Abedini, A, Abedin, B & Zowghi, D 2021, 'Adult learning in online communities of practice: A systematic review', British Journal of Educational Technology, vol. 52, no. 4, pp. 1663-1694.
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AbstractAdult learning is a lifelong process whereby knowledge is formed through the transformation of adults' experience. Research on online adult learning has been on the rise in recent years, thanks to the innovative opportunities provided to adults by digital technologies. Online communities of practice (OCOPs) a one of such opportunities, which offer the potential to bring geographically dispersed adult learners together through a common interest. Despite an increased growth in the use of OCOPs by adults in various professional sectors, there is still a lack of understanding of the characteristics of online adult learning in OCOPs, and the facilitators and hinderers influencing engagement in these communities. This paper presents a comprehensive synthesis of research literature on online adult learning in OCOPs to understand its characteristics and what may facilitate or hinder adults' engagement in these communities. A review has been conducted using a systematic, rigorous and standard procedure, aiming to summarise and synthesise existing research on the topic and to provide analytical criticism. In total, thirty‐seven studies were included in this review. Findings revealed that members of OCOPs are independent, experience‐centred, problem‐centred, self‐motivated, goal‐oriented, and lifelong learners with the purpose to achieve professional outcomes. Moreover, the results revealed how the engagement of adults in OCOPs could lead to improving learning processes. Findings also showed that the level of engagement is influenced by aging, fatigue caused by a busy life, resistance process due to learning new technologies, lack of personal evolution, interactive learning settings, motivation, self‐regulation and competition factors. This study revealed facilitators and hinderers of engagement in OCOPs. The study extended andragogy to digital environments and contributes to the theory by making sen...
Aboutorab, H, Hussain, OK, Saberi, M, Hussain, FK & Chang, E 2021, 'A survey on the suitability of risk identification techniques in the current networked environment', Journal of Network and Computer Applications, vol. 178, pp. 102984-102984.
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Ahmad, S, Alnowibet, K, Alqasem, L, Merigo, JM & Zaindin, M 2021, 'Generalized OWA operators for uncertain queuing modeling with application in healthcare', Soft Computing, vol. 25, no. 6, pp. 4951-4962.
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© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature. The weighted averaging operators are one of the popular methods for aggregating information. In recent years, ordered weighted averaging operators (OWA) have attained a great attention by researchers. These OWA operators due to their versatility are very useful to model many real world situations. Several extensions of OWA operators are presented in the literature which can handle a situation with uncertainty. Although many queuing models have been proposed in numerous healthcare studies, the inclusion of OWA operators is still rare. In this research study, we propose a novel method using the uncertain generalized ordered weighted average and illustrate its application to the uncertain queue modeling in a hospital emergency room; where incoming flux of patients and the required level of service for each patient is unknown and uncertain. The model with multilateral decision making process has been described which will provide several alternatives to decision makers to select the best alternative for their challenging situations. The proposed method has resulted an improved performance of the queuing system, increased customer satisfaction as well as a significant reduction in the operational cost. This study will enable decision makers to operate a flexible and cost-effective system in the event of uncertainty, uncontrollable and unpredicted situations.
Ahmadi Choukolaei, H, Jahangoshai Rezaee, M, Ghasemi, P & Saberi, M 2021, 'Efficient Crisis Management by Selection and Analysis of Relief Centers in Disaster Integrating GIS and Multicriteria Decision Methods: A Case Study of Tehran', Mathematical Problems in Engineering, vol. 2021, pp. 1-22.
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In Iran, location is usually done by temporary relief organizations without considering the necessary standards or conditions. The inappropriate and unscientific location may have led to another catastrophe, even far greater than the initial tragedy. In this study, the proposed locations of crisis management in the region and the optimal points proposed by the Geographic Information System (GIS), taking into account the opinions of experts and without the opinion of experts, were evaluated according to 18 criteria. First, the optimal areas have been evaluated according to standard criteria extracted by GIS and the intended locations of the region for accommodation in times of crisis. Then, the position of each place is calculated concerning each criterion. The resulting matrix of optimal options was qualitatively entered into the Preference Ranking Organization Method for Evaluation (PROMETHEE) for analysis. The triangular fuzzy aggregation method for weighting and standard classification of criteria for extracting optimal areas using GIS and integrating entropy and the Multiobjective Optimization Based on Ratio Analysis (MOORA) method for prioritizing places in the region are considered in this research. Finally, by applying constraints and using net input and output flows, optimal and efficient options are identified by PROMETHEE V. Among the research options, only four options were optimal and efficient. A case study of the Tehran metropolis is provided to show the ability of the proposed approach for selecting the points in three modes, with/without applying weights and applying crisis management.
Akella, A, Singh, AK, Leong, D, Lal, S, Newton, P, Clifton-Bligh, R, Mclachlan, CS, Gustin, SM, Maharaj, S, Lees, T, Cao, Z & Lin, C-T 2021, 'Classifying Multi-Level Stress Responses From Brain Cortical EEG in Nurses and Non-Health Professionals Using Machine Learning Auto Encoder', IEEE Journal of Translational Engineering in Health and Medicine, vol. 9, pp. 1-9.
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Objective
Mental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress.
Methods
To overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers.
Results
The results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%.
Alabsi, MI & Gill, AQ 2021, 'A Review of Passenger Digital Information Privacy Concerns in Smart Airports', IEEE Access, vol. 9, no. 99, pp. 33769-33781.
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Alfaro-García, VG, Merigó, JM, Gil-Lafuente, AM & Monge, RG 2021, 'Group-decision making with induced ordered weighted logarithmic aggregation operators', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1761-1772.
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This paper presents the induced generalized ordered weighted logarithmic aggregation (IGOWLA) operator, this operator is an extension of the generalized ordered weighted logarithmic aggregation (GOWLA) operator. It uses order-induced variables that modify the reordering process of the arguments included in the aggregation. The principal advantage of the introduced induced mechanism is the consideration of highly complex attitude from the decision makers. We study some families of the IGOWLA operator as measures for the characterization of the weighting vector. This paper presents the general formulation of the operator and some special cases, including the induced ordered weighted logarithmic geometric averaging (IOWLGA) operator and the induced ordered weighted logarithmic aggregation (IOWLA). Further generalizations using quasi-arithmetic mean are also proposed. Finally, an illustrative example of a group decision-making procedure using a multi-person analysis and the IGOWLA operator in the area of innovation management is analyzed.
Al-Hadhrami, Y & Hussain, FK 2021, 'DDoS attacks in IoT networks: a comprehensive systematic literature review', World Wide Web, vol. 24, no. 3, pp. 971-1001.
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The Internet of Things (IoT) is a rapidly emerging technology in the consumer and industrial market. This technology has the potential to radically transform the consumer experience, as it will change our daily scenes, starting from the way we drink coffee to how smart objects interact with industrial applications. Such rapid development and deployment face multifarious challenges, including the sheer amount of data generated, network scale, network heterogeneity, as well as security and privacy concerns. In recent years, Distributed Denial-of-Service (DDoS) attacks in IoT networks are considered one of the growing challenges that need to be shed light on. DDoS attacks utilize the limited resources in IoT devices, such as storage limitation and network capacity, that cause this issue in the IoT application. This paper comprehensively reviews the attacks that can lead to DDoS, which eventually will cause serious damage to existing systems. Additionally, the paper investigates the available solutions used to counter these attacks and explore their limitations from the perspective of the constrained device. Furthermore, a detailed analysis of the existing solution placement was implemented, including heterogeneity and their performance for IoT based networks. Finally, the paper will reveal and discuss interesting research direction on the future IoT security and current trends.
Alkalbani, AM & Hussain, W 2021, 'Cloud service discovery method: A framework for automatic derivation of cloud marketplace and cloud intelligence to assist consumers in finding cloud services', International Journal of Communication Systems, vol. 34, no. 8, pp. 1-17.
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SummaryThe increase in the number of cloud services advertisements, needs for cloud services marketplace to enable significant interaction with cloud consumers. Majority of the existing literature has focused on developing algorithms (such as matching algorithms) and assumed the availability of cloud service information. Furthermore, little attention is given to the efficient discovery of cloud services over the internet. Existing approaches unable to describe a user‐friendly method of harvesting related cloud services from the web. Moreover, the existing literature lacks a comprehensive ontology to represent cloud services and a registry for cloud services publication and discovery. The incomplete information prevents discovering accurate services and deriving intelligence from cloud reviews data. The paper presents a framework for automatic derivation of cloud marketplace and cloud intelligence (ADCM&CI) that assist cloud consumers for an effective and efficient cloud service discovery. The framework depends on the capabilities of the Harvester as a Service (HaaS) crawler that provides a user‐friendly interface to extract real‐time cloud dataset. The paper used Protégé OWL a domain‐specific ontology to extract meaningful data from a semi‐structured repository and transform to SaaS ads attribute. The framework conducts sentimental analysis to excerpt the polarity of reviews that assist potential consumers in service selection. The paper considers three measures—precision, recall and F Score as a benchmark and evaluates the accuracy of the proposed approach using machine learning methods—SVM, KNN, Decision Tree and Naïve Bayes algorithms. Through experiments, we validate and demonstrate the suitability of the proposed framework for an effective and efficient cloud service discovery.
Al-Najjar, HAH & Pradhan, B 2021, 'Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks', Geoscience Frontiers, vol. 12, no. 2, pp. 625-637.
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In recent years, landslide susceptibility mapping has substantially improved with advances in machine learning. However, there are still challenges remain in landslide mapping due to the availability of limited inventory data. In this paper, a novel method that improves the performance of machine learning techniques is presented. The proposed method creates synthetic inventory data using Generative Adversarial Networks (GANs) for improving the prediction of landslides. In this research, landslide inventory data of 156 landslide locations were identified in Cameron Highlands, Malaysia, taken from previous projects the authors worked on. Elevation, slope, aspect, plan curvature, profile curvature, total curvature, lithology, land use and land cover (LULC), distance to the road, distance to the river, stream power index (SPI), sediment transport index (STI), terrain roughness index (TRI), topographic wetness index (TWI) and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands. To show the capability of GANs in improving landslide prediction models, this study tests the proposed GAN model with benchmark models namely Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF) and Bagging ensemble models with ANN and SVM models. These models were validated using the area under the receiver operating characteristic curve (AUROC). The DT, RF, SVM, ANN and Bagging ensemble could achieve the AUROC values of (0.90, 0.94, 0.86, 0.69 and 0.82) for the training; and the AUROC of (0.76, 0.81, 0.85, 0.72 and 0.75) for the test, subsequently. When using additional samples, the same models achieved the AUROC values of (0.92, 0.94, 0.88, 0.75 and 0.84) for the training and (0.78, 0.82, 0.82, 0.78 and 0.80) for the test, respectively. Using the additional samples improved the test accuracy of all the models except SVM. As a result, in data-scarce e...
Al-Najjar, HAH, Pradhan, B, Kalantar, B, Sameen, MI, Santosh, M & Alamri, A 2021, 'Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation', Remote Sensing, vol. 13, no. 16, pp. 3281-3281.
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Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has witnessed considerable improvement following advances in machine learning (ML) techniques. However, in most of the previous studies, the distribution of input data was assumed as being, and treated, as normal or Gaussian; this assumption is not always valid as ML is heavily dependent on the quality of the input data. Therefore, we examine the effectiveness of six feature transformations (minimax normalization (Std-X), logarithmic functions (Log-X), reciprocal function (Rec-X), power functions (Power-X), optimal features (Opt-X), and one-hot encoding (Ohe-X) over the 11conditioning factors (i.e., altitude, slope, aspect, curvature, distance to road, distance to lineament, distance to stream, terrain roughness index (TRI), normalized difference vegetation index (NDVI), land use, and vegetation density). We selected the frequent landslide-prone area in the Cameron Highlands in Malaysia as a case study to test this novel approach. These transformations were then assessed by three benchmark ML methods, namely extreme gradient boosting (XGB), logistic regression (LR), and artificial neural networks (ANN). The 10-fold cross-validation method was used for model evaluations. Our results suggest that using Ohe-X transformation over the ANN model considerably improved performance from 52.244 to 89.398 (37.154% improvement).
Al-Najjar, HAH, Pradhan, B, Sarkar, R, Beydoun, G & Alamri, AM 2021, 'A New Integrated Approach for Landslide Data Balancing and Spatial Prediction Based on Generative Adversarial Networks (GAN).', Remote. Sens., vol. 13, pp. 4011-4011.
Aloqaily, AA, Tafavogh, S, Harvey, BL, Catchpoole, DR & Kennedy, PJ 2021, 'Feature prioritisation on big genomic data for analysing gene-gene interactions', International Journal of Bioinformatics Research and Applications, vol. 17, no. 2, pp. 158-158.
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Complex diseases are not caused by single genes but result from intricate non-linear interactions among them. There is a critical need to implement approaches that take into account non-linear gene-gene interactions in searching for markers that jointly cause diseases. Determining the interaction between more than two single nucleotide polymorphisms (SNP) within the whole genome data is computationally expensive and often infeasible. In this paper, we develop an approach to classify patients with Acute Lymphoblastic Leukaemia by analysing multiple SNP interactions. A novel feature prioritisation algorithm called interaction effect quantity (IEQ) selects SNPs with high potential of interaction by analysing their distribution throughout the genomic data and enables deeper analysis of non-linear interactions within large datasets. We show that IEQ enables analyses of interactions between up to four SNPs, with F-measure for classification greater than 89% obtained. Such an analysis is typically much more computationally challenging if IEQ is not implemented.
Altulyan, M, Yao, L, Huang, C, Wang, X & Kanhere, SS 2021, 'Context-Induced Activity Monitoring for On-Demand Things-of-Interest Recommendation in an Ambient Intelligent Environment', Future Internet, vol. 13, no. 12, pp. 305-305.
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Recommendation systems are crucial in the provision of services to the elderly with Alzheimer’s disease in IoT-based smart home environments. In this work, a Reminder Care System (RCS) is presented to help Alzheimer patients live in and operate their homes safely and independently. A contextual bandit approach is utilized in the formulation of the proposed recommendation system to tackle dynamicity in human activities and to construct accurate recommendations that meet user needs without their feedback. The system was evaluated based on three public datasets using a cumulative reward as a metric. Our experimental results demonstrate the feasibility and effectiveness of the proposed Reminder Care System for real-world IoT-based smart home applications.
Alzoubi, Y & Gill, A 2021, 'The Critical Communication Challenges Between Geographically Distributed Agile Development Teams: Empirical Findings', IEEE Transactions on Professional Communication, vol. 64, no. 4, pp. 322-337.
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Background: Although a number of empirical studies have investigated communication challenges during recent years, we still need to discover the most critical challenges that face communication when agile development is geographically distributed. We also need to discover how successful geographically distributed agile development (GDAD) organizations deal with these challenges. Literature review: Most previous studies reported that the critical challenges facing GDAD communication can be categorized into five themes: differences in cultures, different time zones, different spoken languages, different personal skills, and the efficiency and effectiveness of communication tools used. Research questions: 1. What are the challenges of communication between GDAD teams? 2. How can the impact of GDAD communication challenges be mitigated? Methodology: Data were collected by interviewing 12 members of a three-team organization using distributed agile development. These teams are distributed over three countries; the main team located in Australia, the developers' team located in China, and the testers' team located in India. A thematic analysis technique was used to identify communication challenges and practices used to mitigate the effect of these challenges. Results: Our findings reveal that the five challenges are still critical to GDAD. Moreover, we report a new critical challenge of communication in GDAD, the insufficient documentation provided by distributed teams and members. In addition, we recommend several practices to mitigate the impact of these challenges. Conclusions: Communication among distributed agile development teams still faces several critical challenges, and the solutions to these challenges provided in recent years have not been sufficient. This fact prompts the need for more research on how the impact of these challenges can be lessened.
An, Y, Wang, J, Lu, H & Zhao, W 2021, 'Research of a combined wind speed model based on multi‐objective ant lion optimization algorithm', International Transactions on Electrical Energy Systems, vol. 31, no. 12.
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Anjum, M, Voinov, A, Taghikhah, F & Pileggi, SF 2021, 'Discussoo: Towards an intelligent tool for multi-scale participatory modeling', Environmental Modelling & Software, vol. 140, pp. 105044-105044.
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Anwar, MJ, Gill, AQ, Hussain, FK & Imran, M 2021, 'Secure big data ecosystem architecture: challenges and solutions.', EURASIP J. Wirel. Commun. Netw., vol. 2021, pp. 130-130.
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Ashraf, A, Naz, S, Shirazi, SH, Razzak, I & Parsad, M 2021, 'Deep transfer learning for alzheimer neurological disorder detection', Multimedia Tools and Applications, vol. 80, no. 20, pp. 30117-30142.
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Atiqah Rochin Demong, N, Lu, J & Khadeer Hussain, F 2021, 'An Adaptive Personalized Property Investment Risk Analysis Method Based on Data-Driven Approach', International Journal of Information Technology & Decision Making, vol. 20, no. 02, pp. 671-706.
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Risk assessment analysis for investment decisions largely depends on expert judgment using traditional approaches and is lacking in considering investors’ different preferences and limitations. This paper proposes an adaptive personalized property investment risk analysis (APPIRA) method to identify the property investment determinants using a data-driven and personalized approach to weight the risk factors using the multicriteria decision model for optimal solutions. Result for predictive modeling using value prediction technique that measures the median house price depicts that the best method used was nonseasonal ARIMA. Furthermore, classification technique indicates that in each of the three selected suburbs, different property characteristics determined the rental properties desirable. As shown in result, for the investors who plan to invest in property for rental purposes, they need to choose townhouse type or property to make it rentable while for Vaucluse, terrace houses. These results can be applied into practice and will benefit the property industry directly.
Avilés-Ochoa, E, Flores-Sosa, M & Merigó, JM 2021, 'A bibliometric overview of volatility', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1997-2009.
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Price volatility is a matter of importance for making decisions in the finance world. The growing studies regarding volatility have focused on minimizing the risks through modeling, estimating and forecasting. This paper presents a bibliometric overview of the most important authors, institutions and countries that work on the topic. Additionally, a historical analysis of how the agents have interrelated is presented. For the purposes of the analysis and the design of tables and graphics, tools from the Web of Science Core Collection and the VOSviewer software were used. The results show the importance of volatility in the study of business economics and decision making.
Bai, L, Yao, L, Wang, X, Li, C & Zhang, X 2021, 'Deep spatial–temporal sequence modeling for multi-step passenger demand prediction', Future Generation Computer Systems, vol. 121, pp. 25-34.
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Baier-Fuentes, H, Merigó, J, Miranda, L & Martínez-López, F 2021, 'Strategic planning research through fifty years of long range planning: A bibliometric overview', Strategic Management, vol. 26, no. 1, pp. 3-25.
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Long Range Planning (LRP) is the first journal focused on strategic planning. It was created in 1968 by the Long Range Planning Society, and it celebrated its 50 th anniversary in 2018. This event led to the presentation of a complete bibliometric study aimed at identifying the most significant results that occurred in the journal during this period. For this purpose, bibliometric data were collected from the Web of Science Core Collection database, and two bibliometric approaches were used to analyze the journal's publications: a performance analysis and a graphical mapping of the literature. The first of these uses a wide range of productivity and influence indicators that include the number of publications and citations, the h-index, and citations by paper, among others. The second approach uses the VOSviewer software to deliver a graphical view of the various intellectual connections within LRP. The results of both bibliometric approaches are consistent and confirm LRP as a leading journal in strategic planning and management, with increasing participation of authors and universities from countries around the world.
Bano, M, Zowghi, D & Arora, C 2021, 'Requirements, Politics, or Individualism: What Drives the Success of COVID-19 Contact-Tracing Apps?', IEEE Softw., vol. 38, no. 1, pp. 7-12.
