prediction and visual analysis system for graduation destination of undergraduates, PGDUvis, is designed and implemented. A case study and user evaluation on this system was conducted using the academic data of students from five majors who graduated from a university during 2016-2020, and the results illustrate the effectiveness of this method.
Chen, Z, Wang, S, Fu, A, Gao, Y, Yu, S & Deng, RH 2022, 'LinkBreaker: Breaking the Backdoor-Trigger Link in DNNs via Neurons Consistency Check', IEEE Transactions on Information Forensics and Security, vol. 17, pp. 2000-2014.
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Backdoor attacks cause model misbehaving by first implanting backdoors in deep neural networks (DNNs) during training and then activating the backdoor via samples with triggers during inference. The compromised models could pose serious security risks to artificial intelligence systems, such as misidentifying 'stop' traffic sign into '80km/h'. In this paper, we investigate the connection characteristic between the backdoor and the trigger in DNNs and observe the fact that the backdoor is implanted via establishing a link between a cluster of neurons, representing the backdoor, and the triggers. Based on this observation, we design LinkBreaker, a new generic scheme for defending against backdoor attacks. In particular, LinkBreaker deploys a neuron consistency check mechanism for identifying compromised neuron set related to the trigger. Then, the LinkBreaker regulates the model to make predictions based on benign neuron set only and thus breaks the link between the backdoor and the trigger. Compared to previous defenses, LinkBreaker offers a more general backdoor countermeasure that is not only effective against input-agnostic backdoors but also source-specific backdoors, which the later can not be defeated by majority of state-of-the-arts. Besides, LinkBreaker is robust against adversarial examples, which, to a large extent, provides a holistic defense against adversarial example attacks on DNNs, while almost all current backdoor defenses do not have such consideration and capability. Extensive experimental evaluations on real datasets demonstrate that LinkBreaker is with high efficacy of suppressing trigger inputs while incurring no noticeable accuracy deterioration on benign inputs.
Cheng, Z, Ye, D, Zhu, T, Zhou, W, Yu, PS & Zhu, C 2022, 'Multi‐agent reinforcement learning via knowledge transfer with differentially private noise', International Journal of Intelligent Systems, vol. 37, no. 1, pp. 799-828.
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In multi-agent reinforcement learning, transfer learning is one of the key techniques used to speed up learning performance through the exchange of knowledge among agents. However, there are three challenges associated with applying this technique to real-world problems. First, most real-world domains are partially rather than fully observable. Second, it is difficult to pre-collect knowledge in unknown domains. Third, negative transfer impedes the learning progress. We observe that differentially private mechanisms can overcome these challenges due to their randomization property. Therefore, we propose a novel differential transfer learning method for multi-agent reinforcement learning problems, characterized by the following three key features. First, our method allows agents to implement real-time knowledge transfers between each other in partially observable domains. Second, our method eliminates the constraints on the relevance of transferred knowledge, which expands the knowledge set to a large extent. Third, our method improves robustness to negative transfers by applying differentially exponential noise and relevance weights to transferred knowledge. The proposed method is the first to use the randomization property of differential privacy to stimulate the learning performance in multi-agent reinforcement learning system. We further implement extensive experiments to demonstrate the effectiveness of our proposed method.
Chowdhury, RR, Chattopadhyay, S & Adak, C 2022, 'CAHPHF: Context-Aware Hierarchical QoS Prediction With Hybrid Filtering', IEEE Transactions on Services Computing, vol. 15, no. 4, pp. 2232-2247.
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IEEE With the proliferation of Internet-of-Things and continuous growth in the number of web-services at the Internet-scale, service-recommendation is becoming a challenge nowadays. One of the prime aspects influencing the service-recommendation is the Quality-of-Service(QoS) parameter, which depicts the performance of a web-service. In general, the service provider furnishes the QoS values before service deployment. In reality, the QoS values of service vary across different users, time, locations, etc. Therefore, estimating the QoS value of service before its execution is an important task. Thus, QoS-prediction has gained significant attention. Multiple approaches are available in the literature for predicting QoS. However, these approaches are yet to reach the desired accuracy level. Here, we study the QoS-prediction problem across different users and propose a novel solution by considering the contextual information of both services and users. Our proposal includes two key-steps: (a)hybrid-filtering, (b)hierarchical-prediction-mechanism. On one hand, the hybrid-filtering aims to obtain a set of similar users and services, given a target user and a service. On the other hand, the goal of the hierarchical-prediction-mechanism is to estimate the QoS value accurately by leveraging hierarchical-neural-regression. We evaluated our framework on WS-DREAM datasets. The experimental results show our framework outperformed the major state-of-the-art approaches.
Costa, PCS, An, D, Sanders, YR, Su, Y, Babbush, R & Berry, DW 2022, 'Optimal Scaling Quantum Linear-Systems Solver via Discrete Adiabatic Theorem', PRX Quantum, vol. 3, no. 4, p. 040303.
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Recently, several approaches to solving linear systems on a quantum computer have been formulated in terms of the quantum adiabatic theorem for a continuously varying Hamiltonian. Such approaches have enabled near-linear scaling in the condition number κ of the linear system, without requiring a complicated variable-time amplitude amplification procedure. However, the most efficient of those procedures is still asymptotically suboptimal by a factor of log(κ). Here, we prove a rigorous form of the adiabatic theorem that bounds the error in terms of the spectral gap for intrinsically discrete-time evolutions. In combination with the qubitized quantum walk, our discrete adiabatic theorem gives a speed-up for all adiabatic algorithms. Here, we use this combination to develop a quantum algorithm for solving linear systems that is asymptotically optimal, in the sense that the complexity is strictly linear in κ, matching a known lower bound on the complexity. Our O[κlog(1/ µ)] complexity is also optimal in terms of the combined scaling in κ and the precision µ. Compared to existing suboptimal methods, our algorithm is simpler and easier to implement. Moreover, we determine the constant factors in the algorithm, which would be suitable for determining the complexity in terms of gate counts for specific applications.
Cui, L, Guo, L, Gao, L, Cai, B, Qu, Y, Zhou, Y & Yu, S 2022, 'A Covert Electricity-Theft Cyberattack Against Machine Learning-Based Detection Models', IEEE Transactions on Industrial Informatics, vol. 18, no. 11, pp. 7824-7833.
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The advanced metering infrastructure (AMI) in modern networked smart homes brings various advantages. However, smart homes are vulnerable to many cyberattacks, and the most striking one is energy theft. Researchers have developed many countermeasures, fostered by advanced machine learning (ML) techniques. Nevertheless, recent advances are not robust enough in practice, partially due to the vulnerabilities of ML algorithms. In this paper, we present a covert electricity theft strategy through mimicking normal consumption patterns. Such attack is almost impossible to be detected by existing solutions as the manipulated data have little deviation against honest usage records. To address this threat, we initially identify and define two levels of consumption deviations: home-level and interpersonal-level, respectively. Then, we propose a feature extraction method and develop a novel detection model based on deep learning. Extensive experiments show that the presented attack could evade existing mainstream detectors and the proposed countermeasure outperforms existing leading methods.
Cui, L, Qu, Y, Xie, G, Zeng, D, Li, R, Shen, S & Yu, S 2022, 'Security and Privacy-Enhanced Federated Learning for Anomaly Detection in IoT Infrastructures', IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3492-3500.
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Internet of Things (IoT) anomaly detection is significant due to its fundamental roles of securing modern critical infrastructures, such as falsified data injection detection and transmission line faults diagnostic in smart grids. Researchers have proposed various detection methods fostered by machine learning (ML) techniques. Federated learning (FL), as a promising distributed ML paradigm, has been employed recently to improve detection performance due to its advantages of privacy-preserving and lower latency. However, existing FL-based methods still suffer from efficiency, robustness, and security challenges. To address these problems, in this article, we initially introduce a blockchain-empowered decentralized and asynchronous FL framework for anomaly detection in IoT systems, which ensures data integrity and prevents single-point failure while improving the efficiency. Further, we design an improved differentially private FL based on generative adversarial nets, aiming to optimize data utility throughout the training process. To the best of our knowledge, it is the first system to employ a decentralized FL approach with privacy-preserving for IoT anomaly detection. Simulation results on the real-world dataset demonstrate the superior performance from aspects of robustness, accuracy, and fast convergence while maintaining high level of privacy and security protection.
Cui, Z, Chen, H, Cui, L, Liu, S, Liu, X, Xu, G & Yin, H 2022, 'Reinforced KGs reasoning for explainable sequential recommendation', World Wide Web, vol. 25, no. 2, pp. 631-654.
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We explore the semantic-rich structured information derived from the knowledge graphs (KGs) associated with the user-item interactions and aim to reason out the motivations behind each successful purchase behavior. Existing works on KGs-based explainable recommendations focus purely on path reasoning based on current user-item interactions, which generally result in the incapability of conjecturing users’ subsequence preferences. Considering this, we attempt to model the KGs-based explainable recommendation in sequential settings. Specifically, we propose a novel architecture called Reinforced Sequential Learning with Gated Recurrent Unit (RSL-GRU), which is composed of a Reinforced Path Reasoning Network (RPRN) component and a GRU component. RSL-GRU takes users’ sequential behaviors and their associated KGs in chronological order as input and outputs potential top-N items for each user with appropriate reasoning paths from a global perspective. Our RPRN features a remarkable path reasoning capacity, which is regulated by a user-conditioned derivatively action pruning strategy, a soft reward strategy based on an improved multi-hop scoring function, and a policy-guided sequential path reasoning algorithm. Experimental results on four of Amazon’s large-scale datasets show that our method achieves excellent results compared with several state-of-the-art alternatives.
Darwish, A, Halkon, B & Oberst, S 2022, 'Non-Contact Vibro-Acoustic Object Recognition Using Laser Doppler Vibrometry and Convolutional Neural Networks', Sensors, vol. 22, no. 23, pp. 9360-9360.
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Laser Doppler vibrometers (LDVs) have been widely adopted due to their large number of benefits in comparison to traditional contacting vibration transducers. Their high sensitivity, among other unique characteristics, has also led to their use as optical microphones, where the measurement of object vibration in the vicinity of a sound source can act as a microphone. Recent work enabling full correction of LDV measurement in the presence of sensor head vibration unlocks new potential applications, including integration within autonomous vehicles (AVs). In this paper, the common AV challenge of object classification is addressed by presenting and evaluating a novel, non-contact vibro-acoustic object recognition technique. This technique utilises a custom set-up involving a synchronised loudspeaker and scanning LDV to simultaneously remotely solicit and record responses to a periodic chirp excitation in various objects. The 864 recorded signals per object were pre-processed into spectrograms of various forms, which were used to train a ResNet-18 neural network via transfer learning to accurately recognise the objects based only on their vibro-acoustic characteristics. A five-fold cross-validation optimisation approach is described, through which the effects of data set size and pre-processing type on classification accuracy are assessed. A further assessment of the ability of the CNN to classify never-before-seen objects belonging to groups of similar objects on which it has been trained is then described. In both scenarios, the CNN was able to obtain excellent classification accuracy of over 99.7%. The work described here demonstrates the significant promise of such an approach as a viable non-contact object recognition technique suitable for various machine automation tasks, for example, defect detection in production lines or even loose rock identification in underground mines.
Darwish, A, Halkon, B, Rothberg, S, Oberst, S & Fitch, R 2022, 'A comparison of time and frequency domain-based approaches to laser Doppler vibrometer instrument vibration correction', Journal of Sound and Vibration, vol. 520, pp. 116607-116607.
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Davison, C, Akhavan, P, Jan, T, Azizi, N, Fathollahi, S, Taheri, N, Haass, O & Prasad, M 2022, 'Evaluation of Sustainable Digital Currency Exchange Platforms Using Analytic Models', Sustainability, vol. 14, no. 10, pp. 5822-5822.
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This study presents an analytic model to support the general public in evaluating digital currency exchange platforms. Advances in technologies have offered profitable opportunities, but the general public has difficulty accessing appropriate information on digital currency exchange platforms to facilitate their investments and trading. This study aims to provide a decision support system using analytic models that will guide the public in deciding the appropriate digital currency exchange platform for trading and investment. The overarching objective is to support the public in embracing the new era of a dependable, trustworthy, and sustainable digital society. Particularly, this study offers an analytics model that compares numerous well-known digital currency exchange platforms based on the opinions of 34 human expert members on six main criteria to identify the most suitable platform. In this study, the analytic hierarchy process approach, which is a multiple-criteria decision-making method, and Expert Choice software were used for decision support. Using pairwise comparisons of exchanges with respect to the criteria in the software, the weight of each exchange was determined, and these weights became the basis for prioritizing the exchange platform. This study provides valuable insight into how an analytics-driven expert system can support the public in selecting their digital currency exchange platform. This work is an integral part of an effort to help disruptive digital technology become widely accepted by the general public.
Deady, M, Glozier, N, Calvo, R, Johnston, D, Mackinnon, A, Milne, D, Choi, I, Gayed, A, Peters, D, Bryant, R, Christensen, H & Harvey, SB 2022, 'Preventing depression using a smartphone app: a randomized controlled trial', Psychological Medicine, vol. 52, no. 3, pp. 457-466.
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AbstractBackgroundThere is evidence that depression can be prevented; however, traditional approaches face significant scalability issues. Digital technologies provide a potential solution, although this has not been adequately tested. The aim of this study was to evaluate the effectiveness of a new smartphone app designed to reduce depression symptoms and subsequent incident depression amongst a large group of Australian workers.MethodsA randomized controlled trial was conducted with follow-up assessments at 5 weeks and 3 and 12 months post-baseline. Participants were employed Australians reporting no clinically significant depression. The intervention group (N = 1128) was allocated to use HeadGear, a smartphone app which included a 30-day behavioural activation and mindfulness intervention. The attention-control group (N = 1143) used an app which included a 30-day mood monitoring component. The primary outcome was the level of depressive symptomatology (PHQ-9) at 3-month follow-up. Analyses were conducted within an intention-to-treat framework using mixed modelling.ResultsThose assigned to the HeadGear arm had fewer depressive symptoms over the course of the trial compared to those assigned to the control (F3,734.7 = 2.98, p = 0.031). Prevalence of depression over the 12-month period was 8.0% and 3.5% for controls and HeadGear recipients, respectively, with odds of depression caseness amongst the intervention group of 0.43 (
Devitt, SJ 2022, 'Blueprinting quantum computing systems', Journal and Proceedings of the Royal Society of New South Wales, vol. 155, no. 1, pp. 5-39.
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The development of quantum computing systems has been a staple of academic research since the mid-1990s when the first proposal for physical platforms were proposed using Nuclear Magnetic Resonance and Ion-Trap hardware. These first proposals were very basic, essentially consisting of identifying a physical qubit (two-level quantum system) that could be isolated and controlled to achieve universal quantum computation. Over the past thirty years, the nature of quantum architecture design has changed significantly and the scale of investment, groups and companies involved in building quantum computers has increased exponentially. Architectural design for quantum computers examines systems at scale: fully error-corrected machines, potentially consisting of millions if not billions of physical qubits. These designs increasingly act as blueprints for academic groups and companies and are becoming increasingly more detailed, taking into account both the nature and operation of the physical qubits themselves and also peripheral environmental and control infrastructure that is required for each physical system. In this paper, several architectural structures that I have worked on will be reviewed, each of which has been adopted by either a national quantum computing program or a quantum startup. This paper was written in the context of an award with the Royal Society of New South Wales, focused on my personal contributions and impact to quantum computing development, and should be read with that in mind.1
Dietrich, H, Elder, M, Piggott, A, Qiao, Y & Weiß, A 2022, 'The Isomorphism Problem for Plain Groups Is in ΣP3', Leibniz International Proceedings in Informatics, LIPIcs, vol. 219, pp. 26:1-26:14.
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Testing isomorphism of infinite groups is a classical topic, but from the complexity theory viewpoint, few results are known. Sénizergues and the fifth author (ICALP2018) proved that the isomorphism problem for virtually free groups is decidable in PSPACE when the input is given in terms of so-called virtually free presentations. Here we consider the isomorphism problem for the class of plain groups, that is, groups that are isomorphic to a free product of finitely many finite groups and finitely many copies of the infinite cyclic group. Every plain group is naturally and efficiently presented via an inverse-closed finite convergent length-reducing rewriting system. We prove that the isomorphism problem for plain groups given in this form lies in the polynomial time hierarchy, more precisely, in ΣP3. This result is achieved by combining new geometric and algebraic characterisations of groups presented by inverse-closed finite convergent length-reducing rewriting systems developed in recent work of the second and third authors (2021) with classical finite group isomorphism results of Babai and Szemerédi (1984).
Ding, W, Ming, Y, Cao, Z & Lin, C-T 2022, 'A Generalized Deep Neural Network Approach for Digital Watermarking Analysis', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 3, pp. 613-627.
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Technology advancement has facilitated digital content, such as images, being acquired in large volumes. However, requirement from the privacy or legislation perspective still demands the need for intellectual content protection. In this paper, we propose a deep neural network (DNN) based watermarking method to achieve this goal. Instead of training a neural network for protecting a specific image, we train the network on an image dataset and generalize the trained model to protect distinct test images in a bulk manner. Respective evaluations from both the subjective and objective aspects confirm the generality and practicality of our proposed method. To demonstrate the robustness of this general neural watermarking approach, commonly used attacks are applied to the watermarked images to examine the corresponding extracted watermarks, which still retain sufficient recognizable traits for some occasions. Testing on distinctive dataset shows the satisfying generalization of our proposed method, and practice such as loss function adjustment can cater to the capacity requirement of complicated watermark. We also discuss some traits of the trained model, which incur the vulnerability to JPEG compression attack. However, remedy seeking for this can potentially open a window to understand the underlying working principle of DNN in future work. Considering its performance and economy, it is concluded that subsequent studies that generalize our work on utilizing DNN for intellectual content protection might be a promising research trend.
Dinh, TH, Singh, AK, Linh Trung, N, Nguyen, DN & Lin, C-T 2022, 'EEG Peak Detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-Based Ranking', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 1548-1556.
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Correct detection of peaks in electroencephalogram (EEG) signals is of essence due to the significant correlation of those potentials with cognitive performance and disorders. This paper proposes a novel and non-parametric approach to detect prediction error negativity (PEN) in cognitive conflict processing. The PEN candidates are first located from the input signal via an adaptation of a recent effective method for local maxima extraction, processed in a multi-scale manner. The found candidates are then fused and ranked based on their shape and location-based features. False positives caused by candidates' magnitude are eliminated by rotating the sorted candidate list where the one with the second-best ranking score will be identified as PEN. The EEG data collected from a 3D object selection task have been used to verify the efficacy of the proposed approach. Compared with the state-of-the-art peak detection techniques, the proposed method shows an improvement of at least 2.67% in accuracy and 6.27% in sensitivity while requires only about 4 ms to process an epoch. The accuracy and computational efficiency of the proposed technique in the detection of PEN in cognitive conflict processing would lead to promising applications in performance improvement of brain-computer interfaces (BCIs).
Dolmark, T, Sohaib, O, Beydoun, G, Wu, K & Taghikhah, F 2022, 'The Effect of Technology Readiness on Individual Absorptive Capacity Toward Learning Behavior in Australian Universities.', J. Glob. Inf. Manag., vol. 30, no. 1, pp. 1-21.
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Recipient's absorptive capacity (ACAP) is a barrier to knowledge transfer in organizations. The technology readiness (TR) dimensions measure an individual's technological beliefs and aligns with the individual's ACAP. The purpose of this research is to study if technological beliefs have a causal effect onto individual learning capability and behaviour. University's knowledge transfer makes them an ideal context for this research. Through surveying individuals and conducting statistical analysis, the authors provide empirical evidence that there is a causal effect from the TR dimensions to individuals ACAP and their technological learning behaviour at the individual level. The findings could potentially help leverage technology to address said recipient's ACAP. It would also benefit the development of new technologies, in particular in e-learning and tailoring pedagogy.
Dong, F, Lu, J, Song, Y, Liu, F & Zhang, G 2022, 'A Drift Region-Based Data Sample Filtering Method', IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 9377-9390.
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Concept drift refers to changes in the underlying data distribution of data streams over time. A well-trained model will be outdated if concept drift occurs. Once concept drift is detected, it is necessary to understand where the drift occurs to support the drift adaptation strategy and effectively update the outdated models. This process, called drift understanding, has rarely been studied in this area. To fill this gap, this article develops a drift region-based data sample filtering method to update the obsolete model and track the new data pattern accurately. The proposed method can effectively identify the drift region and utilize information on the drift region to filter the data sample for training models. The theoretical proof guarantees the identified drift region converges uniformly to the real drift region as the sample size increases. Experimental evaluations based on four synthetic datasets and two real-world datasets demonstrate our method improves the learning accuracy when dealing with data streams involving concept drift.
Dong, M, Yuan, F, Yao, L, Wang, X, Xu, X & Zhu, L 2022, 'A survey for trust-aware recommender systems: A deep learning perspective', Knowledge-Based Systems, vol. 249, pp. 108954-108954.
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A significant remaining challenge for existing recommender systems is that users may not trust recommender systems for either inaccurate recommendation or lack of explanation. Thus, it becomes critical to embrace a trustworthy recommender system. This survey provides a systematic summary of three categories of trust issues in recommender systems: social-aware recommender systems, which leverage users’ social trust relationships; robust recommender systems, which filter untruthful information, noises and enhance attack resistance; and explainable recommender systems, which provide explanations of the recommended items. We focus on the work based on deep learning techniques, which is an emerging area in the recommendation research.
Dong, Y, Guo, S, Wang, Q, Yu, S & Yang, Y 2022, 'Content Caching-Enhanced Computation Offloading in Mobile Edge Service Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 1, pp. 872-886.
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Cache enhanced computation offloading as a novel offloading paradigm in mobile edge computing (MEC) can reduce more task execution latency than traditional computation offloading by reusing of computation offloading data. However, existing works only focus on the enhancement between computation offloading and data caching but ignore the competition for cache resources between them. To this end, in this paper, we propose a caching enhanced computation offloading algorithm in mobile edge service networks (MESN), by considering the cache resources competition. We formulate a joint optimization problem of content caching and cache-enhanced computation offloading. Furthermore, we give the optimal caching strategy to achieve the equilibrium between the resources competition. By our offloading algorithm caching strategy, the average response time of computation and content request tasks can get further reduction. In addition, we design two low time complexity algorithms, i.e., mixed caching algorithm and enhanced offloading algorithm, to solve the sub-problems, i.e., smart base station (SBS) caching sub-problem and computation offloading sub-problem, transformed by the original optimization problem. The simulation results show that our algorithms can quickly converge and our scheme can reduce 20.52% average response time of all tasks at most compared with other schemes.
Duan, Y, Chen, N, Shen, S, Zhang, P, Qu, Y & Yu, S 2022, 'FDSA-STG: Fully Dynamic Self-Attention Spatio-Temporal Graph Networks for Intelligent Traffic Flow Prediction', IEEE Transactions on Vehicular Technology, vol. 71, no. 9, pp. 9250-9260.
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With the development of transportation and the ever-improving of vehicular technology, Artificial Intelligence (AI) has been popularized in Intelligent Transportation Systems (ITS), especially in Traffic Flow Prediction (TFP). TFP plays an increasingly important role in alleviating traffic pressure caused by regional emergencies and coordinating resource allocation in advance to deployment decisions. However, existing research can hardly model the original intricate structural relationships of the transportation network (TN) due to the lack of in-depth consideration of the dynamic relevance of spatial, temporal, and periodic characteristics. Motivated by this and combined with deep learning (DL), we propose a novel framework entitled Fully Dynamic Self-Attention Spatio-Temporal Graph Networks (FDSA-STG) by improving the attention mechanism using Graph Attention Networks (GATs). In particular, to dynamically integrate the correlations of spatial dimension, time dimension, and periodic characteristics for highly-accurate prediction, we correspondingly devised three components including the spatial graph attention component (SGAT), the temporal graph attention component (TGAT), and the fusion layer. In this case, three groups of similar structures are designed to extract the flow characteristics of recent periodicity, daily periodicity, and weekly periodicity. Extensive evaluation results show the superiority of FDSA-STG from perspectives of prediction accuracy and prediction stability improvements, which also testifies high model adaptability to the dynamic characteristics of the actual observed traffic flow (TF).
Duan, Y, Wang, Z, Wang, J, Wang, Y-K & Lin, C-T 2022, 'Position-aware image captioning with spatial relation', Neurocomputing, vol. 497, pp. 28-38.
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Image caption aims to generate a language description of a given image. The problem can be solved by learning semantic information of visual objects and generating descriptions based on extracted embedding. However, the spatial relationship between visual objects and their static position is not fully explored by existing methods. In this work, we propose a Position-Aware Transformer (PAT) model that extracts both regional and static global visual features and unify both the regional and global by incorporating spatial information aligned to each visual feature. To make a better representation of spatial information and correlation between extracted visual features, we propose and compare three subtle approaches to explore position embedding with spatial relation information explicitly. Moreover, we jointly consider the static global and regional embedding for spatial modeling. Experimental results illustrate that our proposed model achieves competitive performance on the COCO image captioning dataset, where the PAT model could respectively reach 38.7, 28.6, and 58.6 on BLEU-4, METEOR, and ROUGE-L respectively. Extensive experiments suggest that the proposed PAT model could also reach competitive performance on related visual-language tasks including visual question answering (VQA) and multi-modal retrieval. Detailed ablation studies are conducted to report how each part would contribute to the final performance, which could be a good reference for follow-up spatial information representation works.
Duong, TD, Li, Q & Xu, G 2022, 'Stochastic intervention for causal inference via reinforcement learning', Neurocomputing, vol. 482, pp. 40-49.
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Eshkevari, M, Jahangoshai Rezaee, M, Saberi, M & Hussain, OK 2022, 'An end-to-end ranking system based on customers reviews: Integrating semantic mining and MCDM techniques', Expert Systems with Applications, vol. 209, pp. 118294-118294.
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Faehrmann, PK, Steudtner, M, Kueng, R, Kieferova, M & Eisert, J 2022, 'Randomizing multi-product formulas for Hamiltonian simulation', Quantum, vol. 6, pp. 806-806.
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Quantum simulation, the simulation of quantum processes on quantum computers, suggests a path forward for the efficient simulation of problems in condensed-matter physics, quantum chemistry, and materials science. While the majority of quantum simulation algorithms are deterministic, a recent surge of ideas has shown that randomization can greatly benefit algorithmic performance. In this work, we introduce a scheme for quantum simulation that unites the advantages of randomized compiling on the one hand and higher-order multi-product formulas, as they are used for example in linear-combination-of-unitaries (LCU) algorithms or quantum error mitigation, on the other hand. In doing so, we propose a framework of randomized sampling that is expected to be useful for programmable quantum simulators and present two new multi-product formula algorithms tailored to it. Our framework reduces the circuit depth by circumventing the need for oblivious amplitude amplification required by the implementation of multi-product formulas using standard LCU methods, rendering it especially useful for early quantum computers used to estimate the dynamics of quantum systems instead of performing full-fledged quantum phase estimation. Our algorithms achieve a simulation error that shrinks exponentially with the circuit depth. To corroborate their functioning, we prove rigorous performance bounds as well as the concentration of the randomized sampling procedure. We demonstrate the functioning of the approach for several physically meaningful examples of Hamiltonians, including fermionic systems and the Sachdev–Ye–Kitaev model, for which the method provides a favorable scaling in the effort.
Fahmideh, M, Grundy, J, Beydoun, G, Zowghi, D, Susilo, W & Mougouei, D 2022, 'A model-driven approach to reengineering processes in cloud computing.', Inf. Softw. Technol., vol. 144, pp. 106795-106795.
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Context: The reengineering process of large data-intensive legacy software applications (“legacy applications” for brevity) to cloud platforms involves different interrelated activities. These activities are related to planning, architecture design, re-hosting/lift-shift, code refactoring, and other related ones. In this regard, the cloud computing literature has seen the emergence of different methods with a disparate point of view of the same underlying legacy application reengineering process to cloud platforms. As such, the effective interoperability and tailoring of these methods become problematic due to the lack of integrated and consistent standard models. Objective: We design, implement, and evaluate a novel framework called MLSAC (Migration of Legacy Software Applications to the Cloud). The core aim of MLSAC is to facilitate the sharing and tailoring of reengineering methods for migrating legacy applications to cloud platforms. MLSAC achieves this by using a collection of coherent and empirically tested cloud-specific method fragments from the literature and practice. A metamodel (or meta-method) together with corresponding instantiation guidelines is developed from this collection. The metamodel can also be used to create and maintain bespoke reengineering methods in a given scenario of reengineering to cloud platforms. Approach: MLSAC is underpinned by a metamodeling approach that acts as a representational layer to express reengineering methods. The design and evaluation of MLSAC are informed by the guidelines from the design science research approach. Results: Our framework is an accessible guide of what legacy-to-cloud reengineering methods can look like. The efficacy of the framework is demonstrated by modeling real-world reengineering scenarios and obtaining user feedback. Our findings show that the framework provides a fully-fledged domain-specific, yet platform-independent, foundation for the semi-automated representing, maintaining, ...
Faro, B, Abedin, B & Cetindamar, D 2022, 'Hybrid organizational forms in public sector’s digital transformation: a technology enactment approach', Journal of Enterprise Information Management, vol. 35, no. 6, pp. 1742-1763.
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PurposeThe purpose of this paper is to examine how public sector organizations become nimbler while retaining their resilience during digital transformation.Design/methodology/approachThe study adopts a hermeneutic approach in conducting deep expert interviews with 22 senior executives and managers of multiple organizations. The method blends theory and expert views to study digital transformation in the context of enterprise information management.FindingsDrawing on technology enactment framework (TEF), this research poses that organizational form is critical in the enactment of technologies in digital transformation. By extending the TEF, the authors claim that organizations are not in pure bureaucratic or network organizational form during digital transformation; instead, they need a hybrid combination in order to support competing strategic needs for nimbleness and resilience simultaneously. The four hybrid organizational forms presented in this model (4R) allow for networks and bureaucracy to coexist, though at different levels depending on the level of resiliency and nimbleness required at each point in the continuous digital transformation journey.Research limitations/implicationsThe main theoretical contribution of this research is to extend the TEF to illustrate that the need for coexistence of nimbleness with stability in a digital transformation results in a hybrid of networks and bureaucratic organization forms. This research aims to guide public sector organizations' digital transformation with extended the TEF as a tool for building the required organizational forms to influ...
Feng, B, Huang, Y, Tian, A, Wang, H, Zhou, H, Yu, S & Zhang, H 2022, 'DR-SDSN: An Elastic Differentiated Routing Framework for Software-Defined Satellite Networks', IEEE Wireless Communications, vol. 29, no. 6, pp. 80-86.
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Software-defined satellite networking (SDSN) has recently gained unprecedented attention due to its great controllability for traffic delivery. However, it is still facing several fundamental challenges in routing, which mainly involves effective network maintenance, heterogeneous network convergence, and customized flow steering. Hence, in this article, we propose an elastic routing framework for SDSN, aiming to first build a robust control path for signalling exchanges, then offer a transparent channel for IP services based on Loc/ID mappings and associated encapsulations, and finally enable hybrid packet forwarding manners to meet user various demands. Moreover, we define a new 8 B network header for DR-SDSN to further decrease overheads in packet encapsulations, where 16-bit addresses are used instead of 32-bit IPv4 and 128-bit IPv6 addresses. At last, we have implemented a corresponding proof-of-concept prototype with the modified Ryu and OvS, and the Linux socket application programming interface is also extended to handle our protocol with 16-bit addresses. Extensive evaluations are performed and associated results have confirmed the feasibility and advantages of the proposed DR-SDSN framework.
Feng, B, Tian, A, Yu, S, Li, J, Zhou, H & Zhang, H 2022, 'Efficient Cache Consistency Management for Transient IoT Data in Content-Centric Networking', IEEE Internet of Things Journal, vol. 9, no. 15, pp. 12931-12944.
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Since Internet of Things (IoT) communications can enjoy many advantages brought by content-centric networking (CCN) in nature, there is an increasing interest on their integration for better information retrieval and distribution. Nevertheless, different from the conventional multimedia traffic of which contents are hardly changed, IoT data are always transient and updated by their producers according to the actual situation. As a result, if without any effective countermeasures, outdated copies are inevitably stored by CCN routers and then distributed to the associated consumers, degrading both caching efficiency and user experience. In fact, most of related policies take little account of information freshness for cached contents, and how to tackle transient IoT data in CCN is still an ignored but crucial issue required for further explorations. Therefore, in this article, we propose an efficient popularity-based cache consistency management scheme, which aims to guarantee freshness of IoT data returned by on-path routers and avoid heavy signalling costs introduced at the same time. Extensive simulations were performed under both real-world scare-free and binary-tree topologies, and corresponding results have proved the efficiency of the proposed scheme in timely evictions of outdated IoT data stored by CCN in-network caching.
Figuerola-Wischke, A, Gil-Lafuente, AM & Merigó, JM 2022, 'The uncertain ordered weighted averaging adequacy coefficient operator', International Journal of Approximate Reasoning, vol. 148, pp. 68-79.
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Flores‐Sosa, M, Avilés‐Ochoa, E & Merigó, JM 2022, 'Exchange rate and volatility: A bibliometric review', International Journal of Finance & Economics, vol. 27, no. 1, pp. 1419-1442.
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AbstractThe exchange rate is one of the most important prices in open economies. Exchange rate volatility (ERV) has been studied in terms of its measurement, forecast and impact and relationship with other variables. This article proposes a bibliometric analysis of ERV compared with two databases Web of Science and Scopus. The number of data obtained reflects the importance of the topic in scientific research. In addition, we identify authors, institutions and countries of great influence studying currency volatility. The evolution of the study through time shows the increase in attention on the topic. VOS viewer software has been used to create graphic maps and visualize the connections existing in the study.
Flores-Sosa, M, Avilés-Ochoa, E, Merigó, JM & Kacprzyk, J 2022, 'The OWA operator in multiple linear regression', Applied Soft Computing, vol. 124, pp. 108985-108985.
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Flores-Sosa, M, León-Castro, E, Merigó, JM & Yager, RR 2022, 'Forecasting the exchange rate with multiple linear regression and heavy ordered weighted average operators', Knowledge-Based Systems, vol. 248, pp. 108863-108863.
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Fu, A, Yu, S, Zhang, Y, Wang, H & Huang, C 2022, 'NPP: A New Privacy-Aware Public Auditing Scheme for Cloud Data Sharing with Group Users', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 14-24.
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Today, cloud storage becomes one of the critical services, because users can easily modify and share data with others in cloud. However, the integrity of shared cloud data is vulnerable to inevitable hardware faults, software failures or human errors. To ensure the integrity of the shared data, some schemes have been designed to allow public verifiers (i.e., third party auditors) to efficiently audit data integrity without retrieving the entire users' data from cloud. Unfortunately, public auditing on the integrity of shared data may reveal data owners' sensitive information to the third party auditor. In this paper, we propose a new privacy-aware public auditing mechanism for shared cloud data by constructing a homomorphic verifiable group signature. Unlike the existing solutions, our scheme requires at least t group managers to recover a trace key cooperatively, which eliminates the abuse of single-authority power and provides non-frameability. Moreover, our scheme ensures that group users can trace data changes through designated binary tree; and can recover the latest correct data block when the current data block is damaged. In addition, the formal security analysis and experimental results indicate that our scheme is provably secure and efficient.
Fumanal-Idocin, J, Takac, Z, Fernandez, J, Sanz, JA, Goyena, H, Lin, C-T, Wang, Y-K & Bustince, H 2022, 'Interval-Valued Aggregation Functions Based on Moderate Deviations Applied to Motor-Imagery-Based Brain–Computer Interface', IEEE Transactions on Fuzzy Systems, vol. 30, no. 7, pp. 2706-2720.
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In this work we develop moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data to construct interval-valued aggregation functions, and we apply these functions in two Motor-Imagery Brain Computer Interface (MI-BCI) systems to classify electroencephalography signals. To do so, we introduce the notion of interval-valued moderate deviation function and, in particular, we study those interval-valued moderate deviation functions which preserve the width of the input intervals. In order to apply them in a MI-BCI system, we first use fuzzy implication operators to measure the uncertainty linked to the output of each classifier in the ensemble of the system, and then we perform the decision making phase using the new interval-valued aggregation functions. We have tested the goodness of our proposal in two MI-BCI frameworks, obtaining better results than those obtained using other numerical aggregation and interval-valued OWA operators, and obtaining competitive results versus some non aggregation-based frameworks.
Fumanal-Idocin, J, Wang, Y-K, Lin, C-T, Fernandez, J, Sanz, JA & Bustince, H 2022, 'Motor-Imagery-Based Brain–Computer Interface Using Signal Derivation and Aggregation Functions', IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 7944-7955.
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Brain-computer interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is motor imagery (MI). In BCI applications, the electroencephalography (EEG) is a very popular measurement for brain dynamics because of its noninvasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. This difficulty lies in the selection of the correct EEG channels, the signal-to-noise ratio of these signals, and how to discern the redundant information among them. BCI systems are composed of a wide range of components that perform signal preprocessing, feature extraction, and decision making. In this article, we define a new BCI framework, called enhanced fusion framework, where we propose three different ideas to improve the existing MI-based BCI frameworks. First, we include an additional preprocessing step of the signal: a differentiation of the EEG signal that makes it time invariant. Second, we add an additional frequency band as a feature for the system: the sensorimotor rhythm band, and we show its effect on the performance of the system. Finally, we make a profound study of how to make the final decision in the system. We propose the usage of both up to six types of different classifiers and a wide range of aggregation functions (including classical aggregations, Choquet and Sugeno integrals, and their extensions and overlap functions) to fuse the information given by the considered classifiers. We have tested this new system on a dataset of 20 volunteers performing MI-based brain-computer interface experiments. On this dataset, the new system achieved 88.80% accuracy. We also propose an optimized version of our system that is able to obtain up to 90.76%. Furthermore, we find that the pair Choquet/Sugeno integrals and overlap fun...
Gao, H, Huang, J, Tao, Y, Hussain, W & Huang, Y 2022, 'The Joint Method of Triple Attention and Novel Loss Function for Entity Relation Extraction in Small Data-Driven Computational Social Systems', IEEE Transactions on Computational Social Systems, vol. 9, no. 6, pp. 1725-1735.
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Gao, H, Luo, X, Barroso, RJD & Hussain, W 2022, 'Guest editorial: Smart communications and networking: architecture, applications, and future challenges', IET Communications, vol. 16, no. 10, pp. 1021-1024.
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Gao, H, Qin, X, Barroso, RJD, Hussain, W, Xu, Y & Yin, Y 2022, 'Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 1, pp. 66-76.
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Gao, H, Zhang, Y & Hussain, W 2022, 'Special issue on intelligent software engineering', Expert Systems, vol. 39, no. 6.
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Gao, S, Yu, S, Wu, L, Yao, S & Zhou, X 2022, 'Detecting adversarial examples by additional evidence from noise domain', IET Image Processing, vol. 16, no. 2, pp. 378-392.
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Deep neural networks are widely adopted powerful tools for perceptual tasks. However, recent research indicated that they are easily fooled by adversarial examples, which are produced by adding imperceptible adversarial perturbations to clean examples. Here the steganalysis rich model (SRM) is utilized to generate noise feature maps, and they are combined with RGB images to discover the difference between adversarial examples and clean examples. In particular, a two-stream pseudo-siamese network that fuses the subtle difference in RGB images with the noise inconsistency in noise features is proposed. The proposed method has strong detection capability and transferability, and can be combined with any model without modifying its architecture or training procedure. The extensive empirical experiments show that, compared with the state-of-the-art detection methods, the proposed approach achieves excellent performance in distinguishing adversarial samples generated by popular attack methods on different real datasets. Moreover, this method has good generalization, it trained by a specific adversary can defend against other adversaries effectively.
Gao, W, Wu, J & Xu, G 2022, 'Detecting Duplicate Questions in Stack Overflow via Source Code Modeling', International Journal of Software Engineering and Knowledge Engineering, vol. 32, no. 02, pp. 227-255.
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Stack Overflow is one of the most popular Question-Answering sites for programmers. However, it faces the problem of question duplication, where newly created questions are identical to previous questions. Existing works on duplicate question detection in Stack Overflow extract a set of textual features on the question pairs and use supervised learning approaches to classify duplicate question pairs. However, they do not consider the source code information in the questions. While in some cases, the intention of a question is mainly represented by the source code. In this paper, we aim to learn the semantics of a question by combining both text features and source code features. We use word embedding and convolutional neural networks to extract textual features from questions to overcome the lexical gap issue. We use tree-based convolutional neural networks to extract structural and semantic features from source code. In addition, we perform multi-task learning by combining the duplication question detection task with a question tag prediction side task. We conduct extensive experiments on the Stack Overflow dataset and show that our approach can detect duplicate questions with higher recall and MRR compared with baseline approaches on Python and Java programming languages.
García-Orozco, D, Alfaro-García, VG, Merigó, JM, Espitia Moreno, IC & Gómez Monge, R 2022, 'An overview of the most influential journals in fuzzy systems research', Expert Systems with Applications, vol. 200, pp. 117090-117090.
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George, DJ, Sanders, YR, Bagherimehrab, M, Sanders, BC & Brennen, GK 2022, 'Entanglement in quantum field theory via wavelet representations', Physical Review D, vol. 106, no. 3.
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Quantum field theory (QFT) describes nature using continuous fields, but physical properties of QFT are usually revealed in terms of measurements of observables at a finite resolution. We describe a multiscale representation of free scalar bosonic and Ising model fermionic QFTs using wavelets. Making use of the orthogonality and self-similarity of the wavelet basis functions, we demonstrate some well-known relations such as scale-dependent subsystem entanglement entropy and renormalization of correlations in the ground state. We also find some new applications of the wavelet transform as a compressed representation of ground states of QFTs which can be used to illustrate quantum phase transitions via fidelity overlap and holographic entanglement of purification.
Gil Lafuente, AM, Reverter, SB, Merigó, JM & Martínez, AT 2022, 'Preface', Lecture Notes in Networks and Systems, vol. 388 LNNS, pp. v-vi.
Gilberts, R, McGinnis, E, Ransom, M, Pynn, EV, Walker, B, Brown, S, Trehan, P, Jayasekera, P, Veitch, D, Hussain, W, Collins, J, Abbott, RA, Chen, KS & Nixon, J 2022, 'Healing of ExcisionAl wounds on Lower legs by Secondary intention (HEALS) cohort study. Part 2: feasibility data from a multicentre prospective observational cohort study to inform a future randomized controlled trial', Clinical and Experimental Dermatology, vol. 47, no. 10, pp. 1839-1847.
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Abstract Background Compression therapy is considered beneficial for postsurgical lower leg wound healing by secondary intention; however, there is a lack of supportive evidence. To plan a randomized controlled trial (RCT), suitable data are needed. Aim To determine the feasibility of recruitment and estimate recruitment rate; to understand the standard postoperative wound management pathway; to determine uptake of optional additional clinic visits for healing confirmation; and to explore patient acceptability of compression bandaging and plan a future RCT. Methods Participant recruitment was performed from secondary care dermatology clinics, during a period of 22 months. Inclusion criteria were age ≥ 18 years, planned excision of keratinocyte cancer on the lower leg with healing by secondary intention and an ankle–brachial pressure index of ≥ 0.8. Exclusion criteria were planned primary closure/graft or flap; inability to receive, comply with or tolerate high compression; planned compression; or suspected melanoma. Patients were followed up weekly (maximum 6 months) in secondary care clinics and/or by telephone. Information was collected on healthcare resource use, unplanned compression, wound healing and an optional clinic visit to confirm healing. Results This study recruited 58 patients from 9 secondary care dermatology clinics over 22 months. Mean recruitment/centre/month was 0.8 (range 0.1–2.3). Four centres had dedicated Research Nurse support...
Gong, Y, Li, Z, Zhang, J, Liu, W & Zheng, Y 2022, 'Online Spatio-Temporal Crowd Flow Distribution Prediction for Complex Metro System', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 2, pp. 865-880.
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Goodswen, SJ, Kennedy, PJ & Ellis, JT 2022, 'Compilation of parasitic immunogenic proteins from 30 years of published research using machine learning and natural language processing', Scientific Reports, vol. 12, no. 1.
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AbstractThe World Health Organisation reported in 2020 that six of the top 10 sources of death in low-income countries are parasites. Parasites are microorganisms in a relationship with a larger organism, the host. They acquire all benefits at the host’s expense. A disease develops if the parasitic infection disrupts normal functioning of the host. This disruption can range from mild to severe, including death. Humans and livestock continue to be challenged by established and emerging infectious disease threats. Vaccination is the most efficient tool for preventing current and future threats. Immunogenic proteins sourced from the disease-causing parasite are worthwhile vaccine components (subunits) due to reliable safety and manufacturing capacity. Publications with ‘subunit vaccine’ in their title have accumulated to thousands over the last three decades. However, there are possibly thousands more reporting immunogenicity results without mentioning ‘subunit’ and/or ‘vaccine’. The exact number is unclear given the non-standardised keywords in publications. The study aim is to identify parasite proteins that induce a protective response in an animal model as reported in the scientific literature within the last 30 years using machine learning and natural language processing. Source code to fulfil this aim and the vaccine candidate list obtained is made available.
Guan, S, Lu, H, Zhu, L & Fang, G 2022, 'AFE-CNN: 3D Skeleton-based Action Recognition with Action Feature Enhancement', Neurocomputing, vol. 514, pp. 256-267.
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Gui, L, Xu, S, Xiao, F, Shu, F & Yu, S 2022, 'Non-Line-of-Sight Localization of Passive UHF RFID Tags in Smart Storage Systems', IEEE Transactions on Mobile Computing, vol. 21, no. 10, pp. 3731-3743.
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Guo, E, Li, P, Yu, S & Wang, H 2022, 'Efficient Video Privacy Protection Against Malicious Face Recognition Models', IEEE Open Journal of the Computer Society, vol. 3, pp. 271-280.
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The proliferation of powerful facial recognition systems poses a serious threat to user privacy. Attackers could train highly accurate facial recognition models using public data on social platforms. Therefore, recent works have proposed image pre-processing techniques to protect user privacy. Without affecting people's normal viewing, these techniques add special noises into images, so that it would be difficult for attackers to train models with high accuracy. However, existing protection techniques are mainly designed for image data protection, and they cannot be directly applied for video data because of high computational overhead. In this paper, we propose an efficient protection method for video privacy that exploits unique features of video protection to eliminate computation redundancy for computational acceleration. The evaluation results under various benchmarks demonstrate that our method significantly outperforms the traditional methods by reducing computation overhead by 35.5%.
Guo, H, Wang, J, Li, Z, Lu, H & Zhang, L 2022, 'A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory', Resources Policy, vol. 79, pp. 102975-102975.
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Guo, Y, Liu, L, Ba, X, Lu, H, Lei, G, Sarker, P & Zhu, J 2022, 'Characterization of Rotational Magnetic Properties of Amorphous Metal Materials for Advanced Electrical Machine Design and Analysis', Energies, vol. 15, no. 20, pp. 7798-7798.
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Amorphous metal (AM), specifically amorphous ferromagnetic metal, is considered as a satisfactory magnetic material for exploring electromagnetic devices with high-efficiency and high-power density, such as electrical machines and transformers, benefits from its various advantages, such as reasonably low power loss and very high permeability in medium to high frequency. However, the characteristics of these materials have not been investigated comprehensively, which limits its application prospects to good-performance electrical machines that have the magnetic flux density with generally rotational and non-sinusoidal features. The appropriate characterization of AMs under different magnetizations is among the fundamentals for utilizing these materials in electrical machines. This paper aims to extensively overview AM property measurement techniques in the presence of various magnetization patterns, particularly rotational magnetizations, and AM property modeling methods for advanced electrical machine design and analysis. Possible future research tasks are also discussed for further improving AM applications.
Guo, Z, Xiao, F, Sheng, B, Sun, L & Yu, S 2022, 'TWCC: A Robust Through-the-Wall Crowd Counting System Using Ambient WiFi Signals', IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 4198-4211.