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The year 2020 brought us the global pandemic of COVID-19, which is not just a health crisis but a disruption to the fabric of society around the world. With no vaccine yet approved, other measures have been taken all over the world related to lockdowns, social distancing, and contact tracing to quarantine the infected individuals and suppress community transmission. The numerous challenges presented by this novel coronavirus, such as the incubation period, various symptoms, and asymptomatic superspreaders, have exacerbated the challenges of manual contact tracing.
Barolli, L, Hussain, F & Takizawa, M 2021, 'Special issue on intelligent Edge, Fog, Cloud and Internet of Things (IoT)-based services', Computing, vol. 103, no. 3, pp. 357-360.
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Bei, X, Chen, S, Guan, J, Qiao, Y & Sun, X 2021, 'From Independent Sets and Vertex Colorings to Isotropic Spaces and Isotropic Decompositions: Another Bridge between Graphs and Alternating Matrix Spaces', SIAM Journal on Computing, vol. 50, no. 3, pp. 924-971.
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In the 1970s, Lovász built a bridge between graphs and alternating matrix spaces, in the context of perfect matchings [Proceedings of FCT, 1979, pp. 565-574]. A similar connection between bipartite graphs and matrix spaces plays a key role in the recent resolutions of the noncommutative rank problem [A. Garg et al., Proceedings of FOCS, 2016, pp. 109-117; G. Ivanyos, Y. Qiao, and K. V. Subrahmanyam, Comput. Complexity, 26 (2017), pp. 717-763]. In this paper, we lay the foundation for another bridge between graphs and alternating matrix spaces, in the context of independent sets and vertex colorings. The corresponding structures in alternating matrix spaces are isotropic spaces and isotropic decompositions, both useful structures in group theory and manifold theory. We first show that the maximum independent set problem and the vertex c-coloring problem reduce to the maximum isotropic space problem and the isotropic c-decomposition problem, respectively. Next, we show that several topics and results about independent sets and vertex colorings have natural correspondences for isotropic spaces and decompositions. These include algorithmic problems, such as the maximum independent set problem for bipartite graphs, and exact exponential-time algorithms for the chromatic number, as well as mathematical questions, such as the number of maximal independent sets, and the relation between the maximum degree and the chromatic number. These connections lead to new interactions between graph theory and algebra. Some results have concrete applications to group theory and manifold theory, and we initiate a variant of these structures in the context of quantum information theory. Finally, we propose several open questions for further exploration.
Béjanin, JH, Earnest, CT, Sanders, YR & Mariantoni, M 2021, 'Resonant Coupling Parameter Estimation with Superconducting Qubits', PRX Quantum, vol. 2, no. 4, pp. 1-18.
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Today’s quantum computers are composed of tens of qubits interacting with each other and the environment in increasingly complex networks. To achieve the best possible performance when operating such systems, it is necessary to have accurate knowledge of all parameters in the quantum computer Hamiltonian. In this paper, we demonstrate theoretically and experimentally a method to efficiently learn the parameters of resonant interactions for quantum computers consisting of frequency-tunable superconducting qubits. Such interactions include, for example, those with other qubits, resonators, two-level systems, or other wanted or unwanted modes. Our method is based on a significantly improved swap spectroscopy calibration and consists of an offline data collection algorithm, followed by an online Bayesian learning algorithm. The purpose of the offline algorithm is to detect and coarsely estimate resonant interactions from a state of zero knowledge. It produces a quadratic speedup in the scaling of the number of measurements. The online algorithm subsequently refines the estimate of the parameters to accuracy comparable with that of traditional swap spectroscopy calibration but in constant time. We perform an experiment implementing our technique with a superconducting qubit. By combining both algorithms, we observe a reduction of the calibration time by 1 order of magnitude. Our method will improve present medium-scale superconducting quantum computers and will also scale up to larger systems. Finally, the two algorithms presented here can be readily adopted by communities working on different physical implementations of quantum computing architectures.
Bore, JC, Li, P, Jiang, L, Ayedh, WMA, Chen, C, Harmah, DJ, Yao, D, Cao, Z & Xu, P 2021, 'A Long Short-Term Memory Network for Sparse Spatiotemporal EEG Source Imaging', IEEE Transactions on Medical Imaging, vol. 40, no. 12, pp. 3787-3800.
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EEG inverse problem is underdetermined, which poses a long standing challenge in Neuroimaging. The combination of source-imaging and analysis of cortical directional networks enables us to noninvasively explore the underlying neural processes. However, existing EEG source imaging approaches mainly focus on performing the direct inverse operation for source estimation, which will be inevitably influenced by noise and the strategy used to find the inverse solution. Here, we develop a new source imaging technique, Deep Brain Neural Network (DeepBraiNNet), for robust sparse spatiotemporal EEG source estimation. In DeepBraiNNet, considering that Recurrent Neural Network (RNN) are usually 'deep' in temporal dimension and thus suitable for time sequence modelling, the RNN with Long Short-Term Memory (LSTM) is utilized to approximate the inverse operation for the lead field matrix instead of performing the direct inverse operation, which avoids the possible effect of the direct inverse operation on the underdetermined lead field matrix prone to be influenced by noise. Simulations on various source patterns and noise conditions confirmed that the proposed approach could actually recover the spatiotemporal sources well, outperforming existing state of-the-art methods. DeepBraiNNet also estimated sparse MI related activation patterns when it was applied to a real Motor Imagery dataset, consistent with other findings based on EEG and fMRI. Based on the spatiotemporal sources estimated from DeepBraiNNet, we constructed MI related cortical neural networks, which clearly exhibited strong contralateral network patterns for the two MI tasks. Consequently, DeepBraiNNet may provide an alternative way different from the conventional approaches for spatiotemporal EEG source imaging.
Boroon, L, Abedin, B & Erfani, E 2021, 'The Dark Side of Using Online Social Networks', Journal of Global Information Management, vol. 29, no. 6, pp. 1-21.
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Research on online social networks (OSNs) has focused overwhelmingly on their benefits and potential, with their negative effects overlooked. This study builds on the limited existing work on the so-called ‘dark side’ of using OSNs. The authors conducted a systematic review of selected databases and identified 46 negative effects of using OSNs from the users’ perspective, which is a rich spectrum of users’ negative experiences. This article then proposed nomenclature and taxonomy for the dark side of using OSNs by grouping these negative effects into six themes: cost of social exchange, cyberbullying, low performance, annoying content, privacy concerns and security threats. This study then conducted structured interviews with experts to confirm the sense-making and validity of the proposed taxonomy. This study discusses the confirmed taxonomy and outlines directions for future research.
Bui, HT, Hussain, OK, Saberi, M & Hussain, F 2021, 'Assessing the authenticity of subjective information in the blockchain: a survey and open issues', World Wide Web, vol. 24, no. 2, pp. 483-509.
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Cao, Z, John, AR, Chen, H-T, Martens, KE, Georgiades, M, Gilat, M, Nguyen, HT, Lewis, SJG & Lin, C-T 2021, 'Identification of EEG Dynamics During Freezing of Gait and Voluntary Stopping in Patients With Parkinson’s Disease', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1774-1783.
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Mobility is severely impacted in patients with Parkinson's disease (PD), who often experience involuntary stopping from the freezing of gait (FOG). Understanding the neurophysiological difference between “voluntary stopping” and “involuntary stopping” caused by FOG is vital for the detection of and potential intervention for FOG in the daily lives of patients. This study characterised the electroencephalographic (EEG) signature associated with FOG in contrast to voluntary stopping. The protocol consisted of a timed up-and-go (TUG) task and an additional TUG task with a voluntary stopping component, where participants reacted to verbal “stop” and “walk” instructions by voluntarily stopping or walking. Event-related spectral perturbation (ERSP) analysis was performed to study the dynamics of the EEG spectra induced by different walking phases, including normal walking, voluntary stopping and episodes of involuntary stopping (FOG), as well as the transition windows between normal walking and voluntary stopping or FOG. These results demonstrate for the first time that the EEG signal during the transition from walking to voluntary stopping is distinguishable from that during the transition to involuntary stopping caused by FOG. The EEG signature of voluntary stopping exhibits a significantly decreased power spectrum compared with that of FOG episodes, with distinctly different patterns in the delta and low-beta power in the central area. These findings suggest the possibility of a practical EEG-based tool that can accurately predict FOG episodes, excluding the potential confounding of voluntary stopping.
Cao, Z, Lin, C-T, Deng, Y & Weber, G-W 2021, 'Guest Editorial: Fuzzy Systems Toward Human-Explainable Artificial Intelligence and Their Applications', IEEE Transactions on Fuzzy Systems, vol. 29, no. 12, pp. 3577-3578.
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Cao, Z, Wong, K & Lin, C-T 2021, 'Weak Human Preference Supervision for Deep Reinforcement Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 12, pp. 5369-5378.
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The current reward learning from human preferences could be used to resolve complex reinforcement learning (RL) tasks without access to a reward function by defining a single fixed preference between pairs of trajectory segments. However, the judgment of preferences between trajectories is not dynamic and still requires human input over thousands of iterations. In this study, we proposed a weak human preference supervision framework, for which we developed a human preference scaling model that naturally reflects the human perception of the degree of weak choices between trajectories and established a human-demonstration estimator through supervised learning to generate the predicted preferences for reducing the number of human inputs. The proposed weak human preference supervision framework can effectively solve complex RL tasks and achieve higher cumulative rewards in simulated robot locomotion—MuJoCo games—relative to the single fixed human preferences. Furthermore, our established human-demonstration estimator requires human feedback only for less than 0.01% of the agent’s interactions with the environment and significantly reduces the cost of human inputs by up to 30% compared with the existing approaches. To present the flexibility of our approach, we released a video ( https://youtu.be/jQPe1OILT0M ) showing comparisons of the behaviors of agents trained on different types of human input. We believe that our naturally inspired human preferences with weakly supervised learning are beneficial for precise reward learning and can be applied to state-of-the-art RL systems, such as human-autonomy teaming systems.
Cetindamar Kozanoglu, D & Abedin, B 2021, 'Understanding the role of employees in digital transformation: conceptualization of digital literacy of employees as a multi-dimensional organizational affordance', Journal of Enterprise Information Management, vol. 34, no. 6, pp. 1649-1672.
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PurposeMuch of recent academic and professional interest in exploring digital transformation and enterprise systems has focused on the technology or the organizations' external forces, leaving internal factors, in particular employees, overlooked. The purpose of this paper is to explore digital literacy of employees as an organizational affordance to capture contextual factors within which digital technologies are situated and are used.Design/methodology/approachWe used the evidence-based practice for information systems approach, and undertook a systematic literature review of 30 papers coupled with brainstorming with 11 professional experts on the neglected topic of digital literacy and its assessment.FindingsThis paper draws upon affordance theory, and develops a novel framework for conceptualization of digital literacy of employees as an organizational affordance. We do this by distinguishing digital literacy at the individual level and organizational level, and by assessing digital literacy through Information/Cognitive and Social Practice/Articulation affordances.Research limitations/implicationsThe current paper contributes to the notion of organizational affordances by examining the effect of interactions between employee-technology through digital literacy of employees in using digital technologies. We offer a novel conceptualization of digital literacy to improve understanding of the role of employee in digital transformation and utilization of enterprise systems. Thus, our definition of digital literacy offers an extension to the recent discussions in the IS literature regardin...
Cetindamar, D, Katic, M, Burdon, S & Gunsel, A 2021, 'The Interplay among Organisational Learning Culture, Agility, Growth, and Big Data Capabilities', Sustainability, vol. 13, no. 23, pp. 13024-13024.
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This paper examines how an organisational learning culture impacts organisational agility by developing a model based on dynamic capabilities. The model treats agility as a dynamic capability and explains how an organisational learning culture (OLC) triggers a chain reaction through its influence on organisational agility (OA) that ultimately results in company growth. This paper also investigates the role of big data capabilities in transferring learning outcomes into dynamic capabilities. The model is tested through data collected from a survey of 138 Australian companies. Partial least squares structural equation modeling is adopted to empirically demonstrate how agility fully mediates the impact of the learning culture on growth. In addition, this paper further sheds light on the moderating role of big data competencies on the effects of OLC on OA. After presenting the results with implications to theory and practice, the paper ends with suggestions for future studies.
Cetindamar, D, Lammers, T, Kocaoglu, DF & Zhang, Y 2021, 'The Anniversary Tribute of PICMET: 1989-2018.', IEEE Trans. Engineering Management, vol. 68, no. 2, pp. 612-627.
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The Portland International Conference for Management
of Engineering and Technology (PICMET) has become a
world-leading organization in the field of management of engineering
and technology management (MET) since its inception in
1989. PICMET provides a strong platform for academics, industry
professionals, and government representatives to exchange new
knowledge in the field. To celebrate its 30-year journey, this article
examines 20 conferences organized by PICMET covering 6601
accepted papers in order to show the trends in MET research and
implementation through topics, authors, journals, and countries. In
addition to the analysis of the PICMET data, the article delves into
the past ten years (2009–2018) to carry out an in-depth bibliometric
analysis of the citations of more than 3000 PICMET papers available
at Scopus. The detailed analysis sheds light on how PICMET
has developed a rich network of researchers and practitioners
through its conferences over time. PICMET contributes to the
interdisciplinary nature of the MET field and is also affected by
the changes of the field. The article ends with key observations and
a few suggestions for further studies.
Chai, M, Moradi, S, Erfani, E, Asadnia, M, Chen, V & Razmjou, A 2021, 'Application of Machine Learning Algorithms to Estimate Enzyme Loading, Immobilization Yield, Activity Retention, and Reusability of Enzyme–Metal–Organic Framework Biocatalysts', Chemistry of Materials, vol. 33, no. 22, pp. 8666-8676.
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Chang, Y-C, Shi, Y, Dostovalova, A, Cao, Z, Kim, J, Gibbons, D & Lin, C-T 2021, 'Interpretable Fuzzy Logic Control for Multirobot Coordination in a Cluttered Environment', IEEE Transactions on Fuzzy Systems, vol. 29, no. 12, pp. 3676-3685.
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Mobile robot navigation is an essential problem in robotics. We propose a method for constructing and training fuzzy logic controllers (FLCs) to coordinate a robotic team performing collision-free navigation and arriving simultaneously at a target location in an unknown environment. Our FLCs are organized in a multilayered architecture to reduce the number of tunable parameters and improve the scalability of the solution. In addition, in contrast to simple traditional switching mechanisms between target seeking and obstacle avoidance, we develop a novel rule-embedded FLC to improve the navigation performance. Moreover, we design a grouping and merging mechanism to obtain transparent fuzzy sets and integrate this mechanism into the training process for all FLCs, thus increasing the interpretability of the fuzzy models. We train the proposed FLCs using a novel multiobjective hybrid approach combining a genetic algorithm and particle swarm optimization. Simulation results demonstrate the effectiveness of our algorithms in reliably solving the proposed navigation problem.
Chang, Y-C, Wang, Y-K, Pal, NR & Lin, C-T 2021, 'Exploring Covert States of Brain Dynamics via Fuzzy Inference Encoding', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 2464-2473.
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Human brain inherently exhibits latent mental processes which are likely to change rapidly over time. A framework that adopts a fuzzy inference system is proposed to model the dynamics of the human brain. The fuzzy inference system is used to encode real-world data to represent the salient features of the EEG signals. Then, an unsupervised clustering is conducted on the extracted feature space to identify the brain (external and covert) states that respond to different cognitive demands. To understand the human state change, a state transition diagram is introduced, allowing visualization of connectivity patterns between every pair of states. We compute the transition probability between every pair of states to represent the relationships between the states. This state transition diagram is named as the Fuzzy Covert State Transition Diagram (FCOSTD), which helps the understanding of human states and human performance. We then apply FCOSTD on distracted driving experiments. FCOSTD successfully discovers the external and covert states, faithfully reveals the transition of the brain between states, and the route of the state change when humans are distracted during a driving task. The experimental results demonstrate that different subjects have similar states and inter-state transition behaviour (establishing the consistency of the system) but different ways to allocate brain resources as different actions are being taken.
Chen, C-S, Chen, S-K, Lai, C-C & Lin, C-T 2021, 'Sequential Motion Primitives Recognition of Robotic Arm Task via Human Demonstration Using Hierarchical BiLSTM Classifier', IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 502-509.
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IEEE Learning from demonstration (LfD) is an intuitive teaching technology without extensive programming for an operator. In recently LfD research, machine vision is usually used to capture the human-robot interaction. However, it's not reliable during the machining process. In this paper, a novel intuitive high-level kinesthetic teaching technology is proposed by reconstructing the recorded motion information during a human-guided robotic arm. A hierarchical BiLSTM-based machine learning algorithm is proposed in this paper to recognize and segment motion primitives according to the therblig definition. The hybrid sensing interface is used to record and extract the motion features, the velocity profile, force/torque, and gripper information. The motion features, output data via the hybrid sensing interface, are finally used to classify into the target motion primitive by the proposed classifier. The experimental results and comparisons with the state-of-the-art algorithm show that the proposed method can correctly and efficiently synthesize the recorded motion features into a motion primitive sequence. Finally, the recognition results of real-world tasks show that the proposed algorithm can be used to reconstruct the human-guided task and further used to robot command for the KUKA robot. The experimental results of the reconstructed trajectory show that a real-world task can represent and maintain the accuracy in 2.37 mm using the proposed algorithm.
Chen, J, Wu, D, Zhao, Y, Sharma, N, Blumenstein, M & Yu, S 2021, 'Fooling intrusion detection systems using adversarially autoencoder', Digital Communications and Networks, vol. 7, no. 3, pp. 453-460.
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Due to the increasing cyber-attacks, various Intrusion Detection Systems (IDSs) have been proposed to identify network anomalies. Most existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows, and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network flows. Although having been used in the real world widely, the above methods are vulnerable to some types of attacks. In this paper, we propose a novel attack framework, Anti-Intrusion Detection AutoEncoder (AIDAE), to generate features to disable the IDS. In the proposed framework, an encoder transforms features into a latent space, and multiple decoders reconstruct the continuous and discrete features, respectively. Additionally, a generative adversarial network is used to learn the flexible prior distribution of the latent space. The correlation between continuous and discrete features can be kept by using the proposed training scheme. Experiments conducted on NSL-KDD, UNSW-NB15, and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.
Chen, S, Fu, A, Yu, S, Ke, H & Su, M 2021, 'DP-QIC: A differential privacy scheme based on quasi-identifier classification for big data publication', Soft Computing, vol. 25, no. 11, pp. 7325-7339.
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With the advent of the era of big data, data privacy protection has become a valuable topic in the field of data publication. Unfortunately, traditional methods of privacy protection, k-anonymity, and its extensions are not absolutely secure as an adversary with background knowledge can determine the owner of a record. The emergence of differential privacy provides a reasonable alternative for privacy security, but the existing solutions ignore the correlation between sensitive attributes and other attributes. In this paper, we propose a new differential privacy scheme based on quasi-identifier classification for big data publication (DP-QIC). It is a new data publishing scheme based on the obfuscation of attribute correlation. We innovatively present quasi-identifier classification based on sensitive attributes and the privacy ratio for evaluating the data set vulnerability. DP-QIC achieves data privacy-protecting through four steps: data collection, grouping and shuffling, generalization, merging, and noise adding, which retains the overall statistical characteristics of the data set. Moreover, the exponential mechanism and the Laplace mechanism are integrated to ensure higher flexibility and a stronger level of privacy protection, so DP-QIC can be used for privacy processing of different data groups in future development. Finally, we have compared the performance of our scheme with the other two famous schemes in the industry. Experimental results demonstrate that DP-QIC has obvious advantages in data utility, privacy protection, and processing efficiency.
Chen, S, Wang, W, Xia, B, You, X, Peng, Q, Cao, Z & Ding, W 2021, 'CDE-GAN: Cooperative Dual Evolution-Based Generative Adversarial Network', IEEE Transactions on Evolutionary Computation, vol. 25, no. 5, pp. 986-1000.
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Cheng, EJ, Prasad, M, Yang, J, Zheng, DR, Tao, X, Mery, D, Young, KY & Lin, CT 2021, 'A novel online self-learning system with automatic object detection model for multimedia applications', Multimedia Tools and Applications, vol. 80, no. 11, pp. 16659-16681.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. This paper proposes a novel online self-learning detection system for different types of objects. It allows users to random select detection target, generating an initial detection model by selecting a small piece of image sample and continue training the detection model automatically. The proposed framework is divided into two parts: First, the initial detection model and the online reinforcement learning. The detection model is based on the proportion of users of the Haar-like features to generate feature pool, which is used to train classifiers and get positive-negative (PN) classifier model. Second, as the videos plays, the detecting model detects the new sample by Nearest Neighbor (NN) Classifier to get the PN similarity for new model. Online reinforcement learning is used to continuously update classifier, PN model and new classifier. The experiment shows the result of less detection sample with automatic online reinforcement learning is satisfactory.
Cheng, X, Liu, Z, Du, X, Yu, S & Mostarda, L 2021, 'IEEE Access Special Section Editorial: Security and Privacy in Emerging Decentralized Communication Environments', IEEE Access, vol. 9, pp. 68880-68887.
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Chiang, YK, Quan, L, Peng, Y, Sepehrirahnama, S, Oberst, S, Alù, A & Powell, DA 2021, 'Scalable Metagrating for Efficient Ultrasonic Focusing', Physical Review Applied, vol. 16, no. 6, pp. 1-9.
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Acoustic metalenses have been pursued over the past decades due to their pivotal role in a wide variety of applications. Recent research efforts have demonstrated that, at ultrasonic regimes, acoustic levitation can be realized with standing waves, which are created by the interference between incoming and reflected focused waves. However, the conventional gradient-metasurface approach to focus ultrasonic waves is complex, leading to poor scalability. In this work, we propose a design principle for ultrasonic metalenses, based on metagratings - arrays of discrete scatters with coarser features than gradient metasurfaces. We achieve beam focusing by locally controlling the excitation of a single diffraction order with the use of metagratings, with geometry adiabatically varying over the lens aperture. We show that our metalens can effectively focus impinging ultrasonic waves to a focal point with a full width at half maximum of 0.364 of the wavelength. The focusing performance of the metalens is demonstrated experimentally, validating our proposed approach. This metagrating approach to focusing can be adopted for different operating frequencies by scaling the size of the structure, which has coarse features suitable for high-frequency designs, with potential applications ranging from biomedical science to nondestructive testing.
Chivukula, AS, Yang, X, Liu, W, Zhu, T & Zhou, W 2021, 'Game Theoretical Adversarial Deep Learning With Variational Adversaries', IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 11, pp. 3568-3581.
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Chou, KP, Prasad, M, Yang, J, Su, S-Y, Tao, X, Saxena, A, Lin, W-C & Lin, C-T 2021, 'A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications', Multimedia Tools and Applications, vol. 80, no. 11, pp. 16635-16657.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Face detection often plays the first step in various visual applications. Large variants of facial deformations due to head movements and facial expression make it difficult to identify appropriate face region. In this paper, a robust real-time face alignment system, including facial landmarks detection and face rectification, is proposed. A facial landmarks detection model based on regression tree is utilized in the proposed system. In face rectification framework, 2-D geometrical analysis based on pitch, yaw and roll movements is designed to solve the misalignment problem in face detection. The experiments on the two datasets verify the performance significantly improved by the proposed method in the facial recognition task and outperform than those obtained by other alignment methods. Furthermore, the proposed method can achieve robust recognition results even if the amount of training images is not large.
Coronado, FC, Merigó, JM & Cancino, CA 2021, 'Business and management research in Latin America: A country-level bibliometric analysis', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1865-1878.
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Bibliometrics is a scientific discipline that studies quantitatively the bibliographic material of a particular topic. This study analyzes management research published by Latin American countries between 1990 and 2019. The work uses the Web of Science database and provides several country-level bibliometric indicators including the total number of publications and citations, and the h-index. The results indicate that Brazil, Chile and Mexico have constantly led the region’s scientific publications. The temporal evolution shows a significant increase on the number of publications during the last years that seems to continue in the future. The results also show that operations research and finance are the most significant topics in the region.
Correll, P, Feyer, A-M, Phan, P-T, Drake, B, Jammal, W, Irvine, K, Power, A, Muir, S, Ferdousi, S, Moubarak, S, Oytam, Y, Linden, J & Fisher, L 2021, 'Lumos: a statewide linkage programme in Australia integrating general practice data to guide system redesign', Integrated Healthcare Journal, vol. 3, no. 1, pp. e000074-e000074.
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ObjectiveWith ageing of the Australian population, more people are living longer and experiencing chronic or complex health conditions. The challenge is to have information that supports the integration of services across the continuum of settings and providers, to deliver person-centred, seamless, efficient and effective healthcare. However, in Australia, data are typically siloed within health settings, precluding a comprehensive view of patient journeys. Here, we describe the establishment of the Lumos programme—the first statewide linked data asset across primary care and other settings in Australia and evaluate its representativeness to the census population.Methods and analysisRecords extracted from general practices throughout New South Wales (NSW), Australia’s most populous state, were linked to patient records from acute and other settings. Innovative privacy and security technologies were employed to facilitate ongoing and regular updates. The marginal demographic distributions of the Lumos cohort were compared with the NSW census population by calculating multiple measures of representation to evaluate its generalisability.ResultsThe first Lumos programme data extraction linked 1.3 million patients’ general practice records to other NSW health system data. This represented 16% of the NSW population. The demographic distribution of patients in Lumos was >95% aligned to that of the NSW population in the calculated measures of representativeness.ConclusionThe Lumos programme delivers an enduring, regularly updated data resource, providing unique insights about statewide, cross-setting healthcare utilisation. General practice patients represented in the Lumos data asset are representative of the NSW population overall. Lumos ...
Cortes, CAT, Chen, H-T, Sturnieks, DL, Garcia, J, Lord, SR & Lin, C-T 2021, 'Evaluating Balance Recovery Techniques for Users Wearing Head-Mounted Display in VR', IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 1, pp. 204-215.
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Room-scale 3D position tracking allows users to explore the virtual environment by physically walking. However, since the eyesight of the user is blocked by a Head-Mounted Display (HMD), the user might lose her balance because of bumping into real-world obstacles or shifting the body weight onto virtual objects that are inexistent in the real-world. This paper investigates assistive fall recovery methods under the assumption that the onset of the fall is given. Our experiment simulated the forward loss-of-balance with a tether-release protocol. A magnetic lock attached to a counter-weight was released while the subject was in a static leaning posture and engaged in a secondary 3D object selection task. The experiment uses a two by two design that examines two assistive techniques, i.e. video-see-through and auditory warning, at two different timing, i.e. fall onset and 500ms prior to onset. The data from 17 subjects show that the video-see-through triggered 500ms before the onset of fall can effectively help users recover from falls. Surprisingly, the video-see-through at the fall onset has a significant negative impact on the fall recovery providing similar results to the baseline condition (no intervention).
Dai, M, Su, Z, Li, R & Yu, S 2021, 'A Software-Defined-Networking-Enabled Approach for Edge-Cloud Computing in the Internet of Things', IEEE Network, vol. 35, no. 5, pp. 66-73.
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The proliferation of smart devices has led to a huge amount of data streaming in the Internet of Things (IoT). However, the resource-limited devices cannot satisfy the demands of computing-in-tensive but delay-sensitive applications. The data delivery among devices may be tampered with by malicious users. These pose new challenges to provide secure and intelligent services in IoT. Blockchain and reinforcement learning (RL) are promising techniques for establishing a secure environment and intelligent resource management. In this article, we introduce a novel software defined networking (SDN)-enabled architecture for edge-cloud orchestrated computing to support secure and intelligent services in IoT. We first introduce the SDN-enabled architecture by integrating cloud computing, edge computing, and IoT networks. Then we provide several applications of SDN-enabled architecture in edge-cloud orchestrated computing. Next, we propose the blockchain and RL envisioned solutions to implement secure and intelligent services in IoT. Moreover, a case study of blockchain- and RL-enabled secure and intelligent computing offloading is presented to validate its effectiveness. We finally provide our conclusion and discuss several promising research directions.
Dai, Y, Weng, J, Yang, A, Yu, S & Deng, RH 2021, 'A Lightweight and Privacy-Preserving Answer Collection Scheme for Mobile Crowdsourcing', KSII Transactions on Internet and Information Systems, vol. 15, no. 8, pp. 2827-2848.
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Mobile Crowdsourcing (MCS) has become an emerging paradigm evolved from crowdsourcing by employing advanced features of mobile devices such as smartphones to perform more complicated, especially spatial tasks. One of the key procedures in MCS is to collect answers from mobile users (workers), which may face several security issues. First, authentication is required to ensure that answers are from authorized workers. In addition, MCS tasks are usually location-dependent, so the collected answers could disclose workers' location privacy, which may discourage workers to participate in the tasks. Finally, the overhead occurred by authentication and privacy protection should be minimized since mobile devices are resource-constrained. Considering all the above concerns, in this paper, we propose a lightweight and privacy-preserving answer collection scheme for MCS. In the proposed scheme, we achieve anonymous authentication based on traceable ring signature, which provides authentication, anonymity, as well as traceability by enabling malicious workers tracing. In order to balance user location privacy and data availability, we propose a new concept named current location privacy, which means the location of the worker cannot be disclosed to anyone until a specified time. Since the leakage of current location will seriously threaten workers' personal safety, causing such as absence or presence disclosure attacks, it is necessary to pay attention to the current location privacy of workers in MCS. We encrypt the collected answers based on timed-release encryption, ensuring the secure transmission and high availability of data, as well as preserving the current location privacy of workers. Finally, we analyze the security and performance of the proposed scheme. The experimental results show that the computation costs of a worker depend on the number of ring signature members, which indicates the flexibility for a worker to choose an appropriate size of the group...
Daniel, J & Merigó, JM 2021, 'Developing a new multidimensional model for selecting strategic plans in balanced scorecard', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1817-1826.
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The main motivation of this research is to develop an innovative multidimensional model through multi attribute decision making (MADM) methods for strategic plans selection process in the Balanced Scorecard (BSC). The current study adopted MADM analytical methods including AHP, ELECTRE, BORDA, TOPSIS and SAW to rank the initiatives / strategic plans in BSC. Then the results of those methods were compared against each other in order to find a robust model for selecting strategic plans. The correlation coefficient between methods indicated that multidimensional and ELECTRE methods with 0.944 are the best performing and AHP with negative correlation (–0.455) is the worst performing method for selecting strategic plans in BSC. The high correlation demonstrates that the model can be a useful and effective tool to finding the critical aspects of evaluation criteria as well as the gaps to improve company performance for achieving desired level. Developing multidimensional model is the core model for the selection of strategic plans. This study addresses the problem and issues of group decision making process for selecting strategic plans in BSC. It has numerous contributions that particularly includes; 1) Determination of the explicit criteria sub-criteria and criteria to improve ranking strategic plans in BSC, 2) Adopting MADM analytical methods including AHP, ELECTRE, BORDA, TOPSIS and SAW for the selection of strategic plans decision problem in BSC, 3) Developing multidimensional model to address the selection of strategic plans problems in BSC. The proposed model will provide an approach to facilitate strategic plans decision problem in BSC.
Dao, KP, De Cocker, K, Tong, HL, Kocaballi, AB, Chow, C & Laranjo, L 2021, 'Smartphone-Delivered Ecological Momentary Interventions Based on Ecological Momentary Assessments to Promote Health Behaviors: Systematic Review and Adapted Checklist for Reporting Ecological Momentary Assessment and Intervention Studies', JMIR mHealth and uHealth, vol. 9, no. 11, pp. e22890-e22890.
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Background Healthy behaviors are crucial for maintaining a person’s health and well-being. The effects of health behavior interventions are mediated by individual and contextual factors that vary over time. Recently emerging smartphone-based ecological momentary interventions (EMIs) can use real-time user reports (ecological momentary assessments [EMAs]) to trigger appropriate support when needed in daily life. Objective This systematic review aims to assess the characteristics of smartphone-delivered EMIs using self-reported EMAs in relation to their effects on health behaviors, user engagement, and user perspectives. Methods We searched MEDLINE, Embase, PsycINFO, and CINAHL in June 2019 and updated the search in March 2020. We included experimental studies that incorporated EMIs based on EMAs delivered through smartphone apps to promote health behaviors in any health domain. Studies were independently screened. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. We performed a narrative synthesis of intervention effects, user perspectives and engagement, and intervention design and characteristics. Quality appraisal was conducted for all included studies. Results We included 19 papers describing 17 unique studies and comprising 652 participants. Most studies were quasi-experimental (13/17, 76%), had small sample sizes, and great heterogeneity in intervention designs and measurements. EMIs were most popular in the mental health domain (8/17, 47%), followed by substance abuse (3/17, 18%), diet, weight loss, physi...
Ding, W, Ming, Y, Wang, Y-K & Lin, C-T 2021, 'Memory augmented convolutional neural network and its application in bioimages', Neurocomputing, vol. 466, pp. 128-138.
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The long short-term memory (LSTM) network underpins many achievements and breakthroughs especially in natural language processing fields. Essentially, it is endowed with certain memory capabilities to boost its performance. Currently, the volume and speed of big data generation are increasing exponentially, and such data require efficient models to acquire memory augmented knowledge. In this paper, we propose a memory augmented convolutional neural network (MACNN) with utilizing self-organizing maps (SOM) as the memory module. First, we depict the potential challenge about just applying solely a convolutional neural network (CNN) so as to highlight the advantage of augmenting SOM memory for better network generalization. Then, we dissert a corresponding network architecture incorporating memory to instantiate the distributed knowledge representation machanism, which tactically combines both SOM and CNN. Each component of the input vector is connected with a neuron in a two-dimensional lattice. Finally, we test the proposed network on various datasets and the experimental results reveal that MACNN can achieve competitive performance, especially for bioimages datasets. Meanwhile, we further illustrate the learned representations to interpret the SOM behavior and to comprehend the achieved results, which indicates that the proposed memory-incorporating model can exhibit the better performance.
Ding, W, Pal, NR, Lin, C-T, Cheung, Y-M, Cao, Z & Luo, W 2021, 'Guest Editorial Special Issue on Emerging Computational Intelligence Techniques for Decision Making With Big Data in Uncertain Environments', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 5, no. 1, pp. 2-5.
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Ding, W, Pedrycz, W, Triguero, I, Cao, Z & Lin, C-T 2021, 'Multigranulation Supertrust Model for Attribute Reduction', IEEE Transactions on Fuzzy Systems, vol. 29, no. 6, pp. 1395-1408.
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IEEE As big data often contains a significant amount of uncertain, unstructured and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information systems to extract knowledge. Multigranular computing provides a powerful tool for use in big data analysis conducted at different levels of information granularity. In this paper, we present a novel multigranulation super-trust fuzzy-rough set-based attribute reduction (MSFAR) algorithm to support the formation of hierarchies of information granules of higher types and higher orders, which addresses newly emerging data mining problems in big data analysis. First, a multigranulation super-trust model based on the valued tolerance relation is constructed to identify the fuzzy similarity of the changing knowledge granularity with multimodality attributes. Second, an ensemble consensus compensatory scheme is adopted to calculate the multigranular trust degree based on the reputation at different granularities to create reasonable subproblems with different granulation levels. Third, an equilibrium method of multigranular-coevolution is employed to ensure a wide range of balancing of exploration and exploitation and can classify super elitists’ preferences and detect noncooperative behaviors with a global convergence ability and high search accuracy. The experimental results demonstrate that the MSFAR algorithm achieves a high performance in addressing uncertain and fuzzy attribute reduction problems with a large number of multigranularity variables.
Do, NTT, Lin, C-T & Gramann, K 2021, 'Human brain dynamics in active spatial navigation', Scientific Reports, vol. 11, no. 1, pp. 1-12.
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Do, T-TN, Jung, T-P & Lin, C-T 2021, 'Retrosplenial Segregation Reflects the Navigation Load During Ambulatory Movement', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, no. 99, pp. 488-496.
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Spatial navigation is a complex cognitive process based on vestibular, proprioceptive, and visualcues that are integrated and processed by an extensive network of brain areas. The retrosplenial complex (RSC) is an integral part of coordination and translation between spatial reference frames. Previous studies have demonstrated that the RSC is active during a spatial navigation tasks. The specifics of RSC activity under various navigation loads, however, are still not characterized. This study investigated the local information processed by the RSC under various navigation load conditions manipulated by the number of turns in the physical navigation setup. The results showed that the local information processed via the RSC, which was reflected by the segregation network, was higher when the number of turns increased, suggesting that RSC activity is associated with the navigation task load. The present findings shed light on how the brain processes spatial information in a physical navigation task.
Do, T-TN, Lin, C-T & Gramann, K 2021, 'Human brain dynamics in active spatial navigation', Scientific Reports, vol. 11, no. 1, p. 13036.
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AbstractSpatial navigation is a complex cognitive process based on multiple senses that are integrated and processed by a wide network of brain areas. Previous studies have revealed the retrosplenial complex (RSC) to be modulated in a task-related manner during navigation. However, these studies restricted participants’ movement to stationary setups, which might have impacted heading computations due to the absence of vestibular and proprioceptive inputs. Here, we present evidence of human RSC theta oscillation (4–8 Hz) in an active spatial navigation task where participants actively ambulated from one location to several other points while the position of a landmark and the starting location were updated. The results revealed theta power in the RSC to be pronounced during heading changes but not during translational movements, indicating that physical rotations induce human RSC theta activity. This finding provides a potential evidence of head-direction computation in RSC in healthy humans during active spatial navigation.
Do, T-TN, Wang, Y-K & Lin, C-T 2021, 'Increase in Brain Effective Connectivity in Multitasking but not in a High-Fatigue State', IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 3, pp. 566-574.
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IEEE Multitasking has become omnipresent in daily activities, and increased brain connectivity under high workload conditions has been reported. Moreover, the effect of fatigue on neural activity has been shown in participants performing cognitive tasks, but the effect of fatigue on different cognitive workload conditions is unclear. In this study, we investigated the effect of fatigue on changes in effective connectivity (EC) across the brain network under distinctive workload conditions. There were 133 electroencephalography (EEG) datasets collected from sixteen participants over a five-month study in which high-risk, reduced, and normal states of real-world fatigue were identified through a daily sampling system. The participants were required to perform a lane-keeping task (LKT) with/without multimodal dynamic attention-shifting (DAS) tasks. The results show that the EC magnitude is positively correlated with the increased workload in normal and reduced states. However, low EC was discovered in the high-risk state under high workload condition. To the best of our knowledge, this investigation is the first EEG-based longitudinal study of real-world fatigue under multitasking conditions. These results could be beneficial for real-life applications, and adaptive models are essential for monitoring important brain patterns under varying workload demands and fatigue states.
Dolmark, T, Sohaib, O, Beydoun, G & Wu, K 2021, 'The Effect of Individual’s Technological Belief and Usage on Their Absorptive Capacity towards Their Learning Behaviour in Learning Environment', Sustainability, vol. 13, no. 2, pp. 718-718.
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Absorptive capacity is a common barrier to knowledge transfer at the individual level. However, technology absorptive capacity can enhance an individual’s learning behaviour. This study investigates that technology readiness, the tools for knowledge sources, social influences, and social networks influence an individual’s absorptive capacity on an adaptation of the individual learning behaviour. A quantitative approach is used to assess the presence of a causal relationship from the constructs mentioned above. Data were collected from university students in Australia to examine the hypotheses. With 199 responses, a partial least squares structural equation modelling (PLS-SEM) approach was used for the analysis. The results generated mixed findings. Individual’s technological belief in optimism and innovation and social influences had a significantly weaker effect on individual absorptive capacity, which in turn had a significantly weaker impact on their learning behaviour.
Dong, M, Yao, L, Wang, X, Benatallah, B, Zhang, S & Sheng, QZ 2021, 'Gradient Boosted Neural Decision Forest', IEEE Transactions on Services Computing, vol. PP, no. 99, pp. 1-1.