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With the widespread of commercial communication equipment, WiFi signals are ubiquitous in human life. Therefore, utilizing WiFi signals to implement intelligent sensing applications is an inevitable trend. In WiFi sensing applications, through-the-wall crowd counting is a challenging problem. In the through-the-wall scenario, the wireless signal transmitted through the wall will carry a lot of noises and is severely attenuated. Therefore, the influence of human activities on the wireless signal is difficult to extract. To solve this problem, we propose TWCC, a through-the-wall crowd counting system using ambient WiFi signals. TWCC utilizes commercial WiFi equipments to extract the phase difference data of the channel state information (CSI) and transform it to sense the environment. First, TWCC preprocesses the data to remove uncorrelated noise, and then combines the sub-carrier correlation to achieve through-the-wall human detection. When people exist, TWCC extracts features from four domains as feature groups, namely time domain, subcarrier domain, frequency domain, and time-frequency domain. Then TWCC uses different backpropagation (BP) neural networks for the features of the four domains and combines with weighting and threshold judgment to realize the through-the-wall crowd counting detection. Extensive real-world experiments show that TWCC achieves an average recognition accuracy of about 90% and maintains strong robustness to different speeds and environments.
Gupta, D, Borah, P, Sharma, UM & Prasad, M 2022, 'Data-driven mechanism based on fuzzy Lagrangian twin parametric-margin support vector machine for biomedical data analysis', Neural Computing and Applications, vol. 34, no. 14, pp. 11335-11345.
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This paper proposes a fuzzy-based Lagrangian twin parametric-margin support vector machine (FLTPMSVM) to reduce the effect of the outliers presented in biomedical data. The proposed FLTPMSVM assigns the weights to each data sample on the basis of fuzzy membership values to reduce the effect of outliers. This paper also adopts the square of the 2-norm of slack variables to make the objective function more convex. The proposed FLTPMSVM solves simple linearly convergent iterative schemes instead of solving a pair of quadratic programming problems. No external toolbox is required for the proposed FLTPMSVM as compared to the other methods. To establish the applicability of the proposed FLTPMSVM in the area of biomedical data classification, numerical experiments are performed on several biomedical datasets. The proposed FLTPMSVM gives an improved generalization performance and reduced training cost as compared to support vector machine (SVM), twin support vector machine (TWSVM), fuzzy twin support vector machine (FTSVM), twin parametric-margin support vector machine (TPMSVM) and new fuzzy twin support vector machine (NFTSVM).
Hamdi, AMA, Hussain, FK & Hussain, OK 2022, 'Task offloading in vehicular fog computing: State-of-the-art and open issues', Future Generation Computer Systems, vol. 133, pp. 201-212.
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Vehicular fog computing (VFC) has been proposed as a promising solution to overcome the limitations of edge computing. In VFC, the idle resources of moving and parked vehicles can be used for compute-intensive applications of resource-limited vehicles by offloading their tasks to them. For this to succeed, selecting an appropriate target fog node needs to consider various constraints. This paper argues that the selection process should broadly follow the steps needed to form a service level agreement (SLA) to ensure that the right target fog node is selected. We identify the different requirements that need to be addressed in forming such a SLA before surveying the existing literature to determine if the existing approaches of task offloading in VFC address them or not. Based on the analysis, we conclude the paper by discussing open gaps that need to be addressed for efficient task offloading in VFC.
Han, X, Yu, X, Zhu, H, Wang, L, Yu, S, Wang, M & Zheng, M 2022, 'Elastic breakdown via multi-core high frequency discharge for lean-burn ignition', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 236, no. 12, pp. 2661-2680.
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An advanced ignition technique is developed to achieve multi-event breakdown and multi-site ignition using a single coil for ignition quality improvements. The igniter enables a unique elastic breakdown process embracing a series of high-frequency discharge events at the spark gap. The equivalent electric circuits and current/voltage equations are identified and verified for the first time to explain the working principle that governs such an elastic breakdown process. Benchmarking tests are first performed to compare the elastic breakdown ignition with the conventional and advanced multi-electrode ignition systems. The elastic breakdown and spark events are thereafter analyzed through current and voltage measurements and high-speed imaging techniques. Finally, ignition tests in combustion chambers are performed to examine the effects on the ignition process in comparison with conventional coil-based ignition systems. The experiments show that, the elastic breakdown ignition can distribute multiple high-frequency breakdown events at all electrode pairs of a multi-electrode sparkplug while using only one ignition coil, thereby offering significant cost saving advantage and packaging practicability.
Hason Rudd, D, Huo, H & Xu, G 2022, 'Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions', Human-Centric Intelligent Systems, vol. 2, no. 3-4, pp. 70-80.
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AbstractCustomer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Customer acquisition cost can be five to six times that of customer retention, hence investing in customers with churn risk is wise. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and identify effects and possible causes for churn. In general, this study presents a conceptual framework to discover the confounding features that correlate with independent variables and are causally related to those dependent variables that impact churn. We combine different algorithms including the SMOTE, ensemble ANN, and Bayesian networks to address churn prediction problems on a massive and high-dimensional finance data that is usually generated in financial institutions due to employing interval-based features used in Customer Relationship Management systems. The effects of the curse and blessing of dimensionality assessed by utilising the Recursive Feature Elimination method to overcome the high dimension feature space problem. Moreover, a causal discovery performed to find possible interpretation methods to describe cause probabilities that lead to customer churn. Evaluation metrics on validation data confirm the random forest and our ensemble ANN model, with %86 accuracy, outperformed other approaches. Causal analysis results confirm that some independent causal variables representing the level of super guarantee contribution, account growth, and account balance amount were identified as confounding variables that cause customer churn with a high degree of belief. This article provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds.
He, D, Lv, X, Xu, X, Yu, S, Li, D, Chan, S & Guizani, M 2022, 'An Effective Double-Layer Detection System Against Social Engineering Attacks', IEEE Network, vol. 36, no. 6, pp. 92-98.
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He, L, Wang, X, Chen, H & Xu, G 2022, 'Online Spam Review Detection: A Survey of Literature', Human-Centric Intelligent Systems, vol. 2, no. 1-2, pp. 14-30.
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AbstractThe increasingly developed online platform generates a large amount of online reviews every moment, e.g., Yelp and Amazon. Consumers gradually develop the habit of reading previous reviews before making a decision of buying or choosing various products. Online reviews play an vital part in determining consumers’ purchase choices in e-commerce, yet many online reviews are intentionally created to confuse or mislead potential consumers. Moreover, driven by product reputations and merchants’ profits, more and more spam reviews were inserted into online platform. This kind of reviews can be positive, negative or neutral, but they had common features: misleading consumers or damaging reputations. In the past decade, many people conducted research on detecting spam reviews using statistical or deep learning method with various datasets. In view of that, this article first introduces the task of spam online reviews detection and makes a common definition of spam reviews. Then, we comprehensively conclude the existing method and available datasets. Third, we summarize the existing network-based approaches in dealing with this task and propose some direction for future research.
He, Y, Zhang, X, Xia, Z, Liu, Y, Sood, K & Yu, S 2022, 'Joint optimization of Service Chain Graph Design and Mapping in NFV-enabled networks', Computer Networks, vol. 202, pp. 108626-108626.
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Network Function Virtualization (NFV) is an emerging approach to serve diverse demands of network services by decoupling network functions and dedicated network devices. Traffic needs to traverse through a sequence of software-based Virtual Network Functions (VNFs) in a preset order, which is named as Service Function Chain (SFC). Since network operators usually deploy the same type of VNFs in different locations in NFV-enabled networks. How to steer a SFC request to an appropriate path in substrate networks to meet service demands becomes an important issue, which is typically known as SFC mapping. However, the existing research works on SFC mapping often assume that service chain graphs are given in advance. They do not consider VNF interdependency and traffic volume change, which are both theoretically challenging for NFV Management and Orchestration (MANO) framework. To this end, we study the joint optimization of Service Chain Graph Design and Mapping (SCGDM) in NFV-enabled networks. Our objective is to minimize the maximum link load factor to improve the performance of network system. We first formulate the SCGDM problem as an Integer Linear Programming (ILP) model, and prove that it is an NP-hard problem by reduction from a classical Virtual Network Embedding (VNE) problem. Further, we develop an approximation algorithm using randomized rounding method and analyze the approximation performance. Extensive simulation results show that the proposed algorithm effectively reduce the maximum link load factor.
Hesam‐Shariati, N, Chang, W, Wewege, MA, McAuley, JH, Booth, A, Trost, Z, Lin, C, Newton‐John, T & Gustin, SM 2022, 'The analgesic effect of electroencephalographic neurofeedback for people with chronic pain: A systematic review and meta‐analysis', European Journal of Neurology, vol. 29, no. 3, pp. 921-936.
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AbstractBackgroundElectroencephalographic (EEG) neurofeedback has been utilized to regulate abnormal brain activity associated with chronic pain.MethodsIn this systematic review, we synthesized the evidence from randomized controlled trials (RCTs) to evaluate the effect of EEG neurofeedback on chronic pain using random effects meta‐analyses. Additionally, we performed a narrative review to explore the results of non‐randomized studies. The quality of included studies was assessed using Cochrane risk of bias tools, and the GRADE system was used to rate the certainty of evidence.ResultsTen RCTs and 13 non‐randomized studies were included. The primary meta‐analysis on nine eligible RCTs indicated that although there is low confidence, EEG neurofeedback may have a clinically meaningful effect on pain intensity in short‐term. Removing the studies with high risk of bias from the primary meta‐analysis resulted in moderate confidence that there remained a clinically meaningful effect on pain intensity. We could not draw any conclusion from the findings of non‐randomized studies, as they were mostly non‐comparative trials or explorative case series. However, the extracted data indicated that the neurofeedback protocols in both RCTs and non‐randomized studies mainly involved the conventional EEG neurofeedback approach, which targeted reinforcing either alpha or sensorimotor rhythms and suppressing theta and/or beta bands on one brain region at a time. A posthoc analysis of RCTs utilizing the conventional approach resulted in a clinically meaningful effect estimate for pain intensity.ConclusionAlthough there is promising evidence on the analgesic effect of EEG neurofeedback, further studies with larger sample si...
Hu, S, Yu, S, Li, H & Piuri, V 2022, 'Guest Editorial Special Issue on Security, Privacy, and Trustworthiness in Intelligent Cyber–Physical Systems and Internet of Things', IEEE Internet of Things Journal, vol. 9, no. 22, pp. 22044-22047.
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Hu, X, Jin, Z, Zhang, L, Zhou, A & Ye, D 2022, 'Privacy preservation auction in a dynamic social network', Concurrency and Computation: Practice and Experience, vol. 34, no. 16.
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SummaryThe growing popularity of users in online social network gives a big opportunity for online auction. The famous Information Diffusion Mechanism (IDM) is an excellent methods even meet the incentive compatibility and individual rationality. Although the existing auction in online social network has considered the buyers' information has not known by the seller, current mechanism still cannot preserve the information such as prices. In this paper, we propose a novel mechanism which modeled the auction process in online social network and preserved users' privacy by using differential privacy mechanism. Our mechanism can successfully process the auction and at the same time preserve clients' price information from neighbors. We achieved these by adding Laplace noise for its valuation and the number of valuation seller received in the auction process. We also formulate this mechanism on the real network to show the feasibility and effective of the proposed mechanism.
Hu, X, Zhu, T, Zhai, X, Wang, H, Zhou, W & Zhao, W 2022, 'Privacy Data Diffusion Modeling and Preserving in Online Social Network', IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-1.
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With the ubiquity of social media, privacy leakage has become a urgent problemfor social media managers. Studying how the privacy information diffuses through social media has attracted much attention. As a prerequisite, modeling privacy information diffusion is important research. Current approaches for modeling information diffusion are not available for privacy information since they did not consider the propagation features of privacy information in social media. Thispaper discusses the problem of modeling privacy information in social media and its challenges. We first analyse the information diffusion paths in the basic parameters of complex network and the high-order structures. We find that the privacy information is different in propagation features and the size of star structures. Second, a new information diffusion model is illustrated to simulate the diffusion process of information in social media by considering the following three parameters: 1) the probability of users receiving this message, 2) the probability that users have a tendency to forward this message and 3) the interest the users hold for this message. Finally, a block mechanism is designed to congest the diffusion of privacy information in social media.
Huang, C, Yao, L, Wang, X, Sheng, QZ, Dustdar, S, Wang, Z & Xu, X 2022, 'Intent-Aware Interactive Internet of Things for Enhanced Collaborative Ambient Intelligence', IEEE Internet Computing, vol. 26, no. 5, pp. 68-75.
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Huang, W, Zhou, S, Zhu, T & Liao, Y 2022, 'Privately Publishing Internet of Things Data: Bring Personalized Sampling Into Differentially Private Mechanisms', IEEE Internet of Things Journal, vol. 9, no. 1, pp. 80-91.
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Hussain, W 2022, 'Approaching a Large Defect on the Lower Nasal Sidewall—A Twist on a Classic Reconstruction', Dermatologic Surgery, vol. 48, no. 2, pp. 239-241.
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Hussain, W, Gao, H, Raza, MR, Rabhi, FA & Merigó, JM 2022, 'Assessing cloud QoS predictions using OWA in neural network methods', Neural Computing and Applications, vol. 34, no. 17, pp. 14895-14912.
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AbstractQuality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods—Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy.
Hussain, W, Merigó, JM & Raza, MR 2022, 'Predictive intelligence using ANFIS‐induced OWAWA for complex stock market prediction', International Journal of Intelligent Systems, vol. 37, no. 8, pp. 4586-4611.
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Hussain, W, Merigó, JM, Raza, MR & Gao, H 2022, 'A new QoS prediction model using hybrid IOWA-ANFIS with fuzzy C-means, subtractive clustering and grid partitioning', Information Sciences, vol. 584, pp. 280-300.
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Quality of Service (QoS) is one of the key indicators to measure the overall performance of cloud services. The quantitative measurement of the QoS enables the service provider to manage its Service Level Agreement (SLA) in a viable way. It also supports a consumer in service selection and allows measuring the received services to comply with agreed services. There is much existing literature that tries to predict the QoS and assist stakeholders in their decision-making process. However, it is tricky to deal with multidimensional data in time series prediction methods. The computational complexity increases with an increase in data dimension, and it is a challenging task to give precise weights to each time interval. Existing prediction methods could not deal with the intricate reordering of input weights. To address this problem, we propose a novel Clustered Induced Ordered Weighted Averaging (IOWA) Adaptive Neuro-Fuzzy Inference System (ANFIS), (CI-ANFIS) model. This fuzzy time series prediction model reduces data dimension and handles the nonlinear relationship of the cloud QoS dataset. The proposed method uses an intelligent sorting mechanism that regulates uncertainty in prediction while incorporating a fuzzy neural network structure for optimal prediction results. The proposed method employs the IOWA operator to sort input arguments based on associated order-inducing variables and assign customised weights accordingly. The inputs are further classified using three fuzzy clustering methods - fuzzy c-means (FCM), subtractive clustering and grid partitioning. The inputs further pass to the ANFIS structure that takes the benefits of both the fuzzy and neural networks. The fuzzy structure in ANFIS builds understandable rules for cloud stakeholders and deals with uncertain occurrences of data. The model uses a real cloud QoS dataset extracted from the Amazon Elastic Compute Cloud (EC2) US-West instance and predict its behaviour every five minutes for th...
Hussain, W, Raza, MR, Jan, MA, Merigo, JM & Gao, H 2022, 'Cloud Risk Management With OWA-LSTM and Fuzzy Linguistic Decision Making', IEEE Transactions on Fuzzy Systems, vol. 30, no. 11, pp. 4657-4666.
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In a cloud environment, the indemnity of service level agreement (SLA) violations has an adverse effect on the service provider. It leads to the penalty fee, credit amount, license extension, and reputation decline that could significantly impact future business outcomes. Existing approaches are unable to handle complex predictions that can accommodate the temporal influence of Quality of Service (QoS) data. Moreover, no method in a cloud environment considers all possible attitudinal behavior of the service provider to mitigate the risk of an actual violation. This article proposes an SLA violation risk mitigation model that uses ordered weighted average (OWA) in long short-term memory for complex QoS prediction. The OWA operator is weighted with a minimax disparity approach to manage the risk of SLA violation. The approach intelligently predicts deviation in custom prioritized QoS parameter and recommend exigency of mitigating action by considering all possible attitudinal behavior of the service provider. This article uses linguistic variables, fuzzy and interval numbers to handle imprecise information. The analysis results demonstrate the applicability and efficiency of the proposed approach to address complex risk mitigation actions.
Ibrar, I, Yadav, S, Braytee, A, Altaee, A, HosseinZadeh, A, Samal, AK, Zhou, JL, Khan, JA, Bartocci, P & Fantozzi, F 2022, 'Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis', Journal of Membrane Science, vol. 646, pp. 120257-120257.
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Internal concentration polarization (ICP) is currently a major bottleneck in the forward osmosis process. Proper modelling of the internal concentration polarization is therefore vital for improving the process performance and efficiency. This study assessed the feasibility of several machine learning methods for internal concentration polarization prediction, including artificial neural networks, extreme gradient boosting (XGBoost), Categorical boosting (CatBoost), Random forest, and linear regression. Among the many algorithms evaluated, the CatBoost regression outperformed other methods in terms of coefficient of determination (R2) and the mean square error. The CatBoost algorithm's prediction power was then evaluated using non-training (user-provided) data and compared to solution diffusion models. The results indicated that the machine learning algorithms could predict ICP in the process with high accuracy for the provided dataset and excellent generalizability for future testing data. Furthermore, machine learning algorithms may offer insights into the input features that majorly affect ICP modelling in the forward osmosis process.
Ijaz, K, Tran, TTM, Kocaballi, AB, Calvo, RA, Berkovsky, S & Ahmadpour, N 2022, 'Design Considerations for Immersive Virtual Reality Applications for Older Adults: A Scoping Review', Multimodal Technologies and Interaction, vol. 6, no. 7, pp. 60-60.
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Immersive virtual reality (iVR) has gained considerable attention recently with increasing affordability and accessibility of the hardware. iVR applications for older adults present tremendous potential for diverse interventions and innovations. The iVR literature, however, provides a limited understanding of guiding design considerations and evaluations pertaining to user experience (UX). To address this gap, we present a state-of-the-art scoping review of literature on iVR applications developed for older adults over 65 years. We performed a search in ACM Digital Library, IEEE Xplore, Scopus, and PubMed (1 January 2010–15 December 2019) and found 36 out of 3874 papers met the inclusion criteria. We identified 10 distinct sets of design considerations that guided target users and physical configuration, hardware use, and software design. Most studies carried episodic UX where only 2 captured anticipated UX and 7 measured longitudinal experiences. We discuss the interplay between our findings and future directions to design effective, safe, and engaging iVR applications for older adults.
Inan, DI, Beydoun, G & Pradhan, B 2022, 'Disaster Management Knowledge Analysis Framework Validated.', Inf. Syst. Frontiers, vol. 24, no. 6, pp. 2077-2097.
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In Disaster Management (DM), reusing knowledge of best practices from past experiences is envisaged as the best approach for dealing with future disasters. But analysing and modelling processes involved in those experiences is a well-known challenge. But the efficient storage of those processes to allow reuse by others in future DM endeavours is even more challenging and less discussed. Without an efficient process in place, DM knowledge reuse becomes even more remote as the effort incurred gets construed as a hindrance to more pressing activities during the execution of disaster activities. Efficiency has to also be pursued without compromising the effectiveness of the knowledge analysis and reuse. It is important to ensure that knowledge remains meaningful and relevant after it is transformed. This paper presents and validates a DM knowledge analysis framework (DMKAF 2.0) that caters for efficient transformation of DM knowledge intended for reuse. The paper demonstrates that undertaking knowledge transformation and storage in the context of its use is crucial in DM for both, effectiveness and efficiency of the transformation process. Design Science Research methodology guides the research undertaken, by informing enhancements and how the framework is evaluated. A real case study of flood DM from the State Emergency Service of Victoria State Australia is successfully used to validate these enhancements.
Islam, MR, Lu, H, Hossain, MJ & Li, L 2022, 'Coordinating Electric Vehicles and Distributed Energy Sources Constrained by User’s Travel Commitment', IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5307-5317.
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Ivanyos, G, Mittal, T & Qiao, Y 2022, 'Symbolic Determinant Identity Testing and Non-Commutative Ranks of Matrix Lie Algebras', Leibniz International Proceedings in Informatics, LIPIcs, vol. 215.
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One approach to make progress on the symbolic determinant identity testing (SDIT) problem is to study the structure of singular matrix spaces. After settling the non-commutative rank problem (Garg-Gurvits-Oliveira-Wigderson, Found. Comput. Math. 2020; Ivanyos-Qiao-Subrahmanyam, Comput. Complex. 2018), a natural next step is to understand singular matrix spaces whose non-commutative rank is full. At present, examples of such matrix spaces are mostly sporadic, so it is desirable to discover them in a more systematic way. In this paper, we make a step towards this direction, by studying the family of matrix spaces that are closed under the commutator operation, that is, matrix Lie algebras. On the one hand, we demonstrate that matrix Lie algebras over the complex number field give rise to singular matrix spaces with full non-commutative ranks. On the other hand, we show that SDIT of such spaces can be decided in deterministic polynomial time. Moreover, we give a characterization for the matrix Lie algebras to yield a matrix space possessing singularity certificates as studied by Lovász (B. Braz. Math. Soc., 1989) and Raz and Wigderson (Building Bridges II, 2019).
Javed, AR, Shahzad, F, Rehman, SU, Zikria, YB, Razzak, I, Jalil, Z & Xu, G 2022, 'Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects', Cities, vol. 129, pp. 103794-103794.
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Future smart cities are the key to fulfilling the ever-growing demands of citizens. Information and communication advancements will empower better administration of accessible resources. The eventual fate of the world's betterment lies in its urban environment advancement. The fast influx of individuals creates possibility, yet it additionally causes challenges. Creating sustainable, reasonable space in the world's steadily extending cities is tested confronting governments worldwide. The model of the smart cities rise, where the rights and well-being of the smart city citizens are assured, the industry is in action, and the assessment of urban planning from an environmental point of view. This paper presents a survey on analyzing future technologies and requirements for future smart cities. We provide extensive research to identify and inspect the latest technology advancements, the foundation of the upcoming robust era. Such technologies include deep learning (DL), machine learning (ML), internet of things (IoT), mobile computing, big data, blockchain, sixth-generation (6G) networks, WiFi-7, industry 5.0, robotic systems, heating ventilation, and air conditioning (HVAC), digital forensic, industrial control systems, connected and automated vehicles (CAVs), electric vehicles, product recycling, flying Cars, pantry backup, calamity backup and vital integration of cybersecurity to keep the user concerns secured. We provide a detailed review of the existing future smart cities application frameworks. Furthermore, we discuss various technological challenges of future smart cities. Finally, we identify the future dimensions of smart cities to develop smart cities with the precedence of smart living.
Jena, KK, Bhoi, SK, Prasad, M & Puthal, D 2022, 'A fuzzy rule-based efficient hospital bed management approach for coronavirus disease-19 infected patients', Neural Computing and Applications, vol. 34, no. 14, pp. 11361-11382.
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Coronavirus disease-19 (COVID-19) is a very dangerous infectious disease for the entire world in the current scenario. Coronavirus spreads from one person to another person very rapidly. It spreads exponentially throughout the globe. Everyone should be cautious to avoid the spreading of this novel disease. In this paper, a fuzzy rule-based approach using priority-based method is proposed for the management of hospital beds for COVID-19 infected patients in the worst-case scenario where the number of hospital beds is very less as compared to the number of COVID-19 infected patients. This approach mainly attempts to minimize the number of hospital beds as well as emergency beds requirement for the treatment of COVID-19 infected patients to handle such a critical situation. In this work, higher priority has given to severe COVID-19 infected patients as compared to mild COVID-19 infected patients to handle this critical situation so that the survival probability of the COVID-19 infected patients can be increased. The proposed method is compared with first-come first-serve (FCFS)-based method to analyze the practical problems that arise during the assignment of hospital beds and emergency beds for the treatment of COVID-19 patients. The simulation of this work is carried out using MATLAB R2015b.
Jia, M, Gabrys, B & Musial, K 2022, 'Measuring Quadrangle Formation in Complex Networks', IEEE Transactions on Network Science and Engineering, vol. 9, no. 2, pp. 538-551.
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The classic clustering coefficient and the lately proposed closure coefficient quantifies the formation of triangles from two different perspectives, with the focal node at the centre or at the end in an open triad. As many networks are naturally rich in triangles, they become standard metrics to describe and analyse networks. However, their utilities could be limited in many other types of networks, where triangles are relatively few and quadrangles are overrepresented, such as the protein-protein interaction networks, the neural networks and the food webs. Here we propose two quadrangle coefficients, i.e., the i-quad coefficient and the o-quad coefficient, to quantify quadrangle formation in networks, and we further extend them to weighted networks. Through experiments on 16 networks from six different domains, we first reveal the density distribution of the two quadrangle coefficients, and then analyse their correlations with node degree. Finally, we demonstrate that at network-level, adding the average i-quad coefficient and the average o-quad coefficient leads to significant improvement in network classification, while at node-level, the i-quad and o-quad coefficients are useful features to improve link prediction.
Jia, M, Van Alboom, M, Goubert, L, Bracke, P, Gabrys, B & Musial, K 2022, 'Encoding edge type information in graphlets', PLOS ONE, vol. 17, no. 8, pp. e0273609-e0273609.
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Graph embedding approaches have been attracting increasing attention in recent years mainly due to their universal applicability. They convert network data into a vector space in which the graph structural information and properties are maximumly preserved. Most existing approaches, however, ignore the rich information about interactions between nodes, i.e., edge attribute or edge type. Moreover, the learned embeddings suffer from a lack of explainability, and cannot be used to study the effects of typed structures in edge-attributed networks. In this paper, we introduce a framework to embed edge type information in graphlets and generate a Typed-Edge Graphlets Degree Vector (TyE-GDV). Additionally, we extend two combinatorial approaches, i.e., the colored graphlets and heterogeneous graphlets approaches to edge-attributed networks. Through applying the proposed method to a case study of chronic pain patients, we find that not only the network structure of a patient could indicate his/her perceived pain grade, but also certain social ties, such as those with friends, colleagues, and healthcare professionals, are more crucial in understanding the impact of chronic pain. Further, we demonstrate that in a node classification task, the edge-type encoded graphlets approaches outperform the traditional graphlet degree vector approach by a significant margin, and that TyE-GDV could achieve a competitive performance of the combinatorial approaches while being far more efficient in space requirements.
John, AR, Cao, Z, Chen, H-T, Martens, KE, Georgiades, M, Gilat, M, Nguyen, HT, Lewis, SJG & Lin, C-T 2022, 'Predicting the Onset of Freezing of Gait Using EEG Dynamics', Applied Sciences, vol. 13, no. 1, pp. 302-302.
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Freezing of gait (FOG) severely incapacitates the mobility of patients with advanced Parkinson’s disease (PD). An accurate prediction of the onset of FOG could improve the quality of life for PD patients. However, it is imperative to distinguish the possibility of the onset of FOG from that of voluntary stopping. Our previous work demonstrated the neurological differences between the transition to FOG and voluntary stopping using electroencephalogram (EEG) signals. We employed a timed up-and-go (TUG) task to elicit FOG in PD patients. Some of these TUG tasks had an additional voluntary stopping component, where participants stopped walking based on verbal instruction to “stop”. The performance of the convolutional neural network (CNN) in identifying the transition to FOG from normal walking and the transition to voluntary stopping was explored. To the best of our knowledge, this work is the first study to propose a deep learning method to distinguish the transition to FOG from the transition to voluntary stop in PD patients. The models, trained on the EEG data from 17 PD patients who manifested FOG episodes, considering a short two-second transition window for FOG occurrence or voluntary stopping, achieved close to 75% classification accuracy in distinguishing transition to FOG from the transition to voluntary stopping or normal walking. Our results represent an important step toward advanced EEG-based cueing systems for smart FOG intervention, excluding the potential confounding of voluntary stopping.
John, AR, Singh, AK, Do, T-TN, Eidels, A, Nalivaiko, E, Gavgani, AM, Brown, S, Bennett, M, Lal, S, Simpson, AM, Gustin, SM, Double, K, Walker, FR, Kleitman, S, Morley, J & Lin, C-T 2022, 'Unraveling the Physiological Correlates of Mental Workload Variations in Tracking and Collision Prediction Tasks', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, no. 99, pp. 770-781.
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Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control operators routinely work in such complex environments, and we designed tracking and collision prediction tasks to emulate their elementary tasks. The physiological response to the workload variations in these tasks was elucidated to untangle the impact of workload variations experienced by operators. Electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. Our findings indicate that variations in task load in both these tasks are sensitively reflected in EEG, eye activity and HRV data. Multiple regression results also show that operators' performance in both tasks can be predicted using the corresponding EEG, eye activity and HRV data. The results also demonstrate that the brain dynamics during each of these tasks can be estimated from the corresponding eye activity, HRV and performance data. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just 'when' but also 'what' to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in complex work environments.
Joshi, S, Sharma, M, Das, RP, Rosak-Szyrocka, J, Żywiołek, J, Muduli, K & Prasad, M 2022, 'Modeling Conceptual Framework for Implementing Barriers of AI in Public Healthcare for Improving Operational Excellence: Experiences from Developing Countries', Sustainability, vol. 14, no. 18, pp. 11698-11698.
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This study work is among the few attempts to understand the significance of AI and its implementation barriers in the healthcare systems in developing countries. Moreover, it examines the breadth of applications of AI in healthcare and medicine. AI is a promising solution for the healthcare industry, but due to a lack of research, the understanding and potential of this technology is unexplored. This study aims to determine the crucial AI implementation barriers in public healthcare from the viewpoint of the society, the economy, and the infrastructure. The study used MCDM techniques to structure the multiple-level analysis of the AI implementation. The research outcomes contribute to the understanding of the various implementation barriers and provide insights for the decision makers for their future actions. The results show that there are a few critical implementation barriers at the tactical, operational, and strategic levels. The findings contribute to the understanding of the various implementation issues related to the governance, scalability, and privacy of AI and provide insights for decision makers for their future actions. These AI implementation barriers are encountered due to the wider range of system-oriented, legal, technical, and operational implementations and the scale of the usage of AI for public healthcare.
Khan, R, Tao, X, Anjum, A, Malik, SR, Yu, S, Khan, A, Rehman, W & Malik, H 2022, '(τ, m)‐slicedBucket privacy model for sequential anonymization for improving privacy and utility', Transactions on Emerging Telecommunications Technologies, vol. 33, no. 6.
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AbstractIn a real‐world scenario for privacy‐preserving data publishing, the original data are anonymized and released periodically. Each release may vary in number of records due to insert, update, and delete operations. An intruder can combine, that is, correlate different releases to compromise the privacy of the individual records. Most of the literature, such as τ‐safety, τ‐safe (l, k)‐diversity, have an inconsistency in record signatures and adds counterfeit tuples with high generalization that causes privacy breach and information loss. In this paper, we propose an improved privacy model (τ, m)‐slicedBucket, having a novel idea of “Cache” table to address these limitations. We indicate that a collusion attack can be performed for breaching the privacy of τ‐safe (l, k)‐diversity privacy model, and demonstrate it through formal modeling. The objective of the proposed (τ, m)‐slicedBucket privacy model is to set a tradeoff between strong privacy and enhanced utility. Furthermore, we formally model and analyze the proposed model to show that the collusion attack is no longer applicable. Extensive experiments reveal that the proposed approach outperforms the existing models.
Khanna, P, Tanveer, M, Prasad, M & Lin, C-T 2022, 'Artificial intelligence and deep learning for biomedical applications', Multimedia Tools and Applications, vol. 81, no. 10, pp. 13137-13137.
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Khokher, MR, Little, LR, Tuck, GN, Smith, DV, Qiao, M, Devine, C, O’Neill, H, Pogonoski, JJ, Arangio, R & Wang, D 2022, 'Early lessons in deploying cameras and artificial intelligence technology for fisheries catch monitoring: where machine learning meets commercial fishing', Canadian Journal of Fisheries and Aquatic Sciences, vol. 79, no. 2, pp. 257-266.
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Electronic monitoring (EM) is increasingly used to monitor catch and bycatch in wild capture fisheries. EM video data are still manually reviewed and adds to ongoing management costs. Computer vision, machine learning, and artificial intelligence-based systems are seen to be the next step in automating EM data workflows. Here we show some of the obstacles we have confronted and approaches taken as we develop a system to automatically identify and count target and bycatch species using cameras deployed to an industry vessel. A Convolutional Neural Network was trained to detect and classify target and bycatch species groups, and a visual tracking system was developed to produce counts. The multiclass detector achieved a mean average precision of 53.42%. Based on the detection results, the visual tracking system provided automatic fish counts for the test video data. Automatic counts were within two standard deviations of the manual counts for the target species and most times for the bycatch species. Unlike other recent attempts, weather and lighting conditions were largely controlled by mounting cameras under cover.
Kiani, M, Andreu-Perez, J, Hagras, H, Papageorgiou, EI, Prasad, M & Lin, C-T 2022, 'Effective Brain Connectivity for fNIRS With Fuzzy Cognitive Maps in Neuroergonomics', IEEE Transactions on Cognitive and Developmental Systems, vol. 14, no. 1, pp. 50-63.
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King, J-T, John, AR, Wang, Y-K, Shih, C-K, Zhang, D, Huang, K-C & Lin, C-T 2022, 'Brain Connectivity Changes During Bimanual and Rotated Motor Imagery', IEEE Journal of Translational Engineering in Health and Medicine, vol. 10, pp. 1-8.
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Motor imagery-based brain-computer interface (MI-BCI) currently represents a new trend in rehabilitation. However, individual differences in the responsive frequency bands and a poor understanding of the communication between the ipsilesional motor areas and other regions limit the use of MI-BCI therapy. Objective: Bimanual training has recently attracted attention as it achieves better outcomes as compared to repetitive one-handed training. This study compared the effects of three MI tasks with different visual feedback. Methods: Fourteen healthy subjects performed single hand motor imagery tasks while watching single static hand (traditional MI), single hand with rotation movement (rmMI), and bimanual coordination with a hand pedal exerciser (bcMI). Functional connectivity is estimated by Transfer Entropy (TE) analysis for brain information flow. Results: Brain connectivity of conducting three MI tasks showed that the bcMI demonstrated increased communications from the parietal to the bilateral prefrontal areas and increased contralateral connections between motor-related zones and spatial processing regions. Discussion/Conclusion: The results revealed bimanual coordination operation events increased spatial information and motor planning under the motor imagery task. And the proposed bimanual coordination MI-BCI (bcMI-BCI) can also achieve the effect of traditional motor imagery tasks and promotes more effective connections with different brain regions to better integrate motor-cortex functions for aiding the development of more effective MI-BCI therapy. Clinical and Translational Impact Statement The proposed bcMI-BCI provides more effective connections with different brain areas and integrates motor-cortex functions to promote motor imagery rehabilitation for patients' impairment.
Kocaballi, AB, Laranjo, L, Clark, L, Kocielnik, R, Moore, RJ, Liao, QV & Bickmore, T 2022, 'Special Issue on Conversational Agents for Healthcare and Wellbeing', ACM Transactions on Interactive Intelligent Systems, vol. 12, no. 2, pp. 1-3.
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Kong, W, Li, X, Hou, L, Yuan, J, Gao, Y & Yu, S 2022, 'A Reliable and Efficient Task Offloading Strategy Based on Multifeedback Trust Mechanism for IoT Edge Computing', IEEE Internet of Things Journal, vol. 9, no. 15, pp. 13927-13941.
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Facing multidemand tasks and massive heterogeneous resources in an IoT edge computing environment, it is a challenge to obtain reliable and quick response service and allocate application tasks to resource nodes that meet task requirements and user preference. Since IoT edge computing is facing different types of severe attacks, such as message attacks, swing attacks, collusion attack, node attacks, etc., providing a reliable service environment, trust evaluation between edge nodes is necessary. Existing trust computing schemes, however, suffer from a long response period and low malicious detection rate in a dynamic environment. To alleviate these issues, we propose a reliable and efficient task offloading strategy based on the multifeedback trust mechanism (TOSMFTM). First, a reliable and efficient architecture of TOSMFTM is established, which can effectively improve the ability of trust computing and task offloading. Second, according to the broker's dynamic monitoring of data, a multifeedback trust aggregation model based on time attenuation and interaction frequency is proposed to provide a trusted running environment. Third, a trust weight k -means (TWK-means) clustering algorithm is designed based on resource attributes to enhance the reliability of service, and quickly and accurately cluster out resource nodes required by the task. Finally, we construct a task offloading model based on trust clustering to ensure user experience quality and promote system efficiency. Different from existing task processing models, which only focus on task offloading, our method also carries out resource preprocessing, trust evaluation, and resource clustering before task processing. The experiment verifies the effectiveness and feasibility of our TOSMFTM scheme.
Kridalukmana, R, Lu, H & Naderpour, M 2022, 'Self-Explaining Abilities of an Intelligent Agent for Transparency in a Collaborative Driving Context', IEEE Transactions on Human-Machine Systems, vol. 52, no. 6, pp. 1155-1165.
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A critical challenge in human-autonomy teaming is for human players to comprehend their nonhuman teammates (agents). Transparency in agents' behaviors is the key for such comprehension, which may be obtained by embedding a self-explanation ability into the agent to explain its own behaviors. Previous studies have relied on searching for the executed functions and logics to generate explanations for behaviors of goal-following logic-based agents. With the increasing number of functions and logics, current methods, such as component and process-based methods, have become impractical. This article proposes a new method exploiting the agent's artificial situation awareness states for generating explanations that involves several techniques: A Bayesian network, fuzzy theory, and Hamming distance. Our new method is evaluated in a collaborative driving context, in which a significant number of accidents recently occurred around the globe due to the lack of understanding of the autopilot agents. Using an autonomous driving simulator called Carla, two typical scenarios in collaborative driving, namely, traffic light and overtaking situations, are used. The findings show that the new method potentially reduces the search space in generating explanations and exhibits better computational performance and a lower cognitive workload. This work is important to calibrate human trust and to enhance comprehension of the agent.
Kuang, B, Fu, A, Susilo, W, Yu, S & Gao, Y 2022, 'A survey of remote attestation in Internet of Things: Attacks, countermeasures, and prospects', Computers & Security, vol. 112, pp. 102498-102498.
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The explosive growth of the Internet of Things (IoT) devices is an inevitable trend, especially considering the fact that 5G technology facilitates numerous services building on IoT devices. IoT devices deliver great convenience to our daily lives; nevertheless, they are becoming attractive attacking targets. Compromised IoT devices can result in the exposure of user privacy, damage to network security, or even threats to personal safety. In a rush for convenience and marketability, the security of these devices is usually less considered during production and even ignored. Under these circumstances, Remote Attestation (RA) becomes a valuable security service. It outsources the computation and verification burden to a resource-rich party, e.g., server, to ease its on-device implementation, making it suitable for protocol extensions. In this paper, we investigate the state-of-the-art RA schemes from different perspectives, aiming to offer a comprehensive understanding of this security service. Specifically, we summarize the basis of RA. We set up an elaborate adversarial model by systematizing existing RA schemes. Then we put forward the evaluation criteria from protection capability, performance, network adaptability, and attestation quality. According to the adversarial model, we classify existing RA schemes into five categories to show the various characteristics. A comparison of representative proposals enables readers to adopt and design suitable protocols in different application scenarios. Finally, we discuss some open challenges and provision prospects for future research.
Lammers, T, Rashid, L, Kratzer, J & Voinov, A 2022, 'An analysis of the sustainability goals of digital technology start-ups in Berlin', Technological Forecasting and Social Change, vol. 185, pp. 122096-122096.
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Lau, CW, Qu, Z, Draper, D, Quan, R, Braytee, A, Bluff, A, Zhang, D, Johnston, A, Kennedy, PJ, Simoff, S, Nguyen, QV & Catchpoole, D 2022, 'Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts', Scientific Reports, vol. 12, no. 1, p. 11337.
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AbstractThe significant advancement of inexpensive and portable virtual reality (VR) and augmented reality devices has re-energised the research in the immersive analytics field. The immersive environment is different from a traditional 2D display used to analyse 3D data as it provides a unified environment that supports immersion in a 3D scene, gestural interaction, haptic feedback and spatial audio. Genomic data analysis has been used in oncology to understand better the relationship between genetic profile, cancer type, and treatment option. This paper proposes a novel immersive analytics tool for cancer patient cohorts in a virtual reality environment, virtual reality to observe oncology data models. We utilise immersive technologies to analyse the gene expression and clinical data of a cohort of cancer patients. Various machine learning algorithms and visualisation methods have also been deployed in VR to enhance the data interrogation process. This is supported with established 2D visual analytics and graphical methods in bioinformatics, such as scatter plots, descriptive statistical information, linear regression, box plot and heatmap into our visualisation. Our approach allows the clinician to interrogate the information that is familiar and meaningful to them while providing them immersive analytics capabilities to make new discoveries toward personalised medicine.
Li, A, Yang, B, Hussain, FK & Huo, H 2022, 'HSR: Hyperbolic Social Recommender', Information Sciences, vol. 585, pp. 275-288.
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With the prevalence of online social media, users’ social connections have been widely studied and utilized to enhance the performance of recommender systems. In this paper, we explore the use of hyperbolic geometry for social recommendation. We present the Hyperbolic Social Recommender (HSR), a novel social recommendation framework that utilizes hyperbolic geometry to boost the performance. With the help of hyperbolic space, HSR can learn high-quality user and item representations to better model user-item interaction and user-user social relations. Through extensive experiments on four real-world datasets, we show that our proposed HSR outperforms its Euclidean counterpart and state-of-the-art social recommenders in click-through rate prediction and top-K recommendation, demonstrating the effectiveness of social recommendation in the hyperbolic space.
Li, C, Yang, L, Yu, S, Qin, W & Ma, J 2022, 'SEMMI: Multi-party security decision-making scheme for linear functions in the internet of medical things', Information Sciences, vol. 612, pp. 151-167.
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In the Internet of Medical Things(IoMT), developing models using machine learning algorithms can detect and assist users effectively in identifying health issues. Due to the risk of private user information being leaked during the machine learning application process, the widespread application and development of IoMT applications are hampered. Encrypting data is a good way to protect user privacy. However, given the participants’ limited resources, processing and analyzing ciphertext data presents a significant challenge. As a result, this paper proposes a secure and efficient assisted decision-making scheme (SEMMI) that is appropriate for the IoMT applications. SEMMI performs a thorough analysis of each participant's resource constraints, divides the data circulation process into four stages, and constructs a data circulation and ciphertext calculation protocol. Data transmission security is ensured through the use of stream encryption and homomorphic encryption. Each participant sends the ciphertext to the cloud, and the cloud calculates the ciphertext data, effectively relieving each participant's computational load. The security of the final result is guaranteed by matching the result with pre-decryption. The scheme's security and efficiency are demonstrated experimentally. The results indicate that the accuracy loss of each data set under the ciphertext is no more than 3% at most and that the cloud performs most of the calculations for each participant. Finally, SEMMI is applied to the FedAvg algorithm, demonstrating the scheme's universality.
Li, F, Li, Y, Zheng, H, Jiang, L, Gao, D, Li, C, Peng, Y, Cao, Z, Zhang, Y, Yao, D, Xu, T, Yuan, T-F & Xu, P 2022, 'Corrections to “Identification of the General Anesthesia Induced Loss of Consciousness by Cross Fuzzy Entropy-Based Brain Network”', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 2970-2970.
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Li, G, Zhou, H, Feng, B, Zhang, Y & Yu, S 2022, 'Efficient Provision of Service Function Chains in Overlay Networks Using Reinforcement Learning', IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 383-395.
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IEEE Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies facilitate deploying Service Function Chains (SFCs) at clouds in efficiency and flexibility. However, it is still challenging to efficiently chain Virtualized Network Functions (VNFs) in overlay networks without knowledge of underlying network configurations. Although there are many deterministic approaches for VNF placement and chaining, they have high complexity and depend on state information of substrate networks. Fortunately, Reinforcement Learning (RL) brings opportunities to alleviate this challenge as it can learn to make suitable decisions without prior knowledge. Therefore, in this paper, we propose an RL approach for efficient SFC provision in overlay networks, where the same VNFs provided by multiple vendors are with different performance. Specifically, we first formulate the problem into an Integer Linear Programming (ILP) model for benchmarking. Then, we present the online SFC path selection into a Markov Decision Process (MDP) and propose a corresponding policy-gradient-based solution. Finally, we evaluate our proposed approach with extensive simulations with randomly generated SFC requests and a real-world video streaming dataset, and implement an emulation system for feasibility verification. Related results demonstrate that performance of our approach is close to the ILP-based method and better than deep Q-learning, random, and load-least-greedy methods.
Li, K, Lu, J, Zuo, H & Zhang, G 2022, 'Dynamic Classifier Alignment for Unsupervised Multi-Source Domain Adaptation', IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-1.
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Unsupervised domain adaptation leverages the previously gained knowledge from a labeled source domain to tackle the task from a different but similar unlabeled target domain. Most existing methods focus on transferring knowledge from a single source domain, but the information from a single domain may be inadequate to complete the target task. Some previous studies have turned to multi-view representations to enrich the transferable information. However, they simply concatenate multi-view features, which may result in information redundancy. In this paper, we propose a dynamic classifier alignment (DCA) method for multi-source domain adaptation, which aligns classifiers driven from multi-view features via a sample-wise automatic way. As proposed, both the importance of each view and the contribution of each source domain are investigated. To determine the important degrees of multiple views, an importance learning function is built by generating an auxiliary classifier. To learn the source combination parameters, a domain discriminator is developed to estimate the probability of a sample belonging to multiple source domains. Meanwhile, a self-training strategy is proposed to enhance the cross-domain ability of source classifiers with the assistance of pseudo target labels. Experiments on real-world visual datasets show the superiority of the proposed DCA.
Li, K, Lu, J, Zuo, H & Zhang, G 2022, 'Multi-Source Contribution Learning for Domain Adaptation', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 10, pp. 5293-5307.
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Transfer learning becomes an attractive technology to tackle a task from a target domain by leveraging previously acquired knowledge from a similar domain (source domain). Many existing transfer learning methods focus on learning one discriminator with single-source domain. Sometimes, knowledge from single-source domain might not be enough for predicting the target task. Thus, multiple source domains carrying richer transferable information are considered to complete the target task. Although there are some previous studies dealing with multi-source domain adaptation, these methods commonly combine source predictions by averaging source performances. Different source domains contain different transferable information; they may contribute differently to a target domain compared with each other. Hence, the source contribution should be taken into account when predicting a target task. In this article, we propose a novel multi-source contribution learning method for domain adaptation (MSCLDA). As proposed, the similarities and diversities of domains are learned simultaneously by extracting multi-view features. One view represents common features (similarities) among all domains. Other views represent different characteristics (diversities) in a target domain; each characteristic is expressed by features extracted in a source domain. Then multi-level distribution matching is employed to improve the transferability of latent features, aiming to reduce misclassification of boundary samples by maximizing discrepancy between different classes and minimizing discrepancy between the same classes. Concurrently, when completing a target task by combining source predictions, instead of averaging source predictions or weighting sources using normalized similarities, the original weights learned by normalizing similarities between source and target domains are adjusted using pseudo target labels to increase the disparities of weight values, which is desired to improve...
Li, P, Li, C, Bore, JC, Si, Y, Li, F, Cao, Z, Zhang, Y, Wang, G, Zhang, Z, Yao, D & Xu, P 2022, 'L1-norm based time-varying brain neural network and its application to dynamic analysis for motor imagery', Journal of Neural Engineering, vol. 19, no. 2, pp. 026019-026019.