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Tree-based models and deep neural networks are two schools of effective classification methods in machine learning. While tree-based models are robust irrespective of data domain, deep neural networks have advantages in handling high-dimensional data. Adding a differentiable neural decision forest to the neural network can generally help exploit the benefits of both models. Therefore, traditional decision trees diverge into a bagging version (i.e., random forest) and a boosting version (i.e., gradient boost decision tree). In this work, we aim to harness the advantages of both bagging and boosting by applying gradient boost to a neural decision forest. We propose a gradient boost that can learn the residual using a neural decision forest, considering the residual as a part of the final prediction. Besides, we design a structure for learning the parameters of neural decision forest and gradient boost module in contiguous steps, which is extendable to incorporate multiple gradient-boosting modules in an end-to-end manner. Our extensive experiments on several public datasets demonstrate the competitive performance and efficiency of our model against a series of baseline methods in solving various machine learning tasks.
Du, H, Wang, S & Huo, H 2021, 'XFinder: Detecting Unknown Anomalies in Distributed Machine Learning Scenario', Frontiers in Computer Science, vol. 3, pp. 1-13.
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In recent years, the emergence of distributed machine learning has enabled deep learning models to ensure data security and privacy while training efficiently. Anomaly detection for network traffic in distributed machine learning scenarios is of great significance for network security. Although deep neural networks have made remarkable achievements in anomaly detection for network traffic, they mainly focus on closed sets, that is, assuming that all anomalies are known. However, in a real network environment, unknown abnormalities are fatal risks faced by the system because they have no labels and occur before the known anomalies. In this study, we design and implement XFinder, a dynamic unknown traffic anomaly detection framework in distributed machine learning. XFinder adopts an online mode to detect unknown anomalies in real-time. XFinder detects unknown anomalies by the unknowns detector, transfers the unknown anomalies to the prior knowledge base by the network updater, and adopts the online mode to report new anomalies in real-time. The experimental results show that the average accuracy of the unknown anomaly detection of our model is increased by 27% and the average F1-Score is improved by 20%. Compared with the offline mode, XFinder’s detection time is reduced by an average of approximately 33% on three datasets, and can better meet the network requirement.
Easttom, C, Bianchi, L, Valeriani, D, Nam, CS, Hossaini, A, Zapala, D, Roman-Gonzalez, A, Singh, AK, Antonietti, A, Sahonero-Alvarez, G & Balachandran, P 2021, 'A Functional Model for Unifying Brain Computer Interface Terminology', IEEE Open Journal of Engineering in Medicine and Biology, vol. 2, no. 99, pp. 91-96.
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Easttom, C, Bianchi, L, Valeriani, D, Nam, CS, Hossaini, A, Zapała, D, Roman-Gonzalez, A, Singh, AK, Antonietti, A, Sahonero-Alvarez, G & Balachandran, P 2021, 'A functional BCI model by the P2731 working group: control interface', Brain-Computer Interfaces, vol. 8, no. 4, pp. 154-160.
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In order to facilitate communication and collaboration between researchers, Brain–computer interfaces (BCI) require a generally applicable functional model as well as a common vocabulary. The IEEE P2731 working group is in the process of developing such a functional model and a lexicon of BCI terminology. Such a functional model has multiple aspects including the control interface, physiology, transducers, etc. This current paper focuses on the control interface aspects of that model. Having a generally applicable control interface model will facilitate interdisciplinar y research and communication. The control interface is a critical part of the functional model and is described in this current paper. The control interface presented intentionally is intentionally kept general in order to be widely applicable. Some details are specific to a particular application and are thus left to those applications. It does contain the encoder (which also contains a decoder), with a feedback submodule.
Elgharabawy, A, Prasad, M & Lin, C-T 2021, 'Subgroup Preference Neural Network', Sensors, vol. 21, no. 18, pp. 6104-6104.
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Subgroup label ranking aims to rank groups of labels using a single ranking model, is a new problem faced in preference learning. This paper introduces the Subgroup Preference Neural Network (SGPNN) that combines multiple networks have different activation function, learning rate, and output layer into one artificial neural network (ANN) to discover the hidden relation between the subgroups’ multi-labels. The SGPNN is a feedforward (FF), partially connected network that has a single middle layer and uses stairstep (SS) multi-valued activation function to enhance the prediction’s probability and accelerate the ranking convergence. The novel structure of the proposed SGPNN consists of a multi-activation function neuron (MAFN) in the middle layer to rank each subgroup independently. The SGPNN uses gradient ascent to maximize the Spearman ranking correlation between the groups of labels. Each label is represented by an output neuron that has a single SS function. The proposed SGPNN using conjoint dataset outperforms the other label ranking methods which uses each dataset individually. The proposed SGPNN achieves an average accuracy of 91.4% using the conjoint dataset compared to supervised clustering, decision tree, multilayer perceptron label ranking and label ranking forests that achieve an average accuracy of 60%, 84.8%, 69.2% and 73%, respectively, using the individual dataset.
Elman, SJ, Chapman, A & Flammia, ST 2021, 'Free Fermions Behind the Disguise', Communications in Mathematical Physics, vol. 388, no. 2, pp. 969-1003.
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Espinoza-Audelo, LF, León-Castro, E, Mellado-Cid, C, Merigó, JM & Blanco-Mesa, F 2021, 'Uncertain induced prioritized aggregation operators in the analysis of the imports and exports', Journal of Multiple-Valued Logic and Soft Computing, vol. 36, no. 6, pp. 543-568.
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Interval numbers are widely used in many fields to provide information about different scenarios. This paper presents several new uncertain average formulations using the ordered weighted average, prioritized, probabilistic and induced operators. First, the work introduces the uncertain prioritized induced probabilistic ordered weighted average (UPIPOWA) operator that its main applicability is in complex group decision making problems. Also, a wide range of special cases and extensions using quasi-arithmetic means are presented, such is the case of the quasi-arithmetic UPIPOWA (QUPIPOWA) operator. The study analyzes the applicability of this new approach in economic variables, specifically are imports and exports. Particularly, the paper focuses on measuring the imports and exports for Latin America for 2017.
Fahmideh, M, Grundy, JC, Beydoun, G, Zowghi, D, Susilo, W & Mougouei, D 2021, 'A Model-Driven Approach to Reengineering Processes in Cloud Computing.', CoRR, vol. abs/2109.11896.
Fahmideh, M, Low, G & Beydoun, G 2021, 'Conceptualising Cloud Migration Lifecycle.', CoRR, vol. abs/2109.01757.
Faisal, SN, Amjadipour, M, Izzo, K, Singer, JA, Bendavid, A, Lin, C-T & Iacopi, F 2021, 'Non-invasive on-skin sensors for brain machine interfaces with epitaxial graphene', Journal of Neural Engineering, vol. 18, no. 6, pp. 066035-066035.
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Abstract Objective. Brain–machine interfaces are key components for the development of hands-free, brain-controlled devices. Electroencephalogram (EEG) electrodes are particularly attractive for harvesting the neural signals in a non-invasive fashion. Approach. Here, we explore the use of epitaxial graphene (EG) grown on silicon carbide on silicon for detecting the EEG signals with high sensitivity. Main results and significance. This dry and non-invasive approach exhibits a markedly improved skin contact impedance when benchmarked to commercial dry electrodes, as well as superior robustness, allowing prolonged and repeated use also in a highly saline environment. In addition, we report the newly observed phenomenon of surface conditioning of the EG electrodes. The prolonged contact of the EG with the skin electrolytes functionalize the grain boundaries of the graphene, leading to the formation of a thin surface film of water through physisorption and consequently reducing its contact impedance more than three-fold. This effect is primed in highly saline environments, and could be also further tailored as pre-conditioning to enhance the performance and reliability of the EG sensors.
Fan, W, Xiao, F, Chen, X, Cui, L & Yu, S 2021, 'Efficient Virtual Network Embedding of Cloud-Based Data Center Networks into Optical Networks', IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 11, pp. 2793-2808.
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The demand for data center bandwidth has exploded due to the continuous development of cloud computing, causing the use of network resources close to saturation. Optical network has become an encouraging technology for many burgeoning networks and parallel/distributed computing applications because of its huge bandwidth. This article focuses on efficient embedding of data centers into optical networks, which aims to reduce complexity of the network topology by using the parallel transmission characteristics of optical fiber. We first present a novel virtual network embedding (VNE) mathematical model used for optical data center networks. Then we derive a priority of location VNE algorithm according to node proximity sensing and path comprehensive evaluation. Furthermore, we propose routing and wavelength assignment for DCNs into optical networks, and identify the lower bound of the required number of wavelengths. Extensive evaluations show that the proposed embedding algorithm can reduce the average waiting time of virtual network requests by 20 percent, increase the request acceptance rate and revenue-overhead ratio by 13 percent, as compared to the latest VNE algorithm.
Fang, L, Li, Y, Liu, Z, Yin, C, Li, M & Cao, ZJ 2021, 'A Practical Model Based on Anomaly Detection for Protecting Medical IoT Control Services Against External Attacks', IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 4260-4269.
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Feng, B, Zhou, H, Li, G, Zhang, Y, Sood, K & Yu, S 2021, 'Enabling Machine Learning with Service Function Chaining for Security Enhancement at 5G Edges', IEEE Network, vol. 35, no. 5, pp. 196-201.
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With massive sorts of terminals, devices, and machines connecting to 5G, a tremendous surge of data makes cyber-security a pressing issue, and conventional countermeasures are facing unprecedented challenges. Recently, with the rise of ML (Machine Learning) and SDN/NFV-based (Software-Defined Networks/Network Functions Virtualization) SFC (Service Function Chaining) techniques, how to leverage them for security enhancement in MEC (Multi-Access/Mobile Edge Computing) has received much attention. Hence, in this article, we first propose an elastic framework to integrate ML with virtualized SFC, aiming at smart and efficient provision of different services at MEC. Then, we propose an ML-based anomaly detection algorithm used as a kind of service policy for SFC classifiers, which guides the latter for quick traffic classification and subsequent redirections of attack flows. Finally, we build a corresponding prototype system and evaluate the performance of the proposed algorithm through extensive experiments. Related results have confirmed the feasibility and advantages of the proposed framework and algorithm.
Feng, S, Shi, H, Huang, L, Shen, S, Yu, S, Peng, H & Wu, C 2021, 'Unknown hostile environment-oriented autonomous WSN deployment using a mobile robot', Journal of Network and Computer Applications, vol. 182, pp. 103053-103053.
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In this study, we consider the Internet of Things (IoT) with an autonomous deployment framework and seek optimal localizable k-coverage (OLKC) strategies to preserve the connectivity and robustness in IoT networks to assist robots during disaster recovery activities. Therefore, we define localizable k-coverage as the covered region within which a mobile robot can localize itself aided by k neighboring beacon nodes (BNs) in a wireless sensor network (WSN). To this end, we first propose the optimal localizable k-coverage WSN deployment problem (OLKWDP) and present a novel framework that preserves WSN connectivity and robustness for mobile robots. To localize a mobile robot with at least k BNs and overcome the network hole problem that can occur in unknown hostile environments, we propose a hole recovery method for the OLKC achieved by a mobile robot that knows the concurrent mapping, deployment and localization of the WSN. We then present a mapping-to-image transformation method to reveal the interactions between the WSN deployment and the network holes for the OLKC while constructing the online mapping. To solve the OLKWDP, we also develop two optimality conditions to achieve maximum coverage by the proposed OLKC in the unknown hostile environment using the minimum number of sensors. Moreover, we analyze the factors that influence the probability of success of the OLKC and the factors that influence the performance of a mobile robot when determining the WSN deployment. The simulation results illustrate that our framework outperforms the trilateration and spanning tree (TST) method in unknown hostile environment exploration and can achieve the OLKC in a WSN. In 27 simulated situations, our framework achieved average rates of nearly 100% 1-coverage, 91.34% 2-coverage and 89.00% 3-coverage.
Ferro, R, Cordeiro, GA, Ordóñez, REC, Beydoun, G & Shukla, N 2021, 'An Optimization Tool for Production Planning: A Case Study in a Textile Industry', Applied Sciences, vol. 11, no. 18, pp. 8312-8312.
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The textile industry is an important sector of the Brazilian economy, being considered the fifth largest textile industry in the world. To support further growth and development in this sector, this document proposes a process for production analysis through the use of Discrete Event Simulation (DES) and optimization through genetic algorithms. The focus is on production planning for weaving processes and optimization to help make decisions about batch sizing and production scheduling activities. In addition, the correlations between some current technological trends and their implications for the textile industry are also highlighted. Another important contribution of this study is to detail the use of the commercial software Tecnomatix Plant Simulation 13®, to simulate and optimize a production problem by applying genetic algorithms with real production data.
Flores-Sosa, M, Avilés-Ochoa, E, Merigó, JM & Yager, RR 2021, 'Volatility GARCH models with the ordered weighted average (OWA) operators', Information Sciences, vol. 565, pp. 46-61.
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Gao, H, Hussain, W, Yin, Y, Zhao, W & Iqbal, M 2021, 'Editorial: AI-based mobile multimedia computing for data-smart processing', Computer Networks, vol. 195, pp. 108197-108197.
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Gao, S, Yu, S & Yao, S 2021, 'An efficient protein homology detection approach based on seq2seq model and ranking', Biotechnology & Biotechnological Equipment, vol. 35, no. 1, pp. 633-640.
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Evolutionary information is essential for the protein annotation. The number of homologs of a protein retrieved is correlated with the annotations related to the protein structure or function. With the continuous increase in the number of available sequences, fast and effective homology detection methods are particularly important. To increase the efficiency of homology detection, a novel method named CONVERT is proposed in this paper. This method regards homology detection as a translation task and presents a concept of representative protein. Representative proteins are not real proteins. A representative protein corresponds to a protein family, it contains the characteristics of the family. Our method employs the seq2seq model to establish the many-to-one relationship between proteins and representative proteins. Based on the many-to-one relationship, CONVERT converts protein sequences into fixed-length numerical representations, so as to increase the efficiency of homology detection by using numerical comparison instead of sequence alignment. For alignment results, our method adopts ranking to obtain a sorted list. We evaluate the proposed method on two benchmark datasets. The experimental results show that the performances of our method are comparable with the state-of-the-art methods. Meanwhile, our method is ultra-fast and can obtain results in hundreds of milliseconds.
Garcia Marin, J, Biloria, N, Robertson, H & Fornes, M 2021, 'Urban Health and Wellbeing in the Contemporary City', HealthManagement, vol. 21, no. 6.
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This paper explores and debates the intricate connection between our built environment and an increasingly technocentric approach to distinguish health and wellbeing from a multidisciplinary perspective. The authors profess the dire need for rethinking the ‘smart’ within the city by reconsidering models of urban development and focusing on the democratisation of technology for the purpose of enhancing our lived urban experience and psychophysiological wellbeing.
Ghantous, GB & Gill, AQ 2021, 'Evaluating the DevOps Reference Architecture for Multi-cloud IoT-Applications.', SN Comput. Sci., vol. 2, pp. 123-123.
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Gheisari, S, Shariflou, S, Phu, J, Kennedy, PJ, Agar, A, Kalloniatis, M & Golzan, SM 2021, 'A combined convolutional and recurrent neural network for enhanced glaucoma detection', Scientific Reports, vol. 11, no. 1, p. 1945.
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AbstractGlaucoma, a leading cause of blindness, is a multifaceted disease with several patho-physiological features manifesting in single fundus images (e.g., optic nerve cupping) as well as fundus videos (e.g., vascular pulsatility index). Current convolutional neural networks (CNNs) developed to detect glaucoma are all based on spatial features embedded in an image. We developed a combined CNN and recurrent neural network (RNN) that not only extracts the spatial features in a fundus image but also the temporal features embedded in a fundus video (i.e., sequential images). A total of 1810 fundus images and 295 fundus videos were used to train a CNN and a combined CNN and Long Short-Term Memory RNN. The combined CNN/RNN model reached an average F-measure of 96.2% in separating glaucoma from healthy eyes. In contrast, the base CNN model reached an average F-measure of only 79.2%. This proof-of-concept study demonstrates that extracting spatial and temporal features from fundus videos using a combined CNN and RNN, can markedly enhance the accuracy of glaucoma detection.
Gill, AQ 2021, 'A Theory of Information Trilogy: Digital Ecosystem Information Exchange Architecture.', Inf., vol. 12, no. 7, pp. 283-283.
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Information sharing is a critical component of a distributed and multi‐actor digital ecosystem (DE). DE actors, individuals and organisations, require seamless, effective, efficient, and secure architecture for exchanging information. Traditional point‐to‐point and ad hoc integrations hinder the ability of DE actors to do so. The challenge is figuring out how to enable information sharing in a complex DE. This paper addresses this important research challenge and proposes the theory of information trilogy and conceptual DE information exchange architecture, which is inspired by the study of nature and flow of matter, energy, and its states in natural ecosystems. This work is a part of the large DE information framework. The scope of this paper is limited to the emerging concept of DE information exchange. The application of the DE information exchange concept is demonstrated with the help of a geospatial information sharing case study example. The results from this paper can be used by researchers and practitioners for defining the DE information exchange as appropriate to their context. This work also complements Shannon’s mathematical theory of communication.
Gong, Y, Li, Z, Zhang, J, Liu, W, Yin, Y & Zheng, Y 2021, 'Missing Value Imputation for Multi-view Urban Statistical Data via Spatial Correlation Learning', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Goodswen, SJ, Barratt, JLN, Kennedy, PJ, Kaufer, A, Calarco, L & Ellis, JT 2021, 'Machine learning and applications in microbiology', FEMS Microbiology Reviews, vol. 45, no. 5, p. fuab015.
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ABSTRACT To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2021, 'Applying Machine Learning to Predict the Exportome of Bovine and Canine Babesia Species That Cause Babesiosis', Pathogens, vol. 10, no. 6, pp. 660-660.
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Babesia infection of red blood cells can cause a severe disease called babesiosis in susceptible hosts. Bovine babesiosis causes global economic loss to the beef and dairy cattle industries, and canine babesiosis is considered a clinically significant disease. Potential therapeutic targets against bovine and canine babesiosis include members of the exportome, i.e., those proteins exported from the parasite into the host red blood cell. We developed three machine learning-derived methods (two novel and one adapted) to predict for every known Babesia bovis, Babesia bigemina, and Babesia canis protein the probability of being an exportome member. Two well-studied apicomplexan-related species, Plasmodium falciparum and Toxoplasma gondii, with extensive experimental evidence on their exportome or excreted/secreted proteins were used as important benchmarks for the three methods. Based on 10-fold cross validation and multiple train–validation–test splits of training data, we expect that over 90% of the predicted probabilities accurately provide a secretory or non-secretory indicator. Only laboratory testing can verify that predicted high exportome membership probabilities are creditable exportome indicators. However, the presented methods at least provide those proteins most worthy of laboratory validation and will ultimately save time and money.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2021, 'Computational Antigen Discovery for Eukaryotic Pathogens Using Vacceed', vol. 2183, pp. 29-42.
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© Springer Science+Business Media, LLC, part of Springer Nature 2021. Bioinformatics programs have been developed that exploit informative signals encoded within protein sequences to predict protein characteristics. Unfortunately, there is no program as yet that can predict whether a protein will induce a protective immune response to a pathogen. Nonetheless, predicting those pathogen proteins most likely from those least likely to induce an immune response is feasible when collectively using predicted protein characteristics. Vacceed is a computational pipeline that manages different standalone bioinformatics programs to predict various protein characteristics, which offer supporting evidence on whether a protein is secreted or membrane -associated. A set of machine learning algorithms predicts the most likely pathogen proteins to induce an immune response given the supporting evidence. This chapter provides step by step descriptions of how to configure and operate Vacceed for a eukaryotic pathogen of the user’s choice.
Gu, M, Gu, Y, Luo, W, Xu, G, Yang, Z, Zhou, J & Qu, W 2021, 'From text to graph: a general transition-based AMR parsing using neural network', Neural Computing and Applications, vol. 33, no. 11, pp. 6009-6025.
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© 2020, Springer-Verlag London Ltd., part of Springer Nature. Semantic understanding is an essential research issue for many applications, such as social network analysis, collective intelligence and content computing, which tells the inner meaning of language form. Recently, Abstract Meaning Representation (AMR) is attracted by many researchers for its semantic representation ability on an entire sentence. However, due to the non-projectivity and reentrancy properties of AMR graphs, they lose some important semantic information in parsing from sentences. In this paper, we propose a general AMR parsing model which utilizes a two-stack-based transition algorithm for both Chinese and English datasets. It can incrementally parse sentences to AMR graphs in linear time. Experimental results demonstrate that it is superior in recovering reentrancy and handling arcs while is competitive with other transition-based neural network models on both English and Chinese datasets.
Gu, P, Han, Y, Gao, W, Xu, G & Wu, J 2021, 'Enhancing session-based social recommendation through item graph embedding and contextual friendship modeling', Neurocomputing, vol. 419, pp. 190-202.
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Gu, X, Cao, Z, Jolfaei, A, Xu, P, Wu, D, Jung, T-P & Lin, C-T 2021, 'EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 5, pp. 1645-1666.
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Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.
Guan, L, Abbasi, A & Ryan, MJ 2021, 'A simulation-based risk interdependency network model for project risk assessment', Decision Support Systems, vol. 148, pp. 113602-113602.
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Guan, L, Abbasi, A & Ryan, MJ 2021, 'Modelling risk interdependencies to support decision making in project risk management: Analytical and simulation-based methods', Project Governance and Controls Annual Review, vol. 4, no. 1, pp. 24-44.
Guff, T, McMahon, NA, Sanders, YR & Gilchrist, A 2021, 'A resource theory of quantum measurements', Journal of Physics A: Mathematical and Theoretical, vol. 54, no. 22, pp. 225301-225301.
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Abstract Resource theories are broad frameworks that capture how useful objects are in performing specific tasks. In this paper we devise a formal resource theory quantum measurements, focusing on the ability of a measurement to acquire information. The objects of the theory are equivalence classes of positive operator-valued measures, and the free transformations are changes to a measurement device that can only deteriorate its ability to report information about a physical system. We show that catalysis and purification, protocols that are possible in other resource theories, are impossible in our resource theory for quantum measurements. Standard measures of information gain are shown to be resource monotones, and the resource theory is applied to the task of quantum state discrimination.
Gupta, A, Agrawal, RK, Kirar, JS, Andreu-Perez, J, Ding, W-P, Lin, C-T & Prasad, M 2021, 'On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 5, pp. 3080-3092.
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Gupta, D, Choudhury, A, Gupta, U, Singh, P & Prasad, M 2021, 'Computational approach to clinical diagnosis of diabetes disease: a comparative study', Multimedia Tools and Applications, vol. 80, no. 20, pp. 30091-30116.
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© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature. Diabetes is one of the most prevalent non-communicable diseases and is the 6th leading cause of death worldwide. It’s a chronic metabolic disorder which has no cure, however, it is a highly treatable condition, if diagnosed and managed on time to avoid its complications. This paper explores and compares various machine learning (ML) approaches that can help in determining the risk of diabetes at an early stage and aid in improving the medical diagnosis of diabetes. The paper considers two real-world datasets one is a diabetic clinical dataset (DCA) collected from a medical practitioner in the state of Assam, India during the year 2017–2018 and other is public PIMA Indian diabetic dataset. To analyze the various machine learning techniques on DCA and PIMA Indian diabetic datasets for the classification of diabetic and non-diabetic patients, different classifiers like perceptron, Gaussian process, linear discriminant analysis, quadratic discriminant analysis, statistical gradient descent, ridge regression classifier, support vector machines, k-nearest neighbors, decision tree, naïve Bayes, logistic regression, random forest and ELM for multiquadric, RBF, sigmoid activation functions are used. The results of numerical experiments suggested that logistic regression yields better performance in comparison to the other techniques.
Haering, M, Bano, M, Zowghi, D, Kearney, M & Maalej, W 2021, 'Automating the Evaluation of Education Apps With App Store Data.', IEEE Trans. Learn. Technol., vol. 14, no. 1, pp. 16-27.
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Han, B, Tsang, IW, Xiao, X, Chen, L, Fung, S-F & Yu, CP 2021, 'Privacy-Preserving Stochastic Gradual Learning', IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 8, pp. 3129-3140.
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It is challenging for stochastic optimization to handle large-scale sensitive data safely. Duchi et al. recently proposed a private sampling strategy to solve privacy leakage in stochastic optimization. However, this strategy leads to a degeneration in robustness, since this strategy is equal to noise injection on each gradient, which adversely affects updates of the primal variable. To address this challenge, we introduce a robust stochastic optimization under the framework of local privacy, which is called Privacy-pREserving StochasTIc Gradual lEarning (PRESTIGE). PRESTIGE bridges private updates of the primal variable (by private sampling) with gradual curriculum learning (CL). The noise injection leads to similar issue from label noise, but the robust learning process of CL can combat with label noise. Thus, PRESTIGE yields 'private but robust' updates of the primal variable on the curriculum, that is, a reordered label sequence provided by CL. In theory, we reveal the convergence rate and maximum complexity of PRESTIGE. Empirical results on six datasets show that PRESTIGE achieves a good tradeoff between privacy preservation and robustness over baselines.
Han, Y, Gu, P, Gao, W, Xu, G & Wu, J 2021, 'Aspect-level sentiment capsule network for micro-video click-through rate prediction', World Wide Web, vol. 24, no. 4, pp. 1045-1064.
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He, X & Qiao, Y 2021, 'On the Baer–Lovász–Tutte construction of groups from graphs: Isomorphism types and homomorphism notions', European Journal of Combinatorics, vol. 98, pp. 103404-103404.
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Let p be an odd prime. From a simple undirected graph G, through the classical procedures of Baer (1938), Tutte (1947) and Lovász (1989), there is a p-group PG of class 2 and exponent p that is naturally associated with G. Our first result is to show that this construction of groups from graphs respects isomorphism types. That is, given two graphs G and H, G and H are isomorphic as graphs if and only if PG and PH are isomorphic as groups. Our second contribution is a new homomorphism notion for graphs. Based on this notion, a category of graphs can be defined, and the Baer–Lovász–Tutte construction naturally leads to a functor from this category of graphs to the category of groups.
Hesamian, MH, Jia, W, He, X, Wang, Q & Kennedy, PJ 2021, 'Synthetic CT images for semi-sequential detection and segmentation of lung nodules', Applied Intelligence, vol. 51, no. 3, pp. 1616-1628.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Accurately detecting and segmenting lung nodules from CT images play a critical role in the earlier diagnosis of lung cancer and thus have attracted much interest from the research community. However, due to the irregular shapes of nodules, and the low-intensity contrast between the nodules and other lung areas, precisely segmenting nodules from lung CT images is a very challenging task. In this paper, we propose a highly effective and robust solution to this problem by innovatively utilizing the changes of nodule shapes over continuous slices (inter-slice changes) and develop a deep learning based end-to-end system. Different from the existing 2.5D or 3D methods that attempt to explore the inter-slice features, we propose to create a novel synthetic image to depict the unique changing pattern of nodules between slices in distinctive colour patterns. Based on the new synthetic images, we then adopt the deep learning based image segmentation techniques and develop a modified U-Net architecture to learn the unique color patterns formed by nodules. With our proposed approach, the detection and segmentation of nodules can be achieved simultaneously with an accuracy significantly higher than the state of the arts by 10% without introducing high computation cost. By taking advantage of inter-slice information and form the proposed synthetic image, the task of lung nodule segmentation is done more accurately and effectively.
Hewa, TM, Hu, Y, Liyanage, M, Kanhare, SS & Ylianttila, M 2021, 'Survey on Blockchain-Based Smart Contracts: Technical Aspects and Future Research', IEEE Access, vol. 9, pp. 87643-87662.
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Hickey, BA, Chalmers, T, Newton, P, Lin, C-T, Sibbritt, D, McLachlan, CS, Clifton-Bligh, R, Morley, J & Lal, S 2021, 'Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review', Sensors, vol. 21, no. 10, pp. 3461-3461.
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Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.
Hong, G-J, Li, D-L, Pare, S, Saxena, A, Prasad, M & Lin, C-T 2021, 'Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection', Applied Sciences, vol. 11, no. 23, pp. 11268-11268.
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A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft.
Hoseini, SA, Fallahpour, A, Wong, KY, Mahdiyar, A, Saberi, M & Durdyev, S 2021, 'Sustainable Supplier Selection in Construction Industry through Hybrid Fuzzy-Based Approaches', Sustainability, vol. 13, no. 3, pp. 1413-1413.
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Due to increase in the public and stakeholders’ awareness regarding economic, environmental, and social issues, the construction industry tends to follow the sustainability policies and practices in supply chain management. Hence, one of the most crucial aspects for a construction company in this regard is sustainable supplier selection, and, to this end, an accurate and reliable model is required. In this paper a hybrid fuzzy best-worst method and fuzzy inference system model is developed for sustainable supplier selection. In the first phase of this study, after determining 19 criteria in three main aspects, the final weight of each aspect and criterion is obtained using fuzzy best-worst method approach. In the second phase, the most sustainable supplier is selected by running the weighted fuzzy inference system both in aspect and criterion level, providing more accurate results compared to the use of other available models. Finally, two different tests are employed to validate the results and evaluate the robustness of the proposed model. The novel developed model enables the decision-maker to simulate the decision-making process, reduce the calculations loads, consider a large number of criteria in decision making, and resolve the inherited uncertainties in experts’ responses.
Hu, X, Ye, D, Zhu, T & Huo, H 2021, 'A Differentially Private Auction Mechanism in Online Social Networks', Journal of Systems Science and Systems Engineering.
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The growing popularity of users in online social network gives a big opportunity for online auction. The famous Information Diffusion Mechanism (IDM) is an excellent method even meet the incentive compatibility and individual rationality. Although the existing auction in online social network has considered the buyers’ information which is not known by the seller, current mechanism still can not preserve the privacy information of users in online social network. In this paper, we propose a novel mechanism based on the IDM and differential privacy. Our mechanism can successfully process the auction and at the same time preserve clients’ price information from neighbours. We achieved these by adding virtual nodes to each node and Laplace noise for its price in the auction process. We also formulate this mechanism on the real network and the random network, scale-free network to show the feasibility and effectiveness of the proposed mechanism. The evaluation shows that the result of our methods only depend on the noise added to the agents. It is independent from the agents’ original price.
Hua, W, Sui, Y, Wan, Y, Liu, G & Xu, G 2021, 'FCCA: Hybrid Code Representation for Functional Clone Detection Using Attention Networks', IEEE Transactions on Reliability, vol. 70, no. 1, pp. 304-318.
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Code cloning, which reuses a fragment of source code via copy-and-paste with or without modifications, is a common way for code reuse and software prototyping. However, the duplicated code fragments often affect software quality, resulting in high maintenance cost. The existing clone detectors using shallow textual or syntactical features to identify code similarity are still ineffective in accurately finding sophisticated functional code clones in real-world code bases. This article proposes functional code clone detector using attention ( FCCA ), a deep-learning-based code clone detection approach on top of a hybrid code representation by preserving multiple code features, including unstructured (code in the form of sequential tokens) and structured (code in the form of abstract syntax trees and control-flow graphs) information. Multiple code features are fused into a hybrid representation, which is equipped with an attention mechanism that pays attention to important code parts and features that contribute to the final detection accuracy. We have implemented and evaluated FCCA using 275 777 real-world code clone pairs written in Java. The experimental results show that FCCA outperforms several state-of-the-art approaches for detecting functional code clones in terms of accuracy, recall, and F1 score.
Huang, K-C, John, AR, Jung, T-P, Tsai, W-F, Yu, Y-H & Lin, C-T 2021, 'Comparing the Differences in Brain Activities and Neural Comodulations Associated With Motion Sickness Between Drivers and Passengers', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1259-1267.
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It is common to believe that passengers are more adversely affected by motion sickness than drivers. However, no study has compared passengers and drivers' neural activities and drivers experiencing motion sickness (MS). Therefore, this study attempts to explore brain dynamics in motion sickness among passengers and drivers. Eighteen volunteers participated in simulating the driving winding road experiment while their subjective motion sickness levels and electroencephalogram (EEG) signals were simultaneously recorded. Independent Component Analysis (ICA) was employed to isolate MS-related independent components (ICs) from EEG. Furthermore, comodulation analysis was applied to decompose spectra of interest ICs, related to MS, to find the specific spectra-related temporally independent modulators (IMs). The results showed that passengers' alpha band (8-12 Hz) power increased in correlation with the MS level in the parietal, occipital midline and left and right motor areas, and drivers' alpha band (8-12 Hz) power showed relatively smaller increases than those in the passenger. Further, the results also indicate that the enhanced activation of alpha IMs in the passenger than the driver is due to a higher degree of motion sickness. In conclusion, compared to the driver, the passenger experience more conflicts among multimodal sensory systems and demand neuro-physiological regulation.
Huang, Y, Xu, H, Gao, H, Ma, X & Hussain, W 2021, 'SSUR: An Approach to Optimizing Virtual Machine Allocation Strategy Based on User Requirements for Cloud Data Center', IEEE Transactions on Green Communications and Networking, vol. 5, no. 2, pp. 670-681.
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Huo, L, Jiao Li, J, Chen, L, Yu, Z, Hutvagner, G & Li, J 2021, 'Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects', Briefings in Bioinformatics, vol. 22, no. 6, p. bbab229.
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AbstractSingle-cell sequencing is a biotechnology to sequence one layer of genomic information for individual cells in a tissue sample. For example, single-cell DNA sequencing is to sequence the DNA from every single cell. Increasing in complexity, single-cell multi-omics sequencing, or single-cell multimodal omics sequencing, is to profile in parallel multiple layers of omics information from a single cell. In practice, single-cell multi-omics sequencing actually detects multiple traits such as DNA, RNA, methylation information and/or protein profiles from the same cell for many individuals in a tissue sample. Multi-omics sequencing has been widely applied to systematically unravel interplay mechanisms of key components and pathways in cell. This survey overviews recent developments in single-cell multi-omics sequencing, and their applications to understand complex diseases in particular the COVID-19 pandemic. We also summarize machine learning and bioinformatics techniques used in the analysis of the intercorrelated multilayer heterogeneous data. We observed that variational inference and graph-based learning are popular approaches, and Seurat V3 is a commonly used tool to transfer the missing variables and labels. We also discussed two intensively studied issues relating to data consistency and diversity and commented on currently cared issues surrounding the error correction of data pairs and data imputation methods. The survey is concluded with some open questions and opportunities for this extraordinary field.
Hussain, W, Merigo, JM, Gao, H, Alkalbani, AM & Rabhi, FA 2021, 'Integrated AHP-IOWA, POWA Framework for Ideal Cloud Provider Selection and Optimum Resource Management', IEEE Transactions on Services Computing, pp. 1-1.
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Islam, MR, Liu, S, Biddle, R, Razzak, I, Wang, X, Tilocca, P & Xu, G 2021, 'Discovering dynamic adverse behavior of policyholders in the life insurance industry', Technological Forecasting and Social Change, vol. 163, pp. 120486-120486.
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© 2020 Elsevier Inc. Adverse selection (AS) is one of the significant causes of market failure worldwide. Analysis and deep insights into the Australian life insurance market show the existence of adverse activities to gain financial benefits, resulting in loss to insurance companies. Understanding the behavior of policyholders is essential to improve business strategies and overcome fraudulent claims. However, policyholders’ behavior analysis is a complex process, usually involving several factors depending on their preferences and the nature of data such as data which is missing useful private information, the presence of asymmetric information of policyholders, the existence of anomalous information at the cell level rather than the data instance level and a lack of quantitative research. This study aims to analyze the life insurance policyholder's behavior to identify adverse behavior (AB). In this study, we present a novel association rule learning-based approach ‘ARLAS’ to detect the AS behavior of policyholders. In addition to the original data, we further created a synthetic AS dataset by randomly flipping the attribute values of 10% of the records in the test set. The experiment results on 31,800 Australian life insurance users show that the proposed approach achieves significant gains in performance comparatively.
Islam, MR, Lu, H, Hossain, MJ & Li, L 2021, 'Optimal Coordination of Electric Vehicles and Distributed Generators for Voltage Unbalance and Neutral Current Compensation', IEEE Transactions on Industry Applications, vol. 57, no. 1, pp. 1069-1080.
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© 1972-2012 IEEE. To maximize renewable energy usage to combat climate change, the penetration of electric vehicles (EVs) has increased significantly in developed countries. This can cause serious power quality issues, such as increased voltage imbalance and neutral currents, which severely impact the operation of power systems. Although the power quality issue is not a new problem, it requires an improved strategy for the growing penetration of photovoltaic solar energy and EVs in low-voltage distribution grids and their uncoordinated operation. This article presents a new control strategy to reduce the number of coordinated EVs to mitigate voltage unbalance and compensate for the neutral current. The proposed control strategy consists of two controllers arranged in a hierarchical structure with the central controller at the top layer and the local controller at the bottom layer. It is evident that the proposed control strategy reduces the number of EVs that need to be coordinated, and further, EV coordination is not required if the grid imbalance is less. This new hierarchical control strategy can improve power quality and reduce data processing overhead and computational complexity.
Islam, MR, Razzak, I, Wang, X, Tilocca, P & Xu, G 2021, 'Natural language interactions enhanced by data visualization to explore insurance claims and manage risk', Annals of Operations Research.
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Iwanaga, T, Wang, H-H, Hamilton, SH, Grimm, V, Koralewski, TE, Salado, A, Elsawah, S, Razavi, S, Yang, J, Glynn, P, Badham, J, Voinov, A, Chen, M, Grant, WE, Peterson, TR, Frank, K, Shenk, G, Barton, CM, Jakeman, AJ & Little, JC 2021, 'Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach', Environmental Modelling & Software, vol. 135, pp. 104885-104885.
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System-of-systems approaches for integrated assessments have become prevalent in recent years. Such approaches integrate a variety of models from different disciplines and modeling paradigms to represent a socio-environmental (or social-ecological) system aiming to holistically inform policy and decision-making processes. Central to the system-of-systems approaches is the representation of systems in a multi-tier framework with nested scales. Current modeling paradigms, however, have disciplinary-specific lineage, leading to inconsistencies in the conceptualization and integration of socio-environmental systems. In this paper, a multidisciplinary team of researchers, from engineering, natural and social sciences, have come together to detail socio-technical practices and challenges that arise in the consideration of scale throughout the socio-environmental modeling process. We identify key paths forward, focused on explicit consideration of scale and uncertainty, strengthening interdisciplinary communication, and improvement of the documentation process. We call for a grand vision (and commensurate funding) for holistic system-of-systems research that engages researchers, stakeholders, and policy makers in a multi-tiered process for the co-creation of knowledge and solutions to major socio-environmental problems.
Izadikhah, M, Azadi, M, Toloo, M & Hussain, FK 2021, 'Sustainably resilient supply chains evaluation in public transport: A fuzzy chance-constrained two-stage DEA approach', Applied Soft Computing, vol. 113, pp. 107879-107879.
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Owing to today's highly competitive market environments, substantial attention has been focused on sustainably resilient supply chains (SCs) over the last few years. Nevertheless, very few studies have focused on the efficiency evaluation analysis of the sustainability and resilience of SCs as an inevitable essential in any profitable business. This study aims to address this issue by proposing a novel fuzzy chance-constrained two-stage data envelopment analysis (DEA) model as an advanced and rigorous approach in the performance evaluation of sustainably resilient SCs. To the best of our knowledge, the current study is pioneering as it introduces a new fuzzy chance-constrained two-stage method that can be used to undertake the deterministic non-fuzzy programming of the proposed model. The proposed approach is validated and applied to evaluate a real case study including 21 major public transport providers in three megacities. The results demonstrate the advantages of the proposed approach in comparison to the existing approaches in the literature.