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Abstract Objective . Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface offers a promising way to improve the efficiency of motor rehabilitation and motor skill learning. In recent years, the power of dynamic network analysis for MI classification has been proved. In fact, its usability mainly depends on the accurate estimation of brain connection. However, traditional dynamic network estimation strategies such as adaptive directed transfer function (ADTF) are designed in the L2-norm. Usually, they estimate a series of pseudo connections caused by outliers, which results in biased features and further limits its online application. Thus, how to accurately infer dynamic causal relationship under outlier influence is urgent. Approach . In this work, we proposed a novel ADTF, which solves the dynamic system in the L1-norm space (L1-ADTF), so as to restrict the outlier influence. To enhance its convergence, we designed an iteration strategy with the alternating direction method of multipliers, which could be used for the solution of the dynamic state-space model restricted in the L1-norm space. Furthermore, we compared L1-ADTF to traditional ADTF and its dual extension across both simulation and real EEG experiments. Main results . A quantitative comparison between L1-ADTF and other ADTFs in simulation studies demonstrates that fewer bias errors and more desirable dynamic state transformation patterns can be captured by the L1-ADTF. Application to real MI EEG datasets seriously noised by ocular artifacts also reveals the efficiency of the proposed L1-ADTF approach to extract the time-varying brain neural network patterns, even when more complex noises are involved. ...
Li, Q, Wang, Z, Liu, S, Li, G & Xu, G 2022, 'Deep treatment-adaptive network for causal inference', The VLDB Journal, vol. 31, no. 5, pp. 1127-1142.
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AbstractCausal inference is capable of estimating the treatment effect (i.e., the causal effect of treatment on the outcome) to benefit the decision making in various domains. One fundamental challenge in this research is that the treatment assignment bias in observational data. To increase the validity of observational studies on causal inference, representation-based methods as the state-of-the-art have demonstrated the superior performance of treatment effect estimation. Most representation-based methods assume all observed covariates are pre-treatment (i.e., not affected by the treatment) and learn a balanced representation from these observed covariates for estimating treatment effect. Unfortunately, this assumption is often too strict a requirement in practice, as some covariates are changed by doing an intervention on treatment (i.e., post-treatment). By contrast, the balanced representation learned from unchanged covariates thus biases the treatment effect estimation. In light of this, we propose a deep treatment-adaptive architecture (DTANet) that can address the post-treatment covariates and provide a unbiased treatment effect estimation. Generally speaking, the contributions of this work are threefold. First, our theoretical results guarantee DTANet can identify treatment effect from observations. Second, we introduce a novel regularization of orthogonality projection to ensure that the learned confounding representation is invariant and not being contaminated by the treatment, meanwhile mediate variable representation is informative and discriminative for predicting the outcome. Finally, we build on the optimal transport and learn a treatment-invariant representation for the unobserved confounders to alleviate the confounding bias.
Li, Y, Fan, X, Chen, L, Li, B & Sisson, SA 2022, 'Smoothing graphons for modelling exchangeable relational data', Machine Learning, vol. 111, no. 1, pp. 319-344.
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Modelling exchangeable relational data can be described appropriately in graphon theory. Most Bayesian methods for modelling exchangeable relational data can be attributed to this framework by exploiting different forms of graphons. However, the graphons adopted by existing Bayesian methods are either piecewise-constant functions, which are insufficiently flexible for accurate modelling of the relational data, or are complicated continuous functions, which incur heavy computational costs for inference. In this work, we overcome these two shortcomings by smoothing piecewise-constant graphons, which permits continuous intensity values for describing relations, without impractically increasing computational costs. In particular, we focus on the Bayesian Stochastic Block Model (SBM) and demonstrate how to adapt the piecewise-constant SBM graphon to the smoothed version. We first propose the Integrated Smoothing Graphon (ISG) which introduces one smoothing parameter to the SBM graphon to generate continuous relational intensity values. Then, we further develop the Latent Feature Smoothing Graphon (LFSG), which improves the ISG, by introducing auxiliary hidden labels to decompose the calculation of the ISG intensity and enable efficient inference. Experimental results on real-world data sets validate the advantages of applying smoothing strategies to the Stochastic Block Model, demonstrating that smoothing graphons can greatly improve AUC and precision for link prediction without increasing computational complexity.
Li, Y, Liu, Z, Yao, L, Wang, X, McAuley, J & Chang, X 2022, 'An Entropy-Guided Reinforced Partial Convolutional Network for Zero-Shot Learning', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 8, pp. 5175-5186.
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Lin, C-T, Fan, H-Y, Chang, Y-C, Ou, L, Liu, J, Wang, Y-K & Jung, T-P 2022, 'Modelling the Trust Value for Human Agents Based on Real-Time Human States in Human-Autonomous Teaming Systems', Technologies, vol. 10, no. 6, pp. 115-115.
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The modelling of trust values on agents is broadly considered fundamental for decision-making in human-autonomous teaming (HAT) systems. Compared to the evaluation of trust values for robotic agents, estimating human trust is more challenging due to trust miscalibration issues, including undertrust and overtrust problems. From a subjective perception, human trust could be altered along with dynamic human cognitive states, which makes trust values hard to calibrate properly. Thus, in an attempt to capture the dynamics of human trust, the present study evaluated the dynamic nature of trust for human agents through real-time multievidence measures, including human states of attention, stress and perception abilities. The proposed multievidence human trust model applied an adaptive fusion method based on fuzzy reinforcement learning to fuse multievidence from eye trackers, heart rate monitors and human awareness. In addition, fuzzy reinforcement learning was applied to generate rewards via a fuzzy logic inference process that has tolerance for uncertainty in human physiological signals. The results of robot simulation suggest that the proposed trust model can generate reliable human trust values based on real-time cognitive states in the process of ongoing tasks. Moreover, the human-autonomous team with the proposed trust model improved the system efficiency by over 50% compared to the team with only autonomous agents. These results may demonstrate that the proposed model could provide insight into the real-time adaptation of HAT systems based on human states and, thus, might help develop new ways to enhance future HAT systems better.
Lin, C-T, Tian, Y, Wang, Y-K, Do, T-TN, Chang, Y-L, King, J-T, Huang, K-C & Liao, L-D 2022, 'Effects of Multisensory Distractor Interference on Attentional Driving', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 10395-10403.
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Distracted driving refers to multisensory integration and attention shifts between attentional driving and different interferences from different modalities, including visual and auditory stimuli. Here, we compared the behavioral performance with interacting multisensory distractors during attentional driving. Then, the independent component analysis (ICA) and event-related spectral perturbation (ERSP) were applied to investigate the neural oscillation changes. The behavioral results showed that the response times (RTs) increased when distractors appeared in response to attentional driving. Moreover, the RTs were longer when the distractor interference was presented in the auditory modality compared with the visual modality. Eye movement intervals showed shorter tracking saccades under distractor interference. These results may indicate that attentional driving performance was impaired under the exposure to multisensory distractor interference. The ERSPs under visual and auditory distraction exposure showed decreased beta power in the frontal area, increased theta and delta power in the central area, and decreased alpha power in the parietal area. During this process, distracted driving under cross-modal sensory interference required more neural oscillation involvement. Moreover, the visual modality showed increased gamma power in the frontal, central, parietal and occipital areas, while the auditory modality showed decreased gamma power in the frontal area, indicating that auditory interference could intervene in top-down attentional processing.
Lin, C-T, Wang, Y-K, Huang, P-L, Shi, Y & Chang, Y-C 2022, 'Spatial-temporal attention-based convolutional network with text and numerical information for stock price prediction', Neural Computing and Applications, vol. 34, no. 17, pp. 14387-14395.
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AbstractIn the financial market, the stock price prediction is a challenging task which is influenced by many factors. These factors include economic change, politics and global events that are usually recorded in text format, such as the daily news. Therefore, we assume that real-world text information can be used to forecast stock market activity. However, only a few works considered both text and numerical information to predict or analyse stock trends. These works used preprocessed text features as the model inputs; therefore, latent information in text may be lost because the relationships between the text and stock price are not considered. In this paper, we propose a fusion network, i.e. a spatial-temporal attention-based convolutional network (STACN) that can leverage the advantages of an attention mechanism, a convolutional neural network and long short-term memory to extract text and numerical information for stock price prediction. Benefiting from the utilisation of an attention mechanism, reliable text features that are highly relevant to stock value can be extracted, which improves the overall model performance. The experimental results on real-world stock data demonstrate that our STACN model and training scheme can handle both text and numerical data and achieve high accuracy on stock regression tasks. The STACN is compared with CNNs and LSTMs with different settings, e.g. a CNN with only stock data, a CNN with only news titles and LSTMs with only stock data. CNNs considering only stock data and news titles have mean squared errors of 28.3935 and 0.1814, respectively. The accuracy of LSTMs is 0.0763. The STACN can achieve an accuracy of 0.0304, outperforming CNNs and LSTMs in stock regression tasks.
Lin, J, Sun, G, Beydoun, G & Li, L 2022, 'Applying Machine Translation and Language Modelling Strategies for the Recommendation Task of Micro Learning Service', Educational Technology and Society, vol. 25, no. 1, pp. 205-212.
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A newly emerged micro learning service offers a flexible formal, informal, or non-formal online learning opportunity to worldwide users with different backgrounds in real-time. With the assist of big data technology and cloud computing service, online learners can access tremendous fine-grained learning resources through micro learning service. However, big data also causes serious information overload during online learning activities. Hence, an intelligent recommender system is required to filter out not-suitable learning resources and pick the one that matches the learner’s learning requirement and academic background. From the perspective of natural language processing (NLP), this study proposed a novel recommender system that utilises machine translation and language modelling. The proposed model aims to overcome the defects of conventional recommender systems and further enhance distinguish ability of the recommender system for different learning resources.
Lin, J, Sun, G, Shen, J, Pritchard, DE, Yu, P, Cui, T, Xu, D, Li, L & Beydoun, G 2022, 'From computer vision to short text understanding: Applying similar approaches into different disciplines', Intelligent and Converged Networks, vol. 3, no. 2, pp. 161-172.
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Liu, A, Lu, J, Song, Y, Xuan, J & Zhang, G 2022, 'Concept Drift Detection Delay Index', IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-1.
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Data streams may encounter data distribution changes, which can significantly impair the accuracy of models. Concept drift detection tracks data distribution changes and signals when to update models. Many drift detection methods apply thresholds to distinguish between drift or non-drift streams and to claim their method outperforms others with non-aligned drift thresholds. We consider that selecting a proper drift threshold could be more important than developing a new drift detection algorithm, and different drift detection algorithms may end up with very similar performance with aligned drift thresholds. To better understand this process, we propose a novel threshold selection algorithm to align the drift thresholds of a set of algorithms so that they are all at the same sensitivity level. Based on comprehensive experiment evaluations, we observed that several state-of-the-art drift detection algorithms could achieve similar results by aligning their thresholds, providing a novel insight to explain how drift detection algorithms contribute to data stream learning. We noticed that a higher detection sensitivity improves accuracy for data streams with frequent distribution change. The evaluation results are showing that drift thresholds should not be fixed during stream learning. Rather, they should adjust dynamically based on the prevailing conditions of the data stream.
Liu, B, Ding, M, Shaham, S, Rahayu, W, Farokhi, F & Lin, Z 2022, 'When Machine Learning Meets Privacy', ACM Computing Surveys, vol. 54, no. 2, pp. 1-36.
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The newly emerged machine learning (e.g., deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning are still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This article surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning-aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.
Liu, G, Zhang, W, Li, X, Fan, K & Yu, S 2022, 'VulnerGAN: a backdoor attack through vulnerability amplification against machine learning-based network intrusion detection systems', Science China Information Sciences, vol. 65, no. 7.
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Machine learning-based network intrusion detection systems (ML-NIDS) are extensively used for network security against unknown attacks. Existing intrusion detection systems can effectively defend traditional network attacks, however, they face AI based threats. The current known AI attacks cannot balance the escape rate and attack effectiveness. In addition, the time cost of existing AI attacks is very high. In this paper, we propose a backdoor attack called VulnerGAN, which features high concealment, high aggressiveness, and high timeliness. The backdoor can make the specific attack traffic bypass the detection of ML-NIDS without affecting the performance of ML-NIDS in identifying other attack traffic. VulnerGAN uses generative adversarial networks (GAN) to calculate poisoning and adversarial samples based on machine learning model vulnerabilities. It can make traditional network attack traffic escape black-box online ML-NIDS. At the same time, model extraction and fuzzing test are used to enhance the convergence of VulnerGAN. Compared with the state-of-the-art algorithms, the VulnerGAN backdoor attack increases 33.28% in concealment, 18.48% in aggressiveness, and 46.32% in timeliness.
Liu, J, Singh, AK & Lin, C-T 2022, 'Corrections to “Predicting the Quality of Spatial Learning via Virtual Global Landmarks”', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 2971-2971.
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IN THE above article [1], we detected an error in reporting the 10-fold cross-validation result. The correct 10-fold cross-validation result in Table IV is uploaded in this letter. The corrected result of the cross-validation is consistent with our findings [1] that the EEG data associated with virtual global landmark (VGL) [2], [3] stimuli from the VGL group had an overall improvement in Acc and F1 scores compared to local landmarks from the non-VGL group, where Acc and F1 scores improved averagely 14.89% and 21.77%, respectively. (Table Presented).
Liu, J, Singh, AK & Lin, C-T 2022, 'Predicting the Quality of Spatial Learning via Virtual Global Landmarks', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 2418-2425.
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Analyzing the effects landmarks have on spatial learning is an active area of research in the study of human navigation processes and one that is key to understanding the links between human brain dynamics, landmark encoding, and spatial learning outcomes. This article presents a study on whether electroencephalography (EEG) signals related to virtual global landmarks combined with deep learning can be used to predict the accuracy and efficacy of spatial learning. Virtual global landmarks are silhouettes of actual landmarks projected into the navigator's vision via a heads-up display. They serve as a notable frame of reference in addition to the local landmarks we all typically use for route navigation. From a mobile virtual reality scenario involving 55 participants, the results of the study suggest that the EEG data associated with those who were exposed to global landmarks shows a visibly better capacity for predicting the quality of spatial learning levels than those who were not. As such, the EEG features associated with processing VGLs have a greater functional relation to the quality of spatial learning. This finding opens up a future direction of enquiry into landmark encoding and navigational ability. It may also provide a potential avenue for the early diagnosis of Alzheimer's disease.
Liu, J, Singh, AK & Lin, C-T 2022, 'Using virtual global landmark to improve incidental spatial learning', Scientific Reports, vol. 12, no. 1, p. 6744.
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AbstractTo reduce the decline of spatial cognitive skills caused by the increasing use of automated GPS navigation, the virtual global landmark (VGL) system is proposed to help people naturally improve their sense of direction. Designed to accompany a heads-up navigation system, VGL system constantly displays silhouette of global landmarks in the navigator’s vision as a notable frame of reference. This study exams how VGL system impacts incidental spatial learning, i.e., subconscious spatial knowledge acquisition. We asked 55 participants to explore a virtual environment and then draw a map of what they had explored while capturing electroencephalogram (EEG) signals and eye activity. The results suggest that, with the VGL system, participants paid more attention during exploration and performed significantly better at the map drawing task—a result that indicates substantially improved incidental spatial learning. This finding might kickstart a redesigning navigation aids, to teach users to learn a route rather than simply showing them the way.
Liu, J, Singh, AK, Wunderlich, A, Gramann, K & Lin, C-T 2022, 'Redesigning navigational aids using virtual global landmarks to improve spatial knowledge retrieval', npj Science of Learning, vol. 7, no. 1, p. 17.
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AbstractAlthough beacon- and map-based spatial strategies are the default strategies for navigation activities, today’s navigational aids mostly follow a beacon-based design where one is provided with turn-by-turn instructions. Recent research, however, shows that our reliance on these navigational aids is causing a decline in our spatial skills. We are processing less of our surrounding environment and relying too heavily on the instructions given. To reverse this decline, we need to engage more in map-based learning, which encourages the user to process and integrate spatial knowledge into a cognitive map built to benefit flexible and independent spatial navigation behaviour. In an attempt to curb our loss of skills, we proposed a navigation assistant to support map-based learning during active navigation. Called the virtual global landmark (VGL) system, this augmented reality (AR) system is based on the kinds of techniques used in traditional orienteering. Specifically, a notable landmark is always present in the user’s sight, allowing the user to continuously compute where they are in relation to that specific location. The efficacy of the unit as a navigational aid was tested in an experiment with 27 students from the University of Technology Sydney via a comparison of brain dynamics and behaviour. From an analysis of behaviour and event-related spectral perturbation, we found that participants were encouraged to process more spatial information with a map-based strategy where a silhouette of the compass-like landmark was perpetually in view. As a result of this technique, they consistently navigated with greater efficiency and better accuracy.
Liu, J, Wang, X, Shen, S, Fang, Z, Yu, S, Yue, G & Li, M 2022, 'Intelligent Jamming Defense Using DNN Stackelberg Game in Sensor Edge Cloud', IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4356-4370.
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To ensure an accurate power allocation against increasing intelligent jamming attacks on the offloading link of computation tasks, we investigate interactions between a cluster head node and an intelligent jammer using a Stackelberg game framework, under the constraint of the total power to use and the limited knowledge of its own channel gain for each player. In this game, the intelligent jammer gathers channel gain information and processes it using a deep neural network (DNN) to infer the accurate jamming power as an attack strategy. The cluster head node also exploits DNN to infer an accurate transmission power as a defense strategy according to the varying channel gain. We model the optimization of the attack and defense strategies using single channel jamming DNN (SJnet), multiple channel jamming DNN (MJnet), single channel sensor DNN (SSnet), and multiple channel sensor DNN (MSnet) for the single (multiple) channel jamming attacks. In addition, we extend the design to the scenario where the intelligent jammer can launch a hybrid mode jamming attack, and propose a DNN Stackelberg game-based defense scheme. Numerical simulation results demonstrate that our proposed mechanism is superior to other power allocation mechanisms under different scenarios in the sensor edge cloud.
Liu, T, Zhang, W, Li, J, Ueland, M, Forbes, SL, Zheng, WX & Su, SW 2022, 'A Multiscale Wavelet Kernel Regularization-Based Feature Extraction Method for Electronic Nose', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 11, pp. 7078-7089.
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Liu, X, Zhu, T, Jiang, C, Ye, D & Zhao, F 2022, 'Prioritized Experience Replay based on Multi-armed Bandit', Expert Systems with Applications, vol. 189, pp. 116023-116023.
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Experience replay has been widely used in deep reinforcement learning. The learning algorithm allows online reinforcement learning agents to remember and reuse experiences from the past. In order to further improve the sampling efficiency for experience replay, the most useful experiences are expected to be sampled with higher frequency. Existing methods usually designed their sampling strategy according to a few criteria, but they tended to combine different criteria in a linear or fixed manner, where the strategy were static and independent of the agent learner. This ignores the dynamic attribute of the environment and thus can only lead to a suboptimal performance. In this work, we propose a dynamic experience replay strategy according to the interaction between the agent and environment, which is called Prioritized Experience Replay based on Multi-armed Bandit (PERMAB). PERMAB can adaptively combine multiple priority criteria to measure the importance of the experience. In particular, the weight of each assessing criterion can be adaptively adjusted from episode to episode according to their respective contribution to the agent performance, which guarantees useful criterion to be weighted more in its current state. The proposed replay strategy is able to take both sample informativeness and diversity into consideration, which could significantly boosts learning ability and speed of the game agent. Experimental results show that PERMAB accelerates the network learning and achieves a better performance compared to baseline algorithms on seven benchmark environments with various difficulties.
Liu, Z, Li, Y, Yao, L, Wang, X & Nie, F 2022, 'Agglomerative Neural Networks for Multiview Clustering', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 7, pp. 2842-2852.
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Conventional multiview clustering methods seek a view consensus through minimizing the pairwise discrepancy between the consensus and subviews. However, pairwise comparison cannot portray the interview relationship precisely if some of the subviews can be further agglomerated. To address the above challenge, we propose the agglomerative analysis to approximate the optimal consensus view, thereby describing the subview relationship within a view structure. We present an agglomerative neural network (ANN) based on constrained Laplacian rank to cluster multiview data directly without a dedicated postprocessing step (e.g., using K-means). We further extend ANN with a learnable data space to handle data of complex scenarios. Our evaluations against several state-of-the-art multiview clustering approaches on four popular data sets show the promising view-consensus analysis ability of ANN. We further demonstrate ANN's capability in analyzing complex view structures, extensibility through our case study and robustness and effectiveness of data-driven modifications.
Liu, Z, Wang, X, Li, Y, Yao, L, An, J, Bai, L & Lim, E-P 2022, 'Face to purchase: Predicting consumer choices with structured facial and behavioral traits embedding', Knowledge-Based Systems, vol. 235, pp. 107665-107665.
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Loengbudnark, W, Khalilpour, K, Bharathy, G, Taghikhah, F & Voinov, A 2022, 'Battery and hydrogen-based electric vehicle adoption: A survey of Australian consumers perspective', Case Studies on Transport Policy, vol. 10, no. 4, pp. 2451-2463.
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Ma, H, Li, L, Fan, Y, Guo, Y, Jin, Z & Luo, J 2022, 'A Discrete Current Controller for High Power-Density Synchronous Machines', Energies, vol. 15, no. 17, pp. 6396-6396.
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This paper proposes a complex vector discrete current controller based on the flux-linkage data to solve the current loop oscillation problem of high power-density synchronous machines. An offline flux-linkage table measurement method considering cross saturation is introduced, and the data are used to deduce the symmetrical complex vector model. The influence of latch and delay of inverters on the line voltage of machines at high speed is analyzed and compensated during the controller design process. The proposed controller, which only needs to tune one parameter, can deal with the inductance mismatch issues caused by iron core saturation. The controller can be adopted in the current loop of saturated salient or nonsalient synchronous machines. Simulations and experiments have verified the effectiveness of the proposed method.
Ma, H, Lv, K, Zeng, S, Lin, H & Shi, JJ 2022, 'Climbing the Pyramid of Megaproject Social Responsibility: Impacts of External Stakeholders and Project Complexity', Journal of Construction Engineering and Management, vol. 148, no. 11, p. 04022116.
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Mallos, M, Velivela, V, Charlton, A, Keller, C, Frankel, A, Kennedy, P & Catchpoole, D 2022, 'Looking beneath the surface of rhabdomyosarcoma: Artificial intelligence using deep learning can classify with 90% accuracy', Pathology, vol. 54, pp. S38-S38.
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Martin, K, Arbour, S, McGregor, C & Rice, M 2022, 'Silver linings: Observed reductions in aggression and use of restraints and seclusion in psychiatric inpatient care during COVID‐19', Journal of Psychiatric and Mental Health Nursing, vol. 29, no. 2, pp. 381-385.
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Accessible SummaryWhat is known about the subject?In a survey conducted by the World Health Organization (WHO) in the summer of 2020, 93% of countries worldwide acknowledged negative impacts on their mental health services.Previous research during the H1N1 pandemic in 2009 established an increase of patient aggression in psychiatric facilities.What the paper adds to existing knowledge?Despite expected worsening of mental health, our hospital observed reductions in aggressive behaviour among inpatients and subsequent use of coercive interventions by staff in the months following Covid‐19 pandemic restrictions being implemented.The downward trend in incidents observed during the pandemic has suggested that aggression in mental health hospitals may be more situation‐specific and less so a factor of mental illness.What are the implications for practice?We believe that the reduction in aggressive behaviour observed during the pandemic is related to changes in our organization that occurred in response to concerns about patient well‐being; our co‐design approach shifted trust, choice and power. Therefore, practices that support these constructs are needed to maintain the outcomes we experienced.Rather than return to normal in the wake of the pandemic, we are strongly encouraged to sustain the changes we made and continue to find better ways to support and work with the ...
Mckie, I, Narayan, B & Kocaballi, B 2022, 'Conversational Voice Assistants and a Case Study of Long-Term Users: A Human Information Behaviours Perspective', Journal of the Australian Library and Information Association, vol. 71, no. 3, pp. 233-255.
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Meena, MS, Pare, S, Singh, P, Rana, A & Prasad, M 2022, 'A Robust Illumination and Intensity invariant Face Recognition System', International Journal of Circuits, Systems and Signal Processing, vol. 16, pp. 974-984.
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Face recognition has achieved more attention in computer vision with the focus on modelling the expression variations of human. However, in computer vision system, face recognition is a challenging task, due to variation in expressions, poses, and lighting conditions. This paper proposes a facial recognition technique based on 2D Hybrid Markov Model (2D HMM), Cat Swam Optimization (CSO), Local Directional Pattern (LDP), and Tetrolet Transform. Skin segmentation method is used for pre-processing followed by filtering to extract the region of interest. Resultant image is fed to proposed feature extraction method comprising of Tetrolet Transform and LDP. Extracted features are classified using proposed classifier “CSO trained 2D-HMM classification method”. To prove the superiority of method, four face datasets are used, and comparative results are presented. Quantitively results are measured by False Acceptance Rate (FAR), False Rejection Rate (FRR) and Accuracy and the values are 0.0025, 0.0035 and 99.65% respectively
Merino-Arteaga, I, Alfaro-García, VG & Merigó, JM 2022, 'Fuzzy systems research in the United States of America and Canada: A bibliometric overview', Information Sciences, vol. 617, pp. 277-292.
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Mishra, A, Alzoubi, YI, Anwar, MJ & Gill, AQ 2022, 'Attributes impacting cybersecurity policy development: An evidence from seven nations', Computers & Security, vol. 120, pp. 102820-102820.
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Cyber threats have risen as a result of the growing usage of the Internet. Organizations must have effective cybersecurity policies in place to respond to escalating cyber threats. Individual users and corporations are not the only ones who are affected by cyber-attacks; national security is also a serious concern. Different nations' cybersecurity rules make it simpler for cybercriminals to carry out damaging actions while making it tougher for governments to track them down. Hence, a comprehensive cybersecurity policy is needed to enable governments to take a proactive approach to all types of cyber threats. This study investigates cybersecurity regulations and attributes used in seven nations in an attempt to fill this research gap. This paper identified fourteen common cybersecurity attributes such as telecommunication, network, Cloud computing, online banking, E-commerce, identity theft, privacy, and smart grid. Some nations seemed to focus, based on the study of key available policies, on certain cybersecurity attributes more than others. For example, the USA has scored the highest in terms of online banking policy, but Canada has scored the highest in terms of E-commerce and spam policies. Identifying the common policies across several nations may assist academics and policymakers in developing cybersecurity policies. A survey of other nations' cybersecurity policies might be included in the future research.
Mishra, A, Alzoubi, YI, Anwar, MJ & Gill, AQ 2022, 'Attributes impacting cybersecurity policy development: An evidence from seven nations.', Comput. Secur., vol. 120, pp. 102820-102820.
Mishra, A, Alzoubi, YI, Gill, AQ & Anwar, MJ 2022, 'Cybersecurity Enterprises Policies: A Comparative Study', Sensors, vol. 22, no. 2, pp. 538-538.
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Cybersecurity is a critical issue that must be prioritized not just by enterprises of all kinds, but also by national security. To safeguard an organization’s cyberenvironments, information, and communication technologies, many enterprises are investing substantially in cybersecurity these days. One part of the cyberdefense mechanism is building an enterprises’ security policies library, for consistent implementation of security controls. Significant and common cybersecurity policies of various enterprises are compared and explored in this study to provide robust and comprehensive cybersecurity knowledge that can be used in various enterprises. Several significant common security policies were identified and discussed in this comprehensive study. This study identified 10 common cybersecurity policy aspects in five enterprises: healthcare, finance, education, aviation, and e-commerce. We aimed to build a strong infrastructure in each business, and investigate the security laws and policies that apply to all businesses in each sector. Furthermore, the findings of this study reveal that the importance of cybersecurity requirements differ across multiple organizations. The choice and applicability of cybersecurity policies are determined by the type of information under control and the security requirements of organizations in relation to these policies.
Mishra, A, Alzoubi, YI, Gill, AQ & Anwar, MJ 2022, 'Cybersecurity Enterprises Policies: A Comparative Study.', Sensors, vol. 22, pp. 538-538.
Morvan, A, Andersen, TI, Mi, X, Neill, C, Petukhov, A, Kechedzhi, K, Abanin, DA, Michailidis, A, Acharya, R, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Bardin, JC, Basso, J, Bengtsson, A, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Z, Chiaro, B, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Debroy, DM, Del Toro Barba, A, Demura, S, Dunsworth, A, Eppens, D, Erickson, C, Faoro, L, Farhi, E, Fatemi, R, Flores Burgos, L, Forati, E, Fowler, AG, Foxen, B, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Grajales Dau, A, Gross, JA, Habegger, S, Hamilton, MC, Harrigan, MP, Harrington, SD, Hoffmann, M, Hong, S, Huang, T, Huff, A, Huggins, WJ, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, AY, Klimov, PV, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, KW, Lester, BJ, Lill, AT, Liu, W, Locharla, A, Malone, F, Martin, O, McClean, JR, McEwen, M, Meurer Costa, B, Miao, KC, Mohseni, M, Montazeri, S, Mount, E, Mruczkiewicz, W, Naaman, O, Neeley, M, Nersisyan, A, Newman, M, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Olenewa, R, Opremcak, A, Potter, R, Quintana, C, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shvarts, V, Skruzny, J, Smith, WC, Strain, D, Sterling, G, Su, Y, Szalay, M, Torres, A, Vidal, G, Villalonga, B, Vollgraff-Heidweiller, C, White, T, Xing, C, Yao, Z, Yeh, P, Yoo, J, Zalcman, A, Zhang, Y, Zhu, N, Neven, H, Bacon, D, Hilton, J, Lucero, E, Babbush, R, Boixo, S, Megrant, A, Kelly, J, Chen, Y, Smelyanskiy, V, Aleiner, I, Ioffe, LB & Roushan, P 2022, 'Formation of robust bound states of interacting microwave photons', Nature, vol. 612, no. 7939, pp. 240-245.
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AbstractSystems of correlated particles appear in many fields of modern science and represent some of the most intractable computational problems in nature. The computational challenge in these systems arises when interactions become comparable to other energy scales, which makes the state of each particle depend on all other particles1. The lack of general solutions for the three-body problem and acceptable theory for strongly correlated electrons shows that our understanding of correlated systems fades when the particle number or the interaction strength increases. One of the hallmarks of interacting systems is the formation of multiparticle bound states2–9. Here we develop a high-fidelity parameterizable fSim gate and implement the periodic quantum circuit of the spin-½ XXZ model in a ring of 24 superconducting qubits. We study the propagation of these excitations and observe their bound nature for up to five photons. We devise a phase-sensitive method for constructing the few-body spectrum of the bound states and extract their pseudo-charge by introducing a synthetic flux. By introducing interactions between the ring and additional qubits, we observe an unexpected resilience of the bound states to integrability breaking. This finding goes against the idea that bound states in non-integrable systems are unstable when their energies overlap with the continuum spectrum. Our work provides experimental evidence for bound states of interacting photons and discovers their stability beyond the integrability limit.
Mughal, F, Raffe, W, Stubbs, P, Kneebone, I & Garcia, J 2022, 'Fitbits for Monitoring Depressive Symptoms in Older Aged Persons: Qualitative Feasibility Study', JMIR Formative Research, vol. 6, no. 11, pp. e33952-e33952.
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Background In 2022, an estimated 1.105 billion people used smart wearables and 31 million used Fitbit devices worldwide. Although there is growing evidence for the use of smart wearables to benefit physical health, more research is required on the feasibility of using these devices for mental health and well-being. In studies focusing on emotion recognition, emotions are often inferred and dependent on external cues, which may not be representative of true emotional states. Objective The aim of this study was to evaluate the feasibility and acceptability of using consumer-grade activity trackers for apps in the remote mental health monitoring of older aged people. Methods Older adults were recruited using criterion sampling. Participants were provided an activity tracker (Fitbit Alta HR) and completed weekly online questionnaires, including the Geriatric Depression Scale, for 4 weeks. Before and after the study period, semistructured qualitative interviews were conducted to provide insight into the acceptance and feasibility of performing the protocol over a 4-week period. Interview transcripts were analyzed using a hybrid inductive-deductive thematic analysis. Results In total, 12 participants enrolled in the study, and 9 returned for interviews after the study period. Participants had positive attitudes toward being remotely monitored, with 78% (7/9) of participants experiencing no inconvenience throughout the study period. Moreover, 67% (6/9) were interested in trialing our prototype when it is implemented. Participants stated they would feel more comfortable if ...
Nagy, E, Ibrar, I, Braytee, A & Iván, B 2022, 'Study of Pressure Retarded Osmosis Process in Hollow Fiber Membrane: Cylindrical Model for Description of Energy Production', Energies, vol. 15, no. 10, pp. 3558-3558.
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A new mathematical model was developed to predict the cylindrical effect of the membrane performance in the pressure retarded osmosis process. The cylindrical membrane transport layers (the draw side boundary and the porous membrane) were divided into very thin sublayers with constant mass transport parameters, among others with a constant radius in every sublayer. The obtained second-order differential mass balance equations were solved analytically, with constant parameters written for every sublayer. The algebraic equation system involving 2N equations was then solved for the determinant solution. It was shown that the membrane properties, water permeability (A), salt permeability (B), structural parameter (S) and the operating conditions (inlet draw side solute concentration and draw side mass transfer coefficient) affect the water flux strongly, and thus the membrane performance, due to the cylindrical effect caused by the variable surface and volume of the sublayers. This effect significantly depends on the lumen radius. The lower radius means a larger change in the internal surface/volume of sublayers with ΔR thickness. The predicted results correspond to that of the flat-sheet membrane layer at ro = 10,000 μm. At the end of this manuscript, the calculated mass transfer rates were compared to those measured. It was stated that the curvature effect in using a capillary membrane must not be left out of consideration when applying hollow fiber membrane modules due to their relatively low lumen radius. The presented model provides more precise prediction of the performance in the case of hollow fiber membranes.
Nahar, K & Gill, AQ 2022, 'Integrated identity and access management metamodel and pattern system for secure enterprise architecture', Data & Knowledge Engineering, vol. 140, pp. 102038-102038.
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Identity and access management (IAM) is one of the key components of the secure enterprise architecture for protecting the digital assets of the information systems. The challenge is: How to model an integrated IAM for a secure enterprise architecture to protect digital assets? This research aims to address this question and develops an ontology based integrated IAM metamodel for the secure digital enterprise architecture (EA). Business domain and technology agnostic characteristics of the developed IAM metamodel will allow it to develop IAM models for different types of information systems. Well-known design science research (DSR) methodology was adopted to conduct this research. The developed IAM metamodel is evaluated by using the demonstration method. Furthermore, as a part of the evaluation, a pattern system has been developed, consisting of eight IAM patterns. Each pattern offers a solution to a specific IAM related problem. The outcome of this research indicates that enterprise, IAM and information systems architects and academic researchers can use the proposed IAM metamodel and the pattern system to design and implement situation-specific IAM models within the overall context of a secure EA for information systems.
Nahar, K & Gill, AQ 2022, 'Integrated identity and access management metamodel and pattern system for secure enterprise architecture.', Data Knowl. Eng., vol. 140, pp. 102038-102038.
Namisango, F, Kang, K & Beydoun, G 2022, 'How the Structures Provided by Social Media Enable Collaborative Outcomes: A Study of Service Co-creation in Nonprofits.', Inf. Syst. Frontiers, vol. 24, no. 2, pp. 517-535.
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Nascimben, M, Wang, Y-K, King, J-T, Jung, T-P, Touryan, J, Lance, BJ & Lin, C-T 2022, 'Alpha Correlates of Practice During Mental Preparation for Motor Imagery', IEEE Transactions on Cognitive and Developmental Systems, vol. 14, no. 1, pp. 146-155.
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IEEE In this study we quantified performance variations of motor imagery (MI)-based brain-computer interface (BCI) systems induced by practice. Two experimental sessions were recorded from ten healthy subjects while playing a BCI-oriented videogame for two weeks. The analysis focused on the exploration of electroencephalographic changes during mental preparation between novice and practiced subjects. EEG changes were quantified using global field power (GFP), dynamic time warping (TW) and mutual information (MutInf): GFP represents the strength of the electric field, TW measures signal similarities and MutInf signals inter-dependency. Each metric was selected to relate insights extracted from mental preparation to the three experimental hypotheses associating practice with BCI performance. Significant results were identified in lower alpha for GFP and upper alpha for TW and MutInf. GFP in lower alpha during mental preparation assessed not only novice vs practiced variations but also “intra-session” differences. Findings suggest that EEG changes during mental preparation provide a quantitative measure of practice level. These metrics extracted before motor intention could be applied to BCI models targeting MI to monitor a user’s degree of training.
Ni, M, Wang, C, Zhu, T, Yu, S & Liu, W 2022, 'Attacking neural machine translations via hybrid attention learning', Machine Learning, vol. 111, no. 11, pp. 3977-4002.
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AbstractDeep-learning based natural language processing (NLP) models are proven vulnerable to adversarial attacks. However, there is currently insufficient research that studies attacks to neural machine translations (NMTs) and examines the robustness of deep-learning based NMTs. In this paper, we aim to fill this critical research gap. When generating word-level adversarial examples in NLP attacks, there is a conventional trade-off in existing methods between the attacking performance and the amount of perturbations. Although some literature has studied such a trade-off and successfully generated adversarial examples with a reasonable amount of perturbations, it is still challenging to generate highly successful translation attacks while concealing the changes to the texts. To this end, we propose a novel Hybrid Attentive Attack method to locate language-specific and sequence-focused words, and make semantic-aware substitutions to attack NMTs. We evaluate the effectiveness of our attack strategy by attacking three high-performing translation models. The experimental results show that our method achieves the highest attacking performance compared with other existing attacking strategies.
Nie, X, Zhang, A, Chen, J, Qu, Y & Yu, S 2022, 'Blockchain-Empowered Secure and Privacy-Preserving Health Data Sharing in Edge-Based IoMT', Security and Communication Networks, vol. 2022, pp. 1-16.
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Health data sharing, as a booming demand, enables the patients with similar symptoms to connect with each other and doctors to obtain the medical history of patients. Health data are usually collected from edge-based Internet of medical things (IoMT) with devices such as smart wearable devices, smart watches, and smartphones. Since health data are highly private and have great financial value, adversaries ceaselessly launch diverse attacks to obtain private information. All these issues pose great challenges to health data sharing in edge-based IoMT scenarios. Existing research either lacks comprehensive consideration of privacy and security protection or fails to provide a proper incentive mechanism, which expels users from sharing data. In this study, we propose a novel blockchain-assisted data sharing scheme, which allows secure and privacy-preserving profile matching. A bloom filter with hash functions is designed to verify the authenticity of keyword ciphertext. Key-policy attribute-based encryption (KP-ABE) algorithm and smart contracts are employed to achieve secure profile matching. To incentivize users actively participating in profile matching, we devise an incentive mechanism and construct a two-phase Stackelberg game to address pricing problems for data owners and accessing problems of data requesters. The optimal pricing mechanism is specially designed for encouraging more users to participate in health data sharing and maximizing users’ profit. Moreover, security analysis illustrates that the proposed protocol is capable of satisfying various security goals, while performance evaluation shows high scalability and feasibility of the proposed scheme in edge-based IoMT scenarios.
Nie, X, Zhang, A, Chen, J, Qu, Y & Yu, S 2022, 'Time-Enabled and Verifiable Secure Search for Blockchain-Empowered Electronic Health Record Sharing in IoT', Security and Communication Networks, vol. 2022, pp. 1-15.
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The collection and sharing of electronic health records (EHRs) via the Internet of Things (IoT) can enhance the accuracy of disease diagnosis. However, it is challenging to guarantee the secure search of EHR during the sharing process. The advent of blockchain is a promising solution to address the issues, owing to its remarkable features such as immutability and anonymity. In this paper, we propose a novel blockchain-based secure sharing system over searchable encryption and hidden data structure via IoT devices. EHR ciphertexts of data owners are stored in the interplanetary file system (IPFS). A user with proper access permissions can search for the desired data with the data owner’s time-bound authorization and verify the authenticity of the search result. After that, the data user can access the relevant EHR ciphertext from IPFS using a symmetric key. The scheme jointly uses searchable encryption and smart contract to realize secure search, time control, verifiable keyword search, fast search, and forward privacy in IoT scenarios. Performance analysis and proof demonstrate that the proposed protocol can satisfy the design goals. In addition, performance evaluation shows the high scalability and feasibility of the proposed scheme.
Nimmy, SF, Hussain, OK, Chakrabortty, RK, Hussain, FK & Saberi, M 2022, 'Explainability in supply chain operational risk management: A systematic literature review', Knowledge-Based Systems, vol. 235, pp. 107587-107587.
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It is important to manage operational disruptions to ensure the success of supply chain operations. To achieve this aim, researchers have developed techniques that determine the occurrence of operational risk events which assists supply chain operational risk managers develop plans to manage them by detection/monitoring, mitigation/management, or optimization techniques. Various artificial intelligence (AI) approaches have been used to develop such techniques in the broad activities of operational risk management. However, all of these techniques are black box in their working nature. This means that the chosen technique cannot explain why it has given that output and whether it is correct and free from bias. To address this, researchers argue the need for supply chain management professionals to move towards using explainable AI methods for operational risk management. In this paper, we conduct a systematic literature review on the techniques used to determine operational risks and analyse whether they satisfy the requirement of them being explainable. The findings highlight the shortcomings and inspires directions for future research. From a managerial perspective, the paper encourages risk managers to choose techniques for supply chain operational risk management that can be auditable as this will ensure that the risk managers know why they should take a particular risk management action rather than just what they should do to manage the operational risks.
Norouzian-Maleki, P, Izadbakhsh, H, Saberi, M, Hussain, O, Jahangoshai Rezaee, M & GhanbarTehrani, N 2022, 'An integrated approach to system dynamics and data envelopment analysis for determining efficient policies and forecasting travel demand in an urban transport system', Transportation Letters, vol. 14, no. 2, pp. 157-173.
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O’Brien, K, Sood, S & Shete, R 2022, 'Big Data Approach to Visualising, Analysing and Modelling Company Culture: A New Paradigm and Tool for Exploring Toxic Cultures and the Way We Work', THE INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION, vol. 8, no. 2, pp. 48-61.
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This paper explores the use of big data to measure company culture, good and bad, including toxic culture. Culture is a central factor driving employee experiences and contributing to the “great resignation”. Harnessing the key Artificial Intelligence (AI) technology of neural networks using deep learning methodology for NLP provides the capability to extract cultural meanings from a diverse array of organizational information and cultural artefacts ( texts, images, speech and video) available online. Using big data and AI provides a predictive capability surpassing the value of employee survey instruments of the last century providing a rear view of insights. Big data helps break free from the paradigm of only thinking about culture moving at a glacial pace. An innovative methodology and AI technologies help measure and visually plot the organizational culture trajectory within a company cultural landscape. Cultural values, inclusive of toxicity, have the potential for detection across all forms of communications media. A non-invasive approach using a broad range of open data sources overcomes limitations of the traditional survey instruments and approaches for achieving a culture read. The benefits of the approach and the AI technology are the real-time ingestion of ongoing executive and managerial feedback while entirely sidestepping the issues of survey biases and viable samples. The methodology under study for reading a culture moves well beyond traditional text-centric searches, content analyses, dictionaries and text mining, delivering an understanding of the meanings of words, phrases, sentences or even concepts comprising company culture. Embeddings are an ideal neural network breakthrough technology enabling the computation of text as data through creating a meaningful space in which similar word meanings exist in close proximity. Vector algebra in a multidimensional space helps unpack the cultural nuances and biases pent up within...
Olszak, CM, Zurada, JM & Cetindamar, D 2022, 'Business Intelligence & Big Data for Innovative and Sustainable Development of Organizations: Special Issue (SI) Editors: Celina M. Olszak, Jozef Zurada, Dilek Cetindamar.', Inf. Syst. Manag., vol. 39, no. 1, pp. 2-2.
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Pasumarthy, N, Patibanda, R, Tai, YLE, van den Hoven, E, Danaher, J & Khot, RA 2022, 'Gooey Gut Trail: Board Game Play to Understand Human-Microbial Interactions', Proceedings of the ACM on Human-Computer Interaction, vol. 6, no. CHI PLAY, pp. 1-31.
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Our gastrointestinal health is influenced by complex interactions between our gut bacteria and multiple external factors. A wider understanding of these concepts is vital to help make gut-friendly decisions in everyday life; however, its complexity can challenge public understanding if not approached systematically. Research suggests that board games can help to playfully navigate complex subjects. We present Gooey Gut Trail (GGT), a board game to help players understand the multifactorial interactions that influence and sustain gut microbial diversity. Through the embodied enactment of in-game activities, players learn how their habits surrounding diet, physical activity, emotions, and lifestyle influence the gut microbial population. A qualitative field study with 15 participants revealed important facets of our game design that increased participants' awareness, causing them to reflect upon their habits that influence gut health. Drawing upon the study insights, we present five design considerations to aid future playful explorations on nurturing human-microbial relationships.
Peng, S, Cao, L, Zhou, Y, Ouyang, Z, Yang, A, Li, X, Jia, W & Yu, S 2022, 'A survey on deep learning for textual emotion analysis in social networks', Digital Communications and Networks, vol. 8, no. 5, pp. 745-762.
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Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states in texts. Various Deep Learning (DL) methods have developed rapidly, and they have proven to be successful in many fields such as audio, image, and natural language processing. This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research. In this paper, we provide an overview on TEA based on DL methods. After introducing a background for emotion analysis that includes defining emotion, emotion classification methods, and application domains of emotion analysis, we summarize DL technology, and the word/sentence representation learning method. We then categorize existing TEA methods based on text structures and linguistic types: text-oriented monolingual methods, text conversations-oriented monolingual methods, text-oriented cross-linguistic methods, and emoji-oriented cross-linguistic methods. We close by discussing emotion analysis challenges and future research trends. We hope that our survey will assist interested readers in understanding the relationship between TEA and DL methods while also improving TEA development.
Perera, D, Wang, Y-K, Lin, C-T, Nguyen, H & Chai, R 2022, 'Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators', Sensors, vol. 22, no. 16, pp. 6230-6230.
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This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.
Pérez-Escamilla, B, Benrimoj, SI, Martínez-Martínez, F, Gastelurrutia, MÁ, Varas-Doval, R, Musial-Gabrys, K & Garcia-Cardenas, V 2022, 'Using network analysis to explore factors moderating the implementation of a medication review service in community pharmacy', Research in Social and Administrative Pharmacy, vol. 18, no. 3, pp. 2432-2443.
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Background
Implementation factors are hypothesised to moderate the implementation of innovations. Although individual barriers and facilitators have been identified for the implementation of different evidence-based services in pharmacy, relationships between implementation factors are usually not considered.
Objectives
To examine how a network of implementation factors and the position of each factor within this network structure influences the implementation of a medication review service in community pharmacy.
Methods
A mixed methods approach was used. Medication review with follow-up service was the innovation to be implemented over 12 months in community pharmacies. A network analysis to model relationships between implementation factors was undertaken. Two networks were created.
Results
Implementation factors hindering the service implementation with the highest centrality measures were time, motivation, recruitment, individual identification with the organization and personal characteristics of the pharmacists. Three hundred and sixty-nine different interrelationships between implementation factors were identified. Important causal relationships between implementation factors included: workflow-time; characteristics of the pharmacy-time; personal characteristics of the pharmacists-motivation. Implementation factors facilitating the implementation of the service with highest centrality scores were motivation, individual identification with the organization, beliefs, adaptability, recruitment, external support and leadership. Four hundred and fifty-six different interrelationships were identified. The important causal relationships included: motivation-external support; structure-characteristics of the pharmacy; demographics-location of the pharmacy.
Conclusion
Network analysis has proven to be a useful technique to explore networks of factors moderating the implementation of a pharmacy service. Relationships were complex ...
Pietroni, N, Campen, M, Sheffer, A, Cherchi, G, Bommes, D, Gao, X, Scateni, R, Ledoux, F, Remacle, J-F & Livesu, M 2022, 'Hex-Mesh Generation and Processing: a Survey.', CoRR, vol. abs/2202.12670, no. 2, pp. 1-44.
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In this article, we provide a detailed survey of techniques for hexahedral mesh generation. We cover the whole spectrum of alternative approaches to mesh generation, as well as post-processing algorithms for connectivity editing and mesh optimization. For each technique, we highlight capabilities and limitations, also pointing out the associated unsolved challenges. Recent relaxed approaches, aiming to generate not pure-hex but hex-dominant meshes, are also discussed. The required background, pertaining to geometrical as well as combinatorial aspects, is introduced along the way.
Pietroni, N, Dumery, C, Guenot-Falque, R, Liu, M, Vidal-Calleja, TA & Sorkine-Hornung, O 2022, 'Computational Pattern Making from 3D Garment Models.', CoRR, vol. abs/2202.10272, no. 4, pp. 1-14.
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We propose a method for computing a sewing pattern of a given 3D garment model. Our algorithm segments an input 3D garment shape into patches and computes their 2D parameterization, resulting in pattern pieces that can be cut out of fabric and sewn together to manufacture the garment. Unlike the general state-of-the-art approaches for surface cutting and flattening, our method explicitly targets garment fabrication. It accounts for the unique properties and constraints of tailoring, such as seam symmetry, the usage of darts, fabric grain alignment, and a flattening distortion measure that models woven fabric deformation, respecting its anisotropic behavior. We bootstrap a recent patch layout approach developed for quadrilateral remeshing and adapt it to the purpose of computational pattern making, ensuring that the deformation of each pattern piece stays within prescribed bounds of cloth stress. While our algorithm can automatically produce the sewing patterns, it is fast enough to admit user input to creatively iterate on the pattern design. Our method can take several target poses of the 3D garment into account and integrate them into the sewing pattern design. We demonstrate results on both skintight and loose garments, showcasing the versatile application possibilities of our approach.
Pradhan, S, Dyson, LE & Lama, S 2022, 'The nexus between cultural tourism and social entrepreneurship: a pathway to sustainable community development in Nepal', Journal of Heritage Tourism, vol. 17, no. 6, pp. 615-630.
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Cultural tourism offers a pathway to community development and poverty eradication, particularly in developing countries and poor rural communities. In order to ensure that the benefits are spread equitably across the community and that cultural and environmental integrity is maintained over time, active participation of community members supported by outside actors is essential. This paper explores the potential for community-based cultural tourism initiatives in three different regions of Nepal through a series of interviews with 18 experts in the Nepalese tourism industry. The list of tourism programs suggested by the interviewees were interpreted through a community-based entrepreneurship model, focussing on the processes required to produce a sustainable cultural tourism product or service. The research furthers our understanding of the tourism industry in Nepal as well as providing guidance for the implementation of sustainable cultural tourism initiatives using community-based entrepreneurship.
Prior, DD, Saberi, M, Janjua, NK & Jie, F 2022, 'Can i trust you? incorporating supplier trustworthiness into supplier selection criteria', Enterprise Information Systems, vol. 16, no. 8-9, pp. 1-28.
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Pugalia, S & Cetindamar, D 2022, 'Insights on the glass ceiling for immigrant women entrepreneurs in the technology sector', International Journal of Gender and Entrepreneurship, vol. 14, no. 1, pp. 44-68.
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PurposeTechnology sector is the pivotal element for innovation and economic development of any country. Hence, the present article explores past researches looking into challenges faced by immigrant women entrepreneurs in technology sector and their corresponding response strategies.Design/methodology/approachThis study employs a systematic literature review (SLR) technique to collate all the relevant literature looking into the challenges and strategies from immigrant women entrepreneur's perspective and provide a comprehensive picture. Overall, 49 research articles are included in this SLR.FindingsFindings indicate that immigrant status further escalates the human, financial and network disadvantages faced by women who want to start a technology-based venture.Originality/valueThis paper contributes to the literature by categorizing the barriers and strategies on a 3 × 2 matrix reflecting the origins of the barrier or strategy (taking place at the individual, firm or institutional level) versus the type of the barrier or strategy (arising from being an immigrant woman and being a woman in the technology sector). After underlining the dearth of studies in the literature about the complex phenomenon of immigrant WEs in the technology sector, the paper points out several neglected themes for future research.
Puthal, D, Wilson, S, Nanda, A, Liu, M, Swain, S, Sahoo, BPS, Yelamarthi, K, Pillai, P, El-Sayed, H & Prasad, M 2022, 'Decision tree based user-centric security solution for critical IoT infrastructure', Computers and Electrical Engineering, vol. 99, pp. 107754-107754.
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Data processing in real-time brings better business modeling and an intuitive plan of action. Internet of things (IoT), being a source of sensitive data collected and communicated through either public or private networks, requires better security from end to end to uphold integrity, quality, and acceptability of data. Designing an adaptive solution plays a vital role where IoT is deployed for the sensing-as-a-services in the critical infrastructure and near real-time decision making by deploying data analysis in the edge datacenters. Again, securing the system with user's demand and device specifications is a challenging and open research problem. This paper proposed a decision tree based user-centric security approach named DecisionTSec that provides a secure channel for communication in IoT networks, combining edge datacenters in the network edges. Further, the proposed DecisionTSec is validated by experimenting with the real-time testbed for the system performance along with the theoretical security validation.
Qu, Y, Gao, L, Xiang, Y, Shen, S & Yu, S 2022, 'FedTwin: Blockchain-Enabled Adaptive Asynchronous Federated Learning for Digital Twin Networks', IEEE Network, vol. 36, no. 6, pp. 183-190.
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The fast proliferation of digital twin (DT) establishes a direct connection between the physical entity and its deployed digital representation. As markets shift toward mass customization and new service delivery models, the digital representation has become more adaptive and agile by forming digital twin networks (DTN). DTN institutes a real-time single source of truth everywhere. However, there are several issues preventing DTN from further application, which are centralized processing, data falsification, privacy leakage, lack of incentive mechanism, etc. To make DTN better meet the ever-changing demands, we propose a novel blockchain-enabled adaptive asynchronous federated learning (FedTwin) paradigm for privacy-preserving and decentralized DTN. We design Proof-of-Federalism (PoF), which is a tailor-made consensus algorithm for autonomous DTN. In each DT's local training phase, generative adversarial network enhanced differential privacy is used to protect the privacy of local model parameters while a modified Isolation Forest is deployed to filter out the falsified DTs. In the global aggregation phase, an improved Markov decision process is leveraged to select optimal DTs to achieve adaptive asynchronous aggregation while providing a roll-back mechanism to redact the falsified global models. With this paper, we aim to provide insights to the forthcoming researchers and readers in this under-explored domain.
Qu, Y, Xu, C, Gao, L, Xiang, Y & Yu, S 2022, 'FL-SEC: Privacy-Preserving Decentralized Federated Learning Using SignSGD for the Internet of Artificially Intelligent Things', IEEE Internet of Things Magazine, vol. 5, no. 1, pp. 85-90.
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Qu, Z, Lau, CW, Simoff, SJ, Kennedy, PJ, Nguyen, QV & Catchpoole, DR 2022, 'Review of Innovative Immersive Technologies for Healthcare Applications', Innovations in Digital Health, Diagnostics, and Biomarkers, vol. 2, no. 2022, pp. 27-39.
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ABSTRACTImmersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), can connect people using enhanced data visualizations to better involve stakeholders as integral members of the process. Immersive technologies have started to change the research on multidimensional genomic data analysis for disease diagnostics and treatments. Immersive technologies are highlighted in some research for health and clinical needs, especially for precision medicine innovation. The use of immersive technology for genomic data analysis has recently received attention from the research community. Genomic data analytics research seeks to integrate immersive technologies to build more natural human-computer interactions that allow better perception engagements. Immersive technologies, especially VR, help humans perceive the digital world as real and give learning output with lower performance errors and higher accuracy. However, there are limited reviews about immersive technologies used in healthcare and genomic data analysis with specific digital health applications. This paper contributes a comprehensive review of using immersive technologies for digital health applications, including patient-centric applications, medical domain education, and data analysis, especially genomic data visual analytics. We highlight the evolution of a visual analysis using VR as a case study for how immersive technologies step, can by step, move into the genomic data analysis domain. The discussion and conclusion summarize the current immersive technology applications' usability, innovation, and future work in the healthcare domain, and digital health data visual analytics.
Quach, S, Reise, K, McGregor, C, Papaconstantinou, E & Nonoyama, ML 2022, 'A Delphi Survey of Canadian Respiratory Therapists’ Practice Statements on Pediatric Mechanical Ventilation', Respiratory Care, vol. 67, no. 11, pp. 1420-1436.
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BACKGROUND: Pediatric mechanical ventilation practice guidelines are not well established; therefore, the European Society for Paediatric and Neonatal Intensive Care (ESPNIC) developed consensus recommendations on pediatric mechanical ventilation management in 2017. However, the guideline's applicability in different health care settings is unknown. This study aimed to determine the consensus on pediatric mechanical ventilation practices from Canadian respiratory therapists' (RTs) perspectives and consensually validate aspects of the ESPNIC guideline. METHODS: A 3-round modified electronic Delphi survey was conducted; contents were guided by ESPNIC. Participants were RTs with at least 5 years of experience working in standalone pediatric ICUs or units with dedicated pediatric intensive care beds across Canada. Round 1 collected open-text feedback, and subsequent rounds gathered feedback using a 6-point Likert scale. Consensus was defined as ≥ 75% agreement; if consensus was unmet, statements were revised for re-ranking in the subsequent round. RESULTS: Fifty-two RTs from 14 different pediatric facilities participated in at least one of the 3 rounds. Rounds 1, 2, and 3 had a response rate of 80%, 93%, and 96%, respectively. A total of 59 practice statements achieved consensus by the end of round 3, categorized into 10 sections: (1) noninvasive ventilation and high-flow oxygen therapy, (2) tidal volume and inspiratory pressures, (3) breathing frequency and inspiratory times, (4) PEEP and FIO2 , (5) advanced modes of ventilation, (6) weaning, (7) physiological targets, (8) monitoring, (9) general, and (10) equipment adjuncts. Cumulative text feedback guided the formation of the clinical remarks to supplement these practice statements. CONCLUSIONS: This was the first study to survey RTs for their perspectives on the general practice of pediatric mechanical ventilation management in Canada, generally aligning with the ESPNIC guideline. These practice stateme...
Rajawat, AS, Goyal, SB, Bedi, P, Simoff, S, Jan, T & Prasad, M 2022, 'Smart Scalable ML-Blockchain Framework for Large-Scale Clinical Information Sharing', Applied Sciences, vol. 12, no. 21, pp. 10795-10795.
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Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent ”allowed list“ for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results.
Ranjbar, E, Menhaj, MB, Suratgar, AA, Andreu-Perez, J & Prasad, M 2022, 'Modern control design for MEMS tunable capacitors in voltage reference applications: a comparative study', International Journal of Dynamics and Control, vol. 10, no. 2, pp. 483-510.
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The open-loop drive of the MEMS tunable capacitor does not guarantee accurate output voltage in an AC voltage reference source (VRS). For a precise regulation, the capacitor movable plate should track the pull-in point trajectory harmlessly and should be kept at a certain distance from the fixed plate. Achievement of this objective is a highly challenging issue, particularly when measurement noise, unmodeled dynamics, and external disturbance are deemed. In addition, the control effort consumed energy requires minimization in MEMS applications. This paper contemplates different modern control strategies for the capacitance regulation, in a step-by-step manner. The addressed and designed controllers include the simple pole placement state feedback controller (PPSFC), PPSFC equipped with the Luenberger observer (LO), linear quadratic regulator (LQR), LQR equipped with an LO, linear quadratic integrator (LQI), and the Kalman filter-based PPSFC. The design process is considered in an evolutionary way. The simulation yields demonstrate accurate tracking of the pull-in trajectory. Among them, the Kalman filter design shows relatively satisfactory robustness against measurement and modeling errors. Ultimately, the article proposes a linear quadratic Gaussian (LQG) controller to cancel measurement noise, nonlinear dynamics, reject constant external disturbance, and minimize the control effort consumed energy in a sub-optimal manner.
Ranjbar, E, Suratgar, AA, Menhaj, MB & Prasad, M 2022, 'Design of a Fuzzy Adaptive Sliding Mode Control System for MEMS Tunable Capacitors in Voltage Reference Applications', IEEE Transactions on Fuzzy Systems, vol. 30, no. 6, pp. 1838-1852.
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MEMS Tunable Capacitors (TC) are major elements in AC Voltage Reference Sources (VRS). Physical parametric uncertainties, external electrostatic disturbance, and measurement noise malfunction their operation and ruin the preciseness of the VRS output voltage. Our objective problem is the design of a controller to cope with the mentioned parametric uncertainties, noise, and disturbance. Our applied method is the design and employment of a Proportional Integral (PI) Fuzzy Adaptive Sliding Mode Controller (FASMC). Both terms of matched and unmatched uncertainties as well as external disturbance and measurement noise are all addressed in this paper to generate a stable VRS output for the first time. Not only does the paper contribute to the employment of a FASMC to enhance robustness in the drive of the capacitor, but also it benefits the reduction of the chattering effect due to fuzzy regulation in the switching term of the control law. Moreover, the automatic fuzzy adjustment in the controller, which is used for estimation of the coefficients in the sliding surface error dynamical equation, facilitates the specification of those coefficients which can be time-consuming in simulation affairs. Clarifying the importance of the proposed fuzzy adaptive sliding mode controller, this paper reviews some previous controllers for MEMS TC in comparison with the proposed fuzzy controller, demonstrating its enhancement in comparison with previous schemes.
Rastpour, A & McGregor, C 2022, 'Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach', JMIR Mental Health, vol. 9, no. 8, pp. e38428-e38428.
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Background Wait times impact patient satisfaction, treatment effectiveness, and the efficiency of care that the patients receive. Wait time prediction in mental health is a complex task and is affected by the difficulty in predicting the required number of treatment sessions for outpatients, high no-show rates, and the possibility of using group treatment sessions. The task of wait time analysis becomes even more challenging if the input data has low utility, which happens when the data is highly deidentified by removing both direct and quasi identifiers. Objective The first aim of this study was to develop machine learning models to predict the wait time from referral to the first appointment for psychiatric outpatients by using real-time data. The second aim was to enhance the performance of these predictive models by utilizing the system’s knowledge while the input data were highly deidentified. The third aim was to identify the factors that drove long wait times, and the fourth aim was to build these models such that they were practical and easy-to-implement (and therefore, attractive to care providers). Methods We analyzed retrospective highly deidentified administrative data from 8 outpatient clinics at Ontario Shores Centre for Mental Health Sciences in Canada by using 6 machine learning methods to predict the first appointment wait time for new outpatients. We used the system’s knowledge to mitigate the low utility of our data. The data included 4187 patients who received care through 30,342 appointments. Results The average wait time varied widely between d...
Razzak, I, Eklund, P & Xu, G 2022, 'Improving healthcare outcomes using multimedia big data analytics', Neural Computing and Applications, vol. 34, no. 17, pp. 15095-15097.
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Razzak, I, Eklund, P & Xu, G 2022, 'Introduction to the special section on securing IoT-based critical infrastructure (VSI-cei)', Computers and Electrical Engineering, vol. 101, pp. 108118-108118.
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Razzak, I, Moustafa, N, Mumtaz, S & Xu, G 2022, 'One‐class tensor machine with randomized projection for large‐scale anomaly detection in high‐dimensional and noisy data', International Journal of Intelligent Systems, vol. 37, no. 8, pp. 4515-4536.
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Razzak, I, Xu, G & Khan, MK 2022, 'Guest Editorial: Privacy-Preserving Federated Machine Learning Solutions for Enhanced Security of Critical Energy Infrastructures', IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3449-3451.
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Rezazadegan, D, Berkovsky, S, Quiroz, JC, Kocaballi, AB, Wang, Y, Laranjo, L & Coiera, E 2022, 'Symbolic and Statistical Learning Approaches to Speech Summarization: A Scoping Review', Computer Speech & Language, vol. 72, pp. 101305-101305.
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Roberts, AGK, Catchpoole, DR & Kennedy, PJ 2022, 'Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability', NAR Genomics and Bioinformatics, vol. 4, no. 1, p. lqab124.
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ABSTRACT There is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour–normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution analyses are able to identify cancer-related genes. We further demonstrate that differential variability identifies cancer-related genes that are missed by differential expression analysis, and that differential expression and differential variability identify functionally distinct sets of potentially cancer-related genes. These results suggest that differential variability analysis may provide insights into genetic aspects of cancer that would not be revealed by differential expression, and that differential distribution analysis may allow for more comprehensive identification of cancer-related genes than analyses based on changes in mean or variability alone.
Saberi, M, Kamali, N, Tarnian, F & Sadeghipour, A 2022, 'Investigation Phenol, Flavonoids and Antioxidant Activity Content of Capparis spinosa in Three Natural Habitats of Sistan and Baluchestan Province, Iran', Journal of Rangeland Science, vol. 12, no. 2, pp. 191-204.
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Capparis spinosa L. is a shrub plant that in addition to its forage use, has protective importance to prevent from soil erosion in desert areas and important values in treating many diseases as well. The aim of this study was to investigate the amount of phenol, flavonoids and antioxidant activity in different organs of C. spinosa in Sistan, Iranshahr and Saravan counties, Iran. Morphological traits (number of fruits, wet weight of fruit, dry weight of fruit, fruit diameter, number and length of branches, plant height, leaf length, leaf width and root depth) in each habitat were measured from four individuals of C. spinosa randomly. In order to perform phytochemical tests, different parts of the plant (stem, leaves, flowers, fruits, and roots) were randomly collected from the habitats in the post-flowering stage in June 2019. The total phenol and flavonoid contents of all methanol extracts were measured using the spectrophotometric method and antioxidant activity was determined using the free radical trap method. Data analysis was performed as factorial experiment based on a completely randomized design in four replications. Rresults indicated significant differences between different plant organs (P<0.01) in aspect of the antioxidant activity, the amount of total phenol and flavonoids. Also, there was a significant interaction between plant organs and habitats (P<0.01). The results of the means comparison showed that the highest total phenol and total flavonoids were obtained from the methanol extract of the flower 82.8 mg of quercetin equivalent per gram dry weight and 64.3 mg of gallic acid/g of dry weight in Sistan region, respectively, and the highest antioxidant activity was 15.7% in the fruit in Iranshahr region. According to the results, the obtained methanolic extract of C. spinosa flower and fruit in Sistan natural habitats is recommended to the treatment of diseases as a potential source of natural antioxidants.
Sajid, M, Mittal, H, Pare, S & Prasad, M 2022, 'Routing and scheduling optimization for UAV assisted delivery system: A hybrid approach', Applied Soft Computing, vol. 126, pp. 109225-109225.
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This paper proposes a joint-optimization framework for UAV-routing and UAV-route scheduling problems associated with the UAV-assisted delivery system. The mixed-integer linear programming (MILP) models for UAV-routing and UAV-route scheduling problems are proposed considering the effect of incidental processes and the varying payload on travel time. A hybrid genetic and simulated annealing (HGSA) algorithm is proposed for the UAV-routing problem to minimize travel time. In HGSA, genetic algorithm (GA) employs a novel stochastic crossover operator to search for the optimal global position of customers, whereas simulated annealing (SA) utilizes local search operators to avoid the local optima. A UAV-Oriented MinMin (UO-MinMin) algorithm is also proposed to minimize the makespan of the UAV-route scheduling problem. It employs a UAV-oriented view to generate the route-scheduling order with minimal computational efforts without affecting the quality of the makespan. A Monte Carlo simulation-based sensitivity analysis is conducted to evaluate the impact of the hybridization probability of GA and SA in the proposed HGSA algorithm. To assess the performance of the HGSA algorithm, a set P of 24 benchmark instances is adopted and adjusted to meet the constraints of the UAV-Assisted delivery system. The proposed HGSA outperforms the state-of-the-art algorithms such as genetic algorithm (GA), Particle Swarm Optimization & Simulated Annealing algorithm (PSO-SA), Differential Evolution & Simulated Annealing (DE-SA), and Harris-hawks optimization (HHO). For all 24 instances, the aerial routes generated by HGSA have been used to evaluate the effectiveness of the UO-MinMin algorithm for different numbers of UAVs. The proposed UO-MinMin algorithm outperforms the base algorithms such as minimum completion time (MCT) and opportunistic load balancing (OLB).
Sansom, TM, Oberst, S, Richter, A, Lai, JCS, Saadatfar, M, Nowotny, M & Evans, TA 2022, 'Low radiodensity μCT scans to reveal detailed morphology of the termite leg and its subgenual organ', Arthropod Structure & Development, vol. 70, pp. 101191-101191.
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Termites sense tiny substrate-borne vibrations through subgenual organs (SGOs) located within their legs' tibiae. Little is known about the SGOs' structure and physical properties. We applied high-resolution (voxel size 0.45 μm) micro-computed tomography (μCT) to Australian termites, Coptotermes lacteus and Nasutitermes exitiosus (Hill) to test two staining techniques. We compared the effectiveness of a single stain of Lugol's iodine solution (LS) to LS followed by Phosphotungstic acid (PTA) solutions (1% and 2%). We then present results of a soldier of Nasutitermes exitiosus combining μCT with LS + 2%PTS stains and scanning electron microscopy to exemplify the visualisation of their SGOs. The termite's SGO due to its approximately oval shape was shown to have a maximum diameter of 60 μm and a minimum of 48 μm, covering 60 ± 4% of the leg's cross-section and 90.4 ± 5% of the residual haemolymph channel. Additionally, the leg and residual haemolymph channel cross-sectional area decreased around the SGO by 33% and 73%, respectively. We hypothesise that this change in cross-sectional area amplifies the vibrations for the SGO. Since SGOs are directly connected to the cuticle, their mechanical properties and the geometric details identified here may enable new approaches to determine how termites sense micro-vibrations.
Sarker, PC, Guo, Y, Lu, H & Zhu, JG 2022, 'Improvement on parameter identification of modified Jiles-Atherton model for iron loss calculation', Journal of Magnetism and Magnetic Materials, vol. 542, pp. 168602-168602.
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The physical behaviour of a magnetic material can be characterized by Jiles-Atherton (J-A) model where some model parameters are generally identified by optimization techniques. For identification of model parameters using optimization techniques, an error criterion based on the error between the measured and calculated magnetic flux density (B) or magnetic field strength (H) is commonly considered where the relative error in the calculation of iron loss is ignored. Consequently, the calculated iron loss from B-H loop sometimes highly differs from its experimental value. In this paper, the error criteria for J-A model's parameter identification are designed as the combination of the relative iron loss error criterion and the general existing error criterion. Furthermore, a modified J-A model is proposed to improve the agreement between experimental and calculated results especially at the low magnetic induction levels by introducing a scaling factor in the anhysteretic magnetization. The proposed modified J-A model and the effectiveness of the error criteria for its parameter identification are tested by comparing calculated results with the experimental results as well as recently works in the literature.
Saxena, A, Chugh, D, Mittal, H, Sajid, M, Chauhan, R, Yafi, E, Cao, J & Prasad, M 2022, 'A Novel Unsupervised Feature Selection Approach Using Genetic Algorithm on Partitioned Data', Advances in Artificial Intelligence and Machine Learning, vol. 02, no. 04, pp. 500-515.
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A novel feature selection approach is presented in this paper. Sammon’s Stress Function transforms the high dimension data to a lower dimension data set. A data set is divided into small partitions. The features are assigned randomly to these partitions. Using GA with Sammon Error as fitness value, a small, desired number of features are selected from every partition. The combination of the reduced subsets of the features from these partitions is again divided into small partitions. After a certain number of iterating the process, a desired small number of features is obtained. For experimental validation, the proposed method has been tested on 11 standard datasets with three classifiers namely, Decision Tree, MLP and KNN. The classification accuracies obtained by the proposed method is highest on most of the considered datasets against the results reported in literature. Moreover, the proposed method selects comparatively less number of features in comparison to considered methods. The optimistic results obtained from the proposed method justify its strength.
Sepehrirahnama, S & Oberst, S 2022, 'Acoustic Radiation Force and Torque Acting on Asymmetric Objects in Acoustic Bessel Beam of Zeroth Order Within Rayleigh Scattering Limit', Frontiers in Physics, vol. 10.
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Acoustic momentum exchange between objects and the surrounding fluid can be quantified in terms of acoustic radiation force and torque, and depends on several factors including the objects’ geometries. For a one-dimensional plane wave type, the induced torque on the objects with arbitrary shape becomes a function of both, direct polarization and Willis coupling, as a result of shape asymmetry, and has only in-plane components. Here, we investigate, in the Rayleigh scattering limit, the momentum transfer to objects in the non-planar pressure field of an acoustic Bessel beam with axisymmetric wave front. This type of beam is selected since it can be practically realized by an array of transducers that are cylindrically arranged and tilted at the cone angle β which is a proportionality index of the momentum distribution in the transverse and axial propagation directions. The analytical expressions of the radiation force and torque are derived for both symmetric and asymmetric objects. We show the dependence of radiation force and torque on the characteristic parameters β and radial distance from the beam axis. By comparing against the case of a plane travelling plane wave, zero β angle, we demonstrated that the non-planar wavefront of a zeroth order Bessel beam causes an additional radial force and axial torque. We also show that, due to Willis coupling, an asymmetric object experiences greater torques in the θ direction, by minimum of one order of magnitude compared to a plane travelling wave. Further, the components of the partial torques owing to direct polarization and Willis coupling act in the same direction, except for a certain range of cone angle β. Our findings show that a non-planar wavefront, which is quantified by β in the case of a zeroth-order Bessel beam, can be used to con...
Sepehrirahnama, S, Oberst, S, Chiang, YK & Powell, DA 2022, 'Willis Coupling-Induced Acoustic Radiation Force and Torque Reversal', Physical Review Letters, vol. 129, no. 17, p. 174501.
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Acoustic meta-atoms serve as the building blocks of metamaterials, with linear properties designed to achieve functions such as beam steering, cloaking, and focusing. They have also been used to shape the characteristics of incident acoustic fields, which led to the manipulation of acoustic radiation force and torque for development of acoustic tweezers with improved spatial resolution. However, acoustic radiation force and torque also depend on the shape of the object, which strongly affects its scattering properties. We show that by designing linear properties of an object using metamaterial concepts, the nonlinear acoustic effects of radiation force and torque can be controlled. Trapped objects are typically small compared with the wavelength, and are described as particles, inducing monopole and dipole scattering. We extend such models to a polarizability tensor including Willis coupling terms, as a measure of asymmetry, capturing the significance of geometrical features. We apply our model to a three-dimensional, subwavelength meta-atom with maximal Willis coupling, demonstrating that the force and the torque can be reversed relative to an equivalent symmetrical particle. By considering shape asymmetry in the acoustic radiation force and torque, Gorkov's fundamental theory of acoustophoresis is thereby extended. Asymmetrical shapes influence the acoustic fields by shifting the stable trapping location, highlighting a potential for tunable, shape-dependent particle sorting.
Sepehrirahnama, S, Ray Mohapatra, A, Oberst, S, Chiang, YK, Powell, DA & Lim, K-M 2022, 'Acoustofluidics 24: theory and experimental measurements of acoustic interaction force', Lab on a Chip, vol. 22, no. 18, pp. 3290-3313.
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This tutorial review covers theoretical and experimental aspects of acoustic interaction force, as one of the driving forces of acoustophoresis. The non-reciprocity, rotational coupling, viscosity effects, and particle agglomeration are discussed.
Shao, Z, Weng, J, Zhang, Y, Wu, Y, Li, M, Weng, J, Luo, W & Yu, S 2022, 'Peripheral-Free Device Pairing by Randomly Switching Power', IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 6, pp. 4240-4254.
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With the growing popularity of the Internet-of-Things (IoT), a massive amount of purpose-specific, heterogeneous, inexpensive devices have been deployed. To allow these devices to perform their duties and collaborate efficiently, designing a secure and dependable communication channel is necessary. Pairing, as the fundamental procedure for establishing a trustworthy communication channel, has received extensive attention from security researchers. Previous secure pairing protocols depend on auxiliary peripherals (e.g. displays, speakers) to share the secret message, while for those products featuring with low-price, manufacturers would probably adopt insecure pairing methods to reduce the cost, so the devices may be subject to various attacks. To mitigate such a situation, we design a peripheral-free secure pairing protocol, termed SwitchPairing. Our protocol only requires users to connect the pre-pairing devices to the same power source, then randomly presses and releases the switch to generate a shared secret. It does not require additional peripherals and can defense eavesdropping and replay attacks innately. We implement a prototype via two CC2640R2F development boards and invite volunteers to participate in the experiments about bench-marking security and usability. The result of our experiments show that our protocol can fulfill the security and efficient requirement of various IoT applications.
Sharif, O, Islam, MR, Hasan, MZ, Kabir, MA, Hasan, ME, AlQahtani, SA & Xu, G 2022, 'Analyzing the Impact of Demographic Variables on Spreading and Forecasting COVID-19', Journal of Healthcare Informatics Research, vol. 6, no. 1, pp. 72-90.
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The aim of this study is to analyse the coronavirus disease 2019 (COVID-19) outbreak in Bangladesh. This study investigates the impact of demographic variables on the spread of COVID-19 as well as tries to forecast the COVID-19 infected numbers. First of all, this study uses Fisher's Exact test to investigate the association between the infected groups of COVID-19 and demographical variables. Second, it exploits the ANOVA test to examine significant difference in the mean infected number of COVID-19 cases across the population density, literacy rate, and regions/divisions in Bangladesh. Third, this research predicts the number of infected cases in the epidemic peak region of Bangladesh for the year 2021. As a result, from the Fisher's Exact test, we find a very strong significant association between the population density groups and infected groups of COVID-19. And, from the ANOVA test, we observe a significant difference in the mean infected number of COVID-19 cases across the five different population density groups. Besides, the prediction model shows that the cumulative number of infected cases would be raised to around 500,000 in the most densely region of Bangladesh, Dhaka division.
Sharma, R, Saqib, M, Lin, CT & Blumenstein, M 2022, 'A Survey on Object Instance Segmentation', SN Computer Science, vol. 3, no. 6, p. 499.
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AbstractIn recent years, instance segmentation has become a key research area in computer vision. This technology has been applied in varied applications such as robotics, healthcare and intelligent driving. Instance segmentation technology not only detects the location of the object but also marks edges for each single instance, which can solve both object detection and semantic segmentation concurrently. Our survey will give a detail introduction to the instance segmentation technology based on deep learning, reinforcement learning and transformers. Further, we will discuss about its development in this field along with the most common datasets used. We will also focus on different challenges and future development scope for instance segmentation. This technology will provide a strong reference for future researchers in our survey paper.
Shen, J, Miao, T, Lai, J-F, Chen, X, Li, J & Yu, S 2022, 'IMS: An Identity-Based Many-to-Many Subscription Scheme With Efficient Key Management for Wireless Broadcast Systems', IEEE Transactions on Services Computing, vol. 15, no. 3, pp. 1707-1719.
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Shen, S, Zhu, T, Wu, D, Wang, W & Zhou, W 2022, 'From distributed machine learning to federated learning: In the view of data privacy and security', Concurrency and Computation: Practice and Experience, vol. 34, no. 16.
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SummaryFederated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together rather than micromanaging the workforce as in traditional DML. One of the greatest advantages of federated learning is the additional privacy and security guarantees it affords. Federated learning architecture relies on smart devices, such as smartphones and IoT sensors, that collect and process their own data, so sensitive information never has to leave the client device. Rather, clients train a submodel locally and send an encrypted update to the central server for aggregation into the global model. These strong privacy guarantees make federated learning an attractive choice in a world where data breaches and information theft are common and serious threats. This survey outlines the landscape and latest developments in data privacy and security for federated learning. We identify the different mechanisms used to provide privacy and security, such as differential privacy, secure multiparty computation and secure aggregation. We also survey the current attack models, identifying the areas of vulnerability and the strategies adversaries use to penetrate federated systems. The survey concludes with a discussion on the open challenges and potential directions of future work in this increasingly popular learning paradigm.
Shen, S, Zhu, T, Ye, D, Wang, M, Zuo, X & Zhou, A 2022, 'A novel differentially private advising framework in cloud server environment', Concurrency and Computation: Practice and Experience, vol. 34, no. 7.
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SummaryDue to the rapid development of the cloud computing environment, it is widely accepted that cloud servers are important for users to improve work efficiency. Users need to know servers' capabilities and make optimal decisions on selecting the best available servers for users' tasks. We consider the process of learning servers' capabilities by users as a multiagent reinforcement learning process. The learning speed and efficiency in reinforcement learning can be improved by sharing the learning experience among learning agents which is defined as advising. However, existing advising frameworks are limited by the requirement that during advising all learning agents in a reinforcement learning environment must have exactly the same actions. To address the above limitation, this article proposes a novel differentially private advising framework for multiagent reinforcement learning. Our proposed approach can significantly improve the application of conventional advising frameworks when agents have one different action. The approach can also widen the applicable field of advising and speed up reinforcement learning by triggering more potential advising processes among agents with different actions.
Shen, Y, Shen, S, Wu, Z, Zhou, H & Yu, S 2022, 'Signaling game-based availability assessment for edge computing-assisted IoT systems with malware dissemination', Journal of Information Security and Applications, vol. 66, pp. 103140-103140.
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IoT malware dissemination seriously exacerbates the decline of IoT system availability, which deteriorates the users experience. To address the issue, we first predict the optimal IoT malware dissemination strategy based on a signaling game for edge computing-assisted IoT systems. We then develop an algorithm to obtain the solution of the signaling IoT availability assessment game, which is to factually reflect IoT malware dissemination in practice and reasonably express the probability of IoT system nodes being successfully infected by malware. Thus, a state transition diagram of IoT system nodes can be further designed, illustrating intercommunication among all six states during IoT malware dissemination. Upon this state transition diagram, we represent the state transition probability of IoT system nodes in each state utilizing a Markov matrix, and attain the steady-state availability of an IoT system node from reliability theory. Consequently, we deduce metrics to access the steady-state availability of the entire IoT system under typical star-, tree-, and mesh topologies, respectively. We also design the corresponding IoT system availability assessment algorithm from the view of practice. In this manner, an availability assessment mechanism for edge computing-based IoT systems with malware dissemination is constructed. Experiments demonstrate the influence of IoT system features on predicting IoT malware dissemination probability and assessing the steady-state availability of three typical IoT system topologies. Our results can be utilized to lay a theoretical foundation for guiding the implementation of higher availability for edge computing-assisted IoT systems with malware dissemination.
Shi, Y, Zhang, L, Cao, Z, Tanveer, M & Lin, C-T 2022, 'Distributed Semisupervised Fuzzy Regression With Interpolation Consistency Regularization', IEEE Transactions on Fuzzy Systems, vol. 30, no. 8, pp. 3125-3137.
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Recently, distributed semi-supervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over distributed networks with multiple interconnected agents. However, existing DSSL algorithms cannot cope with data uncertainties and may suffer from high computation and communication overhead problems. Hence, we propose a distributed semi-supervised fuzzy regression model, called DSFR to tackle these issues with a two-pronged strategy - first, a structure learning with a distributed fuzzy C-means method (DFCM) that identifies the parameters in the antecedent component of fuzzy if-then rules; and, second, a parameter learning with distributed interpolation consistency regularization (DICR) to obtain the parameters in the consequent component. Since DFCM is both distributed and unsupervised, it can thus extract feature representation from both labeled and unlabeled samples among multiple agents. Meanwhile, DICR expands sample space with interpolated unlabeled instances in a distributed scheme and forces decision boundaries to lie in sparse data areas, thus increasing the models robustness. Both DFCM and DICR are implemented following the alternating direction method of multipliers method. Notably, none of the procedures involve backpropagation, so the model converges very quickly. Further, with the benefit of DFCM and DICR, DSFR is highly scalable to large datasets. Experiments on both artificial and real-world datasets show that this approach yields much lower loss values than the current state-of-the-art DSSL algorithms at a fraction of the computation cost. Our code is available online\footnote{\url{https://github.com/leijiezhang/DSFR}}.
Shu, Y, Li, Q, Liu, L & Xu, G 2022, 'Privileged multi-task learning for attribute-aware aesthetic assessment', Pattern Recognition, vol. 132, pp. 108921-108921.
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Aesthetic attributes are crucial for aesthetics because they explicitly present some photo quality cues that a human expert might use to evaluate a photo's aesthetic quality. However, the aesthetic attributes have not been largely and sufficiently exploited for photo aesthetic assessment. In this paper, we propose a novel approach to photo aesthetic assessment with the help of aesthetic attributes. The aesthetic attributes are used as privileged information (PI), which is often available during training phase but unavailable in prediction phase due to the high collection expense. The proposed framework consists of a deep multi-task network as generator and a fully connected network as discriminator. Deep multi-task network learns the aesthetic attributes and score simultaneously to capture their dependencies and extract better feature representations. Specifically, we use ranking constraint in the label space, similarity constraint and prior probabilities loss in the privileged information space to make the output of multi-task network converge to that of ground truth. Adversarial loss is used to identify and distinguish the predicted privileged information of a deep multi-task network from the ground truth PI distribution. Experimental results on two benchmark databases demonstrate the superiority of the proposed method to state-of-the-art.
Simeng, B, Zhendong, N, Hui, H, Shi, K, Kun, Y & Yuanchi, M 2022, 'Biomedical Text Classification Method Based on Hypergraph Attention Network', Data Analysis and Knowledge Discovery, vol. 6, no. 11, pp. 13-24.
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Objective This paper proposes a new model integrating tag semantics It uses text level hypergraph and cross attention mechanism to capture the organizational structure and grammatical semantics of literature aiming to improve the classification of biomedical texts Methods First we utilized the fine tuned BioBERT to retrieve vector features from the biomedical texts Then we constructed a text level hypergraph to capture the word order semantics and syntactics of the texts Finally we merged the features of text level hypergraph and labelled semantics through the cross attention mechanism network to finish the text classification Results The experimental results on the PM Sentence dataset show that the proposed model is 2 34 percentage points higher than the baseline model in the comprehensive evaluation of F1 indicators Limitations The experimental dataset needs to be expanded to evaluate the model s performance in other fields Conclusions The newly constructed model improves the classification of biomedical texts and provides effective support for knowledge retrieval and mining 2022 Chinese Academy of Sciences All rights reserved
Skarding, J, Hellmich, M, Gabrys, B & Musial, K 2022, 'A Robust Comparative Analysis of Graph Neural Networks on Dynamic Link Prediction', IEEE Access, vol. 10, no. 99, pp. 64146-64160.
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Graph neural networks (GNNs) are rapidly becoming the dominant way to learn on graph-structured data. Link prediction is a near-universal benchmark for new GNN models. Many advanced models such as Dynamic graph neural networks (DGNNs) specifically target dynamic graphs. However, these models, particularly DGNNs, are rarely compared to each other or existing heuristics. Different works evaluate their models in different ways, thus one cannot compare evaluation metrics and their results directly. Motivated by this, we perform a comprehensive comparison study. We compare link prediction heuristics, GNNs, discrete DGNNs, and continuous DGNNs on the dynamic link prediction task. In total we summarize the results of over 3200 experimental runs (≈ 1.5 years of computation time). We find that simple link prediction heuristics perform better than GNNs and DGNNs, different sliding window sizes greatly affect performance, and of all examined graph neural networks, that DGNNs consistently outperform static GNNs. This work is a continuation of our previous work, a foundation of dynamic networks and theoretical review of DGNNs. In combination with our survey, we provide both a theoretical and empirical comparison of DGNNs.
Son, DB, Binh, TH, Vo, HK, Nguyen, BM, Binh, HTT & Yu, S 2022, 'Value-based reinforcement learning approaches for task offloading in Delay Constrained Vehicular Edge Computing', Engineering Applications of Artificial Intelligence, vol. 113, pp. 104898-104898.
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In the age of booming information technology, human-being has witnessed the need for new paradigms with both high computational capability and low latency. A potential solution is Vehicular Edge Computing (VEC). Previous work proposed a Fuzzy Deep Q-Network in Offloading scheme (FDQO) that combines Fuzzy rules and Deep Q-Network (DQN) to improve DQN's early performance by using Fuzzy Controller (FC). However, we notice that frequent usage of FC can hinder the future growth performance of model. One way to overcome this issue is to remove Fuzzy Controller entirely. We introduced an algorithm called baseline DQN (b-DQN), represented by its two variants Static baseline DQN (Sb-DQN) and Dynamic baseline DQN (Db-DQN), to modify the exploration rate base on the average rewards of closest observations. Our findings confirm that these baseline DQN algorithms surpass traditional DQN models in terms of average Quality of Experience (QoE) in 100 time slots by about 6%, but still suffer from poor early performance (such as in the first 5 time slots). Here, we introduce baseline FDQO (b-FDQO). This algorithm has a strategy to modify the Fuzzy Logic usage instead of removing it entirely while still observing the rewards to modify the exploration rate. It brings a higher average QoE in the first 5 time slots compared to other non-fuzzy-logic algorithms by at least 55.12%, prevent the model from getting too bad result over all time slots, while having the late performance as good as that of b-DQN.
Song, F, Li, L, You, I, Yu, S & Zhang, H 2022, 'Optimizing High-Speed Mobile Networks with Smart Collaborative Theory', IEEE Wireless Communications, vol. 29, no. 3, pp. 48-54.
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Although the vision of cognitive and intelligent Internet of Things is fascinating, it is still challenging to maximize its advantages in high-speed mobile networks. In this article, critical obstacles, including inflexible interactions, unreliable connections, and inefficient computations, are focused to establish a better communication solution. First, a concept named mobile-aware resource sharing (MARS) is proposed with straightforward motivation based on smart collaborative theory. Second, inspired by moving velocity and processing capacity, the design details and corresponding optimizations are analyzed from multiple per-spectives. Third, the implementation procedures are introduced to establish slight cooperations, stable associations, and strong virtualizations by considering specific features of fog, edge, and cloud zones. Fourth, both the functionality and performance are validated and discussed in a complex environment. The comparison results illustrate desirable improvements in critical latency and available bandwidth aspects. We expect MARS will be beneficial for both information and transportation communities within the smart city.
Song, Y, Lu, J, Liu, A, Lu, H & Zhang, G 2022, 'A Segment-Based Drift Adaptation Method for Data Streams', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 9, pp. 4876-4889.
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In concept drift adaptation, we aim to design a blind or an informed strategy to update our best predictor for future data at each time point. However, existing informed drift adaptation methods need to wait for an entire batch of data to detect drift and then update the predictor (if drift is detected), which causes adaptation delay. To overcome the adaptation delay, we propose a sequentially updated statistic, called drift-gradient to quantify the increase of distributional discrepancy when every new instance arrives. Based on drift-gradient, a segment-based drift adaptation (SEGA) method is developed to online update our best predictor. Drift-gradient is defined on a segment in the training set. It can precisely quantify the increase of distributional discrepancy between the old segment and the newest segment when only one new instance is available at each time point. A lower value of drift-gradient on the old segment represents that the distribution of the new instance is closer to the distribution of the old segment. Based on the drift-gradient, SEGA retrains our best predictors with the segments that have the minimum drift-gradient when every new instance arrives. SEGA has been validated by extensive experiments on both synthetic and real-world, classification and regression data streams. The experimental results show that SEGA outperforms competitive blind and informed drift adaptation methods.
Soomro, WA, Guo, Y, Lu, H, Jin, J, Shen, B & Zhu, J 2022, 'Experimental Setup for Measurement of AC Loss in HTS under Rotating Magnetic Field', Energies, vol. 15, no. 21, pp. 7857-7857.
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High-temperature superconducting materials have shown great potential for the design of large-scale industry applications. However, they are complicated under AC conditions, resulting in penalties such as power loss or AC loss. This loss has to be considered in order to design reliable and efficient superconducting devices. Furthermore, when superconductors are used in rotating machines, they may be exposed to rotating magnetic fields, which is critical for the design of such machines. Existing AC loss measuring techniques are limited to measuring under one-dimensional AC magnetic fields or transport currents. Therefore, it is essential to develop and investigate robust experimental techniques to investigate the loss mechanism in HTS machines. In this paper, a new and novel experimental technique has been presented to measure AC loss in rotating magnetic field conditions. The loss under rotating magnetic fields is measured and compared by numerical modeling methods, and the results show a strong correlation with the numerical modeling and show the effectiveness of the experimental setup.
Soomro, WA, Guo, Y, Lu, H, Zhu, J, Jin, J & Shen, B 2022, 'Three-Dimensional Numerical Characterization of High-Temperature Superconductor Bulks Subjected to Rotating Magnetic Fields', Energies, vol. 15, no. 9, pp. 3186-3186.
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High-temperature superconductor (HTS) bulks have shown very promising potential for industrial applications due to the ability to trap much higher magnetic fields compared to traditional permanent magnets. In rotating electrical machines, the magnetic field is a combination of alternating and rotating fields. On the contrary, all studies on electromagnetic characterization of HTS presented in the literature so far have only focused on alternating AC magnetic fields and alternating AC loss due to the unavailability of robust experimental techniques and analytical models. This paper presents a numerical investigation on the characterization of HTS bulks subjected to rotating magnetic fields showing AC loss, current density distribution in three-dimensional axes, and trapped field analysis. A three-dimensional numerical model has been developed using H-formulation based on finite element analysis. An HTS cubic sample is magnetized and demagnetized with two-dimensional magnetic flux density vectors rotating in circular orientation around the XOY, XOZ, and YOZ planes.
Syed-Ab-Rahman, SF, Hesamian, MH & Prasad, M 2022, 'Citrus disease detection and classification using end-to-end anchor-based deep learning model', Applied Intelligence, vol. 52, no. 1, pp. 927-938.
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Plant diseases are the primary issue that reduces agricultural yield and production, causing significant economic losses and instability in the food supply. In plants, citrus is a fruit crop of great economic importance, produced and typically grown in about 140 countries. However, citrus cultivation is widely affected by various factors, including pests and diseases, resulted in significant yield and quality losses. In recent years, computer vision and machine learning have been widely used in plant disease detection and classification, which present opportunities for early disease detection and bring improvements in the field of agriculture. Early and accurate detection of plant diseases is crucial to reducing the disease’s spread and damage to the crop. Therefore, this paper employs a two-stage deep CNN model for plant disease detection and citrus diseases classification using leaf images. The proposed model consists of two main stages; (a) proposing the potential target diseased areas using a region proposal network; (b) classification of the most likely target area to the corresponding disease class using a classifier. The proposed model delivers 94.37% accuracy in detection and an average precision of 95.8%. The findings demonstrate that the proposed model identifies and distinguishes between the three different citrus diseases, namely citrus black spot, citrus bacterial canker and Huanglongbing. The proposed model serves as a useful decision support tool for growers and farmers to recognize and classify citrus diseases.
Taghikhah, F, Borevitz, J, Costanza, R & Voinov, A 2022, 'DAESim: A dynamic agro-ecosystem simulation model for natural capital assessment', Ecological Modelling, vol. 468, pp. 109930-109930.
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Taghikhah, F, Voinov, A, Filatova, T & Polhill, JG 2022, 'Machine-assisted agent-based modeling: Opening the black box', Journal of Computational Science, vol. 64, pp. 101854-101854.
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Tao, X, Gong, X, Zhang, X, Yan, S & Adak, C 2022, 'Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-21.
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Tao, X, Zhang, D, Ma, W, Hou, Z, Lu, Z & Adak, C 2022, 'Unsupervised Anomaly Detection for Surface Defects With Dual-Siamese Network', IEEE Transactions on Industrial Informatics, vol. 18, no. 11, pp. 7707-7717.
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Unsupervised anomaly detection in real industrial scenarios is challenging since the small amount of defect-free images contain limited discriminative information, and anomaly defects are unpredictable. Although nowadays image reconstruction-based methods are widely being used in various anomaly detection applications, they cannot effectively learn semantic representation, which leads to imperfect reconstruction. In this article, anomaly detection is formulated as a joint problem of feature reconstruction and inpainting in the dual-siamese framework. The proposed approach forces the network to model the feature distribution from the normal area and capture the semantic context for discriminating normal and abnormal areas. It first uses a Siamese architecture to capture discriminative features of defect-free samples and its corresponding defective samples generated by the defect random generation module. A dense feature fusion module is then employed to obtain the dense feature representation of dual input. The second Siamese network is proposed to reconstruct and inpaint the dual-dense features of the previous stage. Compared to the existing methods that mostly employ single image reconstruction, it is beneficial to simultaneously reconstruct and inpaint the information of dense discriminative features. The experimental results on the MVTec AD datasets and some major real industrial datasets demonstrate that our method achieves state-of-the-art inspection accuracy.
Tian, H, Zhu, T & Zhou, W 2022, 'Fairness and privacy preservation for facial images: GAN-based methods', Computers & Security, vol. 122, pp. 102902-102902.
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Tian, H, Zhu, T, Liu, W & Zhou, W 2022, 'Image fairness in deep learning: problems, models, and challenges', Neural Computing and Applications, vol. 34, no. 15, pp. 12875-12893.