Jahangoshai Rezaee, M, Eshkevari, M, Saberi, M & Hussain, O 2021, 'GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game', Knowledge-Based Systems, vol. 213, pp. 106672-106672.
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Due to its simplicity, versatility and the diversity of applications to which it can be applied, k-means is one of the well-known algorithms for clustering data. The foundation of this algorithm is based on the distance measure. However, the traditional k-means has some weaknesses that appear in some data sets related to real applications, the most important of which is to consider only the distance criterion for clustering. Various studies have been conducted to address each of these weaknesses to achieve a balance between quality and efficiency. In this paper, a novel k-means variant of the original algorithm is proposed. This approach leverages the power of bargaining game modelling in the k-means algorithm for clustering data. In this novel setting, cluster centres compete with each other to attract the largest number of similar objectives or entities to their cluster. Thus, the centres keep changing their positions so that they have smaller distances with the maximum possible data than other cluster centres. We name this new algorithm the game-based k-means (GBK-means) algorithm. To show the superiority and efficiency of GBK-means over conventional clustering algorithms, namely, k-means and fuzzy k-means, we use the following syntactic and real-world data sets: (1) a series of two-dimensional syntactic data sets; and (2) ten benchmark data sets that are widely used in different clustering studies. The evaluation criteria show GBK-means is able to cluster data more accurately than classical algorithms based on eight evaluation metrics, namely F-measure, the Dunn index (DI), the rand index (RI), the Jaccard index (JI), normalized mutual information (NMI), normalized variation of information (NVI), the measure of concordance and error rate (ER).
Jaiswal, A, Kumar, S, Kaiwartya, O, Prasad, M, Kumar, N & Song, H 2021, 'Green computing in IoT: Time slotted simultaneous wireless information and power transfer', Computer Communications, vol. 168, pp. 155-169.
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Simultaneous Wireless Information and Power Transfer (SWIPT) is an emerging field to transmit information and power in IoT network through the same RF signal. Time switching (TS) protocol is more favorable in free space communication than Power Splitting (PS) protocol when the transmitted RF signal is already weak. This is because, transmitted signal loses its power due to attenuation in free space, and using PS design receiver circuit (complex), the received weak signal is further split into two fraction for energy harvesting (EH) and information decoding (ID) simultaneously, that causes inadequacy in SWIPT system. Whereas, using TS design receiver circuit (simple) insert extra delay in the network as EH and ID operations are done in two different time domain one by one. Literature on SWIPT lacks towards cooperation between more energy harvesting in case of free space communication (TS) and critical information transmission in case of delay constraint communication (PS). In this context, this paper presents a time-slotted SWIPT (T-SWIPT) focusing on maximization of energy efficiency in the relay based sensors-enabled IoT network. It enables simultaneous energy harvesting at receiver and neighboring sensors without adding extra delay in the network. The PS ratio, transmission power allotment and energy broadcast time are jointly formulated as non-convex energy efficiency maximization problem. A solution to the problem is presented using Lagrangian dual decomposition and fractional programming. The performance evaluation shows that T-SWIPT attains optimum energy efficiency by trading off transmission power allotment, power-splitting ratio and sink broadcast time slot.
Jena, R, Naik, SP, Pradhan, B, Beydoun, G, Park, H-J & Alamri, A 2021, 'Earthquake vulnerability assessment for the Indian subcontinent using the Long Short-Term Memory model (LSTM)', International Journal of Disaster Risk Reduction, vol. 66, pp. 102642-102642.
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Earthquakes are one of the most destructive and unpredictable natural hazards with a long-term physical, psychological, and economic impact to the society. In the past century, more than 1100 destructive earthquakes occurred, and caused around 1.5 million deaths worldwide. Some recent studies have suggested that a future earthquake in the Himalayan region of magnitude range MW 7.5–8 can cause more than 0.2 million human lives and around 150 billion dollar financial loss. Deep learning methods in recent studies proved very useful in natural hazards forecasting and prediction modelling. Long Short-Term Memory (LSTM) model has been particularly popular in several natural hazard forecasting. In this research, for the first time, LSTM model is implemented with suitable Geospatial Information Systems (GIS) techniques to assess the earthquake vulnerability for whole of India. In India, most of the seismic vulnerability assessment available are at city level or state level using traditional techniques. Several factors such as land use, geology, geomorphology, fault distribution, transportation facility, population density were all used to develop the social, structural, and geotechnical vulnerability maps. The results show that the areas around Delhi, NE region of India, major parts of Gujrat, West Bengal plain exhibit high to very-high seismic vulnerability. This model achieved an accuracy of 87.8%, sensitivity (90%) and specificity (84.9%). The present analysis can be helpful towards prioritization of regions which are in higher need of risk reduction interventions. Also, based on this vulnerability index map, the risk metrics can be attenuated.
Jia, M, Gabrys, B & Musial, K 2021, 'Directed closure coefficient and its patterns', PLOS ONE, vol. 16, no. 6, pp. e0253822-e0253822.
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The triangle structure, being a fundamental and significant element, underlies many theories and techniques in studying complex networks. The formation of triangles is typically measured by the clustering coefficient, in which the focal node is the centre-node in an open triad. In contrast, the recently proposed closure coefficient measures triangle formation from an end-node perspective and has been proven to be a useful feature in network analysis. Here, we extend it by proposing the directed closure coefficient that measures the formation of directed triangles. By distinguishing the direction of the closing edge in building triangles, we further introduce the source closure coefficient and the target closure coefficient. Then, by categorising particular types of directed triangles (e.g., head-of-path), we propose four closure patterns. Through multiple experiments on 24 directed networks from six domains, we demonstrate that at network-level, the four closure patterns are distinctive features in classifying network types, while at node-level, adding the source and target closure coefficients leads to significant improvement in link prediction task in most types of directed networks.
Jiang, C, D'Arienzo, A, Li, W, Wu, S & Bai, Q 2021, 'An Operator-Based Approach for Modeling Influence Diffusion in Complex Social Networks', Journal of Social Computing, vol. 2, no. 2, pp. 166-182.
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Jiang, Y, Zhang, Y, Lin, C, Wu, D & Lin, C-T 2021, 'EEG-Based Driver Drowsiness Estimation Using an Online Multi-View and Transfer TSK Fuzzy System', IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1752-1764.
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Jin, B, Chen, E, Zhao, H, Huang, Z, Liu, Q, Zhu, H & Yu, S 2021, 'Promotion of Answer Value Measurement With Domain Effects in Community Question Answering Systems', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 5, pp. 3068-3079.
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In the area of community question answering (CQA), answer selection and answer ranking are two tasks which are applied to help users quickly access valuable answers. Existing solutions mainly exploit the syntactic or semantic correlation between a question and its related answers (QA), where the multifacet domain effects in CQA are still underexplored. In this paper, we propose a unified model, enhanced attentive recurrent neural network (EARNN), for both answer selection and answer ranking tasks by taking full advantages of both QA semantics and multifacet domain effects (i.e., topic effects and timeliness). Specifically, we develop a serialized long short-term memory to learn the unified representations of QA, where two attention mechanisms at either sentence level or word level are designed for capturing the deep effects of topics. Meanwhile, the emphasis of QA can be automatically distinguished. Furthermore, we design a time-sensitive ranking function to model the timeliness in CQA. To effectively train EARNN, a question-dependent pairwise learning strategy is also developed. Finally, we conduct extensive experiments on a real-world dataset from Quora. Experimental results validate the effectiveness and interpretability of our proposed EARNN model.
Kabade, V, Hooda, R, Raj, C, Awan, Z, Young, AS, Welgampola, MS & Prasad, M 2021, 'Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review', Sensors, vol. 21, no. 22, pp. 7565-7565.
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Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that are often benign and sometimes serious. An individual who develops vertigo can be effectively treated only after a correct diagnosis of the underlying vestibular disorder is reached. Recent advances in artificial intelligence promise novel strategies for the diagnosis and treatment of patients with this common symptom. Human analysts may experience difficulties manually extracting patterns from large clinical datasets. Machine learning techniques can be used to visualize, understand, and classify clinical data to create a computerized, faster, and more accurate evaluation of vertiginous disorders. Practitioners can also use them as a teaching tool to gain knowledge and valuable insights from medical data. This paper provides a review of the literatures from 1999 to 2021 using various feature extraction and machine learning techniques to diagnose vertigo disorders. This paper aims to provide a better understanding of the work done thus far and to provide future directions for research into the use of machine learning in vertigo diagnosis.
Karimi, F, Green, D, Matous, P, Varvarigos, M & Khalilpour, KR 2021, 'Network of networks: A bibliometric analysis', Physica D: Nonlinear Phenomena, vol. 421, pp. 132889-132889.
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This study explores the evolving structure of the rising field of “network of networks” (NoN). Reviewing publications dating back to 1931, we describe the evolution of major NoN research themes in different scientific disciplines and the gradual emergence of an integrated field. We analyse the co-occurrence networks of keywords used in all 7818 scientific publications in Scopus database that mention NoN and other related terms (i.e., “interconnected networks”, “multilayer networks”, “multiplex networks”, “interdependent networks”, “multinetworks”, “multilevel networks”, and “multidimensional networks”). The results show that the NoN began to form as a field mainly in the 1990s around research on neural networks. Diverse aspects of NoN research, indicated by dominant keywords such as “interconnection”, “multilayer”, and “interdependence”, gradually spread to computer and physical sciences. As of 2018, network interdependence – with its application in network resilience and prevention of cascading failure – seems to be one of the key topics attracting broad academic attention. Another noteworthy observation is the emergence of a distinct cluster of terms relevant to nanoscience and nanotechnology. It is envisaged from the analysis that NoN concepts will develop stronger ties with nanoscience with increasing understanding and data acquisition from the molecular, atomic, and subatomic levels.
Kashyap, PK, Kumar, S, Jaiswal, A, Prasad, M & Gandomi, AH 2021, 'Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network', IEEE Sensors Journal, vol. 21, no. 16, pp. 17479-17491.
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Katic, M, Cetindamar, D & Agarwal, R 2021, 'Deploying ambidexterity through better management practices: an investigation based on high-variety, low-volume manufacturing', Journal of Manufacturing Technology Management, vol. 32, no. 4, pp. 952-975.
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PurposeWhilst capabilities in exploiting existing assets and simultaneously exploring new opportunities have proven essential in today's organisations, an understanding of how these so-called ambidextrous capabilities are deployed remains elusive. Thus, the authors aim to investigate the role of better management practices (BMP), as organisational routines, in deploying ambidextrous capabilities in practice.Design/methodology/approachHigh-variety, low-volume (HVLV) manufacturers are adopted as exemplar ambidextrous organisations. A conceptual model was developed where BMP, including human resource management (HRM) and production planning and control (PPC), are considered as mediators in the relationship between ambidextrous capabilities and organisational performance outcomes. Partial least squares structural equation modelling was adopted to analyse the results of a survey undertaken by Australian HVLV manufacturers.FindingsThe results suggest that merely holding ambidextrous capabilities is not enough – demonstrating a fully mediating role of BMP between ambidextrous capabilities and HVLV manufacturer performance outcomes. However, the individual effects of PPC and HRM prove varied in their unique impact on HVLV manufacturer performance.Practical implicationsThis study also provides a rare account of how HVLV manufacturers can leverage their inherently ambidextrous design towards greater organisational performance and highlights critical considerations in the selection of organisational capabilities.Originality...
Ke, Z, Li, Z, Cao, Z & Liu, P 2021, 'Enhancing Transferability of Deep Reinforcement Learning-Based Variable Speed Limit Control Using Transfer Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4684-4695.
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The study aims to evaluate the performance of the transfer learning algorithm to enhance the transferability of a deep reinforcement learning-based variable speed limits (VSL) control. The Double Deep Q Network (DDQN)-based VSL control strategy is proposed for reducing total time spent (TTS) on freeways. A real merging bottleneck is developed in the simulation and considered for the VSL control as the source scenario. Three types of target scenarios are considered, including the overspeed scenarios, adverse weather scenarios, and diverse capacity drop scenarios. A stable testing demand and a fluctuating testing demand are adopted to evaluate the effects of VSL control. The results show that by updating the neural networks, the transfer learning in the DDQN-based VSL control agent successfully transfers knowledge learned in the source scenario to other target scenarios. With the transfer learning, the entire training process is shortened by 32.3% to 69.8%, while keeping a similar maximum reward level, as compared to the VSL control with full learning from scratch. With the transferred DDQN-based VSL strategy, the TTS is reduced by 26.02% to 67.37% with the stable testing demand and 21.31% to 69.98% with the fluctuating testing demand in various scenarios, respectively. The results also show that when the task similarity between the source scenario and target scenario is relatively low, the transfer learning could lead to local optimum and may not achieve the global optimal control effects.
Kennedy, PJ, Catchpoole, DR, Tafavogh, S, Harvey, BL & Aloqaily, AA 2021, 'Feature prioritisation on big genomic data for analysing gene-gene interactions', International Journal of Bioinformatics Research and Applications, vol. 17, no. 2, pp. 158-158.
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Khan, HU, Niazi, M, El-Attar, M, Ikram, N, Khan, SU & Gill, AQ 2021, 'Empirical Investigation of Critical Requirements Engineering Practices for Global Software Development.', IEEE Access, vol. 9, pp. 93593-93613.
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There is a need to identify requirements engineering (RE) practices that are important to global software development (GSD) project success. The objective of this paper is to report our recent empirical study results which aimed to identify the RE practices that are important to GSD projects. This study used an online survey questionnaire to elicit data from 56 RE experts of GSD projects. The survey included 66 RE practices identified by Sommerville et al. for non-GSD projects. The participants were asked to rank each RE practice on a four-point scale to determine the degree of importance of each practice in the context of GSD projects. This research identified a set of six key RE practices that mainly focuses on GSD project stakeholders, scope, standards and requirements traceability management. One common theme that is evident from the RE experts' feedback analysis is the standardization of requirements documents to reduce requirements inconsistencies and improve communication in diverse and distributed GSD project environments Our results show that not all 66 RE best practices are important for GSD projects. We believe that a good understanding of the identified RE practices is vital in developing and implementing the situation-specific RE processes for GSD projects.
Khan, S, Hussain, FK & Hussain, OK 2021, 'Guaranteeing end-to-end QoS provisioning in SOA based SDN architecture: A survey and Open Issues', Future Generation Computer Systems, vol. 119, pp. 176-187.
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Ensuring end-to-end Quality of Services (QoS) is a challenging aspect in traditional network architectures. Software-Defined Network (SDN), as the new norm of the network, has ascended in response to a traditional network's limitations. SDN's benefits are its ability to provide a global networking view, programmability, decouple the data plane with the control plane. Integrating SDN architecture with Service-Oriented Architecture (SOA) paradigm brings a novel network-based notion for service delivery. However, it also introduces new challenges for maintaining the QoS in these networks. Researchers from both academia and industry have proposed and developed several resolutions for QoS management in SDNs. However, gaps still exist in developing and applying such resolutions for QoS management in SOA-based SDNs. This review paper aims to identify these gaps by representing a sketch of the effectiveness of the existing QoS management techniques in SOA-based SDNs. We first identify the four different requirements that QoS management techniques need to meet to be applied in SOA-based SDNs. We then categorize the relevant QoS management approaches into five main categories of QoS based controller design, Resource allocation-based approach, Queue scheduling and management-based approach, QoS-driven optimal routing, and Service Level Agreement (SLA) based quality management in SDN. We then compare the working of techniques in each category against the identified requirements for guaranteeing end-to-end QoS provisioning in SOA based SDN architecture and present directions for future research.
Kurian, JC, Goh, DH-L & John, BM 2021, 'Organizational culture on the Facebook page of an emergency management agency: a thematic analysis', Online Information Review, vol. 45, no. 2, pp. 336-355.
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PurposeThe purpose of this study is to identify organizational cultural factors and overarching themes on emergency management evident across the Facebook page of an emergency management organization. This study also aims to understand the dimensions of social capital that influence the reputation of emergency management organization using the lens of organizational culture.Design/methodology/approachThe organizational cultural factors defined in the literature were used to classify content posted by the organization during a six-month period. The posts were read and analyzed thematically to determine the overarching themes evident across the collected posts. The dimensions of social capital defined in the literature were used to determine its influence on the reputation of an emergency management organization.FindingsThe organizational cultural factors that emerged from the analysis are openness and future orientation without any evidence on risk-taking and flexibility. An analysis of cultural factors indicates that organizational culture facilitates knowledge exchange and knowledge combination. The key themes embedded in the organization's posts are emergency preparedness, communication devices for emergency management, coordination and admiration. The dimensions of social capital that influenced the reputation of emergency management organization were group characteristics, volunteerism, generalized norms and togetherness. Though previous studies have found the influence of culture on social capital, this study extends those findings by identifying the dimensions of culture (i.e. openness and future orientation) that reflects the social capital dimensions (i.e. generalized norms an...
Kwon, S, Tomonaga, A, Lakshmi Bhai, G, Devitt, SJ & Tsai, J-S 2021, 'Gate-based superconducting quantum computing', Journal of Applied Physics, vol. 129, no. 4, pp. 041102-041102.
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In this Tutorial, we introduce basic conceptual elements to understand and build a gate-based superconducting quantum computing system.
Laengle, S, Lobos, V, Merigó, JM, Herrera-Viedma, E, Cobo, MJ & De Baets, B 2021, 'Forty years of Fuzzy Sets and Systems: A bibliometric analysis', Fuzzy Sets and Systems, vol. 402, pp. 155-183.
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© 2020 Elsevier B.V. Fuzzy Sets and Systems is a leading international journal in computer science and applied mathematics that was created in 1978. In 2018, the journal celebrated its 40th anniversary. The aim of this study is to present a bibliometric overview of the leading trends occurring in the journal between 1978 and 2016 by analysing the most productive and influential authors, institutions and countries as well as the publication and citation structure. Additionally, this work presents a graphical visualization of the bibliographic data by using the visualization of similarities (VOS) viewer and the science mapping analysis tool (SciMAT) software. The results show the strong growth of fuzzy set theory over time and a huge diversity of publications from all over the world, especially from Europe, North America and East Asia.
Laranjo, L, Ding, D, Heleno, B, Kocaballi, B, Quiroz, JC, Tong, HL, Chahwan, B, Neves, AL, Gabarron, E, Dao, KP, Rodrigues, D, Neves, GC, Antunes, ML, Coiera, E & Bates, DW 2021, 'Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression', British Journal of Sports Medicine, vol. 55, no. 8, pp. 422-432.
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ObjectiveTo determine the effectiveness of physical activity interventions involving mobile applications (apps) or trackers with automated and continuous self-monitoring and feedback.DesignSystematic review and meta-analysis.Data sourcesPubMed and seven additional databases, from 2007 to 2020.Study selectionRandomised controlled trials in adults (18–65 years old) without chronic illness, testing a mobile app or an activity tracker, with any comparison, where the main outcome was a physical activity measure. Independent screening was conducted.Data extraction and synthesisWe conducted random effects meta-analysis and all effect sizes were transformed into standardised difference in means (SDM). We conducted exploratory metaregression with continuous and discrete moderators identified as statistically significant in subgroup analyses.Main outcome measuresPhysical activity: daily step counts, min/week of moderate-to-vigorous physical activity, weekly days exercised, min/week of total physical activity, metabolic equivalents.ResultsThirty-five studies met inclusion criteria and 28 were included in the meta-analysis (n=7454 participants, 28% women). The meta-analysis showed a small-to-moderate positive effect on physical activity measures (SDM 0.350, 95% CI 0.236 to 0.465, I2=69%, T2=0.051) corresponding to 1850 steps per day (95% CI 1247 to 2457). Interventions including text-messaging and personalisation features were significantly more effective in subgroup analyses and meta...
Leon-Castro, E, Blanco-Mesa, F, Alfaro-Garcia, V, Gil-Lafuente, AM & Merigo, JM 2021, 'Fuzzy systems and applications in innovation and sustainability', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1723-1726.
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Leon-Castro, E, Blanco-Mesa, F, Alfaro-Garcia, V, Gil-Lafuente, AM & Merigo, JM 2021, 'Fuzzy systems in innovation and sustainability', Computational and Mathematical Organization Theory, vol. 27, no. 4, pp. 377-383.
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Fuzzy systems in innovation and sustainability are important topics in literature nowadays. A lot of new formulations in fuzzy systems are being made including interesting applications in different topics. The aim of this special issue is to present different works made in this line of research that were presented in the IV International Congress of Innovation and Sustainability (ICONIS).
Leon-Castro, E, Blanco-Mesa, FR, Gil-Lafuente, AM & Merigo Lindahl, JM 2021, 'Editorial', International Journal of Entrepreneurship and Innovation Management, vol. 25, no. 2-3, pp. 105-109.
León-Castro, E, Espinoza-Audelo, LF, Merigó, JM, Herrera-Viedma, E & Herrera, F 2021, 'Measuring volatility based on ordered weighted average operators: The case of agricultural product prices', Fuzzy Sets and Systems, vol. 422, pp. 161-176.
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León-Castro, E, Perez-Arellano, LA, Olazabal-Lugo, M & Merigó, JM 2021, 'Prioritized Induced Heavy Operators Applied to Political Modelling', International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 29, no. 04, pp. 603-620.
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This paper presents the prioritized induced heavy ordered weighted average (PIHOWA) operator. This operator combines an unbounded weighting vector, an induced vector and a prioritized vector and can be applied to the group decision-making process where the information provided by each decision maker does not have the same importance. An application of this operator is done in governmental transparency in Mexico based on the Open Government Metric (OGM). Among the main results it is possible to visualize how the relative importance of each component can generate important change in the top 10 ranking.
Li, A, Yang, B, Huo, H & Hussain, FK 2021, 'Leveraging implicit relations for recommender systems', Information Sciences, vol. 579, pp. 55-71.
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Li, DL, Prasad, M, Liu, C-L & Lin, C-T 2021, 'Multi-View Vehicle Detection Based on Fusion Part Model With Active Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 5, pp. 3146-3157.
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IEEE Computer vision-based vehicle detection techniques are widely used in real-world applications. However, most of these techniques aim to detect only single-view vehicles, and their performances are easily affected by partial occlusion. Therefore, this paper proposes a novel multi-view vehicle detection system that uses a part model to address the partial occlusion problem and the high variance between all types of vehicles. There are three features in this paper; firstly, different from Deformable Part Model, the construction of part models in this paper is visual and can be replaced at any time. Secondly, this paper proposes some new part models for detection of vehicles according to the appearance analysis of a large number of modern vehicles by the active learning algorithm. Finally, this paper proposes the method that contains color transformation along with the Bayesian rule to filter out the background to accelerate the detection time and increase accuracy. The proposed method outperforms other methods on given dataset.
Li, F, Jiang, L, Liao, Y, Si, Y, Yi, C, Zhang, Y, Zhu, X, Yang, Z, Yao, D, Cao, Z & Xu, P 2021, 'Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study', Journal of Neural Engineering, vol. 18, no. 4, pp. 046097-046097.
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Abstract Objective. Exploring the temporal variability in spatial topology during the resting state attracts growing interest and becomes increasingly useful to tackle the cognitive process of brain networks. In particular, the temporal brain dynamics during the resting state may be delineated and quantified aligning with cognitive performance, but few studies investigated the temporal variability in the electroencephalogram (EEG) network as well as its relationship with cognitive performance. Approach. In this study, we proposed an EEG-based protocol to measure the nonlinear complexity of the dynamic resting-state network by applying the fuzzy entropy. To further validate its applicability, the fuzzy entropy was applied into simulated and two independent datasets (i.e. decision-making and P300). Main results. The simulation study first proved that compared to the existing methods, this approach could not only exactly capture the pattern dynamics in time series but also overcame the magnitude effect of time series. Concerning the two EEG datasets, the flexible and robust network architectures of the brain cortex at rest were identified and distributed at the bilateral temporal lobe and frontal/occipital lobe, respectively, whose variability metrics were found to accurately classify different groups. Moreover, the temporal variability of resting-state network property was also either positively or negatively related to individual cognitive performance. Significance. This outcome suggested the potential of fuzzy entropy for evaluating the temporal variability of the dynamic resting-state brain networks, and the fuzzy entropy is also helpful for uncovering the fluctuating network variability that accounts for the individual decision differences.
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 2021, '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. 29, pp. 2281-2291.
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Li, F, Yi, C, Liao, Y, Jiang, Y, Si, Y, Song, L, Zhang, T, Yao, D, Zhang, Y, Cao, Z & Xu, P 2021, 'Reconfiguration of Brain Network Between Resting State and P300 Task', IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 2, pp. 383-390.
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IEEE Previous studies explore the power spectra from the resting-state condition to the oddball task, but whether brain network existing significant difference is still unclear. Our study aims to address how the brain reconfigures its architecture from a resting-state condition (i.e., baseline) to the P300 task in the visual oddball task. In this study, electroencephalograms (EEGs) were collected from 24 subjects, who were required to only mentally count the number of target stimulus; afterwards, EEG networks constructed in different bands were compared between baseline and task to evaluate the reconfiguration of functional connectivity. Compared to the baseline, our results showed the significantly enhanced delta/theta functional connectivity and decreased alpha default mode network in the progress of brain reconfiguration to the task. Furthermore, the reconfigured coupling strengths were found to relate to P300 amplitudes, which were then regarded as features to train a classifier to differentiate the brain states and the high and low P300 groups with an accuracy of 100% and 77.78%, respectively. The findings of our study help us to under-stand the updates in functional connectivity from resting-state to the oddball task, and the reconfigured network structure has the potential for the selection of good subjects for P300-based brain-computer interface.
Li, M, Cao, Z & Li, Z 2021, 'A Reinforcement Learning-Based Vehicle Platoon Control Strategy for Reducing Energy Consumption in Traffic Oscillations', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 12, pp. 5309-5322.
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The vehicle platoon will be the most dominant driving mode on future roads. To the best of our knowledge, few reinforcement learning (RL) algorithms have been applied in vehicle platoon control, which has large-scale action and state spaces. Some RL-based methods were applied to solve single-agent problems. If we need to tackle multiagent problems, we will use multiagent RL algorithms since the parameters space grows exponentially with the increasing number of agents involved. Previous multiagent RL algorithms generally may provide redundant information to agents, indicating a large amount of useless or unrelated information, which may cause to be difficult for convergence training and pattern extractions from shared information. Also, random actions usually contribute to crashes, especially at the beginning of training. In this study, a communication proximal policy optimization (CommPPO) algorithm was proposed to tackle the above issues. In specific, the CommPPO model adopts a parameter-sharing structure to allow the dynamic variation of agent numbers, which can well handle various platoon dynamics, including splitting and merging. The communication protocol of the CommPPO consists of two parts. In the state part, the widely used predecessor-leader follower typology in the platoon is adopted to transmit global and local state information to agents. In the reward part, a new reward communication channel is proposed to solve the spurious reward and ``lazy agent'' problems in some existing multiagent RLs. Moreover, a curriculum learning approach is adopted to reduce crashes and speed up training. To validate the proposed strategy for platoon control, two existing multiagent RLs and a traditional platoon control strategy were applied in the same scenarios for comparison. Results showed that the CommPPO algorithm gained more rewards and achieved the largest fuel consumption reduction (11.6%).
Li, Y, Shi, W, Liu, Z, Li, J, Wang, Q, Yan, X, Cao, Z & Wang, G 2021, 'Effective Brain State Estimation During Propofol-Induced Sedation Using Advanced EEG Microstate Spectral Analysis', IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 4, pp. 978-987.
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Brain states are patterns of neuronal synchrony, and the electroencephalogram (EEG) microstate provides a promising tool to characterize and analyze the synchronous neural firing. However, the topographical spectral information for each predominate microstate is still unclear during the switch of consciousness, such as sedation, and the practical usage of the EEG microstate is worth probing. Also, the mechanism behind the anesthetic-induced alternations of brain states remains poorly understood. In this study, an advanced EEG microstate spectral analysis was utilized using multivariate empirical mode decomposition in Hilbert-Huang transform. The practicability was further investigated in scalp EEG recordings during the propofol-induced transition of consciousness. The process of transition from the awake baseline to moderate sedation was accompanied by apparent increases in microstate (A, B, and F) energy, especially in the whole-brain delta band, frontal alpha band and beta band. In comparison to other effective EEG-based parameters that commonly used to measure anesthetic depth, using the selected spectral features reached better performance (80% sensitivity, 90% accuracy) to estimate the brain states during sedation. The changes in microstate energy also exhibited high correlations with individual behavioral data during sedation. In a nutshell, the EEG microstate spectral analysis is an effective method to estimate brain states during propofol-induced sedation, giving great insights into the underlying mechanism. The generated spectral features can be promising markers to dynamically assess the consciousness level.
Li, Y, Yin, J & Chen, L 2021, 'SEAL: Semisupervised Adversarial Active Learning on Attributed Graphs', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 3136-3147.
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Li, Z, Xie, H, Xu, G, Li, Q, Leng, M & Zhou, C 2021, 'Towards purchase prediction: A transaction-based setting and a graph-based method leveraging price information', Pattern Recognition, vol. 113, pp. 107824-107824.
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Targeting at boosting business revenue, purchase prediction based on user behavior is crucial to e-commerce. However, it is not a well-explored topic due to a lack of relevant datasets. Specifically, no public dataset provides both price and discount information varying on time, which play an essential role in the user's decision making. Besides, existing learn-to-rank methods cannot explicitly predict the purchase possibility for a specific user-item pair. In this paper, we propose a two-step graph-based model, where the graph model is applied in the first step to learn representations of both users and items over click-through data, and the second step is a classifier incorporating the price information of each transaction record. To evaluate the model performance, we propose a transaction-based framework focusing on the purchased items and their context clicks, which contain items that a user is interested in but fails to choose after comparison. Our experiments show that exploiting the price and discount information can significantly enhance prediction accuracy.
Liao, T, Lei, Z, Zhu, T, Zeng, S, Li, Y & Yuan, C 2021, 'Deep Metric Learning for K Nearest Neighbor Classication', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Lin, C-T & Do, T-TN 2021, 'Direct-Sense Brain–Computer Interfaces and Wearable Computers', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 1, pp. 298-312.
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Brain-computer interfaces (BCIs) allow users to communicate directly with external devices via their brain signals. Recently, BCIs, and wearable computers in particular, have been receiving more attention by government and industry as an alternative means of interacting with technology. Wearable computers can combine highly immersive virtual/augmented/mixed reality experiences for entertainment, health monitoring, utilitarian purposes, and, most importantly at present, research. With wearable computers, researchers can design, simulate, and finely control experiments to examine human-brain dynamics outside the laboratory. Yet despite the power of BCIs, take-up is slow. This form of interaction is unnatural to humans and often requires external stimuli. Further, the response feedback produced by the computer part of the system is nowhere near as quick as our brains. Hence, we undertook a review of the current state-of-the-art in BCI research and distilled the current findings into a stimulus-free BCI, called direct-sense BCIs, that operates directly and seamlessly from our thinking. This is a novel paradigm that, in the short term, could substantially improve the quality of a user's experience with BCI, and, over the long term, lead to much more widespread take-up of BCI technology.
Lin, C-T, Chuang, C-H, Hung, Y-C, Fang, C-N, Wu, D & Wang, Y-K 2021, 'A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks', IEEE Transactions on Cybernetics, vol. 51, no. 10, pp. 4959-4967.
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Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level. Electroencephalography (EEG) has been demonstrated to be effective for monitoring changes in the human brain state and behavior. Thirty-seven subjects participated in this driving experiment and performed a perform lane-keeping task in a visual-reality environment. Three domains, namely, frequency, temporal, and 2-D spatial information, of the EEG channel location were comprehensively considered. A 4-D convolutional neural-network (4-D CNN) algorithm was then proposed to associate all information from the EEG signals and the changes in the human state and behavioral performance. A 4-D CNN achieves superior forecasting performance over 2-D CNN, 3-D CNN, and shallow networks. The results showed a 3.82% improvement in the root mean-square error, a 3.45% improvement in the error rate, and a 11.98% improvement in the correlation coefficient with 4-D CNN compared with 3-D CNN. The 4-D CNN algorithm extracts the significant θ and alpha activations in the frontal and posterior cingulate cortices under distinct fatigue levels. This work contributes to enhancing our understanding of deep learning methods in the analysis of EEG signals. We even envision that deep learning might serve as a bridge between translation neuroscience and further real-world applications.
Lin, C-T, King, J-T, John, AR, Huang, K-C, Cao, Z & Wang, Y-K 2021, 'The Impact of Vigorous Cycling Exercise on Visual Attention: A Study With the BR8 Wireless Dry EEG System', Frontiers in Neuroscience, vol. 15, p. 621365.
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Many studies have reported that exercise can influence cognitive performance. But advancing our understanding of the interrelations between psychology and physiology in sports neuroscience requires the study of real-time brain dynamics during exercise in the field. Electroencephalography (EEG) is one of the most powerful brain imaging technologies. However, the limited portability and long preparation time of traditional wet-sensor systems largely limits their use to laboratory settings. Wireless dry-sensor systems are emerging with much greater potential for practical application in sports. Hence, in this paper, we use the BR8 wireless dry-sensor EEG system to measure P300 brain dynamics while cycling at various intensities. The preparation time was mostly less than 2 min as BR8 system’s dry sensors were able to attain the required skin-sensor interface impedance, enabling its operation without any skin preparation or application of conductive gel. Ten participants performed four sessions of a 3 min rapid serial visual presentation (RSVP) task while resting and while cycling. These four sessions were pre-CE (RSVP only), low-CE (RSVP in 40–50% of max heart rate), vigorous-CE (RSVP in 71–85% of max heart rate) and post-CE (RSVP only). The recorded brain signals demonstrate that the P300 amplitudes, observed at the Pz channel, for the target and non-target responses were significantly different in all four sessions. The results also show decreased reaction times to the visual attention task during vigorous exercise, enriching our understanding of the ways in which exercise can enhance cognitive performance. Even though only a single channel was evaluated in this study, the quality and reliability of the measurement using these dry sensor-based EEG systems is clearly demonstrated by our results. Further, the smooth implementation of the experiment with a dry system and the success of the data analysis demonstrate that wireless dry EEG devices can o...
Lin, Q, Zhu, Y, Lu, H, Shi, K & Niu, Z 2021, 'Improving University Faculty Evaluations via multi-view Knowledge Graph', Future Generation Computer Systems, vol. 117, pp. 181-192.
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Linares-Mustarós, S, Ferrer-Comalat, JC, Corominas-Coll, D & Merigó, JM 2021, 'The weighted average multiexperton', Information Sciences, vol. 557, pp. 355-372.
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© 2020 Experton theory, a generalization of probabilistic set theory, that is of great usefulness to group decision analysis, was first proposed as a means of improving the processing and analysis of opinions issued by experts. This theory produces an information-fusion mathematical object, the experton, which can be used in predictive problems to justify decisions based on well-constructed reasoning. The aim of this paper is to present an aggregative method of several expertons, with the idea that some of the groups of experts involved in producing these expertons may have more influence than others in the decision-making process. In this article, we carry out an aggregation analysis of expertons, not experts, which culminates in the creation of a new mathematical object. This object, which is called the weighted average multiexperton, is coherent with an experton-type object created from a weighting of the initial data provided by all experts. Since the aggregation method presented has been devised to represent the decision-maker's attitude regarding the importance of different groups of experts, this approach represents a new tool for dealing with group decision-making problems. Additionally, the study presents some properties of the new object. Finally, the paper ends with an application for business decision-making.
Liu, C, Bano, M, Zowghi, D & Kearney, M 2021, 'Analysing user reviews of inquiry-based learning apps in science education.', Comput. Educ., vol. 164, pp. 104119-104119.
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© 2020 Elsevier Ltd The science education community is increasingly valuing the use of mobile apps in inquiry-based learning (IBL) to improve learner’ attitudes and their understanding of science concepts. Although there exists a body of research on mobile apps used for IBL in science education, limited attention has been paid to linking apps' features with their pedagogical affordances. Our study addresses this research gap by evaluating science mobile learning apps with respect to IBL pedagogy. Nine functional features of apps that support educational aspects of inquiry-based pedagogy are identified from user reviews, including: fingertip interaction, graphics visualisation, informative materials, location-based services, offline access, search by question, timeline scrolling, user tutorials, and zoom control. The information contained in the version history of the apps is analysed and four educational aspects of IBL supported by the nine functional features are identified as: motivation, conceptualisation, exploration, and conclusion. We have further compared the evolution of the functional features of apps to the educational aspects of inquiry-based pedagogy identified from different versions of apps. The findings of this study show the trend of updated functional features that support IBL and inform practitioners seeking to improve their use of mobile apps to support students' learning in science. We conclude by proposing areas of future research in this field.
Liu, C, Zowghi, D, Kearney, M & Bano, M 2021, 'Inquiry-based mobile learning in secondary school science education: A systematic review.', J. Comput. Assist. Learn., vol. 37, no. 1, pp. 1-23.
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Recent years have seen a growing call for inquiry‐based learning in science education, and mobile technologies are perceived as increasingly valuable tools to support this approach. However, there is a lack of understanding of mobile technology‐supported inquiry‐based learning (mIBL) in secondary science education. More evidence‐based, nuanced insights are needed into how using mobile technologies might facilitate students' engagement with various levels of inquiry and enhance their science learning. We, therefore, conducted a robust systematic literature review (SLR) of the research articles on mIBL in secondary school science education that have been published from 2000 to 2019. We reviewed and analysed 31 empirical studies (34 articles) to explore the types of mIBL, and the benefits and constraints of mIBL in secondary school science education. The findings of this SLR suggest new research areas for further exploration and provide implications for science teachers' selection, use and design of mIBL approaches in their teaching.
Liu, J, Wang, X, Shen, S, Yue, G, Yu, S & Li, M 2021, 'A Bayesian Q-Learning Game for Dependable Task Offloading Against DDoS Attacks in Sensor Edge Cloud', IEEE Internet of Things Journal, vol. 8, no. 9, pp. 7546-7561.
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To enhance dependable resource allocation against increasing DDoS attacks, in this paper, we investigate interactions between a sensor device-edgeVM pair and a DDoS attacker using a game-theoretic framework, under the constraints of the task time, resource budget, and incomplete knowledge of the processing time of machine learning tasks. In this game, the sensor device expects an edgeVM to cooperate and choose its resource allocation strategy with the objective of satisfying the minimum resource required of machine learning tasks at the corresponding sensor device. Similarly, the attacker’s objective is to strategically allocate resources so that the resource constraint of the machine learning tasks is not satisfied. Owing to a lack of complete information of the processing time of the machine learning tasks, this strategic resource allocation problem between the two players is modeled as a Bayesian Q-learning game, in which the optimal strategies of the sensor device-edgeVM pair and the attacker are analyzed. Furthermore, probability distributions are employed by the corresponding players to model the incomplete nature of the game and a greedy Q-learning algorithm is proposed to dependable resource allocation against DDoS attacks. Numerical simulation results demonstrate that the proposed mechanism is superior to other dependable resource allocation mechanisms under incomplete information for DDoS attacks in the sensor edge cloud.
Liu, S, Wang, S, Liu, X, Lin, C-T & Lv, Z 2021, 'Fuzzy Detection Aided Real-Time and Robust Visual Tracking Under Complex Environments', IEEE Transactions on Fuzzy Systems, vol. 29, no. 1, pp. 90-102.
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Liu, X, Yang, B, Chen, H, Musial, K, Chen, H, Li, Y & Zuo, W 2021, 'A Scalable Redefined Stochastic Blockmodel', ACM Transactions on Knowledge Discovery from Data, vol. 15, no. 3, pp. 1-28.