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AbstractIn recent years, it has been revealed that machine learning models can produce discriminatory predictions. Hence, fairness protection has come to play a pivotal role in machine learning. In the past, most studies on fairness protection have used traditional machine learning methods to enforce fairness. However, these studies focus on low dimensional inputs, such as numerical inputs, whereas more recent deep learning technologies have encouraged fairness protection with image inputs through deep model methods. These approaches involve various object functions and structural designs that break the spurious correlations between targets and sensitive features. With these connections broken, we are left with fairer predictions. To better understand the proposed methods and encourage further development in the field, this paper summarizes fairness protection methods in terms of three aspects: the problem settings, the models, and the challenges. Through this survey, we hope to reveal research trends in the field, discover the fundamentals of enforcing fairness, and summarize the main challenges to producing fairer models.
Tianqing, Z, Zhou, W, Ye, D, Cheng, Z & Li, J 2022, 'Resource Allocation in IoT Edge Computing via Concurrent Federated Reinforcement Learning', IEEE Internet of Things Journal, vol. 9, no. 2, pp. 1414-1426.
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Tong, HL, Quiroz, JC, Kocaballi, AB, Ijaz, K, Coiera, E, Chow, CK & Laranjo, L 2022, 'A personalized mobile app for physical activity: An experimental mixed-methods study', DIGITAL HEALTH, vol. 8, pp. 205520762211150-205520762211150.
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Objectives To investigate the feasibility of the be.well app and its personalization approach which regularly considers users’ preferences, amongst university students. Methods We conducted a mixed-methods, pre-post experiment, where participants used the app for 2 months. Eligibility criteria included: age 18–34 years; owning an iPhone with Internet access; and fluency in English. Usability was assessed by a validated questionnaire; engagement metrics were reported. Changes in physical activity were assessed by comparing the difference in daily step count between baseline and 2 months. Interviews were conducted to assess acceptability; thematic analysis was conducted. Results Twenty-three participants were enrolled in the study (mean age = 21.9 years, 71.4% women). The mean usability score was 5.6 ± 0.8 out of 7. The median daily engagement time was 2 minutes. Eighteen out of 23 participants used the app in the last month of the study. Qualitative data revealed that people liked the personalized activity suggestion feature as it was actionable and promoted user autonomy. Some users also expressed privacy concerns if they had to provide a lot of personal data to receive highly personalized features. Daily step count increased after 2 months of the intervention (median difference = 1953 steps/day, p-value <.001, 95% CI 782 to 3112). Conclusions Incorporating users’ preferences in personalized advice provided by a physical activity app was considered feasible and acceptable, with preliminary support for its positive effects on daily step count. Future randomized studies with longer follow up are warranted to determine the effectiveness of personalized mobile apps in promoting physical activity.
Uddin Murad, MA, Cetindamar, D & Chakraborty, S 2022, 'Identifying the Key Big Data Analytics Capabilities in Bangladesh’s Healthcare Sector', Sustainability, vol. 14, no. 12, pp. 7077-7077.
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The study explores the crucial big data analytics capabilities (BDAC) for healthcare in Bangladesh. After a rigorous and extensive literature review, we list a wide range of BDAC and empirically examine their applicability in Bangladesh’s healthcare sector by consulting 51 experts with ample domain knowledge. The study adopted the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Findings highlighted 11 key BDAC, such as using advanced analytical techniques that could be critical in managing big data in the healthcare sector. The paper ends with a summary and puts forward suggestions for future studies.
Unhelkar, B, Joshi, S, Sharma, M, Prakash, S, Mani, AK & Prasad, M 2022, 'Enhancing supply chain performance using RFID technology and decision support systems in the industry 4.0–A systematic literature review', International Journal of Information Management Data Insights, vol. 2, no. 2, pp. 100084-100084.
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Supply Chain processes are continuously marred by myriad factors including varying demands, changing routes, major disruptions, and compliance issues. Therefore, supply chains require monitoring and ongoing optimization. Data science uses real-time data to provide analytical insights, leading to automation and improved decision making. RFID is an ideal technology to source big data, particularly in supply chains, because RFID tags are consumed across supply chain process, which includes scanning raw materials, completing products, transporting goods, and storing products, with accuracy and speed. This study carries out a systematic literature review of research articles published during the timeline (2000-2021) that discuss the role of RFID technology in developing decision support systems that optimize supply chains in light of Industry 4.0. Furthermore, the study offers recommendations on operational efficiency of supply chains while reducing the costs of implementing the RFID technology. The core contribution of this paper is its analysis and evaluation of various RFID implementation methods in supply chains with the aim of saving time effectively and achieving cost efficiencies.
Vaghani, A, Sood, K & Yu, S 2022, 'Security and QoS issues in blockchain enabled next-generation smart logistic networks: A tutorial', Blockchain: Research and Applications, vol. 3, no. 3, pp. 100082-100082.
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The blockchain-enabled smart logistics market is expected to grow worth USD 1620 billion and at a compound annual growth rate of 62.4%. Smart logistics ensures intelligence infrastructure, logistics automation, real-time analysis of supply chain data synchronization of the logistics process, cost transparency, unbroken shipment tracking all the way down to the transportation route, etc. In the smart logistics domain, significant advancement and growth of the Internet of Things (IoT) sensors are evident. However, the connectivity of IoT systems, including Tactile Internet, without proper safeguards creates vulnerabilities that can still be deliberately or inadvertently cause disruption. In view of this, we primarily notice two key issues. Firstly, the logistics domain can be compromised by a variety of natural or man-made activities, which eventually affect the overall network security. Secondly, there are thousands of entities in the supply chain network that use extensive machine-learning algorithms in many scenarios, and they require high-power computational resources. From these two challenges, we note that the first concern can be addressed by adding blockchain to IoT logistic networks. The second issue can be addressed using 6G. This will support 1-μs latency communications, support seamless computing at the edges of networks, and autonomously predict the best optimal location for edge computing. Motivated by this, we have highlighted motivational examples to show the necessity to integrate 6G and blockchain in smart logistic networks. Then, we have proposed a 6G and blockchain-enabled smart logistic high-level framework. We have presented the key intrinsic issues of this framework mainly from the security and resource management context. In this paper, recent state-of-the-art advances in blockchain enabled next-generation smart logistic networks are analyzed. We have also examined why 6G and not 5G would be compatible with the smart network. We ha...
Valipour, M, Yousefi, S, Jahangoshai Rezaee, M & Saberi, M 2022, 'A clustering-based approach for prioritizing health, safety and environment risks integrating fuzzy C-means and hybrid decision-making methods', Stochastic Environmental Research and Risk Assessment, vol. 36, no. 3, pp. 919-938.
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Verhoeven, D, Musial, K, Hambusch, G, Ghannam, S & Shashnov, M 2022, 'Net effects: examining strategies for women’s inclusion and influence in ASX200 company boards', Applied Network Science, vol. 7, no. 1, pp. 1-26.
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AbstractConventional approaches to improving the representation of women on the boards of major companies typically focus on increasing the number of women appointed to these positions. We show that this strategy alone does not improve gender equity. Instead of relying on aggregate statistics (“headcounts”) to evaluate women’s inclusion, we use network analysis to identify and examine two types of influence in corporate board networks: local influence measured by degree centrality and global influence measured by betweenness centrality and k-core centrality. Comparing board membership data from Australia’s largest 200 listed companies in the ASX200 index in 2015 and 2018 respectively, we demonstrate that despite an increase in the number of women holding board seats during this time, their agency in terms of these network measures remains substantively unchanged. We argue that network analysis offers more nuanced approaches to measuring women’s inclusion in organizational networks and will facilitate more successful outcomes for gender diversity and equity.
Verma, S, Wang, C, Zhu, L & Liu, W 2022, 'Attn-HybridNet: Improving Discriminability of Hybrid Features With Attention Fusion', IEEE Transactions on Cybernetics, vol. 52, no. 7, pp. 6567-6578.
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Vo, NNY, Xu, G & Le, DA 2022, 'Causal inference for the impact of economic policy on financial and labour markets amid the COVID-19 pandemic', Web Intelligence, vol. 20, no. 1, pp. 1-19.
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The COVID-19 pandemic has turned the world upside down since the beginning of 2020, leaving most nations worldwide in both health crises and economic recession. Governments have been continually responding with multiple support policies to help people and businesses overcoming the current situation, from “Containment”, “Health” to “Economic” policies, and from local and national supports to international aids. Although the pandemic damage is still not under control, it is essential to have an early investigation to analyze whether these measures have taken effects on the early economic recovery in each nation, and which kinds of measures have made bigger impacts on reducing such negative downturn. Therefore, we conducted a time series based causal inference analysis to measure the effectiveness of these policies, specifically focusing on the “Economic support” policy on the financial markets for 80 countries and on the United States and Australia labour markets. Our results identified initial positive causal relationships between these policies and the market, providing a perspective for policymakers and other stakeholders.
Wang, B, Lu, J, Li, T, Yan, Z & Zhang, G 2022, 'A quantile fusion methodology for deep forecasting', Neurocomputing, vol. 483, pp. 286-298.
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Wang, C, Lu, W, Peng, S, Qu, Y, Wang, G & Yu, S 2022, 'Modeling on Energy-Efficiency Computation Offloading Using Probabilistic Action Generating', IEEE Internet of Things Journal, vol. 9, no. 20, pp. 20681-20692.
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Wireless-powered mobile-edge computing (MEC) emerges as a crucial component in the Internet of Things (IoTs). It can cope with the fundamental performance limitations of low-power networks, such as wireless sensor networks or mobile networks. Although computation offloading and resource allocation in MEC have been studied with different optimization objectives, performance optimization in larger-scale systems still needs to be further improved. More importantly, energy efficiency is also a key issue as well as computation offloading and resource allocation for wireless-powered MEC. In this article, we investigate the joint optimization of computation rate and energy consumption under limited resources, and propose an online offloading model to search for the asymptotically optimal offloading and resource allocation strategy. First, the joint optimization problem is modeled as a mixed integer programming (MIP) problem. Second, a deep reinforcement learning (DRL)-based method, energy efficiency computation offloading using probabilistic action generating (ECOPG), is designed to generate the joint optimization policy for computation offloading and resource allocation. Finally, to avoid the curse of dimensionality in large network scales, an action exploration mechanism based on probability is introduced to accelerate the convergence rate by targeted sampling and dynamic experience replay. The experimental results demonstrate that the proposed methods significantly outperform other DRL-based methods in energy consumption, and gain better computation rate and execution efficiency at the same time. With the expansion of the network scale, the improvements become more apparent.
Wang, D, Zhang, X, Wan, Y, Yu, D, Xu, G & Deng, S 2022, 'Modeling Sequential Listening Behaviors With Attentive Temporal Point Process for Next and Next New Music Recommendation', IEEE Transactions on Multimedia, vol. 24, pp. 4170-4182.
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Wang, D, Zhang, X, Xiang, Z, Yu, D, Xu, G & Deng, S 2022, 'Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention', IEEE Transactions on Cybernetics, vol. 52, no. 11, pp. 11893-11905.
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Wang, H, Ding, S, Yang, S, Liu, C, Yu, S & Zheng, X 2022, 'Guided Activity Prediction for Minimally Invasive Surgery Safety Improvement in the Internet of Medical Things', IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4758-4768.
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With the application of the Internet of Medical Things (IoMT) in minimally invasive surgery (MIS), surgeons now have a better chance at hard-to-treat cases by carrying out more complicated MIS workflows. However, a scheduled surgical workflow is often required to be updated based on the patient's internal tissue states. Perioperative complications could occur if in-time adjustments are lacking in the operating rooms when needed. To help manage the uncertainty of live surgical workflows in the IoMT environment, we propose a MIS safety improvement framework. It helps surgeons in predicting surgical workflows with limited MIS video frames by embedding our proposed model GuidedNet. To predict future surgical activities, we first build three isomorphic neural networks to capture the spatiotemporal information. Then, we establish a guidance fusion module to handle the contextual information. It guides the GuidedNet to recognize the surgical stage. Moreover, we build a novel joint loss function to train the GuidedNet to predict the future surgical stage. We evaluate the approach on a large data set that contains 80 cholecystectomy videos (Cholec-80) and compare it with the state of the art. Experiments show that the GuidedNet can assist surgeons in carrying out MIS as well as guide the next stage of surgery for improving surgical safety. Comparing to the state of the art, our approach can obtain better predict accuracy (up to 79%) with less computing resource consumption. The result also shows that our approach has a high application prospect in video classification in other Internet of Things scenarios.
Wang, J, An, Y, Li, Z & Lu, H 2022, 'A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting', Energy, vol. 251, pp. 123960-123960.
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Wang, J, Li, Q, Ma, X & Lu, H 2022, 'Distribution parameter-determining method comparison for airborne wind energy potential assessment in the eastern coastal area of China', Sustainable Energy Technologies and Assessments, vol. 52, pp. 102161-102161.
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Wang, J, Liang, J, Yao, J, Song, HX, Yang, XT, Wu, FC, Ye, Y, Li, JH & Wu, T 2022, 'Meta-analysis of clinical trials focusing on hypertonic dextrose prolotherapy (HDP) for knee osteoarthritis', Aging Clinical and Experimental Research, vol. 34, no. 4, pp. 715-724.
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Wang, J, Wang, S, Zeng, B & Lu, H 2022, 'A novel ensemble probabilistic forecasting system for uncertainty in wind speed', Applied Energy, vol. 313, pp. 118796-118796.
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The quantification of wind speed uncertainty is of great significance for real-time control of wind turbines and power grid dispatching. However, the intermittence and fluctuation of wind energy present great challenges in modeling its uncertainty; research in this field is limited. A quantile regression bi-directional long short-term memory network (QrBiLStm) and a novel ensemble probabilistic forecasting strategy are proposed in this study to explore ensemble probabilistic forecasting. To verify the reliability of the proposed ensemble probabilistic forecasting system, the uncertainties of wind speed at wind farms in China were modeled as a case study. The results of comparative experiments including 15 other models demonstrate the superiority of this ensemble probabilistic forecasting system in terms of sharpness while maintaining high interval coverage. More specifically, it was observed that the prediction interval coverage probability obtained by the proposed system is above 97%, and the sharpness is improved by at least 24.21% as compared with the commonly used single models. The proposed ensemble probabilistic forecasting system can accurately quantify the uncertainty of wind speed, and also reduce the operation cost of power systems by improving the efficiency of wind energy utilization.
Wang, K, Ling, Y, Zhang, Y, Yu, Z, Wang, H, Bai, G, Ooi, BC & Dong, JS 2022, 'Characterizing Cryptocurrency-themed Malicious Browser Extensions', Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 6, no. 3, pp. 1-31.
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Due to the surging popularity of various cryptocurrencies in recent years, a large number of browser extensions have been developed as portals to access relevant services, such as cryptocurrency exchanges and wallets. This has stimulated a wild growth of cryptocurrency themed malicious extensions that cause heavy financial losses to the users and legitimate service providers. They have shown their capability of evading the stringent vetting processes of the extension stores, highlighting a lack of understanding of this emerging type of malware in our community. In this work, we conduct the first systematic study to identify and characterize cryptocurrency-themed malicious extensions. We monitor seven official and third-party extension distribution venues for 18 months (December 2020 to June 2022) and have collected around 3600 unique cryptocurrency-themed extensions. Leveraging a hybrid analysis, we have identified 186 malicious extensions that belong to five categories. We then characterize those extensions from various perspectives including their distribution channels, life cycles, developers, illicit behaviors, and illegal gains. Our work unveils the status quo of the cryptocurrency-themed malicious extensions and reveals their disguises and programmatic features on which detection techniques can be based. Our work serves as a warning to extension users, and an appeal to extension store operators to enact dedicated countermeasures. To facilitate future research in this area, we release our dataset of the identified malicious extensions and open-source our analyzer.
Wang, K, Lu, J, Liu, A, Song, Y, Xiong, L & Zhang, G 2022, 'Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation', Neurocomputing, vol. 491, pp. 288-304.
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As an excellent ensemble algorithm, Gradient Boosting Decision Tree (GBDT) has been tested extensively with static data. However, real-world applications often involve dynamic data streams, which suffer from concept drift problems where the data distribution changes overtime. The performance of GBDT model is degraded when applied to predict data streams with concept drift. Although incremental learning can help to alleviate such degrading, finding a perfect learning rate (i.e., the iteration in GBDT) that suits all time periods with all their different drift severity levels can be difficult. In this paper, we convert the issue of determining an optimal learning rate into the issue of choosing the best adaptive iterations when tuning GBDT. We theoretically prove that drift severity is closely related to the convergence rate of model. Accordingly, we propose a novel drift adaptation method, called adaptive iterations (AdIter), that automatically chooses the number of iterations for different drift severities to improve the prediction accuracy for data streams under concept drift. In a series of comprehensive tests with seven state-of-the-art drift adaptation methods on both synthetic and real-world data, AdIter yielded superior accuracy levels.
Wang, K, Wang, J, Zeng, B & Lu, H 2022, 'An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization', Applied Energy, vol. 314, pp. 118938-118938.
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During an era of rapid growth in electricity demand throughout society, accurate forecasting of electricity loads has become increasingly important to guarantee a stable power supply. Nevertheless, historical models do not address the structure of the data itself, and a single model cannot accurately determine the nonlinear characteristics of the data. This would not allow for accurate and stable predictions. With the aim of filling this gap, this paper proposes an innovative intelligent power load point-interval forecasting system. The system discretizes the time series, then performs efficient dimensionality reduction by fuzzification, and multi-level optimization of five benchmark deep learning models by the proposed multi-objective optimization algorithm, and finally analyzes the uncertainty of the prediction results. Experiments comparing the developed prediction system with other models were conducted on three datasets, and the prediction results were discussed for validation from multiple perspectives. The simulation results show that the proposed model has superior prediction accuracy, robustness and uncertainty analysis capability, and can provide accurate deterministic prediction information and fluctuation interval analysis to ensure the long-term safety and stability and operation of the grid.
Wang, P, Li, L, Wang, R, Zheng, X, He, J & Xu, G 2022, 'Learning persona-driven personalized sentimental representation for review-based recommendation', Expert Systems with Applications, vol. 203, pp. 117317-117317.
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Wang, P, Li, L, Xie, Q, Wang, R & Xu, G 2022, 'Social dual-effect driven group modeling for neural group recommendation', Neurocomputing, vol. 481, pp. 258-269.
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Wang, Q, Liu, D, Carmichael, MG, Aldini, S & Lin, C-T 2022, 'Computational Model of Robot Trust in Human Co-Worker for Physical Human-Robot Collaboration', IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3146-3153.
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Trust is key to achieving successful Human-Robot Interaction (HRI). Besides trust of the human co-worker in the robot, trust of the robot in its human co-worker should also be considered. A computational model of a robot's trust in its human co-worker for physical human-robot collaboration (pHRC) is proposed. The trust model is a function of the human co-worker's performance which can be characterized by factors including safety, robot singularity, smoothness, physical performance and cognitive performance. Experiments with a collaborative robot are conducted to verify the developed trust model.
Wang, Q, Zhou, Y, Cao, Z & Zhang, W 2022, 'M2SPL: Generative multiview features with adaptive meta-self-paced sampling for class-imbalance learning', Expert Systems with Applications, vol. 189, pp. 115999-115999.
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Class-imbalance learning is an important research area and draws continued attention in various real-world applications for many years. Undersampling is a key method of class-imbalance learning in order to obtain a balanced class distribution, but it may discard potentially crucial samples and may be influenced by outliers or noises in imbalanced data. Multiview learning methods have shown that models trained on different views can help each other to improve their performances and robustness, but the existing imbalance learning approaches rely only on single-view samples. In this paper, we propose a multiview feature imbalance sampling method via meta self-paced learning (M2SPL) to effectively choose high-quality samples and separate adjacent features to improve the robustness of the trained model. There are two advantages of our proposed method: (1) An adaptive reweight generation process acts as a pivotal part in our M2SPL. The adaptive density-based reweight samples learning mechanism considers noisy and intractable samples to improve the robustness of model. (2) The multiview feature learning can avoid the large value of the loss function to learn a robust model from original data, and can enhance the discrimination capability of the model. Comparison with the existing sampling approaches shows that our proposed M2SPL approach significantly improves the classification performance, with increases in the F1-score and G-mean of 15.4% and 12.5%, respectively, on average. Finally, our experimental results pass the Friedman and Holm tests, indicating that our model has a significant improvement over existing methods.
Wang, S, Cao, Y, Chen, X, Yao, L, Wang, X & Sheng, QZ 2022, 'Adversarial Robustness of Deep Reinforcement Learning Based Dynamic Recommender Systems', Frontiers in Big Data, vol. 5, p. 822783.
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Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including interactive recommendation systems. The latent embedding space of those techniques makes adversarial attacks challenging to detect at an early stage. Recent advance in causality shows that counterfactual can also be considered one of the ways to generate the adversarial samples drawn from different distribution as the training samples. We propose to explore adversarial examples and attack agnostic detection on reinforcement learning (RL)-based interactive recommendation systems. We first craft different types of adversarial examples by adding perturbations to the input and intervening on the casual factors. Then, we augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data. Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods. Our extensive experiments show that most adversarial attacks are effective, and both attack strength and attack frequency impact the attack performance. The strategically-timed attack achieves comparative attack performance with only 1/3 to 1/2 attack frequency. Besides, our white-box detector trained with one crafting method has the generalization ability over several other crafting methods.
Wang, S, Li, Z, Cao, Z, Jolfaei, A & Cao, Q 2022, 'Jam-Absorption Driving Strategy for Improving Safety Near Oscillations in a Connected Vehicle Environment Considering Consequential Jams', IEEE Intelligent Transportation Systems Magazine, vol. 14, no. 2, pp. 41-52.
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Wang, W, Zhang, Y, Sui, Y, Wan, Y, Zhao, Z, Wu, J, Yu, PS & Xu, G 2022, 'Reinforcement-Learning-Guided Source Code Summarization Using Hierarchical Attention', IEEE Transactions on Software Engineering, vol. 48, no. 1, pp. 102-119.
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Wang, X, Chen, H-T & Lin, C-T 2022, 'Error-related potential-based shared autonomy via deep recurrent reinforcement learning', Journal of Neural Engineering, vol. 19, no. 6, pp. 066023-066023.
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Abstract Objective. Error-related potential (ErrP)-based brain–computer interfaces (BCIs) have received a considerable amount of attention in the human–robot interaction community. In contrast to traditional BCI, which requires continuous and explicit commands from an operator, ErrP-based BCI leverages the ErrP, which is evoked when an operator observes unexpected behaviours from the robot counterpart. This paper proposes a novel shared autonomy model for ErrP-based human–robot interaction. Approach. We incorporate ErrP information provided by a BCI as useful observations for an agent and formulate the shared autonomy problem as a partially observable Markov decision process. A recurrent neural network-based actor-critic model is used to address the uncertainty in the ErrP signal. We evaluate the proposed framework in a simulated human-in-the-loop robot navigation task with both simulated users and real users. Main results. The results show that the proposed ErrP-based shared autonomy model enables an autonomous robot to complete navigation tasks more efficiently. In a simulation with 70% ErrP accuracy, agents completed the task 14.1% faster than in the no ErrP condition, while with real users, agents completed the navigation task 14.9% faster. Significance. The evaluation results confirmed that the shared autonomy via deep recurrent reinforcement learning is an effective way to deal with uncertain human feedback in a complex human–robot interaction task.
Wang, X, Xu, H, Wang, X, Xu, X & Wang, Z 2022, 'A Graph Neural Network and Pointer Network-Based Approach for QoS-Aware Service Composition', IEEE Transactions on Services Computing, pp. 1-14.
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Wang, Y, Jin, D, He, D, Musial, K & Dang, J 2022, 'Community Detection in Social Networks Considering Social Behaviors', IEEE Access, vol. 10, pp. 109969-109982.
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The study of community detection in networks has drawn great attention in recent years. To find communities and to understand community semantics, both network topology and network content are utilized. Unfortunately, none of them can explain the driving factors of generating community structure with semantics, which is significant for understanding the mechanisms of community generation. Our observations on a large number of networks show that specific user social behaviors are underlying factors for the generation of community structure. We exploit four types of social behaviors that widely exist in networks, i.e., reciprocity of interactions, posting preference, multitopic preference, and temporal variation of topics. We investigate their impacts on the formation process of links and content in networks, during which communities with topics form. Our analysis shows that they are highly related to community structure. Consequently, a generative community detection model SBCD (social behavior-based community detection) is proposed by combining network topology and content, in which the above social behaviors play a core role. The model is evaluated on two real datasets. The experimental results show that SBCD outperforms state-of-the-art baselines. Finally, a case study illustrates several significant observations with respect to the proposed social behaviors.
Wang, Y, Wang, X, Xu, S, He, C, Zhang, Y, Ren, J & Yu, S 2022, 'FlexMon: A flexible and fine-grained traffic monitor for programmable networks', Journal of Network and Computer Applications, vol. 201, pp. 103344-103344.
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Accurate and fine-grained traffic measurements are crucial for various network management tasks. Recent researches introduce counter-based and sketch-based approaches to traffic measurement. However, implementing accurate and fine-grained traffic measurements is very challenging due to the rigid constraints of measurement resources. The counter-based approaches are limited by the memory space constraints that prevent covering each flow in the network, and the sketch-based approaches produce inefficient throughput and lower measurement accuracy. Emerging programmable networking techniques provide programmable, flexible, and fine-grained traffic control capabilities, paving the way for realizing fine-grained and accurate traffic measurements. In this paper, we aim to design efficient traffic measurement schemes for programmable networks. We first propose a single-node traffic measurement scheme called FlexMon to accurately measure fine-grained flows in a single network node. The FlexMon separates large flows from small ones and uses dedicated flow rules and sketches to measure large and small flows, respectively. Then, to further improve the measurement performance by efficiently leveraging the network-wide measurement resource, we propose a network-wide traffic measurement scheme and extend FlexMon to support network-wide measurement. We implement the FlexMon on FPGA and CPU to process five typical measurement tasks. Experimental results show that both the single-node and network-wide measurement schemes can achieve much faster speed and higher accuracy compared to the state-of-the-art.
Wang, Y, Zhang, A, Zhang, P, Qu, Y & Yu, S 2022, 'Security-Aware and Privacy-Preserving Personal Health Record Sharing Using Consortium Blockchain', IEEE Internet of Things Journal, vol. 9, no. 14, pp. 12014-12028.
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With the fast boom of Internet of Medical Things (IoMT) devices and an increasing focus on personal health, personal health data are extensively collected by IoMT and stored as personal health records (PHRs). PHRs are frequently shared for accurate diagnosis, prognosis prediction, health advice consulting, etc. Since PHRs are highly private, the data sharing process leads to wide-ranging concerns on privacy leakage and security compromise. Existing research has shown that the centralized systems, as the mainstream mode, are under the great risks. Motivated by this, we propose a consortium blockchain based PHR management and sharing scheme, which is both security-aware and privacy-preserving. We adopt the interplanetary file system (IPFS) to store PHR ciphertext of IoMT. Then, Zero-knowledge proof can provide evidence for verifying keyword index authentication on blockchain. Moreover, the scheme jointly leverages modified attribute-based cryptographic primitives and tailor-made smart contracts to achieve secure search, privacy preservation, and personalized access control in IoMT scenarios. Security analysis is conducted to show the designed protocols attain the expected design goals. This is followed by extensive evaluation results derived from real-world datasets, which demonstrate the superiority of the proposed scheme over current leading ones.
Wang, Z, Jiang, Y, Han, F, Yu, S, Li, W, Ji, Y & Cai, W 2022, 'A thermodynamic configuration method of combined supercritical CO2 power system for marine engine waste heat recovery based on recuperative effects', Applied Thermal Engineering, vol. 200, pp. 117645-117645.
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Wang, Z, Xiao, F & Cao, Z 2022, 'Uncertainty measurements for Pythagorean fuzzy set and their applications in multiple-criteria decision making', Soft Computing, vol. 26, no. 19, pp. 9937-9952.
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Wen, J, Gabrys, B & Musial, K 2022, 'Toward Digital Twin Oriented Modeling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey', IEEE Access, vol. 10, no. 99, pp. 66886-66923.
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This paper aims to provide a comprehensive critical overview on how entities and their interactions in Complex Networked Systems (CNS) are modelled across disciplines as they approach their ultimate goal of creating a Digital Twin (DT) that perfectly matches the reality. We propose four complexity dimensions for the network representation and five generations of models for the dynamics modelling to describe the increasing complexity level of the CNS that will be developed towards achieving DT (e.g. CNS dynamics modelled offline in the 1st generation v.s. CNS dynamics modelled simultaneously with a two-way real time feedback between reality and the CNS in the 5th generation). Based on that, we propose a new framework to conceptually compare diverse existing modelling paradigms from different perspectives and create unified assessment criteria to evaluate their respective capabilities of reaching such an ultimate goal. Using the proposed criteria, we also appraise how far the reviewed current state-of-the-art approaches are from the idealised DTs. Finally, we identify and propose potential directions and ways of building a DT-orientated CNS based on the convergence and integration of CNS and DT utilising a variety of cross-disciplinary techniques.
Wen, Y, Liu, B, Cao, J, Xie, R, Song, L & Li, Z 2022, 'IdentityMask: Deep Motion Flow Guided Reversible Face Video De-Identification', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 12, pp. 8353-8367.
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Wen, Y, Liu, B, Ding, M, Xie, R & Song, L 2022, 'IdentityDP: Differential private identification protection for face images', Neurocomputing, vol. 501, pp. 197-211.
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Because of the explosive growth of face photos as well as their widespread dissemination and easy accessibility in social media, the security and privacy of personal identity information become an unprecedented challenge. Meanwhile, the convenience brought by advanced identity-agnostic computer vision technologies is attractive. Therefore, it is important to use face images while taking careful consideration in protecting people's identities. Given a face image, face de-identification, also known as face anonymization, refers to generating another image with similar appearance and the same background, while the real identity is hidden. Although extensive efforts have been made, existing face de-identification techniques are either insufficient in photo-reality or incapable of well-balancing privacy and utility. In this paper, we focus on tackling these challenges to improve face de-identification. We propose IdentityDP, a face anonymization framework that combines a data-driven deep neural network with a differential privacy (DP) mechanism. This framework encompasses three stages: facial representations disentanglement, ∊-IdentityDP perturbation and image reconstruction. Our model can effectively obfuscate the identity-related information of faces, preserve significant visual similarity, and generate high-quality images that can be used for identity-agnostic computer vision tasks, such as detection, tracking, etc. Different from the previous methods, we can adjust the balance of privacy and utility through the privacy budget according to practical demands and provide a diversity of results without pre-annotations. Extensive experiments demonstrate the effectiveness and generalization ability of our proposed anonymization framework.
Wu, EQ, Lin, C-T, Zhu, L-M, Tang, ZR, Jie, Y-W & Zhou, G-R 2022, 'Fatigue Detection of Pilots’ Brain Through Brains Cognitive Map and Multilayer Latent Incremental Learning Model', IEEE Transactions on Cybernetics, vol. 52, no. 11, pp. 12302-12314.
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This work proposes a nonparametric prior induced deep sum-logarithmic-multinomial mixture (DSLMM) model to detect pilots' cognitive states through the developed brain power map. DSLMM uses multinormal distribution to infer the latent variable of each neuron in the first layer of the network. These latent variables obeyed a sum-logarithmic distribution that is backpropagated to its observation vector and the number of neurons in the next layer. Multinormal distribution is used to segment the extended observation vector to form a matrix associated with the width of the next layer. This work also proposes an adaptive topic-layer stochastic gradient Riemann (ATL-SGR) Markov chain Monte Carlo (MCMC) inference method to learn its global parameters without heuristic assumptions. The experimental results indicate that DSLMM can extract more probability distribution contained in the brain power map layer by layer, and achieve higher pilot cognition detection accuracy.
Wu, K, Beydoun, G, Sohaib, O & Gill, A 2022, 'The Co-construct/ Co-evolving Process between Organization's Absorptive Capacity and Enterprise System Practice under Changing Context: The Case of ERP Practice.', Inf. Syst. Frontiers, vol. 24, no. 6, pp. 2123-2138.
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AbstractLong term sustainability in a competitive and changing environment requires an organization to continuously learn and adapt. The ability to access and use new knowledge is contingent on the organisational absorptive capacity (AC). In this paper, we focus on how an organization’s absorptive capacity and its enterprise system practices develop and co-evolve over time. Analysing a fifteen years’ ERP practice in an organisational context, this study synthesizes a new AC analysis framework that takes into account the dynamic nature of AC. This high-level analysis coupled with a longitudinal view resolves inconsistent results between current AC studies and suggest further directions for organisational AC research.
Wu, T, Ma, H, Wang, C, Qiao, S, Zhang, L & Yu, S 2022, 'Heterogeneous representation learning and matching for few-shot relation prediction', Pattern Recognition, vol. 131, pp. 108830-108830.
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The recent explosive development of knowledge graphs (KGs) in artificial intelligence tasks coupled with incomplete or partial information has triggered considerable research interest in relation prediction. However, many challenges still remain unsolved: (i) the previous relation prediction methods require a significant amount of training instances (i.e., head-tail entity pairs) for every relation, which is infeasible in practical scenarios; and (ii) the representation learning of entities and relations always assumes that all local neighbors and their features contribute equally to the embedding, not sufficiently considering the heterogeneity of the information; and (iii) the state-of-the-art methods usually require a lot of training time, resulting in a high cost in real-world applications. To overcome these challenges, we propose a heterogeneous representation learning and matching approach, Multi-metric Feature Extraction Network (MFEN for short), for few-shot relation prediction in KGs. Our method focuses on knowledge graphs to sufficiently explore the topological structure and node content in graphs. Rather than taking the average of the embeddings of all relational neighbors, a heterogeneity-aware representation learning method is proposed to generate high-expressive embeddings, which capture the heterogenous roles of the relational neighbors of given entity and all of their features via a convolutional encoder. To learn the expressive representations efficiently, a single-layer CNN architecture with multi-scale filters is devised. In addition, multiple heuristic metrics are combined to efficiently improve the accuracy of similarity calculation. The proposed MFEN model is evaluated on two representative benchmark datasets NELL and Wiki. Extensive experiments have demonstrated that our method gets more than 5% accuracy improvement and three times speedup to state-of-the-art models. Code is available on https://github.com/summer-funny/MFEN.
Xiao, F, Cao, Z & Lin, C-T 2022, 'A Complex Weighted Discounting Multisource Information Fusion With Its Application in Pattern Classification', IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-16.
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Complex evidence theory (CET) is an effective method for uncertainty reasoning in knowledge-based systems with good interpretability that has recently attracted much attention. However, approaches to improve the performance of uncertainty reasoning in CET-based expert systems remains an open issue. One key to performance improvement is the adequate management of conflict from multisource information. In this paper, a generalized correlation coefficient, namely, the complex evidential correlation coefficient (CECC), is proposed for the complex mass functions or complex basic belief assignments (CBBAs) in CET. On this basis, a complex conflict coefficient is proposed to measure the conflict between CBBAs; when CBBAs turn into classic BBAs, the complex correlation and conflict coefficients will degrade into traditional coefficients. The complex conflict coefficient satisfies nonnegativity, symmetry, boundedness, extreme consistency, and insensitivity to refinement properties, which are desirable for conflict measurement. Several numerical examples validate through comparisons the superiority of the complex conflict coefficient. In this context, a weighted discounting multisource information fusion algorithm, which is called the CECC-WDMSIF, is designed based on the CECC to improve the performance of CET-based expert systems. By applying the CECC-WDMSIF method to the pattern classification of diverse real-world datasets, it is demonstrated that the proposed CECC-WDMSIF outperforms well-known related approaches with higher classification accuracy and robustness.
Xiao, M, Li, H, Huang, Q, Yu, S & Susilo, W 2022, 'Attribute-Based Hierarchical Access Control With Extendable Policy', IEEE Transactions on Information Forensics and Security, vol. 17, pp. 1868-1883.
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Attribute-based encryption scheme is a promising mechanism to realize one-to-many fine-grained access control which strengthens the security in cloud computing. However, massive amounts of data and various data sharing requirements bring great challenges to the complex but isolated and fixed access structures in most of the existing attribute-based encryption schemes. In this paper, we propose an attribute-based hierarchical encryption scheme with extendable policy, called Extendable Hierarchical Ciphertext-Policy Attribute-Based Encryption (EH-CP-ABE), to improve the data sharing efficiency and security simultaneously. The scheme realizes the function of hierarchical encryption, in which, data with hierarchical access control relationships could be encrypted together flexibly to improve the efficiency. The scheme also achieves external and internal extension of the access structure to further encrypt newly added hierarchical data without updating the original ciphertexts or with only a minor update depending on the data sharing requirements, which simplifies the encryption process and greatly reduces the computation overhead. We formally prove the security of the scheme is IND-CCA secure in the random oracle model based on bilinear Diffie-Hellman assumption, and we also implement our scheme to demonstrate its efficiency and practicality.
Xing, Q, Wang, J, Lu, H & Wang, S 2022, 'Research of a novel short-term wind forecasting system based on multi-objective Aquila optimizer for point and interval forecast', Energy Conversion and Management, vol. 263, pp. 115583-115583.
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Facing the increasing depletion of traditional energy resources and the worsening environmental issues, wind energy sources have been widely considered. As an essential renewable energy resource, wind energy features abundant deposits, extensive distribution, non-pollution, etc. In recent years, wind power generation occupies a non-negligible position in the electric power industry. Stable and reliable power system operation demands accurate wind speed prediction (WSP), but the inherent randomness of wind speed sequences complicates their fluctuations and causes them to be uncontrollable. In this paper, an innovative WSP system is proposed, which combines data pre-processing technique, benchmark model selection, an advanced optimizer for point forecast and interval forecast. Furthermore, this paper theoretically demonstrates that the weights allocated by this optimizer are Pareto optimal solutions. Six interval data from two sites in China are utilized to validate the forecasting performance of our developed model. The experimental results indicate that the developed model can achieve superior accuracy compared to the tested models in all cases for point forecast, and also obtains the forecasting interval with high coverage and low width error, which is an extremely crucial instruction to guarantee the security and stability of the power system.
Xiong, H, Huang, X, Yang, M, Wang, L & Yu, S 2022, 'Unbounded and Efficient Revocable Attribute-Based Encryption With Adaptive Security for Cloud-Assisted Internet of Things', IEEE Internet of Things Journal, vol. 9, no. 4, pp. 3097-3111.
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Xiong, H, Yao, T, Wang, H, Feng, J & Yu, S 2022, 'A Survey of Public-Key Encryption With Search Functionality for Cloud-Assisted IoT', IEEE Internet of Things Journal, vol. 9, no. 1, pp. 401-418.
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Nowadays, Internet of Things (IoT) is an attractive system to provide broad connectivity of a wide range of applications, and clouds are natural promoters. Cloud-assisted IoT combines the advantages of cloud computing and IoT, which is able to collect data from the real world and maximizes the value of the collected data by the means of data sharing and data analysis. Meanwhile, secure and convenient data retrieval in cloud servers becomes an important requirement for both enterprises and individual users. Public-key encryption with search functionality (shorten as PKE-SF) is a widely used cryptographic technique that allows users to retrieve encrypted data without decryption. PKE-SF mainly contains the primitives of public-key encryption with keyword search (PKE-KS), public-key encryption with equality test (PKE-ET), and plaintext-checkable encryption (PCE). In light of the overwhelming variety and multitude of PKE-SF schemes, this survey presents these schemes from different perspectives to provide better comprehension for beginners and advanced researchers. More concretely, this survey concentrates on the state of the art of PKE-SF by analyzing the design rationale, examining the framework and security model, and assessing the existing schemes in accordance with theoretic efficiency, security properties, and experimental performance. Furthermore, we discuss the extensions of traditional PKE-SF schemes which feature with the access control delegation, conjunctive keyword search, certificate-free, and offline keyword guessing attack resilience. Finally, we point out some promising directions for readers.
Xiong, P, Li, G, Ren, W & Zhu, T 2022, 'LOPO: a location privacy preserving path optimization scheme for spatial crowdsourcing', Journal of Ambient Intelligence and Humanized Computing, vol. 13, no. 12, pp. 5803-5818.
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Xiong, P, Liang, L, Zhu, Y & Zhu, T 2022, 'PriTxt: A privacy risk assessment method for text data based on semantic correlation learning', Concurrency and Computation: Practice and Experience, vol. 34, no. 5.
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SummaryPrivacy risk assessment plays a fundamental role in privacy preservation, as it determines the extent to which subsequent processing (such as generalization and obfuscation), should be applied to the sensitive data. However, most existing works on privacy risk assessment have focused on structured data, while unstructured text data remain relatively underexplored due to the complexity of natural language. In this article, we propose a novel method, PriTxt, for evaluating the privacy risk associated with text data by exploiting the semantic correlation. Using definitions derived from the General Data Protection Regulation (GDPR), a de facto standard of privacy preservation in practice, PriTxt first defines the private features that related to individual privacy in order to locate the sensitive words. By using the word2vec algorithm, a word‐embedding model is further constructed to identify the quasi‐sensitive words that are semantically correlated to the private features. The privacy risk of a given text is finally evaluated by aggregating the weighted risks of the sensitive and the quasi‐sensitive words in the text. Experiments on real‐world datasets demonstrate that the proposed PriTxt is effective for conducting risk assessment on text data and further outperforms the traditional methods.
Xu, Q, Su, Z, Lu, R & Yu, S 2022, 'Ubiquitous Transmission Service: Hierarchical Wireless Data Rate Provisioning in Space-Air-Ocean Integrated Networks', IEEE Transactions on Wireless Communications, vol. 21, no. 9, pp. 7821-7836.
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Space-air-ocean integrated networks (SAOINs), composed of low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and unmanned surface vehicles (USVs), have been advocated to provide seamless, high-rate, and reliable wireless transmission services for USVs. However, due to the restrictions of limited resources (e.g., spectrum bandwidth, transmission power, etc.), diverse demands of USVs, and selfishness of both UAVs and LEOs, there comes a significant challenge to provision high-quality wireless data rate for USVs to achieve their satisfied quality of experience (QoE). To this end, in this paper, we propose a hierarchical on-demand wireless data rate provisioning scheme to provide ubiquitous transmission services for USVs. Specifically, we first devise a hierarchical wireless data rate provisioning framework. The LEO satellite with an extensive wireless coverage is utilized to provide LEO satellite-to-UAV (L2U) data rate for UAVs with a certain L2U data rate price. Each UAV is employed to provide UAV-to-USV (U2U) data rate for covered multiple USVs with a certain U2U data rate price. We then propose a modified three-stage Stackelberg game to model the wireless data rate assignments among LEO satellites, UAVs, and USVs, where the time-varying data rate demands of USVs are considered to formulate the utility maximization problem. Afterwards, the backward induction approach is leveraged to attain the Stackelberg equilibrium as the solution of the formulated problem, where the closed-form expressions on the optimal strategies of both USVs and UAVs under different data rate budgets are obtained by the nonlinear programming method. Besides, an accelerated conjugate gradient descent (ACGD) based iteration algorithm is also designed to obtain the optimal strategies of the LEO satellites on the L2U data rate prices. At last, extensive simulations are carried out to demonstrate that the proposed scheme can significantly increase the utilities of USVs, ...
Xu, Q, Su, Z, Yu, S & Wang, Y 2022, 'Trust Based Incentive Scheme to Allocate Big Data Tasks with Mobile Social Cloud', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 113-124.
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Xu, Y, Fang, M, Chen, L, Xu, G, Du, Y & Zhang, C 2022, 'Reinforcement Learning With Multiple Relational Attention for Solving Vehicle Routing Problems', IEEE Transactions on Cybernetics, vol. 52, no. 10, pp. 11107-11120.
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In this article, we study the reinforcement learning (RL) for vehicle routing problems (VRPs). Recent works have shown that attention-based RL models outperform recurrent neural network-based methods on these problems in terms of both effectiveness and efficiency. However, existing RL models simply aggregate node embeddings to generate the context embedding without taking into account the dynamic network structures, making them incapable of modeling the state transition and action selection dynamics. In this work, we develop a new attention-based RL model that provides enhanced node embeddings via batch normalization reordering and gate aggregation, as well as dynamic-aware context embedding through an attentive aggregation module on multiple relational structures. We conduct experiments on five types of VRPs: 1) travelling salesman problem (TSP); 2) capacitated VRP (CVRP); 3) split delivery VRP (SDVRP); 4) orienteering problem (OP); and 5) prize collecting TSP (PCTSP). The results show that our model not only outperforms the learning-based baselines but also solves the problems much faster than the traditional baselines. In addition, our model shows improved generalizability when being evaluated in large-scale problems, as well as problems with different data distributions.
Yan, Z, Yang, LT, Li, T, Miche, Y, Yu, S & Yau, SS 2022, 'Guest Editorial: Trust, Security and Privacy of 6G', IEEE Network, vol. 36, no. 4, pp. 100-102.
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Yang, H, Chen, L, Pan, S, Wang, H & Zhang, P 2022, 'Discrete embedding for attributed graphs', Pattern Recognition, vol. 123, pp. 108368-108368.
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Yang, K, Lu, J, Wan, W, Zhang, G & Hou, L 2022, 'Transfer learning based on sparse Gaussian process for regression', Information Sciences, vol. 605, pp. 286-300.
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Transfer learning is to use the knowledge obtained from the source domain to improve the learning efficiency when the target domain has insufficient labeled data. For regression problems, when the conditional distribution function and the marginal distribution function of the source domain and the target domain are different, how to effectively extract similar knowledge for transfer learning is still a problem. In this paper, we propose a transfer learning method for regression problem based on the sparse Gaussian process (GP). GP models are very popular in regression modeling, as they have the capability to produce uncertainty estimation, however, they cannot be used directly for transfer learning. We propose an adaptive neural kernel network (ANKN) to ensure that the GP model can effectively transfer knowledge. Additionally, although many sparse GP methods are proposed to solve the time consumption problem of the GP models in large datasets, they cannot maintain the transfer performance. We propose a transfer inducing point (TIP) algorithm for data selection in large datasets to maintain the transfer performance. The experiments with transfer regression problems on both real-world small datasets and large datasets indicate that the our method significantly increases prediction accuracy and effectiveness.
Yang, L, Li, C, Cheng, Y, Yu, S & Ma, J 2022, 'Achieving privacy-preserving sensitive attributes for large universe based on private set intersection', Information Sciences, vol. 582, pp. 529-546.
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Nowadays, an increasing amount of data has been sent to the cloud for analysis and storage, and data security in the cloud has been widely concerned. Among them, CP-ABE is regarded as one of the most promising technologies to protect outsourced data. However, in most CP-ABE schemes, attackers may obtain user privacy information from policy plaintext. More likely, partial policy hiding programme neither satisfies full policy hiding nor applies to the needs of the large universe. In this paper,we hide both attribute values and names in policy by using private set intersection (PSI). The CP-ABE programme not only supports a complete hiding policy, but also can calculate the authorization relationship and mapping relationship between user passwords and keys. We use a polynomial-based PSI and a recursive algorithm to calculate the authorization relationship. And with the help of the algorithm and the label vector, the mapping is determined under communication restrictions. Through the outsourcing index, we achieve an efficient hiding strategy and effectively reduce the user's computing overhead. Finally, theoretical analysis and experiments show that our model has better performance while effectively protecting sensitive attributes.
Yang, X, Liu, W & Liu, W 2022, 'Tensor Canonical Correlation Analysis Networks for Multi-View Remote Sensing Scene Recognition', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 6, pp. 2948-2961.
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Yang, Z, Pan, J, Chen, J, Zi, Y, Oberst, S, Schwingshackl, CW & Hoffmann, N 2022, 'A novel unknown-input and single-output approach to extract vibration patterns via a roving continuous random excitation', ISA Transactions, vol. 129, pp. 675-686.
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Yao, H, Liu, C, Zhang, P, Wu, S, Jiang, C & Yu, S 2022, 'Identification of Encrypted Traffic Through Attention Mechanism Based Long Short Term Memory', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 241-252.
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Network traffic classification has become an important part of network management, which is beneficial for achieving intelligent network operation and maintenance, enhancing the network quality of service (QoS), and for network security. Given the rapid development of various applications and protocols, more and more encrypted traffic has emerged in networks. Traditional traffic classification methods exhibited the unsatisfied performance since the encrypted traffic is no longer in plain text. In this work, we modeled the time-series network traffic by the recurrent neural network (RNN). Moreover, the attention mechanism was introduced for assisting network traffic classification in the form of the following two models, the attention aided long short term memory (LSTM) as well as the hierarchical attention network (HAN). Finally, relying on the ISCX VPN-NonVPN dataset, extensive experiments were conducted, showing that the proposed methods achieved 91.2 percent in accuracy while the highest accuracy of other methods was 89.8 percent relying on the same dataset.
Ye, D, Shen, S, Zhu, T, Liu, B & Zhou, W 2022, 'One Parameter Defense—Defending Against Data Inference Attacks via Differential Privacy', IEEE Transactions on Information Forensics and Security, vol. 17, pp. 1466-1480.
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Machine learning models are vulnerable to data inference attacks, such as membership inference and model inversion attacks. In these types of breaches, an adversary attempts to infer a data record's membership in a dataset or even reconstruct this data record using a confidence score vector predicted by the target model. However, most existing defense methods only protect against membership inference attacks. Methods that can combat both types of attacks require a new model to be trained, which may not be time-efficient. In this paper, we propose a differentially private defense method that handles both types of attacks in a time-efficient manner by tuning only one parameter, the privacy budget. The central idea is to modify and normalize the confidence score vectors with a differential privacy mechanism which preserves privacy and obscures membership and reconstructed data. Moreover, this method can guarantee the order of scores in the vector to avoid any loss in classification accuracy. The experimental results show the method to be an effective and timely defense against both membership inference and model inversion attacks with no reduction in accuracy.
Ye, D, Zhu, T, Cheng, Z, Zhou, W & Yu, PS 2022, 'Differential Advising in Multiagent Reinforcement Learning', IEEE Transactions on Cybernetics, vol. 52, no. 6, pp. 5508-5521.
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Agent advising is one of the main approaches to improve agent learning performance by enabling agents to share advice. Existing advising methods have a common limitation that an adviser agent can offer advice to an advisee agent only if the advice is created in the same state as the advisee's concerned state. However, in complex environments, it is a very strong requirement that two states are the same, because a state may consist of multiple dimensions and two states being the same means that all these dimensions in the two states are correspondingly identical. Therefore, this requirement may limit the applicability of existing advising methods to complex environments. In this article, inspired by the differential privacy scheme, we propose a differential advising method that relaxes this requirement by enabling agents to use advice in a state even if the advice is created in a slightly different state. Compared with the existing methods, agents using the proposed method have more opportunity to take advice from others. This article is the first to adopt the concept of differential privacy on advising to improve agent learning performance instead of addressing security issues. The experimental results demonstrate that the proposed method is more efficient in complex environments than the existing methods.
Yu, E, Song, Y, Zhang, G & Lu, J 2022, 'Learn-to-adapt: Concept drift adaptation for hybrid multiple streams', Neurocomputing, vol. 496, pp. 121-130.
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Existing concept drift adaptation (CDA) methods aim to continually update outdated classifiers in a single-labeled stream scenario. However, real-world data streams are massive, with hybrids of labeled and unlabeled streams. In this paper, we discuss CDA in multiple data streams that may contain unlabeled drifting streams. To address this realistic and complex problem, we rethink the concept drift problem by adopting a meta-learning approach and introduce a Learn-to-Adapt framework (L2A). The L2A framework simultaneously 1) makes adaptations for drifting labeled streams, and 2) leverages knowledge from labeled drifting streams to make adaptations for unlabeled stream prediction. In L2A, a meta-representor with an adapter in the meta-training stage is designed to learn the invariant representations for drifting streams, enabling the model to quickly produce a good generalization of new concepts with limited training samples. In the online stage, the meta-representor will be adapted continually under the control of the adapter and will contribute to adapting the classifiers for unlabeled drifting stream prediction. Compared to existing CDA methods which mostly only adapt the classifiers, L2A adapts the feature extractor and classifier in a feedback process, which is advanced in dealing with more complex and high-dimensional data streams.
Yu, H, Lu, J & Zhang, G 2022, 'Continuous Support Vector Regression for Nonstationary Streaming Data', IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3592-3605.
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Quadratic programming is the process of solving a special type of mathematical optimization problem. Recent advances in online solutions for quadratic programming problems (QPPs) have created opportunities to widen the scope of applications for support vector regression (SVR). In this vein, efforts to make SVR compatible with streaming data have been met with substantial success. However, streaming data with concept drift remain problematic because the trained prediction function in SVR tends to drift as the data distribution drifts. Aiming to contribute a solution to this aspect of SVR's advancement, we have developed continuous SVR (C-SVR) to solve regression problems with nonstationary streaming data, that is, data where the optimal input-output prediction function can drift over time. The basic idea of C-SVR is to continuously learn a series of input-output functions over a series of time windows to make predictions about different periods. However, strikingly, the learning process in different time windows is not independent. An additional similarity term in the QPP, which is solved incrementally, threads the various input-output functions together by conveying some learned knowledge through consecutive time windows. How much learned knowledge is transferred is determined by the extent of the concept drift. Experimental evaluations with both synthetic and real-world datasets indicate that C-SVR has better performance than most existing methods for nonstationary streaming data regression.
Yu, H, Lu, J & Zhang, G 2022, 'MORStreaming: A Multioutput Regression System for Streaming Data', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 8, pp. 4862-4874.
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With the continuous generation of huge volumes of streaming data, streaming data regression has become more complicated. A regressor that predicts two or more outputs, i.e., multioutput regression, is commonly used in many applications. However, current multioutput regressors use a batch method to handle data, which presents compatibility issues for streaming data as they need to be analyzed online. To address this issue, we present a multioutput regression system, called MORStreaming, for streaming data. MORStreaming uses an instance-based model to make predictions because this model can quickly adapt to change by only storing new instances or by throwing away old instances. However, learning instances in our regression system are constrained by online demand and need to consider the relationship between outputs. Therefore, MORStreaming consists of two algorithms: 1) an online algorithm based on topology networks which is designed to learn the instances and 2) an online algorithm based on adaptive rules which is designed to learn the correlation between outputs automatically. Experiments involving both artificial and real-world datasets indicate MORStreaming can achieve superior performance compared with other multioutput methods.
Yu, H, Lu, J & Zhang, G 2022, 'Topology Learning-Based Fuzzy Random Neural Networks for Streaming Data Regression', IEEE Transactions on Fuzzy Systems, vol. 30, no. 2, pp. 412-425.
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IEEE As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN system can inherit the advantages of neural networks. However, for streaming data regression, EFN systems still have several drawbacks: 1) determining fuzzy sets is not robust to data sequence; 2) determining fuzzy rules is complex due to subspaces that can approximate to a Takagi-Sugeno-Kang (TSK) rule need to be obtained, and many parameters need to be optimized; 3) it is difficult to detect and adapt to changes in the data distribution, i.e., concept drift, if the output is a continuous variable. Hence, in this paper, a novel evolving-fuzzy-neuro system, called the topology learning-based fuzzy random neural network (TLFRNN), is proposed. In TLFRNN, an online topology learning algorithm is designed to self-organize each layer of TLFRNN. Different from current EFN systems, TLFRNN learns multiple fuzzy sets to reduce the impact of noises on each fuzzy set, and a randomness layer is designed, which assigning the probability of each fuzzy set. Also, TLFRNN does not utilize TSK rules instead uses a simple inference which considering fuzzy and random information of data simultaneously. More importantly, in TLFRNN, concept drift can be detected and adapted easily and rapidly. The experiments demonstrate that TLFRNN achieves superior performance compared to other EFSs.
Yu, H, Lu, J, Liu, A, Wang, B, Li, R & Zhang, G 2022, 'Real-Time Prediction System of Train Carriage Load Based on Multi-Stream Fuzzy Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 15155-15165.
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When a train leaves a platform, knowing the carriage load (the number of passengers in each carriage) of this train will support train managers to guide passengers at the next platform to choose carriages to avoid congestion. This capacity has become critical since the onset of the pandemic. However, with the dynamicity of passengers and the speed of trains improved (about 3 minutes travel between stations) as well as the station stop period reduced (60–90 second per station), the real-time prediction is more challenging. This paper presents an intelligent system, which is developed in collaboration with Sydney Trains, for real-time predicting carriage load across a city passenger train network. The system comprises three innovations. First, a fuzzy time-matching method significantly improves prediction accuracy in the uncertain situations and allows noisy historical data to be used for training. Second, the LightGBM model is extended with an incremental learning scheme to make forecasting in real-time possible. Third, a new multi-stream learning strategy that merges data streams with similar concept drift patterns is pioneered to increase the amount of suitable training data while reducing generalization errors. A comprehensive suite of practical tests on real-world datasets demonstrates the merit of these solutions.
Yu, H, Zhang, Q, Liu, T, Lu, J, Wen, Y & Zhang, G 2022, 'Meta-ADD: A meta-learning based pre-trained model for concept drift active detection', Information Sciences, vol. 608, pp. 996-1009.
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Concept drift is a phenomenon that commonly happened in data streams and need to be detected, because it means the statistical properties of a target variable, which the model is trying to predict, change over time in an unseen way. Most current detection methods are based on a hypothesis test framework. As a result, in these detection methods, a hypothesis test is need to be set, and more importantly, cannot obtain the type of drift. The setting of a hypothesis test requires an understanding of data streams, and cannot obtain the type of concept drift results in the loss of drift information. Hence, in this paper, to get rid of the setting of hypothesis test, and obtain the type of concept drift, we propose Active Drift Detection based on Meta learning (Meta-ADD), a novel framework that learns to classify concept drift by offline pre-training a model on data stream with known drifts, then online fine-tuning model to improve detection accuracy. Specifically, in the pre-trained phase, we extract meta-features based on the error rates of various concept drift, after which a pre-trained model called meta-detector is developed via a prototypical neural network by representing various concept drift classes as corresponding prototypes. In the detection phase, the meta-detector is fine-tuned to adapt to the real data stream via a simple stream-based active learning. Hence, Meta-ADD does not need a hypothesis test to detect concept drifts and identify their types automatically, which can directly support drift understand. The experiment results verify the effectiveness of Meta-ADD.
Yu, X, Wang, L, Yu, S, Wang, M & Zheng, M 2022, 'Flame kernel development with radiofrequency oscillating plasma ignition', Plasma Sources Science and Technology, vol. 31, no. 5, pp. 055004-055004.
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Abstract In this paper a radiofrequency oscillating plasma discharge is investigated under various initial pressures up to 5 atm in a constant-volume combustion chamber. The oscillating plasma is suppressed by elevated pressure, both in length and branch number. The ignition performance under elevated background pressure is investigated, and the results are compared with spark events with a similar ignition energy. Under ambient conditions, the oscillating plasma discharge generates multiple streamers that are much longer than a spark gap, resulting in a much bigger initial flame kernel. Under elevated background pressures, fewer streamers with much smaller sizes are observed, thus the advantage of an oscillating plasma discharge over a spark discharge is compromised. Prolonged duration of an oscillating plasma discharge consistently demonstrates a positive impact on flame propagation speed, but neither prolonged duration nor enhanced discharge current has a noticeable impact on flame kernel growth for the spark ignition cases. Both oscillating plasma and spark are used to treat non-combustible propane–air mixtures under background pressures from 1 to 5 atm.
Zeng, S, Zhou, J, Zhang, C & Merigó, JM 2022, 'Intuitionistic fuzzy social network hybrid MCDM model for an assessment of digital reforms of manufacturing industry in China', Technological Forecasting and Social Change, vol. 176, pp. 121435-121435.
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Digital reform requires enterprises to use digital technology to create a deep integration between their production, management, and operational processes, and generate a data chain for the entire process, thereby meeting the personalized requirements and expectations of customers. The achievements of digital reform in manufacturing enterprises need to be evaluated scientifically, which can help the enterprises adjust their development strategies for a digital reform in a timely manner. We therefore propose a multi-criteria model based on a social network for assessing a digital reform under an intuitionistic fuzzy environment, wherein an intuitionistic fuzzy hybrid average and geometric operator is proposed to aggregate evaluation information more effectively than with existing methods. In addition, because the trust relationships between experts can affect their decisions, a social network is introduced to determine the weights assigned to these experts. Finally, a case study of four manufacturing enterprises is presented to verify the effectiveness of the proposed method.
Zhang, C, Cui, L, Yu, S & Yu, JJQ 2022, 'A Communication-Efficient Federated Learning Scheme for IoT-Based Traffic Forecasting', IEEE Internet of Things Journal, vol. 9, no. 14, pp. 11918-11931.
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Federated Learning (FL) is widely adopted in traffic forecasting tasks involving large-scale IoT-enabled sensor data since its decentralization nature enables data providers’ privacy to be preserved. When employing state-of-the-art deep learning-based traffic predictors in FL systems, the existing FL frameworks confront overlarge communication overhead when transmitting these models’ parameter updates since the modelling depth and breadth renders them incorporating enormous number of parameters. In this paper, we propose a practical FL scheme, namely, Clustering-based hierarchical and Two-step-optimized FL (CTFed), to tackle this issue. The proposed scheme follows a divide et impera strategy that clusters the clients into multiple groups based on the similarity between their local models’ parameters. We integrate the particle swarm optimization algorithm and devises a two-step approach for local model optimization. This scheme enables only one but representative local model update from each cluster to be uploaded to the central server, thus reduces the communication overhead of the model updates transmission in FL. CTFed is orthogonal to the gradient compression-or sparsification-based approaches so that they can orchestrate to optimize the communication overhead. Extensive case studies on three real-world datasets and three state-of-the-art models demonstrate the outstanding training efficiency, accurate prediction performance and robustness to unstable network environments of the proposed scheme.
Zhang, C, Zhu, Y, Markos, C, Yu, S & Yu, JJQ 2022, 'Toward Crowdsourced Transportation Mode Identification: A Semisupervised Federated Learning Approach', IEEE Internet of Things Journal, vol. 9, no. 14, pp. 11868-11882.
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Privacy-preserving Transportation Mode Identification (TMI) is among the key challenges towards future intelligent transportation systems. With recent developments in federated learning (FL), crowdsourcing has emerged as a promising cost-effective data source for training powerful TMI classifiers without compromising users’ data privacy. However, existing TMI approaches have relied heavily on the availability of transportation mode labels, which is often limited in real-world applications. While recent semi-supervised studies have partially addressed this issue by assigning pseudo-labels to unlabeled data, such practice often degrades classification performance as more unlabeled data are incorporated. In response to this issue, we present a semi-supervised FL scheme for TMI termed Mean Teacher Semi-Supervised Federated Learning (MTSSFL). MTSSFL trains a deep neural network ensemble under a novel semi-supervised FL framework, achieving highly accurate and privacy-protected crowdsourced TMI without depending on the availability of massive labeled data. MTSSFL introduces consistency-updating to insert the global model in the gradient updates of the local models that only have unlabeled data to improve their training. We also devise mean-teacher-averaging, a secure parameter aggregation mechanism that further boosts the global model’s TMI performance without requiring additional training. Our extensive case studies on a real-world dataset demonstrate that MTSSFL’s classification accuracy is merely 1.1% lower than the state-of-the-art semi-supervised TMI approach while being the only one to satisfy FL’s privacy-preserving constraints. In addition, MTSSFL can achieve high accuracy with less training overhead due to the proposed semi-supervised learning design.
Zhang, G, Liu, B, Zhu, T, Zhou, A & Zhou, W 2022, 'Visual privacy attacks and defenses in deep learning: a survey', Artificial Intelligence Review, vol. 55, no. 6, pp. 4347-4401.
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Zhang, K, Song, X, Zhang, C & Yu, S 2022, 'Challenges and future directions of secure federated learning: a survey', Frontiers of Computer Science, vol. 16, no. 5, p. 165817.
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UNLABELLED: Federated learning came into being with the increasing concern of privacy security, as people's sensitive information is being exposed under the era of big data. It is an algorithm that does not collect users' raw data, but aggregates model parameters from each client and therefore protects user's privacy. Nonetheless, due to the inherent distributed nature of federated learning, it is more vulnerable under attacks since users may upload malicious data to break down the federated learning server. In addition, some recent studies have shown that attackers can recover information merely from parameters. Hence, there is still lots of room to improve the current federated learning frameworks. In this survey, we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated learning. Several open issues and existing solutions in federated learning are discussed. We also point out the future research directions of federated learning. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available in the online version of this article at 10.1007/s11704-021-0598-z.
Zhang, L, Huang, S & Liu, W 2022, 'Enhancing Mixture-of-Experts by Leveraging Attention for Fine-Grained Recognition', IEEE Transactions on Multimedia, vol. 24, pp. 4409-4421.
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Zhang, L, Huang, S & Liu, W 2022, 'Learning sequentially diversified representations for fine-grained categorization', Pattern Recognition, vol. 121, pp. 108219-108219.
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Zhang, L, Zhu, T, Xiong, P, Zhou, W & Yu, PS 2022, 'More than Privacy', ACM Computing Surveys, vol. 54, no. 7, pp. 1-37.
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The vast majority of artificial intelligence solutions are founded on game theory, and differential privacy is emerging as perhaps the most rigorous and widely adopted privacy paradigm in the field. However, alongside all the advancements made in both these fields, there is not a single application that is not still vulnerable to privacy violations, security breaches, or manipulation by adversaries. Our understanding of the interactions between differential privacy and game theoretic solutions is limited. Hence, we undertook a comprehensive review of literature in the field, finding that differential privacy has several advantageous properties that can make more of a contribution to game theory than just privacy protection. It can also be used to build heuristic models for game-theoretic solutions, to avert strategic manipulations, and to quantify the cost of privacy protection. With a focus on mechanism design, the aim of this article is to provide a new perspective on the currently held impossibilities in game theory, potential avenues to circumvent those impossibilities, and opportunities to improve the performance of game-theoretic solutions with differentially private techniques.
Zhang, T, Zhu, T, Li, J, Han, M, Zhou, W & Yu, PS 2022, 'Fairness in Semi-Supervised Learning: Unlabeled Data Help to Reduce Discrimination', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 4, pp. 1763-1774.
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Zhang, T, Zhu, T, Liu, R & Zhou, W 2022, 'Correlated data in differential privacy: Definition and analysis', Concurrency and Computation: Practice and Experience, vol. 34, no. 16.
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SummaryDifferential privacy is a rigorous mathematical framework for evaluating and protecting data privacy. In most existing studies, there is a vulnerable assumption that records in a dataset are independent when differential privacy is applied. However, in real‐world datasets, records are likely to be correlated, which may lead to unexpected data leakage. In this survey, we investigate the issue of privacy loss due to data correlation under differential privacy models. Roughly, we classify existing literature into three lines: (1) using parameters to describe data correlation in differential privacy, (2) using models to describe data correlation in differential privacy, and (3) describing data correlation based on the framework of Pufferfish. First, a detailed example is given to illustrate the issue of privacy leakage on correlated data in real scenes. Then our main work is to analyze and compare these methods, and evaluate situations that these diverse studies are applied. Finally, we propose some future challenges on correlated differential privacy.
Zhang, W, Wang, D, Yu, S, He, H & Wang, Y 2022, 'Repeatable Multi-Dimensional Virtual Network Embedding in Cloud Service Platform', IEEE Transactions on Services Computing, vol. 15, no. 6, pp. 3499-3512.
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Virtual network embedding (VNE) can effectively deploy virtual networks (VNs) onto shared substrate network (SN) resources. However, with the consistent changing scalability and diversity demands of VNs, traditional VNE methods prove to be a challenging task for current cloud service platforms. Thus, we model a repeatable multi-dimensional virtual network embedding (RMD-VNE) problem for implementing multi-dimensional virtual networks (MD-VNs) that involves real servers, virtual machines, containers, and network simulators. The MD-VN is preprocessed and embedded via a heuristic method denoted as ReMiDvne. Following its transformation for the containers and simulation networks, the MD-VN topology undergoes a process of coarsening, partitioning, and uncoarsening. ReMiDvne then applies a topology-aware repeatable embedding solution to complete the embedding stage. Experimental results demonstrate that ReMiDvne outperforms seven baseline approaches through small-, 1,000- and 10,000-scale VNE simulation experiments. Remarkably, ReMiDvne improves the average rates of acceptance ratio, revenue, and revenue-cost ratio by up to 40.45%, 40.45%, and 299.03%, respectively, and reduces the average rate of cost by up to 64.16%. Furthermore, real-world VNE experiments are conducted based on the OpenStack platform. The results reveal the ability of ReMiDvne to efficiently reduce communication costs by up to 45.93% and 63.43% for download and upload, respectively.
Zhang, X, Xu, Z, Fan, L, Yu, S & Qu, Y 2022, 'Near-Optimal Energy-Efficient Algorithm for Virtual Network Function Placement', IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 553-567.
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To accommodate heterogeneous and sophisticated network services, Network Function Virtualization (NFV) is invented as a hopeful networking technology. The most distinct feature of NFV is that it separates network functions from physical hardware. In the NFV architecture, various types of Virtual Network Functions (VNFs) are placed on specific software-based middleboxes by telecom providers. Traffic traverses through a sequence of Virtual Network Functions (VNFs) in pre-defined order, which is named as Service Function Chain (SFC). However, how to effectively place VNFs at different locations and steer SFC requests while minimizing energy consumption is still an open problem. Accordingly, we investigate on the joint optimization of VNF placement and traffic steering for energy efficiency in telecom networks. We first present the power consumption model in NFV-enabled telecom networks, and then formulate the studied problem as an Integer Linear Programming (ILP) model. Since the problem is proved as NP-hard, we design a polynomial algorithm that can achieve near-optimal performances based on the Markov approximation technique. In addition, our algorithm can be extended to an online version to serve dynamic arriving SFC requests. The online algorithm achieves a near-optimal long-term averaged performance. Extensive simulation results show that compared with the benchmark algorithms, in the offline and online scenario, our algorithm can reduce up to 14.08 and 13.72 percent power consumption in telecom networks, respectively.
Zhang, Y, Li, B, Wu, J, Liu, B, Chen, R & Chang, J 2022, 'Efficient and Privacy-Preserving Blockchain-Based Multifactor Device Authentication Protocol for Cross-Domain IIoT', IEEE Internet of Things Journal, vol. 9, no. 22, pp. 22501-22515.
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Zhang, Y, Liu, F, Fang, Z, Yuan, B, Zhang, G & Lu, J 2022, 'Learning From a Complementary-Label Source Domain: Theory and Algorithms', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 7667-7681.
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Zhang, Y, Wang, M, Saberi, M & Chang, E 2022, 'Analysing academic paper ranking algorithms using test data and benchmarks: an investigation', Scientometrics, vol. 127, no. 7, pp. 4045-4074.
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AbstractResearch on academic paper ranking has received great attention in recent years, and many algorithms have been proposed to automatically assess a large number of papers for this purpose. How to evaluate or analyse the performance of these ranking algorithms becomes an open research question. Theoretically, evaluation of an algorithm requires to compare its ranking result against a ground truth paper list. However, such ground truth does not exist in the field of scholarly ranking due to the fact that there does not and will not exist an absolutely unbiased, objective, and unified standard to formulate the impact of papers. Therefore, in practice researchers evaluate or analyse their proposed ranking algorithms by different methods, such as using domain expert decisions (test data) and comparing against predefined ranking benchmarks. The question is whether using different methods leads to different analysis results, and if so, how should we analyse the performance of the ranking algorithms? To answer these questions, this study compares among test data and different citation-based benchmarks by examining their relationships and assessing the effect of the method choices on their analysis results. The results of our experiments show that there does exist difference in analysis results when employing test data and different benchmarks, and relying exclusively on one benchmark or test data may bring inadequate analysis results. In addition, a guideline on how to conduct a comprehensive analysis using multiple benchmarks from different perspectives is summarised, which can help provide a systematic understanding and profile of the analysed algorithms.
Zhao, J, Li, H, Qu, L, Zhang, Q, Sun, Q, Huo, H & Gong, M 2022, 'DCFGAN: An adversarial deep reinforcement learning framework with improved negative sampling for session-based recommender systems', Information Sciences, vol. 596, pp. 222-235.
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Zhao, J, Zhang, T, Sun, Q, Huo, H & Gong, M 2022, 'A novel initialization method of fixed point continuation for recommendation systems', Expert Systems with Applications, vol. 210, pp. 118346-118346.
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Zhao, J, Zheng, S, Huo, H, Gong, M, Zhang, T & Qu, L 2022, 'Fast weighted CP decomposition for context-aware recommendation with explicit and implicit feedback', Expert Systems with Applications, vol. 204, pp. 117591-117591.
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Zhao, Y, Chen, J, Zhang, J, Wu, D, Blumenstein, M & Yu, S 2022, 'Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks', Concurrency and Computation: Practice and Experience, vol. 34, no. 7.
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SummaryIn the age of the Internet of Things (IoT), large numbers of sensors and edge devices are deployed in various application scenarios; Therefore, collaborative learning is widely used in IoT to implement crowd intelligence by inviting multiple participants to complete a training task. As a collaborative learning framework, federated learning is designed to preserve user data privacy, where participants jointly train a global model without uploading their private training data to a third party server. Nevertheless, federated learning is under the threat of poisoning attacks, where adversaries can upload malicious model updates to contaminate the global model. To detect and mitigate poisoning attacks in federated learning, we propose a poisoning defense mechanism, which uses generative adversarial networks to generate auditing data in the training procedure and removes adversaries by auditing their model accuracy. Experiments conducted on two well‐known datasets, MNIST and Fashion‐MNIST, suggest that federated learning is vulnerable to the poisoning attack, and the proposed defense method can detect and mitigate the poisoning attack.
Zhao, Y, Liu, B, Zhu, T, Ding, M & Zhou, W 2022, 'Private‐encoder: Enforcing privacy in latent space for human face images', Concurrency and Computation: Practice and Experience, vol. 34, no. 3.
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SummaryThe explosive growth of various computer vision technologies generates a tremendous amount of visual data online every day. In addition to bringing convenience and revolutionizing our daily life, image data also reveal a wide range of sensitive information and pose unprecedented privacy leakage risks. Particularly, in the case of photos contain human faces, people can easily access those face images on social media without any consent, and the misuse of personal information could cause serious privacy violation to individuals. Therefore, it is essential to consider sanitizing people's identity information when using images containing human faces. As a result, there has been rapid development in the area of facial anonymization, also called image de‐identification. However, due to the emergence of numerous deep‐learning based attacks, traditional anonymization methods such as blurring and mosaic are weak and ineffective to protect individual's privacy in face images. To respond to this challenge, this article proposes a novel de‐identification method that utilizes a deep neural network. The proposed framework encompasses two modules: encoder network and generator network. The encoder transforms a face image into a high‐semantic latent vector of codes, which will be de‐identified according to the differential privacy criterion. The generator leverages the unconditional generative adversarial network to synthesize high‐quality images based on the modified latent codes from the encoder. Extensive experimental results indicate that our proposed model can protect image privacy while keeping the processed image visual realistic.
Zhou, H, Wang, Z, Wang, J & Yu, S 2022, 'Ternary MXenes-based nanostructure enabled fire-safe and mechanic-robust EP composites with markedly impeded toxicants releases', Composites Part A: Applied Science and Manufacturing, vol. 162, pp. 107137-107137.
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Zhou, W-H, Vijayan, MK, Wang, X-W, Lu, Y-H, Gao, J, Jiao, Z-Q, Ren, R-J, Chang, Y-J, Shen, Z-S, Rohde, PP & Jin, X-M 2022, 'Reducing circuit complexity in optical quantum computation using 3D architectures', Optics Express, vol. 30, no. 18, pp. 32887-32887.
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Integrated photonic architectures based on optical waveguides are one of the leading candidates for the future realisation of large-scale quantum computation. One of the central challenges in realising this goal is simultaneously minimising loss whilst maximising interferometric visibility within waveguide circuits. One approach is to reduce circuit complexity and depth. A major constraint in most planar waveguide systems is that beamsplitter transformations between distant optical modes require numerous intermediate SWAP operations to couple them into nearest neighbour proximity, each of which introduces loss and scattering. Here, we propose a 3D architecture which can significantly mitigate this problem by geometrically bypassing trivial intermediate operations. We demonstrate the viability of this concept by considering a worst-case 2D scenario, where we interfere the two most distant optical modes in a planar structure. Using femtosecond laser direct-writing technology we experimentally construct a 2D architecture to implement Hong-Ou-Mandel interference between its most distant modes, and a 3D one with corresponding physical dimensions, demonstrating significant improvement in both fidelity and efficiency in the latter case. In addition to improving fidelity and efficiency of individual non-adjacent beamsplitter operations, this approach provides an avenue for reducing the optical depth of circuits comprising complex arrays of beamsplitter operations.
Zhou, Y, Fu, Y, Luo, Z, Hu, M, Wu, D, Sheng, QZ & Yu, S 2022, 'The Role of Communication Time in the Convergence of Federated Edge Learning', IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 3241-3254.
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Federated Edge Learning (FEL) enables a massive number of edge devices (e.g. smart phones) to train machine learning models collaboratively. Due to the inherent unreliability of participating edge devices and the unpredictable network conditions, the final model accuracy is largely determined by the communication time between edge devices and the parameter server (PS). However, there exists very limited work to quantify the influence of the communication time in FEL and show how to minimize its negative impacts from a theoretic perspective. In this paper, we are among the first to develop a formal model of the communication time in FEL and its influence on the final model accuracy. In our work, the set of edge devices involved in each global iteration is defined as the ECP (Engaged Client Pool). We model the communication time cost as a function with respect to the response time distribution of individual devices and the ECP size. By incorporating communication time cost into the convergence rate analysis, we propose the ECPA and H-ECPA algorithms to automatically adjust the size of the ECP so as to maximize the model accuracy in both homogeneous and heterogeneous networks. We also analyze how the tail shape of response time affects the convergence rate, and prove that the heavy tail shape can significantly lower the model accuracy. Finally, we conduct extensive experiments with real datasets, and the results confirm the correctness of our analysis and demonstrate the superiority of our proposed algorithms.
Zhou, Y, Liu, J, Wang, JH, Wang, J, Liu, G, Wu, D, Li, C & Yu, S 2022, 'USST: A two-phase privacy-preserving framework for personalized recommendation with semi-distributed training', Information Sciences, vol. 606, pp. 688-701.
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Personalized recommendations are becoming indispensable for assisting online users in discovering items of interest. However, existing recommendation algorithms rely heavily on the collection of personal information, which poses significant privacy concerns to users. In this paper, we propose a two-phase privacy-preserving framework called user sampling and semi-distributed training (USST) for personalized recommendations, which can protect user privacy while ensuring high recommendation accuracy. In the USST framework, rather than directly training the model with all user records, a shared model is first trained with a small set of records contributed by sampled users (e.g., paid users and volunteers). This shared model is then distributed to each user, who further trains a personalized model using personal information. Thus, the USST guarantees that all unsampled users never disclose their private information. To validate the effectiveness and practicality of USST, we designed two USST-based privacy-preserving recommendation algorithms, USST-SVD and USST-NCF based on SVD and NCF algorithms, respectively. We conducted evaluations using MovieLens and Netflix Prize datasets, and the results show that, using only 20% of sampled users’ records, the recommendation accuracy of USST-based algorithms is very close to that of all users’ records. Thus, USST can significantly improve the level of privacy protection in recommender systems.
Zhou, Y, Shang, Y, Cao, Y, Li, Q, Zhou, C & Xu, G 2022, 'API-GNN: attribute preserving oriented interactive graph neural network', World Wide Web, vol. 25, no. 1, pp. 239-258.
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AbstractAttributed graph embedding aims to learn node representation based on the graph topology and node attributes. The current mainstream GNN-based methods learn the representation of the target node by aggregating the attributes of its neighbor nodes. These methods still face two challenges: (1) In the neighborhood aggregation procedure, the attributes of each node would be propagated to its neighborhoods which may cause disturbance to the original attributes of the target node and cause over-smoothing in GNN iteration. (2) Because the representation of the target node is derived from the attributes and topology of its neighbors, the attributes and topological information of each neighbor have different effects on the representation of the target node. However, this different contribution has not been considered by the existing GNN-based methods. In this paper, we propose a novel GNN model named API-GNN (Attribute Preserving Oriented Interactive Graph Neural Network). API-GNN can not only reduce the disturbance of neighborhood aggregation to the original attribute of target node, but also explicitly model the different impacts of attribute and topology on node representation. We conduct experiments on six public real-world datasets to validate API-GNN on node classification and link prediction. Experimental results show that our model outperforms several strong baselines over various graph datasets on multiple graph analysis tasks.
Zhou, Y, Wang, J, Li, Z & Lu, H 2022, 'Short-term photovoltaic power forecasting based on signal decomposition and machine learning optimization', Energy Conversion and Management, vol. 267, pp. 115944-115944.
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Owing to the continuous increase in the proportion of solar generation accounting for the total global generation, real-time management of solar power has become indispensable. Moreover, accurate prediction of photovoltaic power is emerging as an important link to support grid operations and reflect real-life scenarios. Various studies have led to the design of several forecasting models. Nevertheless, most predictors do not focus on the effects of the factors of photovoltaic modules on the forecast results. To fill this gap, in this paper, a novel multivariable hybrid prediction system combining signal decomposition, artificial intelligence models, deep learning models, and a swarm intelligence optimization strategy is proposed. This system fully utilizes independent variable features (including the module temperature) to efficiently enhance the precision and efficiency of photovoltaic forecasting. In particular, it is proved that a Pareto-optimal solution can be obtained using the designed system. Using three datasets obtained from Safi-Morocco, the presented system is verified by comparative experiments, and its remarkable advantages in terms of forecasting are demonstrated. Specifically, using the three datasets, the symmetric mean absolute percentage errors obtained by the presented forecast system are 2.129%, 2.335%, and 3.654%, respectively, which are significantly lower than those achieved with other comparison models. Furthermore, a comprehensive and rational evaluation methodology is employed to assess the predictive capability of the developed system. The evaluation results show that the system is effective in improving the forecasting efficiency and outperforms other benchmark models.
Zhou, Y, Wang, J, Lu, H & Zhao, W 2022, 'Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition', Chaos, Solitons & Fractals, vol. 157, pp. 111982-111982.
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Short-term wind power prediction has a considerable effect on improving the productivity of wind energy systems and increasing economic benefits. In recently years, various wind velocity predictive models have been designed to raise the prediction effect. However, numerous predictive systems are limited by single type, and many ordinary predictive systems ignore the advantage of optimized parameters and the significance of data preparation, which bring about the lower predictive precision. To fill this gap, in this article, a novel predictive system is come up, which is on the basis of data denoising strategy, statistical predictive systems, artificial intelligence forecasting system and multi-objective optimization strategy. After using the data denoising strategy for denoising, the reconstructed data is used for the forecasting of different sub-systems, to obtain stable forecasting results, multi-objective dragonfly algorithm is used to estimate the weight coefficient of sub-systems. To evaluate the availability of the designed predictive system, five wind velocity datasets from different wind farms are used for the purpose of a case research. According four experiments and four analyses, it can be concluded that the designed combined system has a well predictive effect in short-term wind speed prediction. And it is in favor of grid regulation and operation.
Zhu, C, Xiao, F & Cao, Z 2022, 'A generalized Rényi divergence for multi-source information fusion with its application in EEG data analysis', Information Sciences, vol. 605, pp. 225-243.
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Zhu, C, Ye, D, Zhu, T & Zhou, W 2022, 'Time-optimal and privacy preserving route planning for carpool policy', World Wide Web, vol. 25, no. 3, pp. 1151-1168.
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AbstractTo alleviate the traffic congestion caused by the sharp increase in the number of private cars and save commuting costs, taxi carpooling service has become the choice of many people. Current research on taxi carpooling services has focused on shortening the detour distances. While with the development of intelligent cities, efficiently match passengers and vehicles and planning routes become urgent. And the privacy between passengers in the taxi carpooling service also needs to be considered. In this paper, we propose a time-optimal and privacy-preserving carpool route planning system via deep reinforcement learning. This system uses the traffic information around the carpooling vehicle to optimize passengers’ travel time, not only to efficiently match passengers and vehicles but also to generate detailed route planning for carpooling vehicles. We conducted experiments on an Internet of Vehicles simulator CARLA, and the results demonstrate that our method is better than other advanced methods and has better performance in complex environments.
Zhu, R, Li, G, Wang, P, Xu, M & Yu, S 2022, 'DRL-Based Deadline-Driven Advance Reservation Allocation in EONs for Cloud–Edge Computing', IEEE Internet of Things Journal, vol. 9, no. 21, pp. 21444-21457.
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The ongoing roll-out of cloud-edge computing and Internet of Things (IoT) has been simulating the boom of new advance reservation (AR) services, such as bulk-data migration and virtual machine backup, driving the development of substrate elastic optical networks (EONs). These AR requests are initial-delay-insensitive if they are guaranteed to be completed before a predefined deadline. Therefore, the routing, modulation, and spectrum assignment (RMSA) problem is extended to the time-spectrum domain rather than the single spectrum domain. Traditional heuristic RMSA algorithms follow static procedures under handcrafted rules and assumptions, and thus cannot be optimized automatically. To solve this problem, we propose a deep reinforced deadline-driven allocation (DRDA) algorithm. To the best of our knowledge, this work is the first to leverage deep reinforcement learning (DRL) methods to solve the AR resource allocation problem. Moreover, compared with the single experiment scenario of many existing works, the DRDA algorithm is evaluated in both static and dynamic scenarios. Simulation results show that our DRDA algorithm outperforms the other leading algorithms in both static scenario and dynamic scenario.
Zogan, H, Razzak, I, Wang, X, Jameel, S & Xu, G 2022, 'Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media', World Wide Web, vol. 25, no. 1, pp. 281-304.
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AbstractThe ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words’ importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.
Zurita, G, Mulet-Forteza, C, Merigo, J, Lobos-Ossandon, V & Ogata, H 2022, 'A Bibliometric Overview of the IEEE Transactions on Learning Technologies', IEEE Transactions on Learning Technologies, vol. 15, no. 6, pp. 656-672.
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Abahussein, S, Cheng, Z, Zhu, T, Ye, D & Zhou, W 1970, 'Privacy-Preserving in Double Deep-Q-Network with Differential Privacy in Continuous Spaces', Springer International Publishing, pp. 15-26.
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Adak, C, Sharma, P & Chanda, S 1970, 'DAZeTD: Deep Analysis of Zones in Torn Documents', Springer International Publishing, pp. 515-529.
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Ahadi, A & Mathieson, L 1970, 'A Bibliometrics Analysis of Australasian Computing Education Conference Proceedings', Proceedings of the 24th Australasian Computing Education Conference, ACE '22: Australasian Computing Education Conference, ACM.
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Ahadi, A, Kitto, K, Rizoiu, MA & Musial, K 1970, 'Skills Taught vs Skills Sought: Using Skills Analytics to Identify the Gaps between Curriculum and Job Markets', Proceedings of the 15th International Conference on Educational Data Mining, EDM 2022, International Conference on Educational Data Mining, International Educational Data Mining Society, Durham, pp. 538-542.
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Higher education often aims to create job-ready graduates. Thus, the skills and knowledge taught in professional degrees are expected to align with the needs of the labor market. However, the dynamic nature of the job market makes it challenging to ensure that this alignment occurs. In this study, we show how Skills Analytics can be used to identify critical skills in the workforce, mapping these to the curriculum offerings of a university. This enables us to identify skill gaps between what is taught and what is needed in the job market. Methods are presented that allow universities to test the alignment of their curriculum offerings with the job market. Where gaps are identified, this would enable universities to update their curriculum more rapidly to produce graduates equipped with up-to-date skills required by the local job market. Our contributions include: a new method for ranking skills in curricula based on their relative importance in the job market; and proof of concept methods to find skills gaps between curriculum offerings and an identified job market that can lead to curriculum redesign and enhancements.
Alharbi, M & Hussain, FK 1970, 'Blockchain-Based Identity Management for Personal Data: A Survey', Springer International Publishing, pp. 167-178.
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Alhosaini, H, Wang, X, Yao, L, Chen, Y & Xu, G 1970, 'Caching Hierarchical Skylines for Efficient Service Composition on Service Graphs', 2022 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (IEEE SCC 2022), IEEE International Conference on Services Computing (IEEE SCC), IEEE COMPUTER SOC, SPAIN, Barcelona, pp. 1-9.
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Service-oriented computing (SOC) is a paradigm for developing applications by reusing existing services. Through a standardized publishing, discovery, and composition process, SOC enables the orchestration of multiple (including third-party) services to constitute new applications. Hereby the quality of a composite service is fundamentally determined by its constituent services. To satisfy users’ non-functional requirements, it is important to identify the optimal set of constituent services to participate in the composition. Practical applications usually require the optimal set to be identified with high efficiency and accuracy. This poses challenges to existing service composition methods as they either provide no accuracy guarantee or are inapplicable to large-scale problems. The challenges become more evident when considering service graphs, which contain multiple execution paths that could multiply the computational overhead. In this paper, we propose a hierarchical skyline-based approach for highly efficient service composition, which maintains and reuses varying levels of service skylines to accelerate service composition. We discuss how the skylines can be selectively computed, lazily updated, and efficiently retrieved for reuse. Experiments demonstrate the effectiveness of our approach.
Alhosaini, H, Wang, X, Yao, L, Chen, Y & Xu, G 1970, 'Caching Hierarchical Skylines for Efficient Service Composition on Service Graphs', 2022 IEEE International Conference on Services Computing (SCC), 2022 IEEE International Conference on Services Computing (SCC), IEEE, Barcelona, Spain.
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Alhosaini, H, Wang, X, Yao, L, Yang, Z, Hussain, F & Lim, E-P 1970, 'Harnessing Confidence for Report Aggregation in Crowdsourcing Environments', 2022 IEEE International Conference on Services Computing (SCC), 2022 IEEE International Conference on Services Computing (SCC), IEEE, Barcelona, Spain, pp. 305-314.
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Crowdsourcing is an effective means of accomplishing human intelligence tasks by leveraging the collective wisdom of crowds. Given reports of various accuracy degrees from workers, it is important to make wise use of these reports to derive accurate task results. Intuitively, a task result derived from a sufficient number of reports bears lower uncertainty, and higher uncertainty otherwise. Existing report aggregation research, however, has largely neglected the above uncertainty issue. In this regard, we propose a novel report aggregation framework that defines and incorporates a new confidence measure to quantify the uncertainty associated with tasks and workers, thereby enhancing result accuracy. In particular, we employ a link analysis approach to propagate confidence information, subgraph extraction techniques to prioritize workers, and a progressive approach to gradually explore and consolidate workers’ reports associated with less confident workers and tasks. The framework is generic enough to be combined with existing report aggregation methods. Experiments on four real-world datasets show it improves the accuracy of several competitive state-of-the-art methods.
Ali, A & Hussian, W 1970, 'Challenges and Issues of the Internet of Things: Factoring Elements from the Social, Political and Information Systems', Springer International Publishing, pp. 73-83.
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Altulyan, MS, Huang, C, Yao, L, Wang, X & Kanhere, S 1970, 'Deep Reinforcement Learning for Dynamic Things of Interest Recommendation in Intelligent Ambient Environment', Australasian Joint Conference on Artificial Intelligence, Australasian Joint Conference on Artificial Intelligence, Springer International Publishing, Hybrid (Sydney, online), pp. 393-404.
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Anwar, MJ, Gill, AQ & Proper, HA 1970, 'A Conceptual Model to Assess the Maturity Of Information Security Audit Process.', PoEM Workshops, Practice of Enterprise Modelling 2022 Workshops and Models at Work, CEUR-WS.org, London, UK.
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One of the critical aspects of information security management is the security audit, both internal and external audits. The fundamental challenge for organisations is the effective design and implementation of the information security audits to better understand their information security capability. In this paper, we present insights from an action design research (ADR) project and propose a conceptual model to assess the maturity of security audit processes. The results of this research can be used to create an improvement plan, which will guide organisations to reach their target process maturity level. The maturity model proposed in this paper was evaluated by way of feedback workshops in the target organization. The model forms the basis for future work for generalising the research into a formal reference architecture (involving models and principles) for audit process maturity.
Bakhanova, E, Anjum, M, Garcia, JA, Raffe, WL & Voinov, A 1970, 'GAMIFICATION OF DISCUSSOO: AN ONLINE AI-BASED FORUM FOR SERIOUS DISCUSSIONS', 16th International Conference on Interfaces and Human Computer Interaction, IHCI 2022, and 15th International Conference on Game and Entertainment Technologies 2022, GET 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022, pp. 157-164.
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Engagement in the discussion process is one of the common challenges of asynchronous online forums. It becomes especially crucial if the discussion is organized over a serious topic about a complex problem with a group of diverse stakeholders. Gamification gives much promise in addressing this challenge. In this paper, we propose possible game design solutions to the engagement challenge for an existing online AI-based platform Discussoo and reflect on the results from the expert interviews and an experiment with students.
Bakhanova, E, Garcia Marin, J, Raffe, W & Voinov, A 1970, 'Gamified process of conceptual model developmentwith stakeholders', Proceedings of the International Environmental Modelling and Software Society Conference 2022, Proceedings of the International Environmental Modelling and Software Society Conference 2022, Brussels, Belgium.
Benedict, G, Sullivan, C & Gill, A 1970, 'Governance Challenges of AI-enabled Decentralized Autonomous Organizations: Toward a Research Agenda', https://aisel.aisnet.org/icis2022/blockchain/blockchain/2/, International Conference on Information Systems, AIS, Copenhagen, Denmark, pp. 1-9.
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The emergence of novel applications using distributed ledger technologies (DLTs) has gathered pace since the introduction of Bitcoin and the subsequent release of the Ethereum platform for decentralized applications (dApps). Such decentrally governed DLT systems are accelerating the displacement of intermediaries in regulated contexts such as the financial system and challenging the efficacy of governance regimes that have conventionally levered governance controls on identifiable, accountable decision-makers. The governance challenges of DLT systems are exacerbated by the arrival of digital autonomous organizations (DAOs) that use on-ledger decision-making mechanisms to further displace or eliminate human decision-makers. When DAOs are augmented with artificial intelligence (AI), their potent combination of computational power and access to large on-platform data sets and resources, signals a significant disruption to conventional institutional, regulatory, and legal governance regimes. This paper discusses the governance challenges of AI-enabled DAOs and presents a research agenda to address these challenges.
Beyhan, B, Akcomak, IS & Cetindamar, D 1970, 'How do technology-based accelerators build their legitimacy as new organizations in an emerging entrepreneurship ecosystem?', Journal of Entrepreneurship in Emerging Economies, European Academy of Management, Dublin, Ireland.
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Purpose: This paper aims to understand technology-based accelerators’ legitimation efforts in an emerging entrepreneurship ecosystem. Design/methodology/approach: This research is based on qualitative inductive methodology using ten Turkish technology-based accelerators. Findings: The analysis indicates that accelerators’ legitimation efforts are shaped around crafting a distinctive identity and mobilizing allies around this identity; and establishing new collaborations to enable collective action. Further, the authors observe two types of technology-based accelerators, namely, “deal flow makers” and “welfare stimulators” in Turkey. These variations among accelerators affect how they build their legitimacy. Different types of accelerators make alliances with different actors in the entrepreneurship ecosystem. Accelerators take collective action to build a collective identity and simultaneously imply how they are distinguished from other organizations in the same category and the ones in the old category. Originality/value: This study presents a framework to understand how accelerators use strategies and actions to legitimize themselves as new organizations and advocate new norms, values and routines in an emerging entrepreneurship ecosystem. The framework also highlights how different accelerators support legitimacy building by managing the judgments of diverse audiences and increasing the variety of resources these audiences provide to the ecosystem.
Bui, H, Hussain, OK, Saberi, M & Hussain, FK 1970, 'Proof of Earnestness- Subjective information’s Trustworthiness in Blockchains as a Service', 2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), IEEE.
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Cao, J, Liu, B, Wen, Y, Zhu, Y, Xie, R, Song, L, Li, L & Yin, Y 1970, 'Hiding Among Your Neighbors: Face Image Privacy Protection with Differential Private k-anonymity', 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, pp. 1-6.
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The development of modern social media allows millions of private photos to be uploaded and shared, which provides a wide range of image acquisition but extremely threatens personal image privacy. Face de-identification is treated as an important privacy protection tool in multimedia data processing by modifying image identity information. Although there exist many traditional methods widely used to hide sensitive private information, they all fail to balance the trade-off between privacy and utility in qualitative and quantitative manners and cannot generate de-identified results with satisfactory visual perception. In this paper, we propose a novel face image privacy protection method with differential private k-anonymity, which can not only generate de-identified results with good image quality but also control the balance between privacy protection and image utility according to different application scenarios. The framework consists of the following three steps: facial attributes prediction, privacy-preserving attributes obfuscation, and naturally realistic de-identificated image generation. Our extensive experiments demonstrate the stability and effectiveness of the proposed model.
Caruana, A, Bandara, M, Catchpoole, D & Kennedy, PJ 1970, 'Beyond Topics: Discovering Latent Healthcare Objectives from Event Sequences', AI 2021: Advances in Artificial Intelligence, Springer International Publishing, pp. 368-380.
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A meaningful understanding of clinical protocols and patient pathways helps improve healthcare outcomes. Electronic health records (EHR) reflect real-world treatment behaviours that are used to enhance healthcare management but present challenges; protocols and pathways are often loosely defined and with elements frequently not recorded in EHRs, complicating the enhancement. To solve this challenge, healthcare objectives associated with healthcare management activities can be indirectly observed in EHRs as latent topics. Topic models, such as Latent Dirichlet Allocation (LDA), are used to identify latent patterns in EHR data. However, they do not examine the ordered nature of EHR sequences, nor do they appraise individual events in isolation. Our novel approach, the Categorical Sequence Encoder (CaSE) addresses these shortcomings. The sequential nature of EHRs is captured by CaSE’s event-level representations, revealing latent healthcare objectives. In synthetic EHR sequences, CaSE outperforms LDA by up to 37% at identifying healthcare objectives. In the real-world MIMIC-III dataset, CaSE identifies meaningful representations that could critically enhance protocol and pathway development.
Chen, X, Yao, L, McAuley, J, Guan, W, Chang, X & Wang, X 1970, 'Locality-Sensitive State-Guided Experience Replay Optimization for Sparse Rewards in Online Recommendation', Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, Madrid, Spain, pp. 1316-1325.
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Online recommendation requires handling rapidly changing user preferences. Deep reinforcement learning (DRL) is an effective means of capturing users' dynamic interest during interactions with recommender systems. Generally, it is challenging to train a DRL agent in online recommender systems because of the sparse rewards caused by the large action space (e.g., candidate item space) and comparatively fewer user interactions. Leveraging experience replay (ER) has been extensively studied to conquer the issue of sparse rewards. However, they adapt poorly to the complex environment of online recommender systems and are inefficient in learning an optimal strategy from past experience. As a step to filling this gap, we propose a novel state-aware experience replay model, in which the agent selectively discovers the most relevant and salient experiences and is guided to find the optimal policy for online recommendations. In particular, a locality-sensitive hashing method is proposed to selectively retain the most meaningful experience at scale and a prioritized reward-driven strategy is designed to replay more valuable experiences with higher chance. We formally show that the proposed method guarantees the upper and lower bound on experience replay and optimizes the space complexity, as well as empirically demonstrate our model's superiority to several existing experience replay methods over three benchmark simulation platforms.
Chin Derix, E, Wah Leong, T & Prior, J 1970, '“It's A Drag”: Exploring How to Improve Parents’ Experiences of Managing Mobile Device Use During Family Time', CHI Conference on Human Factors in Computing Systems, CHI '22: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-20.
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Chockalingam, M & Singh, A 1970, 'Assessing Virtual Reality's potential to influence emotional states from negative to provide an instant positive effect', 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, pp. 1-9.
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Chowdhury, T, Cheraghian, A, Ramasinghe, S, Ahmadi, S, Saberi, M & Rahman, S 1970, 'Few-Shot Class-Incremental Learning for 3D Point Cloud Objects', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Switzerland, pp. 204-220.
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Few-shot class-incremental learning (FSCIL) aims to incrementally fine-tune a model (trained on base classes) for a novel set of classes using a few examples without forgetting the previous training. Recent efforts address this problem primarily on 2D images. However, due to the advancement of camera technology, 3D point cloud data has become more available than ever, which warrants considering FSCIL on 3D data. This paper addresses FSCIL in the 3D domain. In addition to well-known issues of catastrophic forgetting of past knowledge and overfitting of few-shot data, 3D FSCIL can bring newer challenges. For example, base classes may contain many synthetic instances in a realistic scenario. In contrast, only a few real-scanned samples (from RGBD sensors) of novel classes are available in incremental steps. Due to the data variation from synthetic to real, FSCIL endures additional challenges, degrading performance in later incremental steps. We attempt to solve this problem using Microshapes (orthogonal basis vectors) by describing any 3D objects using a pre-defined set of rules. It supports incremental training with few-shot examples minimizing synthetic to real data variation. We propose new test protocols for 3D FSCIL using popular synthetic datasets (ModelNet and ShapeNet) and 3D real-scanned datasets (ScanObjectNN and CO3D). By comparing state-of-the-art methods, we establish the effectiveness of our approach in the 3D domain. Code is available at: https://github.com/townim-faisal/FSCIL-3D.
Chuahan, R, Gola, N, Yafi, E, Farez, M & Prasad, M 1970, 'Smart Cities with Recognizance in Air Quality', 2022 International Visualization, Informatics and Technology Conference (IVIT), 2022 International Visualization, Informatics and Technology Conference (IVIT), IEEE, pp. 130-135.
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The worldwide populace keeps on developing at a consistent speed, and more individuals are moving to urban communities. This led to the generation of idea of smart cities which eventually builds the sustainable environment around the world by advancing technologies which can implacably applied to understand and control various processes of the city which are subjected on water, air and energy. In current study of approach, the focus relies specifically on atmospheric pollutants which arise due to industries, factories, mining, and the combustion of fossil fuels. These activities release air pollutants that are harmful to all living things including Sulphur dioxide, nitrogen dioxide, carbon monoxide, ozone, and various others air pollutants. Additionally, it is a major risk factor for several health conditions, including bronchitis, lung cancer, heart problems, throat and eye disorders, asthma, skin infections, and respiratory system ailments. The aim of the current study was to conduct discrete factor analysis to analyze the factors which are responsible for degradation of the air quality. The proposed study is carried out in two phases, with the first phase measured the variation in the AQI (Air Quality Index) value of different smart cities of India for years 2015-2020, whereas in second phase we analyze the contribution of different gases such as NO2, NO, benzene, toluene, xylene, O3, CO, SO2, NOx towards the AQI value.
Coluccia, A, Fascista, A, Schumann, A, Sommer, L, Dimou, A, Zarpalas, D, Sharma, N, Nalamati, M, Eryuksel, O, Ozfuttu, KA, Akyon, FC, Sahin, K, Buyukborekci, E, Cavusoglu, D, Altinuc, S, Xing, D, Unlu, HU, Evangeliou, N, Tzes, A, Nayak, A, Bouazizi, M, Ahmad, T, Gonçalves, A, Rigault, B, Jain, R, Matsuo, Y, Prendinger, H, Jajaga, E, Rushiti, V, Ramadani, B & Pavleski, D 1970, 'Drone-vs-Bird Detection Challenge at ICIAP 2021', Springer International Publishing, pp. 410-421.
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Du, J, Yao, L, Wang, X, Guo, B & Yu, Z 1970, 'Hierarchical Task-aware Multi-Head Attention Network', Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, Madrid, pp. 1933-1937.
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Neural Multi-task Learning is gaining popularity as a way to learn multiple tasks jointly within a single model. While related research continues to break new ground, two major limitations still remain, including (i) poor generalization to scenarios where tasks are loosely correlated; and (ii) under-investigation on global commonality and local characteristics of tasks. Our aim is to bridge these gaps by presenting a neural multi-task learning model coined Hierarchical Task-aware Multi-headed Attention Network (HTMN). HTMN explicitly distinguishes task-specific features from task-shared features to reduce the impact caused by weak correlation between tasks. The proposed method highlights two parts: Multi-level Task-aware Experts Network that identifies task-shared global features and task-specific local features, and Hierarchical Multi-Head Attention Network that hybridizes global and local features to profile more robust and adaptive representations for each task. Afterwards, each task tower receives its hybrid task-adaptive representation to perform task-specific predictions. Extensive experiments on two real datasets show that HTMN consistently outperforms the compared methods on a variety of prediction tasks.
Duan, W, Xuan, J, Qiao, M & Lu, J 1970, 'Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), pp. 6550-6558.
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Graph Convolutional Neural Networks (GCNs) have been generally accepted to be an effective tool for node representations learning. An interesting way to understand GCNs is to think of them as a message passing mechanism where each node updates its representation by accepting information from its neighbours (also known as positive samples). However, beyond these neighbouring nodes, graphs have a large, dark, all-but forgotten world in which we find the non-neighbouring nodes (negative samples). In this paper, we show that this great dark world holds a substantial amount of information that might be useful for representation learning. Most specifically, it can provide negative information about the node representations. Our overall idea is to select appropriate negative samples for each node and incorporate the negative information contained in these samples into the representation updates. Moreover, we show that the process of selecting the negative samples is not trivial. Our theme therefore begins by describing the criteria for a good negative sample, followed by a determinantal point process algorithm for efficiently obtaining such samples. A GCN, boosted by diverse negative samples, then jointly considers the positive and negative information when passing messages. Experimental evaluations show that this idea not only improves the overall performance of standard representation learning but also significantly alleviates over-smoothing problems.
Durvasula, N, Srinivasan, A & Dickerson, J 1970, 'Forecasting Patient Outcomes in Kidney Exchange', Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}, International Joint Conferences on Artificial Intelligence Organization.
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Kidney exchanges allow patients with end-stage renal disease to find a lifesaving living donor by way of an organized market. However, not all patients are equally easy to match, nor are all donor organs of equal quality---some patients are matched within weeks, while others may wait for years with no match offers at all. We propose the first decision-support tool for kidney exchange that takes as input the biological features of a patient-donor pair, and returns (i) the probability of being matched prior to expiry, and (conditioned on a match outcome), (ii) the waiting time for and (iii) the organ quality of the matched transplant. This information may be used to inform medical and insurance decisions. We predict all quantities (i, ii, iii) exclusively from match records that are readily available in any kidney exchange using a quantile random forest approach. To evaluate our approach, we developed two state-of-the-art realistic simulators based on data from the United Network for Organ Sharing that sample from the training and test distribution for these learning tasks---in our application these distributions are distinct. We analyze distributional shift through a theoretical lens, and show that the two distributions converge as the kidney exchange nears steady-state. We then show that our approach produces clinically-promising estimates using simulated data. Finally, we show how our approach, in conjunction with tools from the model explainability literature, can be used to calibrate and detect bias in matching policies.
Farhood, H, Saberi, M & Najafi, M 1970, 'Human-in-the-Loop Optimization for Artificial Intelligence Algorithms', Springer International Publishing, pp. 92-102.
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Fattoruso, V, Sepehrirahnama, S, Tofigh, F, Lai, JCS, Nowotny, M & Oberst, S 1970, 'CONSIDERATION ON HOW TO IMPROVE GROUND REACTION FORCE MEASUREMENTS IN SMALL WALKING INSECTS', Proceedings of the International Congress on Sound and Vibration, 28th International Congress on Sound and Vibration, Singapore.
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Micro-vibrations caused by the motion of insects, provide a content-rich signal that may be perceived by nestmates, competitors or predators. Knowing the ground reaction forces of a single leg impacting the surface can provide quantitative information about the interaction with the substrate, the substrate itself, physiological and behavioural state of an individual, through mechanistic constraints and the diversity of the gait. Micro-force plates have been used for measuring the ground reaction forces in the order of micro-Newton, using highly sensitive strain gauges attached to compliant load-bearing parts of an underlying mechanical structure. However, their calibration and signal-to-noise-ratio are some of the main challenges of designing these highly sensitive systems. For fine movement analysis, the micro-force plates need to be coupled to high speed video recording systems; the synchronisation of the camera and force plate represents another challenge. For an existing micro-force plate designed for ant measurements, which showed linear signal response in the calibrated force with a lower limit of 120 μN, the linearity of force measurement and sensitivity of the device are investigated in a lower force range, extending the opportunity to study also insects with a lighter footfall. We take into account the difficulties of adapting such devices to the insects' needs related to the environment (i.e. temperature, light...) and morphology (i.e. dimension, weight...). Based on the experiments of the force plate, we consider how to design an experimental setup that overcomes many of the behavioural and technical challenges, to enable more efficient and accurate measurements for insects with body weights less than 5 mg.
Garcia, JA & Tenorio, JF 1970, 'Assessing the capabilities of the HTC Vive as a tool to assess the risk of falling in older people', 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, pp. 1-6.
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Guo, S, Su, Z, Tian, Z & Yu, S 1970, 'Utility-Aware Privacy-Preserving Federated Learning through Information Bottleneck', 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), IEEE.
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Hanna, B, Xu, G, Wang, X & Hossain, J 1970, 'Blockchain-based solutions for humanitarian supply chain management', AMCIS 2022 Proceedings, Americas Conference on Information Systems, AMCIS, Minneapolis, USA, pp. 195-218.
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The outbreak of the novel COVID-19 demonstrates how pandemics disturb supply chains (SC) all across the world. Policymakers and private-sector partners are increasingly acknowledging that we cannot tackle today's issues without leveraging the promise of new technology. Blockchain technology is increasingly being adopted to help humanitarian efforts in various fields. This paper presents conceptual research designed to assess how Blockchain distributed ledger technology can be leveraged to enhance humanitarian supply chain management (HSCM). This paper fills the present research gap on the Blockchain's potential implications for HSCM by proposing a framework built on the foundations of five prominent institutional economic theories: social exchange theory, principal-agent theory, transaction cost theory, resource-based view, and network theory. These theories could be utilized to generate research topics that are theory-based and industry-relevant. This conceptual framework assists institutions in making decisions about how to recover and rebuild their SC during disasters.
Hanna, B, Xu, G, Wang, X & Hossain, J 1970, 'Data-Driven Computational Algorithms for Predicting Electricity Consumption Missing Values: A Comparative Study', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE, Adelaide, Australia.
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Hason Rudd, D, Huo, H & Xu, G 1970, 'Causal Analysis of Customer Churn Using Deep Learning', 2021 International Conference on Digital Society and Intelligent Systems (DSInS), 2021 International Conference on Digital Society and Intelligent Systems, IEEE, Chengdu, China, pp. 319-324.
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Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Two main business marketing strategies play vital roles to increase market share dollar- value: gaining new and preserving existing customers. Customer acquisition cost can be five to six times that for customer retention, hence investing in customers with churn risk is smart. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and assist enterprises to identify effects and possible causes for churn and subsequently use that knowledge to apply tailored incentives. This paper proposes a framework using a deep feedforward neural network for classification accompanied by a sequential pattern mining method on high-dimensional sparse data. We also propose a causal Bayesian network to predict cause probabilities that lead to customer churn. Evaluation metrics on test data confirm the XGBoost and our deep learning model outperformed previous techniques. Experimental analysis confirms that some independent causal variables representing the level of super guarantee contribution, account growth, and customer tenure were identified as confounding factors for customer churn with a high degree of belief. This paper provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds.
Hason Rudd, D, Huo, H & Xu, G 1970, 'Leveraged Mel Spectrograms Using Harmonic and Percussive Components in Speech Emotion Recognition', Advances in Knowledge Discovery and Data Mining, Springer International Publishing, pp. 392-404.
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Hason Rudd, D, Huo, H & Xu, G 1970, 'Predicting Financial Literacy Via Semi-Supervised Learning', AI 2021: Advances in Artificial Intelligence, Springer International Publishing, pp. 304-319.
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Hayati, H, Eager, D & Oberst, S 1970, 'Recurrence Plot Qualification Analysis of the Greyhound Rotary Gallop Gait', Springer International Publishing, Sapienza University of Rome, Italy (online), pp. 331-341.
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Huang, W, Li, Y, Du, W, Yin, J, Xu, RYD, Chen, L & Zhang, M 1970, 'TOWARDS DEEPENING GRAPH NEURAL NETWORKS: A GNTK-BASED OPTIMIZATION PERSPECTIVE', ICLR 2022 - 10th International Conference on Learning Representations.
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Graph convolutional networks (GCNs) and their variants have achieved great success in dealing with graph-structured data. Nevertheless, it is well known that deep GCNs suffer from the over-smoothing problem, where node representations tend to be indistinguishable as more layers are stacked up. The theoretical research to date on deep GCNs has focused primarily on expressive power rather than trainability, an optimization perspective. Compared to expressivity, trainability attempts to address a more fundamental question: Given a sufficiently expressive space of models, can we successfully find a good solution via gradient descent-based optimizers? This work fills this gap by exploiting the Graph Neural Tangent Kernel (GNTK), which governs the optimization trajectory under gradient descent for wide GCNs. We formulate the asymptotic behaviors of GNTK in the large depth, which enables us to reveal the dropping trainability of wide and deep GCNs at an exponential rate in the optimization process. Additionally, we extend our theoretical framework to analyze residual connection-based techniques, which are found to be merely able to mitigate the exponential decay of trainability mildly. Inspired by our theoretical insights on trainability, we propose Critical DropEdge, a connectivity-aware and graph-adaptive sampling method, to alleviate the exponential decay problem more fundamentally. Experimental evaluation consistently confirms using our proposed method can achieve better results compared to relevant counterparts with both infinite-width and finite-width.
Huang, Y, Feng, B, Dong, P, Tian, A & Yu, S 1970, 'A Multi-objective based Inter-Layer Link Allocation Scheme for MEO/LEO Satellite Networks', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Austin, TX, USA, pp. 1301-1306.
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Recently, there is a growing interest in Double-Layered Satellite Networks (DLSN) which integrate Medium-Earth-Orbit (MEO) and Low-Earth-Orbit (LEO) satellites for provision of mobile and personal services. However, it is still in the early stage with several challenges unaddressed, and one of the key problems is the inter-layer link allocations between MEO and LEO satellites, as DLSN topology is dynamically changed over the time and satellites are with the limited number of connections onboard. To this end, we propose a corresponding Inter-layer Link Allocation (ILA) scheme in this paper, taking the visible duration between satellites, transmitting power consumed onboard and geographical distributions of user load into account, aiming to maximize the utilization efficiency of DLSN inter-layer links. Then, we formulate it as a constrained multi-objective linear programming problem and evaluate its performance with other three benchmarks. Numerical results have demonstrated that the proposed ILA scheme can decrease the number of ILL handovers and average inter-satellite distance, with load balanced between LEO and MEO satellites.
Jayan Chirayath Kurian, J 1970, 'Digital workplaces: Generating value for community-based emergency services', Queensland Government Disaster Management Forum, Brisbane.
Jayan Chirayath Kurian, J, John, BM & Lang, A 1970, 'Redesigning Learning Spaces During a Pandemic', Copenhagen.
Jia, M, Alboom, MV, Goubert, L, Bracke, P, Gabrys, B & Musial, K 1970, 'Analysing Egocentric Networks via Local Structure and Centrality Measures: A Study on Chronic Pain Patients', 2022 International Conference on Information Networking (ICOIN), 2022 International Conference on Information Networking (ICOIN), IEEE, SOUTH KOREA, pp. 152-157.
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Typical centrality measures assess the importance of a node based on the distances to other nodes, shortest paths passing through it, or the eigen-structure of the adjacency matrix. Local structure measures, on the other hand, capture network topological features by measuring how a motif is constructed from a substructure. In this paper, we discuss the suitability of several centrality measures and local structure measures in egocentric networks and investigate the relationships among them. Through experiments on 303 ego social networks of chronic pain patients, we find that patients of lower pain grade indeed have better connections in their networks than those of higher pain grade, and that including centrality measures and local structure measures as additional features leads to significant improvement in a machine learning task that predicts the patients' pain grades.
Jia, M, Van Alboom, M, Goubert, L, Bracke, P, Gabrys, B & Musial, K 1970, 'Analysing Ego-Networks via Typed-Edge Graphlets: A Case Study of Chronic Pain Patients', Complex Networks & Their Applications X, Springer International Publishing, pp. 514-526.
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Graphlets, being the fundamental building blocks, are essential for understanding and analysing complex networks. The original notion of graphlets, however, is unable to encode edge attributes in many types of networks, especially in egocentric social networks. In this paper, we introduce a framework to embed edge type information in graphlets and generate a Typed-Edge Graphlets Degree Vector (TyE-GDV). Through applying the proposed method to a case study of chronic pain patients, we find that not only a patient’s social network structure could inform his/her perceived pain grade, but also particular types of social relationships, such as friends, colleagues and healthcare workers, are more important in understanding the effect of chronic pain. Further, we demonstrate that including TyE-GDV as additional features leads to significant improvement in a typical machine learning task.
Jiao, S, Zhang, G, Navasardyan, S, Chen, L, Zhao, Y, Wei, Y & Shi, H 1970, 'Mask Matching Transformer for Few-Shot Segmentation', Advances in Neural Information Processing Systems.
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In this paper, we aim to tackle the challenging few-shot segmentation task from a new perspective. Typical methods follow the paradigm to firstly learn prototypical features from support images and then match query features in pixel-level to obtain segmentation results. However, to obtain satisfactory segments, such a paradigm needs to couple the learning of the matching operations with heavy segmentation modules, limiting the flexibility of design and increasing the learning complexity. To alleviate this issue, we propose Mask Matching Transformer (MM-Former), a new paradigm for the few-shot segmentation task. Specifically, MM-Former first uses a class-agnostic segmenter to decompose the query image into multiple segment proposals. Then, a simple matching mechanism is applied to merge the related segment proposals into the final mask guided by the support images. The advantages of our MM-Former are two-fold. First, the MM-Former follows the paradigm of decompose first and then blend, allowing our method to benefit from the advanced potential objects segmenter to produce high-quality mask proposals for query images. Second, the mission of prototypical features is relaxed to learn coefficients to fuse correct ones within a proposal pool, making the MM-Former be well generalized to complex scenarios or cases. We conduct extensive experiments on the popular COCO-20i and Pascal-5i benchmarks. Competitive results well demonstrate the effectiveness and the generalization ability of our MM-Former. Code is available at github.com/Picsart-AI-Research/Mask-Matching-Transformer.
Jin, C, Bell, JA, Deverell, L, Gates, F, Gorodo, I, Hossain, S, Lin, CT, Melencio, M, Nguyen, M, Nguyen, V, Singh, A & Zhu, H 1970, 'Acoustic touch: An auditory sensing paradigm to support close reaching for people who are blind', Proceedings of the International Congress on Acoustics, 24th International Congress on Acoustics, Korea.
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This work explores an auditory sensory augmentation paradigm we call acoustic touch, to assist people who are blind with reaching for close objects. The sensory augmentation system is constructed based on the Nreal augmented-reality glasses using a custom application running on an android phone. The system recognizes and localizes objects visually using cameras in the glasses, then renders objects as sound within a limited field-of-view, so we shall refer to the glasses as a foveated audio device. The repetition of the sound varies depending on the location of the object within the field of view of the foveated audio device. Psychophysical tests of the spatial perception of multiple objects are conducted comparing the acoustic touch paradigm with two other conditions: (1) a verbal clock face description of object locations and (2) a sequential audio presentation of the objects using Bluetooth speakers located with the objects. We report on the results of the psychophysical study with blind and blindfolded sighted participants.
Khan, IA & Hussain, FK 1970, 'Regression Analysis Using Machine Learning Approaches for Predicting Container Shipping Rates', Springer International Publishing, pp. 269-280.
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Khan, S & Hussain, FK 1970, 'Software-Defined Overlay Network Implementation and Its Use for Interoperable Mission Network in Military Communications', Springer International Publishing, pp. 554-565.
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Khwaji, A, Alsahafi, Y & Hussain, FK 1970, 'Conceptual Framework of Blockchain Technology Adoption in Saudi Public Hospitals Using TOE Framework', Springer International Publishing, pp. 78-89.
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Klettner, A, Sainty, R & Cetindamar Kozanoglu, D 1970, 'Corporate purpose as a signalling mechanism to facilitate and guide stakeholder governance', European Academy of Management, Winterthur, Switzerland, pp. 1-30.
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There are strong indications that acceptance of the shareholder primacy view of the corporation is on the decline and a stakeholder theory approach to corporate governance is becoming more mainstream. Yet we have very little idea on how stakeholder governance can be achieved in practice, nor how it might be understood theoretically. Certified B Corps are at the front of this movement with their commitment to achieving both profit and a positive impact on society and the environment. Through interviews with 18 B Corp leaders in Australia and New Zealand we explore emerging theories of stakeholder governance and how it interacts with corporate purpose. We use signalling theory to understand stakeholder governance as a proactive process of communication of priorities rather than a reactive process of stakeholder management. We find that an organisation-specific corporate purpose acts as a signal to pre-empt and prevent stakeholder conflicts. A unique corporate purpose makes conflicts less likely but also provides an ethical compass for decision-making in situations where conflict is unavoidable. Together, corporate purpose and a commitment to stakeholder governance raise the legitimacy of non-shareholder stakeholders and increase their relative salience.
Knight, J, Johnston, A & Berry, A 1970, 'Machine Art: Exploring Abstract Human Animation Through Machine Learning Methods', Proceedings of the 8th International Conference on Movement and Computing, MOCO '22: 8th International Conference on Movement and Computing, ACM, pp. 1-7.
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Visual media and performance art have a symbiotic relationship. They support one another and engage the audience by providing an experience or telling a story. This comparative study explores the accuracy, efficiency, and cost factors of using machine learning based motion capture methods in performance art. There is extensive research in the field of machine learning methods for human pose estimation, but the outputs of such work are rarely used as inputs for performance art. In this paper we present a practice-based research project that involves producing animations that match a performer's movements using machine learning based motion capture methods. We use human poses derived from low-cost video capture as an input into high-resolution abstract forms that accompany and synchronise with dance performances. A single-camera approach is examined and compared to existing methods. We find that compared with existing motion capture methods the machine learning based methods require less setup time, and less equipment is required resulting in considerably lower cost. This research suggests that machine learning has considerable potential to improve the quality of human pose estimation in performance art, visual effects and motion capture, and make it more accessible for arts companies with limited resources.
Li, A, Yang, B, Huo, H & Hussain, F 1970, 'Hypercomplex Graph Collaborative Filtering', Proceedings of the ACM Web Conference 2022, WWW '22: The ACM Web Conference 2022, ACM.
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Li, C, Yang, L, Yu, S, Qin, W & Ma, J 1970, 'SEMMI: Multi-party Security Decision-making Scheme Under the Internet of Medical Things', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE, pp. 2792-2797.
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In the Internet of Medical Things, the intelligent auxiliary decision-making system uses machine learning algorithms to analyze medical data for disease diagnosis, auxiliary intervention, and analysis and early warning. However, in the process of medical data transmission, processing, and storage, a large amount of private information is also at risk of leakage. Therefore, this article proposes a smart classification and decision-making program in the Internet of Medical Things scenario-SEMMI, which can effectively deal with the risk of data leakage in the process of medical data processing. At the same time, it reduces the huge computing and storage pressure caused by encryption and decryption operations in medical institutions. In the scheme, data collection, processing, transmission, storage and calculation are completed by ciphertext. In addition, in view of the relatively weak computing and storage capabilities of sensor nodes, we use chaos theory to construct a stream cipher algorithm to ensure the security of transmission from sensor to user; the homomorphic encryption algorithm is used to ensure the computability of the ciphertext and the security of storage. Through security analysis, it can be concluded that this scheme can resist attacks from adversaries; at the same time, the experimental results show that the scheme has good performance in terms of calculation, storage overhead, accuracy, and so on.
Li, J, Yao, L, Li, B, Wang, X & Sammut, C 1970, 'Multi-agent Transformer Networks for Multimodal Human Activity Recognition', Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM '22: The 31st ACM International Conference on Information and Knowledge Management, ACM, pp. 1135-1145.
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Human activity recognition has become an important challenge yet to resolve while also having promising benefits in various applications for years. Existing approaches have made great progress by applying deep-learning and attention-based methods. However, the deep learning-based approaches may not fully exploit the features to resolve multimodal human activity recognition tasks. Also, the potential of attention-based methods still has not been fully explored to better extract the multimodal spatial-temporal relationship and produce robust results. In this work, we propose Multi-agent Transformer Network (MATN), a multi-agent attention-based deep learning algorithm, to address the above issues in multimodal human activity recognition. We first design a unified representation learning layer to encode the multimodal data, which preprocesses the data in a generalized and efficient way. Then we develop a multimodal spatial-temporal transformer module that applies the attention mechanism to extract the salient spatial-temporal features. Finally, we use a multi-agent training module to collaboratively select the informative modalities and predict the activity labels. We have extensively conducted experiments to evaluate MATN's performance on two public multimodal human activity recognition datasets. The results show that our model has achieved competitive performance compared to the state-of-the-art approaches, which also demonstrates scalability, effectiveness, and robustness.
Li, K, Lu, J, Zuo, H & Zhang, G 1970, 'Source-Free Multi-Domain Adaptation with Generally Auxiliary Model Training', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, Padua, Italy, pp. 1-8.
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Unsupervised domain adaptation transfers gained knowledge from labeled source domain(s) to a similar unlabeled target domain by eliminating the domain shifts. Most existing domain adaptation methods require the access to source data to match the source and target distributions. However, data privacy concerns make it difficult or impossible to share source data, leading to failures in existing domain adaptation methods. Admittedly, a few previous studies deal with domain adaptation without source data, but they rarely pay heed to data free domain adaptation with multiple source domains containing richer knowledge. In this paper, we propose a new multi-source data-free domain adaptation method- generally auxiliary model training (GAM)- which fits the source models to the target domain under the supervision of pseudo target labels rather than matching data distributions. To collect high-quality initial pseudo target labels, our approach learns both specific and general source models to improve the generality of source models based on auxiliary learning. Going further, we introduce a class balanced coefficient of each category based on the number of samples to reduce the misclassification often caused by data imbalance. Experiments on real-world classification datasets show that the propsosed generally auxiliary training has a superiority over the baselines.
Li, Y, Bei, X, Qiao, Y, Tao, D & Chen, Z 1970, 'Heterogeneous Multi-commodity Network Flows over Time', Computer Science – Theory and Applications, Springer International Publishing, pp. 238-255.
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In the 1950’s, Ford and Fulkerson introduced dynamic flows by incorporating the notion of time into the network flow model (Oper. Res., 1958). In this paper, motivated by real-world applications including route planning and evacuations, we extend the framework of multi-commodity dynamic flows to the heterogeneous commodity setting by allowing different transit times for different commodities along the same edge. We first show how to construct the time-expanded networks, a classical technique in dynamic flows, in the heterogeneous setting. Based on this construction, we give a pseudopolynomial-time algorithm for the quickest flow problem when there are two heterogeneous commodities. We then present a fully polynomial-time approximation scheme when the nodes have storage for any number of heterogeneous commodities. The algorithm is based on the condensed time-expanded network technique introduced by Fleischer and Skutella (SIAM J. Comput., 2007).
Li, Z, Wang, X, Yao, L, Chen, Y, Xu, G & Lim, E-P 1970, 'Graph Neural Network with Self-attention and Multi-task Learning for Credit Default Risk Prediction', International Conference on Web Information Systems Engineering, International Conference on Web Information Systems Engineering, Springer International Publishing, Biarritz, France, pp. 616-629.
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Lister, R 1970, 'Some thoughts on designing eye movement studies for novice programmers', Proceedings of the Tenth International Workshop on Eye Movements in Programming, ICSE '22: 44th International Conference on Software Engineering, ACM.
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Liu, K, Zhao, F, Chen, H, Li, Y, Xu, G & Jin, H 1970, 'DA-Net', Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM '22: The 31st ACM International Conference on Information and Knowledge Management, ACM, pp. 1289-1298.
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Predicting future events in dynamic knowledge graphs has attracted significant attention. Existing work models the historical information in a holistic way, which achieves satisfactory performance. However, in real-world scenarios, the influence of historical information on future events is changing over time. Therefore, it is difficult to distinguish the historical information of different roles by invariably embedding historical entities with simple vector stacking. Furthermore, it is laborious to explicitly learn a distributed representation of each historical repetitive fact at different timestamps. This poses a challenge to the widely adopted codec-based architectures. In this paper, we propose a novel model for predicting future events, namely Distributed Attention Network (DA-Net). Rather than obtaining the fixed representations of historical events, DA-Net attempts to learn the distributed attention of future events on repetitive facts at different historical timestamps inspired by human cognitive theory. In human cognitive theory, when humans make a decision, similar historical events are replayed during memory recall. Based on memory, the original intention is adjusted according to their recent knowledge developments, making the action more reasonable to the context. Experiments on four benchmark datasets demonstrate a substantial improvement of DA-Net on multiple evaluation metrics.
Liu, K, Zhao, F, Xu, G, Wang, X & Jin, H 1970, 'Temporal Knowledge Graph Reasoning via Time-Distributed Representation Learning', 2022 IEEE International Conference on Data Mining (ICDM), 2022 IEEE International Conference on Data Mining (ICDM), IEEE, Orlando, Florida, USA.
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Liu, W, Xie, K, Pang, L, Bailey, J, Cao, L & Zhang, Y 1970, 'Deep Learning for Search and Recommendation', Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM '22: The 31st ACM International Conference on Information and Knowledge Management, ACM.
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Liu, Y, Yao, L, Li, B, Wang, X & Sammut, C 1970, 'Social Graph Transformer Networks for Pedestrian Trajectory Prediction in Complex Social Scenarios', Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM '22: The 31st ACM International Conference on Information and Knowledge Management, ACM, pp. 1339-1349.
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Liu, Z, Liu, A, Zhang, G & Lu, J 1970, 'An Empirical Study of Fuzzy Decision Tree for Gradient Boosting Ensemble', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 34th Australasian Joint Conference on Artificial Intelligence (AI), Springer International Publishing, Univ Technol Sydney, ELECTR NETWORK, pp. 716-727.
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Gradient boosting has been proved to be an effective ensemble learning paradigm to combine multiple weak learners into a strong one. However, its improved performance is still limited by decision errors caused by uncertainty. Fuzzy decision trees are designed to solve the uncertainty problems caused by the collected information’s limitation and incompleteness. This paper investigates whether the robustness of gradient boosting can be improved by using fuzzy decision trees even when the decision conditions and objectives are fuzzy. We first propose and implement a fuzzy decision tree (FDT) by referring to two widely cited fuzzy decision trees. Then we propose and implement a fuzzy gradient boosting decision tree (FGBDT), which integrates a set of FDTs as weak learners. Both the algorithms can be set as non-fuzzy algorithms by parameters. To study whether fuzzification can improve the proposed algorithms in classification tasks, we pair the algorithms with their non-fuzzy algorithms and run comparison experiments on UCI Repository datasets in the same settings. The experiments show that the fuzzy algorithms perform better than their non-fuzzy algorithms in many classical classification tasks. The code is available at github.com/ZhaoqingLiu/FuzzyTrees.
Ma, R, Pang, G, Chen, L & van den Hengel, A 1970, 'Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation', Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, ACM, pp. 704-714.
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Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are abnormal in their structure and/or the features of their nodes, as compared to other graphs. One of the challenges in GAD is to devise graph representations that enable the detection of both locally- and globally-anomalous graphs, i.e., graphs that are abnormal in their fine-grained (node-level) or holistic (graph-level) properties, respectively. To tackle this challenge we introduce a novel deep anomaly detection approach for GAD that learns rich global and local normal pattern information by joint random distillation of graph and node representations. The random distillation is achieved by training one GNN to predict another GNN with randomly initialized network weights. Extensive experiments on 16 real-world graph datasets from diverse domains show that our model significantly outperforms seven state-of-the-art models. Code and datasets are available at https://git.io/GLocalKD.
Ma, W, Chang, Y-C, Wang, Y-K & Lin, C-T 1970, 'Human-Autonomous Teaming Framework Based on Trust Modelling', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 707-718.
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With the development of intelligent technology, autonomous agents are no longer just simple tools; they have gradually become our partners. This paper presents a trust-based human-autonomous teaming (HAT) framework to realize tactical coordination between human and autonomous agents. The proposed trust-based HAT framework consists of human and autonomous trust models, which leverage a fusion mechanism to fuse multiple performance metrics to generate trust values in real-time. To obtain adaptive trust models for a particular task, a reinforcement learning algorithm is used to learn the fusion weights of each performance metric from human and autonomous agents. The adaptive trust models enable the proposed trust-based HAT framework to coordinate actions or decisions of human and autonomous agents based on their trust values. We used a ball-collection task to demonstrate the coordination ability of the proposed framework. Our experimental results show that the proposed framework can improve work efficiency.
Madhisetty, S 1970, 'Understanding Risks of Sharing Images in the Context of Deepfakes Technology', Springer International Publishing, pp. 132-140.
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Martínez, AT, Gil-Lafuente, AM, Keropyan, A & MerigóLindahl, JM 1970, 'Application of the Forgotten Effects Theory to the Qualitative Analysis of the Operational Risk Events', Springer International Publishing, pp. 261-270.
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McGregor, C & Inibhunu, C 1970, 'A Framework for the Design, Development, Testing and Deployment of Reliable Big Data Platforms', 2022 IEEE International Conference on Big Data (Big Data), 2022 IEEE International Conference on Big Data (Big Data), IEEE, pp. 2660-2666.
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We consider the problem of reliability in big data science projects that are comprised of multiple computing platforms and complex architectures that harness data. Specifically on their ability to capture, process and analyze streaming high frequency data from vast complex systems reliably with effective scalability for deployment in vast domains such as clinical care, smart cities or within extreme climatic work environments. This paper introduces a framework to enable reliable data science projects by integrating multiple computing principles of autonomy, local responsibility, fault tolerance, symmetry, decentralization, well-understood building blocks, and simplicity. The designed framework is applied in the development of a decoupled data pipeline demonstrated through a case study on pre-deployment acclimation strategies that is continuously monitored to ensure reliability and availability is effectively quantified.
Milton, J, Halkon, B, Oberst, S, Chiang, YK & Powell, D 1970, 'SONAR-BASED BURIED OBJECT DETECTION VIA STATISTICS OF RECURRENCE PLOT QUANTIFICATION MEASURES', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Society of Acoustics, Singapore, Singapore, pp. 1-8.
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Active sonar has been successfully deployed for naval mine countermeasures (MCM) to detect, localise, and classify mines and mine-like objects (MLOs). One of the most challenging problems in MCM operations is the detection and classification of (partially) covered objects; traditional image-based sonar processing techniques cannot readily detect objects within the seabed. In this paper, a processing technique that utilises recurrence plot quantification analysis, a class of nonlinear time series analysis, is proposed for improved covered MLO detection in raw sonar signals. Recurrence plots are binary, graphical visualisations of the recurrence matrix generated from time series data. Following an embedding process to reconstruct a copy of the dynamics in phase space, recurrence plot quantification analysis measures can be extracted and further statistically analysed. Using computationally generated sonar signals extracted from simplified representations of real-world relevant scenarios, this study explores the application of such an approach and its sensitivity to the user-defined parameters for detecting the presence of an MLO, irrespective of the level of burial.
Mirdad, A & Hussain, FK 1970, 'Blockchain-Based Pharmaceutical Supply Chain: A Literature Review', Springer International Publishing, pp. 106-115.
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Recently, blockchain technology was introduced to the public in order to provide a secure environment that is immutable, consensus-based and transparent in the finance technology world. However, there have been many efforts to apply blockchain to other fields where trust and transparency are a requirement. The ability to reliably share pharmaceutical information between various stakeholders is essential. The use of blockchain technology adds traceability and visibility to supply chains such as pharmaceuticals to provide all the information from end to end. Currently, the data is stored and managed by large manufacturers and pharmacy retailers using their own centralized systems. Several existing approaches and methods that allow pharmaceutical information to be stored and shared between the healthcare provider and other stakeholders in a centralized manner have been discussed in the literature. Due to the lack of comprehensive literature review studies that focus on pharmaceutical supply chain using blockchain technology, thus this paper highlights and addresses this gap. This paper reviews several studies which have applied blockchain technology in pharmaceutical supply chains. This paper overviews the knowledge on blockchain technology, discusses and explores the most recent and relevant studies that adopt blockchain technology in the field of pharmaceutical supply chains, describes the challenges associated with blockchain technology, and presents some ideas for future work.
Mughal, F, Raffe, W, Stubbs, P & Garcia, J 1970, 'Towards depression monitoring and prevention in older populations using smart wearables: Quantitative Findings', 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, pp. 1-8.
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Depression has become a growing concern over the recent years. Since the start of the COVID-19 pandemic, depression among all age groups has increased significantly. As mental health is often stigmatized among older aged people, it is less openly discussed or treated. We propose a mental health monitoring approach that limits explicit user interaction, using Fitbit smartwatch data to determine depressive tendencies in older-aged people. We analysed physiological user data extracted from a Fitbit Alta HR device and use this data to train a machine learning model to detect depressive tendencies. While this is not a diagnostic tool, the aim is to identify physiological signs early on and direct the user toward professional medical guidance and treatment. We trained 19 predictive models on our dataset, the gradient boosting regressor outperformed all other models. The best performing model achieved at R-square of 0.32 although most models were poorly performing. Due to the limited sample size, there is a risk of model overfitting. Although these preliminary results are promising for one model, they would need to be replicated in a larger sample of older people, who exhibit a wider range of depressive tendencies.
Murad, MAU, Kozanoglu, DC & Chakraborty, S 1970, 'Public Procurement, Big Data Analytics Capabilities, and Healthcare Supply Chain Sustainability', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, University of Hawaii, Hawai, pp. 296-303.
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Big data analytics (BDA) is considered the most critical supply chain activity for organizations. Implementing BDA requires specialized infrastructure coupled with specialized analytical expertise. Most of the existing research focuses on building BDA capabilities or perceived benefits of organizations' BDA capabilities. However, the benefits of having BDA capabilities, neither immediately visible nor straightforward. Optimizing procurement is one of the many intermediate factors that influence BDA capabilities' impact on the supply chain's sustainability performance. This paper has analyzed the existing literature to develop a conceptual framework to investigate the relationships among procurement optimization, BDA capabilities, and healthcare sustainable supply chain.
Nalamati, M, Saqib, M, Sharma, N & Blumenstein, M 1970, 'Exploring Transformers for Intruder Detection in Complex Maritime Environment', Springer International Publishing, pp. 428-439.
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Nerse, C & Oberst, S 1970, 'NUMERICAL VIBRATION ANALYSIS OF HONEYBEE COMB STRUCTURES', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Singapore.
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Since ancient times much has been written about the geometrical perfection of honeybee comb structures. The hexagonal shape, trademark of the comb cell, has been credited for auxetic mechanical properties and efficient storage of honey. More recent studies on Apis mellifera ligustica have shown that bees have complex nest-building practices through ecological and behavioural evolution. Although mostly dominated by hexagonal cells, the comb structure is shown to feature imperfections due to uneven distribution of worker and drone cells, as well as tilting and merging of cluster of cells. The shape and conditions of the substrate in which the hive is built upon also affects the expansion of the comb structure. Experimental studies have shown that the honeybee comb may have unusual physical properties of vibration amplification and phase reversal. However, the confined nature of these studies poses challenges in understanding the physical mechanisms. In this study, we examine the sensitivity of geometrical and viscoelastic material properties of a honeybee comb on structural vibration transmission. For this purpose, a finite element model of a comb has been developed to obtain modal and frequency response characteristics. The results have shown that lateral deflection of the walls may contribute to efficient vibration transmission at certain resonant frequencies of the cells. Findings might elucidate on why certain frequencies have been observed in experiments, irrespective of the shape and the boundary conditions of the overall honeycomb, and how bees may use this feature to communicate within the colony.
Nerse, C, Oberst, S, Moore, S & MacGillivray, I 1970, 'ASSESSMENT OF FLANKING TRANSMISSIONS IN MEASUREMENTS OF SOUND TRANSMISSION LOSS OF MULTILAYER PANELS', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Singapore.
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The sound transmission loss measurements of small-sized panels ideally require perfect sealing of the panel frame and a rigid construction of the filler wall that encloses the panels. In practice, suppression of flanking transmission is achieved by having a sufficient isolation between both the source and the receiver rooms and blocking the indirect transmission by installing additional elements on the surfaces of both rooms. At the outer edges of the panel, the frame is supported by acoustically reflective materials and insulations to reduce the energy propagating into the wall. The sound transmission loss of the panels can be improved by installing layers that contribute to additional or more efficient dissipation. These layers are installed in such a way that they are tightly bolted into the frame with a niche being introduced on sides to further secure the panel within the opening. However, for panels with alternating layers of solid and porous materials, or with acoustic cavities, the structural rigidity of the supporting frame and joints are the primary factors that cause the flanking transmission. In this study, we investigate the extent of this transmission, and identify the vibration transmission paths and assess their negligibility in measurement of the sound transmission loss of the multilayer panels. A source-path-receiver approach has been proposed for ranking the critical transmission paths for different panel configurations. For this purpose, a numerical framework has been developed to measure the acoustic response of the room and vibration response of the structural elements at operating conditions. A finite element model in COMSOL is set to validate the results and is compared with an in-house analytical solution which shows good agreements. Assessment of the vibration and acoustic signals at sub-structures reveals transmission paths that are significant for the performance evaluation of multilayer panels.
Nisal, S, Patibanda, R, Saini, A, Van Den Hoven, E & Mueller, FF 1970, 'TouchMate: Understanding the Design of Body Actuating Games using Physical Touch', Extended Abstracts of the Annual Symposium on Computer-Human Interaction in Play, CHI PLAY '22: The Annual Symposium on Computer-Human Interaction in Play, ACM.
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Oliveira, FT, Tong, BW, Garcia, JA & Gay, VC 1970, 'CogWorldTravel: Design of a Game-Based Cognitive Screening Instrument', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Joint Conference on Serious Games (JCSG), Springer International Publishing, Bauhaus Univ Weimar, Weimar, GERMANY, pp. 125-139.
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Cognitive Screening Instruments are helpful in the early detection of cognitive changes and possible underlying dementia. These instruments test all major cognitive domains of an individual. Serious games have been investigated as an alternative approach for cognitive assessment because of their ability to motivate. Previous work mostly focused on finding out whether it is feasible to use a serious game for such purpose. We decided to investigate further how a serious game can be engaging and fun while prioritizing the cognitive assessment. In this paper, we describe the design, development, and evaluation of CogWorldTravel, a serious game that has the potential to be used for cognitive screening as it measures at least one aspect of each cognitive domain. CogWorldTravel features six game tasks that involve recognition memory, attention, working memory, language, immediate memory span, processing speed, inhibition, recognition of emotions, visuoconstructional, perceptual-motor, and planning abilities. The serious game also accommodates age-related changes and considers the gameplay preferences of older adults.
Oliveira, FTV, Garcia, JA & Gay, VC 1970, 'Evaluation of CogWorldTravel: A Serious Game for Cognitive Screening', 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, pp. 1-8.
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As the world population is growing older, there is an urge to develop new technologies to support older adults, who are at a greater risk for the onset of dementia. Cognitive Screening Instruments (CSIs) can be used to screen for dementia. While there are a significant number of available well-researched and accepted CSIs, they are associated with drawbacks. Serious games have been investigated as an alternative instrument to overcome the constraints of traditional methods. The use of serious games for cognitive screening is still a relatively new field of research, with previous works mostly focusing on finding out whether there is a correlation or not between games and cognitive performance. Serious games that engage older adults and meet the criteria of CSIs remain an open challenge. To address this challenge, we developed CogWorldTravel, a serious game for the cognitive screening of older adults. In this paper, we describe the results of the evaluation of CogWorldTravel, which consisted of conducting semi-structured interviews with five experts in dementia assessment. Results suggest that the game involves recognition memory, attention, working memory, language, immediate memory span, processing speed, inhibition, recognition of emotions, visuoconstructional, perceptual-motor, and planning abilities.
Ordibazar, AH, Hussain, O & Saberi, M 1970, 'A Recommender System and Risk Mitigation Strategy for Supply Chain Management Using the Counterfactual Explanation Algorithm', Springer International Publishing, pp. 103-116.
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Pan, L, Yao, L, Zhang, W & Wang, X 1970, 'Mitigating Multi-class Unintended Demographic Bias in Text Classification with Adversarial Learning', Web Information Systems Engineering – WISE 2022, International Conference on Web Information Systems Engineering, Springer International Publishing, Biarritz, France, pp. 386-394.
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Text classification enables higher efficiency on text data queries in information retrieval. However, unintended demographic bias can impair text toxicity classification. Thus, we propose a novel debiasing framework utilizing Adversarial Learning on word embeddings of multi-class sensitive demographic words to alleviate this bias. Slight adjustment over word embeddings with flipped sensitive indices is achieved, and the modified word embeddings are used in the downstream classification task to realize Demographic Parity. The experimental results validate the effectiveness of our proposed method in mitigating multi-class unintended demographic bias without impairing the original classification accuracy.
Pang, L, Liu, W, Wu, L, Xie, K, Guo, S, Chalapathy, R & Wen, M 1970, 'Applied Machine Learning Methods for Time Series Forecasting', Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM '22: The 31st ACM International Conference on Information and Knowledge Management, ACM.
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Patibanda, R, Van Den Hoven, E & Mueller, FF 1970, 'Towards Understanding the Design of Body-Actuated Play', Extended Abstracts of the Annual Symposium on Computer-Human Interaction in Play, CHI PLAY '22: The Annual Symposium on Computer-Human Interaction in Play, ACM.
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Pizarro, V, Merigó, JM, Valenzuela, L & Aciares, S 1970, 'A Bibliometric Study of Key Journals in Corporate Social Responsibility', Springer International Publishing, pp. 205-216.
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Qu, Y, Chen, S, Gao, L, Cui, L, Sood, K & Yu, S 1970, 'Personalized Privacy-Preserving Medical Data Sharing for Blockchain-based Smart Healthcare Networks', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE, pp. 4229-4234.
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With the growing proliferation of intelligent end devices and data analytics techniques, real momentum towards the development of smart healthcare networks (SHN) has already been evident. Multiple parties in SHNs continuously exchange medical data in order to achieve a precise diagnosis and process optimization. Privacy issue emerges since medical data are susceptible, while the combination of a series of medical data may lead to further privacy leakage. Adversaries launch unceasingly launch poisoning attacks, a dominant attack to maliciously manipulate data, severely impact the authenticity of the data transmitting over the SHNs, leading to misdiagnosing or even physical damage. In this paper, we propose a personalized differential privacy model built upon blockchain, in which the community density is exploited to customize the degree of privacy protection and inject corresponding noise data. Besides using blockchain as the underlying network architecture to defeat poisoning attacks. The proposed model can guarantee the authentication of the differentially private data, traceability of data, and single-point failure avoidance in SHN. Evaluation and extensive results using real-world data sets demonstrate the superiority of the proposed model.
Qu, Z, Tegegne, Y, Simoff, SJ, Kennedy, PJ, Catchpoole, DR & Nguyen, QV 1970, 'Enhancing Understandability of Omics Data with SHAP, Embedding Projections and Interactive Visualisations', Communications in Computer and Information Science, Springer Nature Singapore, pp. 58-72.
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Uniform Manifold Approximation and Projection (UMAP) is a new and effective non-linear dimensionality reduction (DR) method recently applied in biomedical informatics analysis. UMAP’s data transformation process is complicated and lacks transparency. Principal component analysis (PCA) is a conventional and essential DR method for analysing single-cell datasets. PCA projection is linear and easy to interpret. The UMAP is more scalable and accurate, but the complex algorithm makes it challenging to endorse the users’ trust. Another challenge is that some single-cell data have too many dimensions, making the computational process inefficient and lacking accuracy. This paper uses linkable and interactive visualisations to understand UMAP results by comparing PCA results. An explainable machine learning model, SHapley Additive exPlanations (SHAP) run on Random Forest (RF), is used to optimise the input single-cell data to make UMAP and PCA processes more efficient. We demonstrate that this approach can be applied to high-dimensional omics data exploration to visually validate informative molecule markers and cell populations identified from the UMAP-reduced dimensionality space.
Raza, MR, Hussain, W & Varol, A 1970, 'Performance Analysis of Deep Approaches on Airbnb Sentiment Reviews', 2022 10th International Symposium on Digital Forensics and Security (ISDFS), 2022 10th International Symposium on Digital Forensics and Security (ISDFS), IEEE, Maltepe, TURKEY.
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Consumer reviews in the Airbnb marketplace are one of the key attributes to measure the quality of services and the main determinant of consumer rentals decisions. Such feedback can impact both a new and repeated consumer's choice decision. The way to manage poor reviews can help to save or damage the host's reputation. Sentiment analysis enables an Airbnb host to get an insight into the business, pinpoint degradation of the specific component of compound services and assist in managing it proactively. Multiple Deep Learning algorithms have been used for Natural Language Processing (NLP). For optimal sentiment management in the Airbnb marketplace, it is crucial to identify the right algorithm. The paper uses multiple Deep Learning algorithms to identify different aspects of guest reviews and analyze their accuracies. The paper uses four accuracy measurement benchmarks - Precision, Recall, F1-score and Support to analyze results. The analysis shows that the GRU method achieves the best results with the highest classification metrics values as compared to RNN and LSTM.
Saini, A, Huang, H, Patibanda, R, Overdevest, N, Van Den Hoven, E & Mueller, FF 1970, 'SomaFlatables: Supporting Embodied Cognition through Pneumatic Bladders', Adjunct Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, UIST '22: The 35th Annual ACM Symposium on User Interface Software and Technology, ACM, pp. 1-4.
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Applying the theory of Embodied Cognition through design allows us to create computational interactions that engage our bodies by modifying our body schema. However, in HCI, most of these interactive experiences have been stationed around creating sensing-based systems that leverage our body's position and movement to offer an experience, such as games using Nintendo Wii and Xbox Kinect. In this work, we created two pneumatic inflatables-based prototypes that actuate our body to support embodied cognition in two scenarios by altering the user's body schema. We call these 'SomaFlatables'and demonstrate the design and implementation of these inflatables based prototypes that can move and even extend our bodies, allowing for novel bodily experiences. Furthermore, we discuss the future work and limitations of the current implementation.
Sansom, T, Sepehrirahnama, S, Halkon, B, Lai, JCS & Oberst, S 1970, 'LASER INTENSITY-INDUCED DAMAGE EFFECTS ON DYNAMIC CHARACTERISATION OF WINGS OF THE EUROPEAN HONEYBEE (APIS MELLIFERA)', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Singapore.
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Micromechanical and mesoscopic structures including biological tissue, insect appendages or hearing organs can be dynamically characterised through laser Doppler vibrometry (LDV). LDV measures surface vibrations with high spatial resolution, and high dynamic and frequency ranges without causing obvious damage to the specimens. Generally speaking, higher laser intensities lead to higher signal-to-noise ratios, desirable for accurate vibration measurements. However, for certain wavelengths and too high intensity values, the LDV, though only 1 mW output, may damage organic tissue. We aim to illustrate LDV measurements by studying the vibration characteristics of forewings (N= 5) of the European honeybee (Apis mellifera, Hymenoptera). We qualify the level of damage caused by a laser vibrometer with a Helium-Neon laser (532 nm) of a microsystems analyser using a white light-microscope. We monitor the change in the first three eigenfrequencies and the non-damaging intensity level at which the forced-vibration response (FRF) of the wings can still be measured. The first three frequencies at 0.48±0.04 kHz, 1.05±0.06 kHz, and 1.55±0.12 kHz, and their mode shapes of damaged wings are compared against those reported in literature and show ca. 15% frequency deviation. Assuming the stiff element hypothesis, the wing's first bending mode is expected to be at higher frequencies (485±37 Hz) than the approximate wing-beat frequency (234±13.9 Hz). Implementing a finite element model of the wing using a reinforced membrane geometry approach, the measurement results of the undamaged wings are verified. Our results indicate that the intensity levels in LDV measurements on bee wings need to be carefully monitored. The established experimental methodology based on non-damaging laser intensity can also be used for studies of other insects' filigree structures such as their appendages and their vibration and acoustic sensing organs.
Schuhmann, AH, Kleinfeller, N, Sepehrirahnama, S, Oberst, S, Adams, C & Melz, T 1970, 'Numerical analysis on defect detection using structural intensity in solid bodies', Proceedings of the International Congress on Acoustics.
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Analysing structural intensity (SI) offers the possibility to assess the transmission of wave energy within a structure. Measurement of SI has been mainly focused on thin shells and beams. In this work a measurement method is presented to evaluate SI within solid, homogeneous, and isotropic bodies. The method is based on the reciprocity principle, a fundamental assumption in linear vibroacoustics. It allows the reconstruction of the structural intensity field within the bulk of a solid body from the measured surface velocities on the exterior boundaries. From the preliminary results, we demonstrate the capability of this method in approximating the spatial variation of the reconstructed stress and velocity fields using finite element simulation results. Inspired by the reciprocity-based method, we also demonstrate a cavity detection technique using the structural intensity measured along a closed path on a surface of a solid block. Despite some discrepancies in the estimate of the magnitude, the method works well in principle for the benchmark problem of a rectangular cube and it can be verified using our recently set up experimental test. Our proposed method provides an alternative energy-based SI detection technique that may perform as well as those exploiting velocity/acceleration or strain/stress.
Sepehrirahnama, S, McManus, H & Oberst, S 1970, 'ACOUSTIC LEVITATOR-TWEEZER USING PRE-PROGRAMMED ACOUSTIC HOLOGRAMS', Proceedings of the International Congress on Sound and Vibration, 28th International Congress on Sound and Vibration 2022, Singapore.
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Objects in an acoustic field are subjected to acoustic radiation forces, which depend on the objects' scattering behaviour and becomes comparable to the objects' weight for sizes smaller than a few millimeters. This led to manipulation techniques with ultrasonic waves in fluids. In current acoustic levitators, naturally asymmetric objects undergo unwanted spin and rigid-body oscillations. We developed a design of an acoustic manipulator with the ability to levitate and tweeze in vertical and horizontal directions, respectively. This is realised, using three separate transducer arrays and a discretized, reflective floor, inspired by the MIT inForm machine. The floor is made of nine movable pins to change the surface topography and, consequently, manipulate the acoustic field. In this study, we implemented square, staircase, and flat surface configurations to apply pre-defined acoustic holograms for manipulating levitated objects. The two side arrays generate a strong horizontal trap for holding the objects stably at a point where the acoustic radiation force is near zero. The top array and the adjustable floor generate a radiation force as large as an object's weight at the point of levitation, indicated by its levitation height. The object responds to the change of pins by altering its original position in the chamber. Preliminary results obtained at a transducer driving frequency of 40 kHz indicate that an asymmetric object such as a Bee's wing can be levitated stably for more than half an hour with minimal response to external disturbances, and without using phased-array technique. Owing to acoustic radiation force, the measurements are contactless and potentially non-invasive or minimally invasive, dependent on the object. The suggested device design can be potentially employed in the study of delicate biological samples including insects' appendages, such as wings, legs or other filigree structures such as electronic components, wires or MEMS with d...
Sheikh, MA, Khan, GZ & Hussain, FK 1970, 'Systematic Analysis of DDoS Attacks in Blockchain', 2022 24th International Conference on Advanced Communication Technology (ICACT), 2022 24th International Conference on Advanced Communication Technology (ICACT), IEEE.
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Sheikh, MA, Khattak, F, Khan, GZ & Hussain, FK 1970, 'Secured Land Title Transfer System in Australia using VPN based Blockchain Network', 2022 24th International Conference on Advanced Communication Technology (ICACT), 2022 24th International Conference on Advanced Communication Technology (ICACT), IEEE.
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Shen, L, Zhang, Y, Wang, J & Bai, G 1970, 'Better Together: Attaining the Triad of Byzantine-robust Federated Learning via Local Update Amplification', Proceedings of the 38th Annual Computer Security Applications Conference, ACSAC: Annual Computer Security Applications Conference, ACM.
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Shen, Y, Li, L, Xie, Q, Li, X & Xu, G 1970, 'A Two-Tower Spatial-Temporal Graph Neural Network for Traffic Speed Prediction', Springer International Publishing, pp. 406-418.
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Smith, J, Bhandari, A, Yuksel, B & Kocaballi, AB 1970, 'An Embodied Conversational Agent to Minimize the Effects of Social Isolation During Hospitalization', ACIS 2022 - Australasian Conference on Information Systems, Proceedings, Australasian Conference on Information Systems, Melbourne.
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Social isolation and loneliness contribute to the development of depression and anxiety. Comorbidity of mental health issues in hospitalized patients increases the length of stay in hospital by up to 109% and costs the healthcare sector billions of dollars each year. This study aims to understand the potential suitability of embodied conversational agents (ECAs) to reduce feelings of social isolation and loneliness among hospital patients. To facilitate this, a video prototype of an ECA was developed for use in single-occupant hospital rooms. The ECA was designed to act as an intelligent assistant, a rehabilitation guide, and a conversational partner. A co-design workshop involving five healthcare professionals was conducted. The thematic analysis of the workshop transcripts identified some major themes including improving health literacy, reducing the time burden on healthcare professionals, preventing secondary mental health issues, and supporting higher acceptance of digital technologies by elderly patients.
Sun, Y, Han, Y, Zhang, Y, Chen, M, Yu, S & Xu, Y 1970, 'DDoS Attack Detection Combining Time Series-based Multi-dimensional Sketch and Machine Learning', 2022 23RD ASIA-PACIFIC NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM (APNOMS 2022), 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS), IEEE, JAPAN, Takamatsu, pp. 37-42.
Sun, Y, Han, Y, Zhang, Y, Chen, M, Yu, S & Xu, Y 1970, 'DDoS Attack Detection Combining Time Series-based Multi-dimensional Sketch and Machine Learning', 2022 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS), 2022 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS), IEEE, pp. 01-06.
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Machine learning-based DDoS attack detection methods are mostly implemented at the packet level with expensive computational time costs, and the space cost of those sketch-based detection methods is uncertain. This paper proposes a two-stage DDoS attack detection algorithm combining time series-based multi-dimensional sketch and machine learning technologies. Besides packet numbers, total lengths, and protocols, we construct the time series-based multi-dimensional sketch with limited space cost by storing elephant flow information with the Boyer-Moore voting algorithm and hash index. For the first stage of detection, we adopt CNN to generate sketch-level DDoS attack detection results from the time series-based multi-dimensional sketch. For the sketch with potential DDoS attacks, we use RNN with flow information extracted from the sketch to implement flow-level DDoS attack detection in the second stage. Experimental results show that not only is the detection accuracy of our proposed method much close to that of packet-level DDoS attack detection methods based on machine learning, but also the computational time cost of our method is much smaller with regard to the number of machine learning operations.
Tahira, A, Hussain, W & Ali, A 1970, 'Review-Based Recommender System for Hedonic and Utilitarian Products in IoT Framework', Springer International Publishing, pp. 221-232.
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Tang, G, Duong, DH, Joux, A, Plantard, T, Qiao, Y & Susilo, W 1970, 'Practical Post-Quantum Signature Schemes from Isomorphism Problems of Trilinear Forms', Advances in Cryptology – EUROCRYPT 2022, Springer International Publishing, pp. 582-612.
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Tian, Y, Do, T-TN, Wang, Y-K & Lin, C-T 1970, 'The effect of different sensory modalities on inattentional blindness in a virtual environment for attentional loss improvement', 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, pp. 1-6.
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Failure to notice salient objects even looking directly at them happens when individuals' attention is preoccupied, known as inattentional blindness (IB). As a form of attentional loss, IB occurrence might cause severe outcomes due to limited cognitive resources. Varied methods have been explored to reduce the IB effect and avoid neglect of critical information. Attenuating attentional loss via aided guidance with different sensory modalities intervention could be a possible way to address this issue. This study investigates how different sensory modalities affect the cognitive performance and IB effect from behaviour and neural changes in the human brain and how could we apply this in attention training for attentional loss improvement. Two experimental sessions were conducted, with a multisensory oddball task designed in virtual reality (VR) as the main task to attract individuals' attention. In session 1, participants responded to the main task without being informed of the unexpected task-irrelevant patterns in the background, while in session 2, they were informed of the unexpected patterns but still attended to the main task. Thus, participants were divided into IB (unaware of the pattern) and Aware (aware of the pattern) groups based on their awareness of patterns in the first session. Our results revealed that this VR-based design successfully induced the IB occurrence, with four out of nine participants reporting being unaware of the unexpected patterns. Further, the multisensory oddball task showed better performance in cross-modal stimuli (visual-auditory, VA) with higher accuracy and shorter reaction time than in uni-modal (A or V) conditions. Interestingly, in session 1, the IB group showed better performance than the Aware group, indicating that the IB group was not distracted during the task since they were unaware of the patterns. These findings supported our aims to explore the impact of different sensory modalities on cognitive perform...
Tian, Z, Zhang, C, Cui, L & Yu, S 1970, 'GSMI: A Gradient Sign Optimization Based Model Inversion Method', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 34th Australasian Joint Conference on Artificial Intelligence (AI), Springer International Publishing, Univ Technol Sydney, ELECTR NETWORK, pp. 67-78.
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The vulnerabilities of deep learning models on security and privacy have attracted a lot of attentions. Researchers have revealed the possibility of reconstructing training data of a target model. However, the performances of current works are highly rely on auxiliary datasets. In this paper, we investigate the model inversion problem under a strict restriction, where the adversary aims to reconstruct plausible samples of the target class without help of auxiliary information. To solve this challenge, we propose a Gradient Sign Model Inversion (GSMI) method based on the idea of adversarial examples generation. Specifically, we make three modifications on a popular adversarial examples generation method i-FGSM to generate plausible samples. 1) increasing the number of attack iterations and 2) superposing noises to reveal more obvious features learned by target model. 3) removing subtle noises to make reconstructed samples more plausible. However, we find samples generated by GSMI still contain noisy components. Furthermore, we adopt the idea of image adjacent regions to design a two-pass components selection algorithm to generate more reasonable sample of the target class. Through experiments, we find that the inversion samples of GSMI are close to real target class samples with some fluctuations on different classes. In addition, we also provide detail analysis for reasons of limitations on the optimization-based model inversion methods.
Tofigh, F, Sepehrirahnama, S, Lai, JCS & Oberst, S 1970, 'CHARACTERISING AND CALIBRATING PIEZO ACTUATORS FOR MICRO-EXCITATION FOR VIBRATION PLAYBACK IN BI-OASSAYS OF INSECTS', Proceedings of the International Congress on Sound and Vibration, 28th Intenational Congress on Sound and Vibration, Singapore.
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Micro-vibration signals in bioassays under controlled environmental conditions in biotremology require a device that can generate a similar level of vibration response as caused by the insect. Since bioassays often need to be run in environmental cabinets, the space available is limited, and structures to be excited should not be mass loaded. Considering the properties of piezo actuators in generating very short strokes with high frequency and fast response times, stacked arrangements were found suitable for micro-excitation based on a given approximation of a Dirac delta impulse, approximating in the first instance the impact signal of a walking insect. However, at below the current limit of miniaturised force and displacement actuators, it is essential to characterise and calibrate the piezo actuators to ensure they are producing the desired signal at the point of contact on a given structure. Here we established a methodology for driving piezo actuators at the order of μm/s to generate low-amplitude impulsive excitations. The methodology includes finding the transfer function of the piezo actuator and an aluminum and a wood beam (Pinus radiata) of 20x10mm2 cross section and 200mm length. The reaction force from the piezo actuator was measured from about 40mN down to 2mΝ for travel ranges between 1.2μm and 11μm. The results showed that the force varies linearly from 5-19μm for the ceramic, and 0.6μm to 1.4μm for the PI and the MTK actuators with an input voltage ranging from 2-10V. The measurement setup improved using an anechoic chamber to reduce the noise level by one order of magnitude, compared to reported results in literature, and ensure excitation amplitudes as low as ±10nm/s can be measured. The presented methodology allows developing affordable micro-excitors in the future for playback bioassays in confined spaces which cause minimal mass loading on the test specimen.
Vahdati, F, Atif, A & Saberi, M 1970, 'A machine learning-based depression detection on social media platforms for adolescents: A work in progress narrative review', 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), IEEE, Gold Coast, pp. 1-6.
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A rise in depression episodes has prompted an increased focus on depression detection. This research paper aims to review the literature to discover the pros and cons of proposed solutions for this critical social problem. In this
narrative review, specifically, we looked at machine learning
(ML) based techniques that analyse text data from social media to diagnose depression symptoms. A thorough search technique across several databases for relevant articles, specifically Google Scholar, PubMed, Medline, ERIC, PsycINFO, and BioMed databases, were used to perform a narrative evaluation. Terms and definitions were used to filter the article titles, abstracts, and full texts. Approaches based on machine learning and text data from social media may be helpful in the diagnosis of depression and might be used in conjunction with other mental health services.
Vo, H, Tang, M, Zheng, XJ & Yu, S 1970, 'BI-GAN', Proceedings of the 17th ACM Workshop on Mobility in the Evolving Internet Architecture, ACM MobiCom '22: The 28th Annual International Conference on Mobile Computing and Networking, ACM, pp. 31-36.
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Federated Learning is a growing advanced collaborative machine learning framework that aims to preserve user-privacy data. However, multiple researchers have investigated attack methods from the server side via gradient inversion techniques or Generative Adversarial Networks (GAN) to reconstruct the raw data distributions from users. In this paper, we propose Batch Inversion GAN (BI-GAN), a novel membership inference attack that can recover user-level batch images from local updates, utilizing both gradient inversion techniques and GAN. Our attack is more stealthy since it only requires access to gradients and does not interfere with the global model performance and is more robust in terms of image batch recovery and victim classification. The experiments show that our attack recovers higher quality images of the victim with higher accuracy compared to other attacks.
Wan, Y, He, Y, Bi, Z, Zhang, J, Sui, Y, Zhang, H, Hashimoto, K, Jin, H, Xu, G, Xiong, C & Yu, PS 1970, 'NaturalCC', Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings, ICSE '22: 44th International Conference on Software Engineering, ACM.
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Wan, Y, Zhang, S, Zhang, H, Sui, Y, Xu, G, Yao, D, Jin, H & Sun, L 1970, 'You see what I want you to see: poisoning vulnerabilities in neural code search', Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE '22: 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ACM.
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Wan, Y, Zhao, W, Zhang, H, Sui, Y, Xu, G & Jin, H 1970, 'What do they capture?', Proceedings of the 44th International Conference on Software Engineering, ICSE '22: 44th International Conference on Software Engineering, ACM.
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Wang, S, Liu, Y, Chen, L & Zhang, C 1970, 'Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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Unsupervised anomaly detection (AD) is a challenging task in realistic applications. Recently, there is an increasing trend to detect anomalies with deep neural networks (DNN). However, most popular deep AD detectors cannot protect the network from learning contaminated information brought by anomalous data, resulting in unsatisfactory detection performance and overfitting issues. In this work, we identify one reason that hinders most existing DNN-based anomaly detection methods from performing is the wide adoption of the Empirical Risk Minimization (ERM). ERM assumes that the performance of an algorithm on an unknown distribution can be approximated by averaging losses on the known training set. This averaging scheme thus ignores the distinctions between normal and anomalous instances. To break through the limitations of ERM, we propose a novel Diminishing Empirical Risk Minimization (DERM) framework. Specifically, DERM adaptively adjusts the impact of individual losses through a well-devised aggregation strategy. Theoretically, our proposed DERM can directly modify the gradient contribution of each individual loss in the optimization process to suppress the influence of outliers, leading to a robust anomaly detector. Empirically, DERM outperformed the state-of-the-art on the unsupervised AD benchmark consisting of 18 datasets.
Wang, S, Zhao, G, Xu, C, Han, Z & Yu, S 1970, 'A NTRU-Based Access Authentication Scheme for Satellite Terrestrial Integrated Network', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE, pp. 3629-3634.
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The Satellite Terrestrial Integrated Network(STIN) has become an indispensable part of the future network. However, the limited resources, long delay communications and highly exposed channels of SGIN are vulnerable to network attacks, which make the access authentication scheme as the first line of defense in network security. Most of the existing access authentication schemes are based on discrete logarithm and large integer factorization problems, which cannot resist quantum attacks, and the number of interactions between entities are too large. Therefore, we propose a lightweight and certificateless anonymous access authentication scheme based on lattice to solve this problem. We introduce Number Theory Research Unit (NTRU) scheme into the key generation process, to improve the utilization rate of equipment resources and ensure the legitimacy of the communication entity. The performance evaluation results demonstrate that our scheme only needs twice satellite-ground interactions to complete mutual authentication.
Wang, W, Liu, S, Liu, A, Liang, CJ & Yu, S 1970, 'Locally Random Sampling for Practical Privacy Protection in Federated Learning', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE, pp. 528-533.
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Federated learning (FL) is an emerging solution for machine learning model training in edge/fog computing systems. Unlike traditional systems that collect and train models on clouds, FL allows multiple edge/fog nodes to train a global model collaboratively without revealing their local data to clouds. Compared with traditional systems, it is inherited with better privacy protection ability. Although the basic privacy protection is inherited in FL, the privacy leakage from shard models is still unsolved. Existing solutions attempt to enhance the privacy of shared model parameters by adding differential privacy (DP) noise. However, these solutions all suffer from accuracy loss and convergence problems owing to the injected noise. In this paper, we propose a novel federated learning protocol to solve the above problem. The model trained on a carefully selected sampling subset can achieve the same level privacy protection as DP while preserving the model accuracy. Experimentally, we proved that our protocol achieves better model accuracy in the same privacy guarantee compared with noise injecting DP methods.
Wang, X, Li, Q, Yu, D & Xu, G 1970, 'Off-policy Learning over Heterogeneous Information for Recommendation', Proceedings of the ACM Web Conference 2022, WWW '22: The ACM Web Conference 2022, ACM, pp. 2348-2359.
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Reinforcement learning has recently become an active topic in recommender system research, where the logged data that records interactions between items and users feedback is used to discover the policy. Much off-policy learning, referring to the procedure of policy optimization with access only to logged feedback data, has been a popular research topic in reinforcement learning. However, the log entries are biased in that the logs over-represent actions favored by the recommender system, as the user feedback contains only partial information limited to the particular items exposed to the user. As a result, the policy learned from such off-line logged data tends to be biased from the true behaviour policy. In this paper, we are the first to propose a novel off-policy learning augmented by meta-paths for the recommendation. We argue that the Heterogeneous information network (HIN), which provides rich contextual information of items and user aspects, could scale the logged data contribution for unbiased target policy learning. Towards this end, we develop a new HIN augmented target policy model (HINpolicy), which explicitly leverages contextual information to scale the generated reward for target policy. In addition, being equipped with the HINpolicy model, our solution adaptively receives HIN-augmented corrections for counterfactual risk minimization, and ultimately yields an effective policy to maximize the long run rewards for the recommendation. Finally, we extensively evaluate our method through a series of simulations and large-scale real-world datasets, obtaining favorable results compared with state-of-the-art methods.
Wang, X, Li, Q, Yu, D, Wang, Z, Chen, H & Xu, G 1970, 'MGPolicy', Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 1369-1378.
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Off-policy learning has drawn huge attention in recommender systems (RS), which provides an opportunity for reinforcement learning to abandon the expensive online training. However, off-policy learning from logged data suffers biases caused by the policy shift between the target policy and the logging policy. Consequently, most off-policy learning resorts to inverse propensity scoring (IPS) which however tends to be over-fitted over exposed (or recommended) items and thus fails to explore unexposed items. In this paper, we propose meta graph enhanced off-policy learning (MGPolicy), which is the first recommendation model for correcting the off-policy bias via contextual information. In particular, we explicitly leverage rich semantics in meta graphs for user state representation, and then train the candidate generation model to promote an efficient search in the action space. lMoreover, our MGpolicy is designed with counterfactual risk minimization, which can correct poicy learning bias and ultimately yield an effective target policy to maximize the long-run rewards for the recommendation. We extensively evaluate our method through a series of simulations and large-scale real-world datasets, achieving favorable results compared with state-of-the-art methods. Our code is currently available online.
Wu, G, Li, Z, Shen, Y, Zhang, H, Shen, S & Yu, S 1970, 'A Deep Reinforcement Learning Approach to Edge-based IDS Packets Sampling', 2022 5th International Conference on Data Science and Information Technology (DSIT), 2022 5th International Conference on Data Science and Information Technology (DSIT), IEEE, pp. 1-6.
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Edge computing expands the Internet of Things (IoT) by allowing partial computing tasks to be migrated to edge servers and alleviate the computing pressure of terminal devices. However, the edge servers will be attacked through malicious network traffic. A well-designed edge-based IDS plays a significant role in protecting from malicious attacks. In this paper, we employ a gated recurrent unit classifier to perform intrusion detection due to its characteristics on long-term memory of inputs and light structure. Furthermore, to reduce the cost of performing intrusion detection when facing a large volume of data, we propose an actor-critic network based on deep reinforcement learning for packets sampling to work on some packets by ignoring the others. The system we designed can achieve great classification performance through partial packets. We use dataset CIC-IDS-2017 to evaluate our model, and the accuracy of classification reaches 97% while the proportion of detection packets is 26% and below. Evaluations of our approach on UNSW-NB15 and CIC-IDS-2017 show that our model maintains under 26% and 18% of the selected packets, respectively.
Wu, G, Zhao, Y, Shen, Y, Zhang, H, Shen, S & Yu, S 1970, 'DRL-based Resource Allocation Optimization for Computation Offloading in Mobile Edge Computing', IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, ELECTR NETWORK.
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Mobile edge computing (MEC) provides a new development direction for emerging computing-intensive applications because it can improve computing performance and lower the threshold for users to use such applications. However, designing an effective computation offloading strategy to determine which tasks should be uninstalled to an edge server is still a crucial challenge. To this end, we propose a computation offload scheme based on dynamic resource allocation to optimize computing performance and energy consumption in MEC systems. We further formulate the resource allocation as a partially observable Markov decision process, which is solved by a policy gradient deep reinforcement learning method. Compared with other existing solutions, simulation results show that our proposal reduces the computational latency and energy consumption.
Wu, X, Qi, L, Xu, X, Yu, S, Dou, W & Zhang, X 1970, 'Crowdsourcing-based Multi-Device Communication Cooperation for Mobile High-Quality Video Enhancement', Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, ACM, ELECTR NETWORK, pp. 1140-1148.
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The widespread use of mobile devices propels the development of new-fashioned video applications like 3D (3-Dimensional) stereo video and mobile cloud game via web or App, exerting more pressure on current mobile access network. To address this challenge, we adopt the crowdsourcing paradigm to offer some incentive for guiding the movement of recruited crowdsourcing users and facilitate the optimization of the movement control decision. In this paper, based on a practical 4G (4th-Generation) network throughput measurement study, we formulate the movement control decision as a cost-constrained user recruitment optimization problem. Considering the intractable complexity of this problem, we focus first on a single crowdsourcing user case and propose a pseudo-polynomial time complexity optimal solution. Then, we apply this solution to solve the more general problem of multiple users and propose a graph-partition-based algorithm. Extensive experiments show that our solutions can improve the efficiency of real-time D2D communication for mobile videos.
Xia, X, Yin, H, Yu, J, Wang, Q, Xu, G & Nguyen, QVH 1970, 'On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation', Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM.
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Xiao, T, Halkon, B, Oberst, S, Wang, S & Qiu, X 1970, 'SOUND FIELD MEASUREMENT AT AN ENCLOSURE OPENING USING REFRACTO-VIBROMETRY', Proceedings of the International Congress on Sound and Vibration, International Congress on Sound and Vibration, Singapore.
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A sound field can be measured by an array of microphones distributed across the area of interest or by moving a smaller number of microphones sequentially. Such procedures can be time-consuming and expensive when high spatial resolution is required. Furthermore, the presence of physical microphones might disturb the sound field. Refracto-vibrometry is based on the acousto-optic effect. It can serve as an alternative method to measure sound pressure at all the points of interest without disturbing the sound field. In this paper, three methods, the filtered back-projection, the truncated singular value decomposition and the Tikhonov regularisation methods, are used to evaluate the sound field at an enclosure opening. Comparison with a microphone array shows that the Tikhonov regularisation method yields the best result.
Xie, F, Zhang, Y, Wei, H & Bai, G 1970, 'UQ-AAS21: A Comprehensive Dataset of Amazon Alexa Skills', Springer International Publishing, pp. 159-173.
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Xie, F, Zhang, Y, Yan, C, Li, S, Bu, L, Chen, K, Huang, Z & Bai, G 1970, 'Scrutinizing Privacy Policy Compliance of Virtual Personal Assistant Apps', Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, ASE '22: 37th IEEE/ACM International Conference on Automated Software Engineering, ACM.
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Xing, Y, Zhao, G, Xu, C, Cheng, K & Yut, S 1970, 'The Converged Scheduling for Time Sensitive Mission in Satellite Formation Flying', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE.
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Xu, C, Qu, Y, Xiang, Y, Gao, L, Smith, D & Yu, S 1970, 'BASS: Blockchain-Based Asynchronous SignSGD for Robust Collaborative Data Mining', 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, Shenzhen, China, pp. 1-7.
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Federated learning (FL) is a machine learning framework for collaborative data mining in many scenarios (e.g. Internet of Things) due to its privacy-preserving feature. However, various attacks arise security concerns of FL, such as poisoning, backdoor, and DDoS attacks. Several blockchain-based FL schemes strengthen credibility and security without considering the increased communication overhead. Some existing work compresses local updated gradients to sign vectors to lower communication overhead at the expense of model accuracy. To address the above concerns, this paper offers a blockchain-based asynchronous SignSGD (BASS) scheme. A novel asynchronous sign aggregation algorithm is introduced to ensure model accuracy even if the local updated gradients are compressed to sign vectors. Considering the unstable network connection on IoT, a consensus algorithm that elects multiple leader nodes enables reliable global model aggregation. The introduced blockchain improves credibility and security without downgrading efficiency. Empirical studies show that BASS outperforms other schemes in efficiency, model accuracy, and security.
Xu, D, Yang, H, Rizoiu, M-A & Xu, G 1970, 'Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks', Advanced Data Mining and Applications, Springer Nature Switzerland, pp. 520-534.
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The rapid advances in automation technologies, such as artificialintelligence (AI) and robotics, pose an increasing risk of automation foroccupations, with a likely significant impact on the labour market. Recentsocial-economic studies suggest that nearly 50\% of occupations are at highrisk of being automated in the next decade. However, the lack of granular dataand empirically informed models have limited the accuracy of these studies andmade it challenging to predict which jobs will be automated. In this paper, westudy the automation risk of occupations by performing a classification taskbetween automated and non-automated occupations. The available information is910 occupations' task statements, skills and interactions categorised byStandard Occupational Classification (SOC). To fully utilize this information,we propose a graph-based semi-supervised classification method named\textbf{A}utomated \textbf{O}ccupation \textbf{C}lassification based on\textbf{G}raph \textbf{C}onvolutional \textbf{N}etworks (\textbf{AOC-GCN}) toidentify the automated risk for occupations. This model integrates aheterogeneous graph to capture occupations' local and global contexts. Theresults show that our proposed method outperforms the baseline models byconsidering the information of both internal features of occupations and theirexternal interactions. This study could help policymakers identify potentialautomated occupations and support individuals' decision-making before enteringthe job market.
Xu, Y, Fang, M, Chen, L, Du, Y, Zhou, J & Zhang, C 1970, 'Perceiving the World: Question-guided Reinforcement Learning for Text-based Games', Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, pp. 538-560.
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Text-based games provide an interactive way to study natural language processing. While deep reinforcement learning has shown effectiveness in developing the game playing agent, the low sample efficiency and the large action space remain to be the two major challenges that hinder the DRL from being applied in the real world. In this paper, we address the challenges by introducing world-perceiving modules, which automatically decompose tasks and prune actions by answering questions about the environment. We then propose a two-phase training framework to decouple language learning from reinforcement learning, which further improves the sample efficiency. The experimental results show that the proposed method significantly improves the performance and sample efficiency. Besides, it shows robustness against compound error and limited pre-training data.
Yang, C, Wang, X, Yao, L, Jiang, J & Xu, G 1970, 'An Explanation Module for Deep Neural Networks Facing Multivariate Time Series Classification', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 3-14.
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Deep neural networks currently achieve state-of-the-art performance in many multivariate time series classification (MTSC) tasks, which are crucial for various real-world applications. However, the black-box characteristic of deep learning models impedes humans from obtaining insights into the internal regulation and decisions made by classifiers. Existing explainability research generally requires constructing separate explanation models to work with deep learning models or process their results, thus calling for additional development efforts. We propose a novel explanation module pluggable into existing deep neural networks to explore variable importance for explaining MTSC. We evaluate our module with popular deep neural networks on both real-world and synthetic datasets to demonstrate its effectiveness in generating explanations for MTSC. Our experiments also show the module improves the classification accuracy of existing models due to the comprehensive incorporation of temporal features.
Yang, C, Wang, X, Yao, L, Jiang, J & Xu, G 1970, 'Pluggable Explanation for Deep Neural Networks-based Multivariate Time Series Classification', Australasian Joint Conference on Artificial Intelligence, Australasian Joint Conference on Artificial Intelligence, hybrid (Sydney, online).
Yang, C, Wang, X, Yao, L, Long, G, Jiang, J & Xu, G 1970, 'Attentional Gated Res2net for Multivariate Time Series Classification', ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Singapore.
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Yang, H, Chen, H, Pan, S, Li, L, Yu, PS & Xu, G 1970, 'Dual Space Graph Contrastive Learning', Proceedings of the ACM Web Conference 2022, WWW '22: The ACM Web Conference 2022, ACM, pp. 1238-1247.
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Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain. Graph contrastive learning is one of the unsupervised graph representation learning methods, which recently attracts attention from researchers and has achieved state-of-the-art performances on various tasks. The key to the success of graph contrastive learning is to construct proper contrasting pairs to acquire the underlying structural semantics of the graph. However, this key part is not fully explored currently, most of the ways generating contrasting pairs focus on augmenting or perturbating graph structures to obtain different views of the input graph. But such strategies could degrade the performances via adding noise into the graph, which may narrow down the field of the applications of graph contrastive learning. In this paper, we propose a novel graph contrastive learning method, namely Dual Space Graph Contrastive (DSGC) Learning, to conduct graph contrastive learning among views generated in different spaces including the hyperbolic space and the Euclidean space. Since both spaces have their own advantages to represent graph data in the embedding spaces, we hope to utilize graph contrastive learning to bridge the spaces and leverage advantages from both sides. The comparison experiment results show that DSGC achieves competitive or better performances among all the datasets. In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.
Yang, Z, Zheng, B, Wang, X, Li, G & Zhou, X 1970, 'minIL: A Simple and Small Index for String Similarity Search with Edit Distance', 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, Kuala Lumpur, Malaysia.
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Yao, Y, Wang, X, Ma, Y, Fang, H, Wei, J, Chen, L, Anaissi, A & Braytee, A 1970, 'Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks', 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp. 1-10.
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Class imbalance occurs in many real-world applications, including image classification, where the number of images in each class differs significantly. With imbalanced data, the generative adversarial networks (GANs) leans to majority class samples. The two recent methods, Balancing GAN (BAGAN) and improved BAGAN (BAGAN-GP), are proposed as an augmentation tool to handle this problem and restore the balance to the data. The former pre-trains the autoencoder weights in an unsupervised manner. However, it is unstable when the images from different categories have similar features. The latter is improved based on BAGAN by facilitating supervised autoencoder training, but the pre-training is biased towards the majority classes. In this work, we propose a novel Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks (CAPGAN) as an augmentation tool to generate realistic synthetic images. In particular, we utilize a conditional convolutional variational autoencoder with supervised and balanced pre-training for the GAN initialization and training with gradient penalty. Our proposed method presents a superior performance of other state-of-the-art methods on the highly imbalanced version of MNIST, Fashion-MNIST, CIFAR-10, and two medical imaging datasets. Our method can synthesize high-quality minority samples in terms of Fréchet inception distance, structural similarity index measure and perceptual quality. The source code is available at https://github.com/alibraytee/CAPGAN.
Yu, D, Li, Q, Wang, X, Wang, Z, Cao, Y & Xu, G 1970, 'Semantics-Guided Disentangled Learning for Recommendation', Advances in Knowledge Discovery and Data Mining, Springer International Publishing, pp. 249-261.
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Although traditional recommendation methods trained on observational interaction information have engendered a significant impact in real-world applications, it is challenging to disentangle users’ true interests from interaction data. Recent disentangled learning methods emphasize on untangling users’ true interests from historical interaction records, which however overlook auxiliary information to correct bias. In this paper, we design a novel method called SeDLR (Semantics Disentangled Learning Recommendation) to bridge this gap. Particularly, by leveraging rich heterogeneous information networks (HIN), SeDLR is capable of untangling high-order user-item relationships into multiple independent components according to their semantic user intents. In addition, SeDLR offers reliable explanations for the disentangled graph embeddings by the designed Monte Carlo edge-drop component. Finally, we conduct extensive experiments on two benchmark datasets and achieve state-of-the-art performance compared against recent strong baselines.
Yue, Z, Guo, P, Zhang, Y & Liang, C 1970, 'Learning Feature Alignment Architecture for Domain Adaptation', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE.
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Yue, Z, Lin, B, Zhang, Y & Liang, C 1970, 'Effective, Efficient and Robust Neural Architecture Search', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE.
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Yuksel, B & Kocaballi, AB 1970, 'Conversational Agents to Support Couple Therapy', Proceedings of the 34th Australian Conference on Human-Computer Interaction, OzCHI '22: 34th Australian Conference on Human-Computer Interaction, ACM, Australian Conference on Human-Computer Interaction.
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Zhang, C, Chen, H, Zhang, S, Xu, G & Gao, J 1970, 'Geometric Inductive Matrix Completion', Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, ACM, pp. 1337-1346.
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Collaborative filtering is a central task in a broad range of recommender systems. As traditional methods train latent variables for user/item individuals under a transductive setting, it requires re-training for out-of-sample inferences. Inductive matrix completion (IMC) solves this problem by learning transformation functions upon engineered features, but it sacrifices model expressiveness and highly depends on feature qualities. In this paper, we propose Geometric Inductive Matrix Completion (GIMC) by introducing hyperbolic geometry and a unified message passing scheme into this generic task. The proposed method is the earliest attempt utilizing capacious hyperbolic space to enhance the capacity of IMC. It is the first work defining continuous explicit feedback prediction within non-Euclidean space by introducing hyperbolic regression for vertex interactions. This is also the first to provide comprehensive evidence that edge semantics can significantly improve recommendations, which is ignored by previous works. The proposed method outperforms the state-of-the-art algorithms with less than 1% parameters compared to its transductive counterparts. Extensive analysis and ablation studies are conducted to reveal the design considerations and practicability for a positive impact to the research community.
Zhang, C, Zhang, S, Yu, S & Yu, JJQ 1970, 'Graph-Based Traffic Forecasting via Communication-Efficient Federated Learning', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Austin, TX, pp. 2041-2046.
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The existing Federated Learning (FL) systems encounter an enormous communication overhead when employing GNN-based models for traffic forecasting tasks since these models commonly incorporate enormous number of parameters to be transmitted in the FL systems. In this paper, we propose a FL framework, namely, C lustering-based hierarchical and T wo-step- optimized FL (CTFL), to overcome this practical problem. CTFL employs a divide-and-conquer strategy, clustering clients based on the closeness of their local model parameters. Furthermore, we incorporate the particle swarm optimization algorithm in CTFL, which employs a two-step strategy for optimizing local models. This technique enables the central server to upload only one representative local model update from each cluster, thus reducing the communication overhead associated with model update transmission in the FL. Comprehensive case studies on two real-world datasets and two state-of-the-art GNN-based models demonstrate the proposed framework's outstanding training efficiency and prediction accuracy, and the hyperparameter sensitivity of CTFL is also investigated.
Zhang, S, Chen, H, Sun, X, Li, Y & Xu, G 1970, 'Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation', Proceedings of the ACM Web Conference 2022, WWW '22: The ACM Web Conference 2022, ACM, pp. 1322-1330.
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Graph contrastive learning is the state-of-the-art unsupervised graph representation learning framework and has shown comparable performance with supervised approaches. However, evaluating whether the graph contrastive learning is robust to adversarial attacks is still an open problem because most existing graph adversarial attacks are supervised models, which means they heavily rely on labels and can only be used to evaluate the graph contrastive learning in a specific scenario. For unsupervised graph representation methods such as graph contrastive learning, it is difficult to acquire labels in real-world scenarios, making traditional supervised graph attack methods difficult to be applied to test their robustness. In this paper, we propose a novel unsupervised gradient-based adversarial attack that does not rely on labels for graph contrastive learning. We compute the gradients of the adjacency matrices of the two views and flip the edges with gradient ascent to maximize the contrastive loss. In this way, we can fully use multiple views generated by the graph contrastive learning models and pick the most informative edges without knowing their labels, and therefore can promisingly support our model adapted to more kinds of downstream tasks. Extensive experiments show that our attack outperforms unsupervised baseline attacks and has comparable performance with supervised attacks in multiple downstream tasks including node classification and link prediction. We further show that our attack can be transferred to other graph representation models as well.
Zhang, S, Fu, J, Zhang, Z, Yu, S, Mao, S & Lin, Y 1970, 'Maximum Focal Inter-Class Angular Loss with Norm Constraint for Automatic Modulation Classification', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE.
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Zhang, Y, Wang, M, Zipperle, M, Abbasi, A & Saberi, M 1970, 'S-index: Significance of Academic Authors to Individual Publication Venues', 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), IEEE.
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Zhang, Z, Fang, M, Chen, L & Namazi Rad, MR 1970, 'Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics', Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, pp. 3886-3893.
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Recent work incorporates pre-trained word embeddings such as BERT embeddings into Neural Topic Models (NTMs), generating highly coherent topics. However, with high-quality contextualized document representations, do we really need sophisticated neural models to obtain coherent and interpretable topics? In this paper, we conduct thorough experiments showing that directly clustering high-quality sentence embeddings with an appropriate word selecting method can generate more coherent and diverse topics than NTMs, achieving also higher efficiency and simplicity.
Zhao, R, Zhao, F, Xu, G, Zhang, S & Jin, H 1970, 'Can Language Models Serve as Temporal Knowledge Bases?', Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 2024-2037.
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Recent progress regarding the use of language models (LMs) as knowledge bases (KBs) has shown that language models can act as structured knowledge bases for storing relational facts. However, most existing works only considered the LM-as-KB paradigm in a static setting, which ignores the analysis of temporal dynamics of world knowledge. Furthermore, a basic function of KBs, i.e., the ability to store conflicting information (i.e., 1-N, N-1 and N-M relations), is underexplored. In this paper, we formulate two practical requirements for treating LMs as temporal KBs: (i) the capacity to store temporally-scoped knowledge that contains conflicting information and (ii) the ability to use stored knowledge for temporally-scoped knowledge queries. We introduce a new dataset called LAMA-TK which is aimed at probing temporally-scoped knowledge, and investigate the two above requirements to explore the LM-as-KB paradigm in the temporal domain. On the one hand, experiments show that LMs can memorize millions of temporally-scoped facts with relatively high accuracy and transfer stored knowledge to temporal knowledge queries, thereby expanding the LM-as-KB paradigm to the temporal domain. On the other hand, we show that memorizing conflicting information, which has been neglected by previous works, is still challenging for LMs and hinders the memorization of other unrelated one-to-one relationships.
Zheng, T, Verma, S & Liu, W 1970, 'Interpretable Binaural Ratio for Visually Guided Binaural Audio Generation', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE.
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Zheng, X & Huo, H 1970, 'Enhancing group polarity of temporal patterns for rumour detection on Twitter', 2022 9th International Conference on Behavioural and Social Computing (BESC), 2022 9th International Conference on Behavioural and Social Computing (BESC), IEEE.
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Zhou, Y, Liu, J, Yang, Z, Liu, T, Meng, X, Zhou, Z, Anaissi, A & Braytee, A 1970, 'VGG-FusionNet: A Feature Fusion Framework from CT scan and Chest X-ray Images based Deep Learning for COVID-19 Detection', 2022 IEEE International Conference on Data Mining Workshops (ICDMW), 2022 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp. 1-9.
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