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Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1
Liu, X, Yang, B, Song, W, Musial, K, Zuo, W, Chen, H & Yin, H 2021, 'A block-based generative model for attributed network embedding', World Wide Web, vol. 24, no. 5, pp. 1439-1464.
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Attributed network embedding has attracted plenty of interest in recent years. It aims to learn task-independent, low-dimensional, and continuous vectors for nodes preserving both topology and attribute information. Most of the existing methods, such as random-walk based methods and GCNs, mainly focus on the local information, i.e., the attributes of the neighbours. Thus, they have been well studied for assortative networks (i.e., networks with communities) but ignored disassortative networks (i.e., networks with multipartite, hubs, and hybrid structures), which are common in the real world. To model both assortative and disassortative networks, we propose a block-based generative model for attributed network embedding from a probability perspective. Specifically, the nodes are assigned to several blocks wherein the nodes in the same block share the similar linkage patterns. These patterns can define assortative networks containing communities or disassortative networks with the multipartite, hub, or any hybrid structures. To preserve the attribute information, we assume that each node has a hidden embedding related to its assigned block. We use a neural network to characterize the nonlinearity between node embeddings and node attributes. We perform extensive experiments on real-world and synthetic attributed networks. The results show that our proposed method consistently outperforms state-of-the-art embedding methods for both clustering and classification tasks, especially on disassortative networks.
Liu, Z, Xiao, F, Lin, C-T, Kang, BH & Cao, Z 2021, 'A Generalized Golden Rule Representative Value for Multiple-Criteria Decision Analysis', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 5, pp. 3193-3204.
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Liu, Z, Yao, L, Wang, X, Monaghan, JJM, Schaette, R, He, Z & McAlpine, D 2021, 'Generalizable Sample-Efficient Siamese Autoencoder for Tinnitus Diagnosis in Listeners With Subjective Tinnitus', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, no. 99, pp. 1452-1461.
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Electroencephalogram (EEG)-based neurofeedback has been widely studied for tinnitus therapy in recent years. Most existing research relies on experts' cognitive prediction, and studies based on machine learning and deep learning are either data-hungry or not well generalizable to new subjects. In this paper, we propose a robust, data-efficient model for distinguishing tinnitus from the healthy state based on EEG-based tinnitus neurofeedback. We propose trend descriptor, a feature extractor with lower fineness, to reduce the effect of electrode noises on EEG signals, and a siamese encoder-decoder network boosted in a supervised manner to learn accurate alignment and to acquire high-quality transferable mappings across subjects and EEG signal channels. Our experiments show the proposed method significantly outperforms state-of-the-art algorithms when analyzing subjects' EEG neurofeedback to 90dB and 100dB sound, achieving an accuracy of 91.67%-94.44% in predicting tinnitus and control subjects in a subject-independent setting. Our ablation studies on mixed subjects and parameters show the method's stability in performance.
Logan, J, Kennedy, PJ & Catchpoole, D 2021, 'The Untapped Social Impact of Artificial Intelligence for Breast Cancer Screening in Developing Countries: A Critical Commentary of DeepMind', Innovations in Digital Health, Diagnostics, and Biomarkers, vol. 1, no. 2, pp. 29-32.
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Lu, H, Zhu, Y, Yuan, Y, Gong, W, Li, J, Shi, K, Lv, Y, Niu, Z & Wang, F-Y 2021, 'Social Signal-Driven Knowledge Automation: A Focus on Social Transportation', IEEE Transactions on Computational Social Systems, vol. 8, no. 3, pp. 737-753.
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Lu, J, Zheng, X, Tang, L, Zhang, T, Sheng, QZ, Wang, C, Jin, J, Yu, S & Zhou, W 2021, 'Can Steering Wheel Detect Your Driving Fatigue?', IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 5537-5550.
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Automated Driving System (ADS) has attracted increasing attention but the state-of-the-art ADS largely depend on vehicle driving parameters and facial features, which lacks reliability. Approaches using physiological based sensors (e.g., electroencephalogram or electrocardiogram) are either too clumsy to wear or impractical to install. In this paper, we propose a novel driver fatigue detection method by embedding surface electromyography (sEMG) sensors on a steering wheel. Compared with the existing methods, our approach is able to collect bio-signals in a non-intrusive way and detect driver fatigue at an earlier stage. The experimental results show that our approach outperforms existing methods with the weighted average F1 scores of about 90%. We also propose promising future directions to deploy this approach in real-life settings, such as applying multimodal learning using several supplementary sensors.
Lv, Y, Miao, J, Liang, J, Chen, L & Qian, Y 2021, 'BIC-based node order learning for improving Bayesian network structure learning', Frontiers of Computer Science, vol. 15, no. 6, p. 156337.
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Node order is one of the most important factors in learning the structure of a Bayesian network (BN) for probabilistic reasoning. To improve the BN structure learning, we propose a node order learning algorithm based on the frequently used Bayesian information criterion (BIC) score function. The algorithm dramatically reduces the space of node order and makes the results of BN learning more stable and effective. Specifically, we first find the most dependent node for each individual node, prove analytically that the dependencies are undirected, and then construct undirected subgraphs UG. Secondly, the UG is examined and connected into a single undirected graph UGC. The relation between the subgraph number and the node number is analyzed. Thirdly, we provide the rules of orienting directions for all edges in UGC, which converts it into a directed acyclic graph (DAG). Further, we rank the DAG’s topology order and describe the BIC-based node order learning algorithm. Its complexity analysis shows that the algorithm can be conducted in linear time with respect to the number of samples, and in polynomial time with respect to the number of variables. Finally, experimental results demonstrate significant performance improvement by comparing with other methods.
Maeda, EE, Haapasaari, P, Helle, I, Lehikoinen, A, Voinov, A & Kuikka, S 2021, 'Black Boxes and the Role of Modeling in Environmental Policy Making', Frontiers in Environmental Science, vol. 9.
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Modeling is essential for modern science, and science-based policies are directly affected by the reliability of model outputs. Artificial intelligence has improved the accuracy and capability of model simulations, but often at the expense of a rational understanding of the systems involved. The lack of transparency in black box models, artificial intelligence based ones among them, can potentially affect the trust in science driven policy making. Here, we suggest that a broader discussion is needed to address the implications of black box approaches on the reliability of scientific advice used for policy making. We argue that participatory methods can bridge the gap between increasingly complex scientific methods and the people affected by their interpretations
Mai, L, Ding, Y, Zhang, X, Fan, L, Yu, S & Xu, Z 2021, 'Energy efficiency with service availability guarantee for Network Function Virtualization', Future Generation Computer Systems, vol. 119, pp. 140-153.
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Following the trend of Network Function Virtualization (NFV), dedicated hardware middleboxes are replaced with innovative and flexible software middleboxes also known as Virtual Network Functions (VNFs). An ordered sequence of VNFs composing a logical service is called Service Function Chain (SFC). VNFs are generally run on commodity servers. In this way, the capital and operational expenditures of buying and maintaining dedicated hardware for telecom operators can be greatly reduced. One of the key issues in NFV is the optimal VNF placement and service chaining to achieve energy efficiency. However, the current NFV energy saving approaches seem to consider energy minimization as the only objective to be optimized. Little or no attention is given to other important aspects, e.g., service availability, which is paramountly important to fulfill Service Level Agreement (SLA) for telecom operators. This paper investigates the energy efficiency optimization with service availability guarantee in NFV-enabled networks. We firstly propose a novel green orchestration NFV architecture. Then, an energy-efficient VNF placement framework guaranteeing service availability is presented under the proposed architecture, and evaluated by extensive simulations. Open research issues and technical challenges in this emerging area are also presented.
Mann, RL 2021, 'Simulating quantum computations with Tutte polynomials', npj Quantum Information, vol. 7, no. 1, pp. 1-8.
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AbstractWe establish a classical heuristic algorithm for exactly computing quantum probability amplitudes. Our algorithm is based on mapping output probability amplitudes of quantum circuits to evaluations of the Tutte polynomial of graphic matroids. The algorithm evaluates the Tutte polynomial recursively using the deletion–contraction property while attempting to exploit structural properties of the matroid. We consider several variations of our algorithm and present experimental results comparing their performance on two classes of random quantum circuits. Further, we obtain an explicit form for Clifford circuit amplitudes in terms of matroid invariants and an alternative efficient classical algorithm for computing the output probability amplitudes of Clifford circuits.
Mann, RL & Helmuth, T 2021, 'Efficient algorithms for approximating quantum partition functions', Journal of Mathematical Physics, vol. 62, no. 2, pp. 022201-022201.
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We establish a polynomial-time approximation algorithm for partition functions of quantum spin models at high temperature. Our algorithm is based on the quantum cluster expansion of Netočný and Redig and the cluster expansion approach to designing algorithms due to Helmuth, Perkins, and Regts. Similar results have previously been obtained by related methods, and our main contribution is a simple and slightly sharper analysis for the case of pairwise interactions on bounded-degree graphs.
Mastio, E & Dovey, K 2021, 'Contextual insight as an antecedent to strategic foresight', Futures, vol. 128, pp. 102715-102715.
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Mas-Tur, A, Roig-Tierno, N, Sarin, S, Haon, C, Sego, T, Belkhouja, M, Porter, A & Merigó, JM 2021, 'Co-citation, bibliographic coupling and leading authors, institutions and countries in the 50 years of Technological Forecasting and Social Change', Technological Forecasting and Social Change, vol. 165, pp. 120487-120487.
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Maus Esfahani, N, Catchpoole, D & Kennedy, PJ 2021, 'SMCKAT, a Sequential Multi-Dimensional CNV Kernel-Based Association Test', Life, vol. 11, no. 12, pp. 1302-1302.
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Copy number variants (CNVs) are the most common form of structural genetic variation, reflecting the gain or loss of DNA segments compared with a reference genome. Studies have identified CNV association with different diseases. However, the association between the sequential order of CNVs and disease-related traits has not been studied, to our knowledge, and it is still unclear that CNVs function individually or whether they work in coordination with other CNVs to manifest a disease or trait. Consequently, we propose the first such method to test the association between the sequential order of CNVs and diseases. Our sequential multi-dimensional CNV kernel-based association test (SMCKAT) consists of three parts: (1) a single CNV group kernel measuring the similarity between two groups of CNVs; (2) a whole genome group kernel that aggregates several single group kernels to summarize the similarity between CNV groups in a single chromosome or the whole genome; and (3) an association test between the CNV sequential order and disease-related traits using a random effect model. We evaluate SMCKAT on CNV data sets exhibiting rare or common CNVs, demonstrating that it can detect specific biologically relevant chromosomal regions supported by the biomedical literature. We compare the performance of SMCKAT with MCKAT, a multi-dimensional kernel association test. Based on the results, SMCKAT can detect more specific chromosomal regions compared with MCKAT that not only have CNV characteristics, but the CNV order on them are significantly associated with the disease-related trait.
Maus Esfahani, N, Catchpoole, D, Khan, J & Kennedy, PJ 2021, 'MCKAT: a multi-dimensional copy number variant kernel association test', BMC Bioinformatics, vol. 22, no. 1, p. 588.
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AbstractBackgroundCopy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific disease traits is growing. However, due to the specific multi-dimensional characteristics of the CNVs, methods for testing the association between CNVs and the disease-related traits are still underdeveloped. We propose a novel multi-dimensional CNV kernel association test (MCKAT) in this paper. We aim to find significant associations between CNVs and disease-related traits using kernel-based methods.ResultsWe address the multi-dimensionality in CNV characteristics. We first design a single pair CNV kernel, which contains three sub-kernels to summarize the similarity between two CNVs considering all CNV characteristics. Then, aggregate single pair CNV kernel to the whole chromosome CNV kernel, which summarizes the similarity between CNVs in two or more chromosomes. Finally, the association between the CNVs and disease-related traits is evaluated by comparing the similarity in the trait with kernel-based similarity using a score test in a random effect model. We apply MCKAT on genome-wide CNV datasets to examine the association between CNVs and disease-related traits, which demonstrates the potential usefulness the proposed method has for the CNV association tests. We compare the performance of MCKAT with CKAT, a uni-dimensional kernel method. Based on the results, MCKAT indicates stronger evidence, smallerp-value, in detecting significant associations between CNVs and disease-related traits in both rare and common CNV datasets.ConclusionA...
Ming, Y, Wu, D, Wang, Y-K, Shi, Y & Lin, C-T 2021, 'EEG-Based Drowsiness Estimation for Driving Safety Using Deep Q-Learning', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 5, no. 4, pp. 583-594.
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IEEE Fatigue is the most vital factor of road fatalities, and one manifestation of fatigue during driving is drowsiness. In this paper, we propose using deep Q-learning to study the correlation between drowsiness and driving performance. This study is carried out by analyzing an electroencephalogram (EEG) dataset captured during a simulated endurance driving test. Driving safety research using EEG data represents an important brain-computer interface (BCI) paradigm from an application perspective. To formulate the drowsiness estimation problem as an optimization of a Q-learning task, we adapt the terminologies in the driving test to fit the reinforcement learning framework. Based on that, a deep Q-network (DQN) is tailored by referring to the latest DQN technologies. The designed network merits the characteristics of the EEG data and can generate actions to indirectly estimate drowsiness. The results show that the trained model can trace the variations of mind state in a satisfactory way against the testing EEG data, which confirms the feasibility and practicability of this new computation paradigm. By comparison, it also reveals that our method outperforms the supervised learning counterpart and is superior for real applications. To the best of our knowledge, we are the first to introduce the deep reinforcement learning method to this BCI scenario, and our method can potentially be generalized to other BCI cases.
Moallemi, EA, de Haan, FJ, Hadjikakou, M, Khatami, S, Malekpour, S, Smajgl, A, Smith, MS, Voinov, A, Bandari, R, Lamichhane, P, Miller, KK, Nicholson, E, Novalia, W, Ritchie, EG, Rojas, AM, Shaikh, MA, Szetey, K & Bryan, BA 2021, 'Evaluating Participatory Modeling Methods for Co‐creating Pathways to Sustainability', Earth's Future, vol. 9, no. 3.
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AbstractThe achievement of global sustainability agendas, such as the Sustainable Development Goals, relies on transformational change across society, economy, and environment that are co‐created in a transdisciplinary exercise by all stakeholders. Within this context, environmental and societal change is increasingly understood and represented via participatory modeling for genuine engagement with multiple collaborators in the modeling process. Despite the diversity of participatory modeling methods to promote engagement and co‐creation, it remains uncertain what the extent and modes of participation are in different contexts, and how to select the suitable methods to use in a given situation. Based on a review of available methods and specification of potential contextual requirements, we propose a unifying framework to guide how collaborators of different backgrounds can work together and evaluate the suitability of participatory modeling methods for co‐creating sustainability pathways. The evaluation of method suitability promises the integration of concepts and approaches necessary to address the complexities of problems at hand while ensuring robust methodologies based on well‐tested evidence and negotiated among participants. Using two illustrative case studies, we demonstrate how to explore and evaluate the choice of methods for participatory modeling in varying contexts. The insights gained can inform creative participatory approaches to pathway development through tailored combinations of methods that best serve the specific sustainability context of particular case studies.
Moscato, P, Mathieson, L & Haque, MN 2021, 'Augmented intuition: a bridge between theory and practice', Journal of Heuristics, vol. 27, no. 4, pp. 497-547.
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Motivated by the celebrated paper of Hooker (J Heuristics 1(1): 33–42, 1995) published in the first issue of this journal, and by the relative lack of progress of both approximation algorithms and fixed-parameter algorithms for the classical decision and optimization problems related to covering edges by vertices, we aimed at developing an approach centered in augmenting our intuition about what is indeed needed. We present a case study of a novel design methodology by which algorithm weaknesses will be identified by computer-based and fixed-parameter tractable algorithmic challenges on their performance. Comprehensive benchmarkings on all instances of small size then become an integral part of the design process. Subsequent analyses of cases where human intuition “fails”, supported by computational testing, will then lead to the development of new methods by avoiding the traps of relying only on human perspicacity and ultimately will improve the quality of the results. Consequently, the computer-aided design process is seen as a tool to augment human intuition. It aims at accelerating and foster theory development in areas such as graph theory and combinatorial optimization since some safe reduction rules for pre-processing can be mathematically proved via theorems. This approach can also lead to the generation of new interesting heuristics. We test our ideas with a fundamental problem in graph theory that has attracted the attention of many researchers over decades, but for which seems it seems to be that a certain stagnation has occurred. The lessons learned are certainly beneficial, suggesting that we can bridge the increasing gap between theory and practice by a more concerted approach that would fuel human imagination from a data-driven discovery perspective.
Motahari, R, Saeidi Sough, Y, Aboutorab, H & Saberi, M 2021, 'Joint optimization of maintenance and inventory policies for multi-unit systems', International Journal of System Assurance Engineering and Management, vol. 12, no. 3, pp. 587-607.
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Moussa, L, Benrimoj, S, Musial, K, Kocbek, S & Garcia-Cardenas, V 2021, 'Data-driven approach for tailoring facilitation strategies to overcome implementation barriers in community pharmacy', Implementation Science, vol. 16, no. 1.
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Abstract Background Implementation research has delved into barriers to implementing change and interventions for the implementation of innovation in practice. There remains a gap, however, that fails to connect implementation barriers to the most effective implementation strategies and provide a more tailored approach during implementation. This study aimed to explore barriers for the implementation of professional services in community pharmacies and to predict the effectiveness of facilitation strategies to overcome implementation barriers using machine learning techniques. Methods Six change facilitators facilitated a 2-year change programme aimed at implementing professional services across community pharmacies in Australia. A mixed methods approach was used where barriers were identified by change facilitators during the implementation study. Change facilitators trialled and recorded tailored facilitation strategies delivered to overcome identified barriers. Barriers were coded according to implementation factors derived from the Consolidated Framework for Implementation Research and the Theoretical Domains Framework. Tailored facilitation strategies were coded into 16 facilitation categories. To predict the effectiveness of these strategies, data mining with random forest was used to provide the highest level of accuracy. A predictive resolution percentage was established for each implementation strategy in relation to the barriers that were resolved by that particular strategy. Results During the 2-year programme, 1131 barriers and facilitation strategies were recorded by change facilitators. The most frequently identified barriers...
Mulet-Forteza, C, Lunn, E, Merigó, JM & Horrach, P 2021, 'Research progress in tourism, leisure and hospitality in Europe (1969–2018)', International Journal of Contemporary Hospitality Management, vol. 33, no. 1, pp. 48-74.
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PurposeThis study aims to present a bibliometric overview of articles published in the field of tourism, leisure and hospitality and analyzed by researchers mainly affiliated with European institutions.Design/methodology/approachThe authors conducted a bibliometric study of journals included in the Web of Science related to the field of tourism, leisure and hospitality in 2019. The review incorporates various techniques to determine the field’s structure from a scientific and intellectual perspective.FindingsThe results are valuable for several reasons. First, they will support researchers in identifying those topics with the greatest potential for advancing research in this field. Second, they will constitute an important aid in the design of new policies for journal publishers.Practical implicationsThis study can lead to advances in the tourism, leisure and hospitality field, as it identifies the publication trends of researchers who are mainly affiliated with European institutions. It also offers useful information for practitioners and academics in their endeavor to identify gaps in the extant literature and future trends.Originality/valueNo other studies have analyzed this field for a period of this length.
Naseem, U, Razzak, I, Khan, SK & Prasad, M 2021, 'A Comprehensive Survey on Word Representation Models: From Classical to State-of-the-Art Word Representation Language Models', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 20, no. 5, pp. 1-35.
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Word representation has always been an important research area in the history of natural language processing (NLP). Understanding such complex text data is imperative, given that it is rich in information and can be used widely across various applications. In this survey, we explore different word representation models and its power of expression, from the classical to modern-day state-of-the-art word representation language models (LMS). We describe a variety of text representation methods, and model designs have blossomed in the context of NLP, including SOTA LMs. These models can transform large volumes of text into effective vector representations capturing the same semantic information. Further, such representations can be utilized by various machine learning (ML) algorithms for a variety of NLP-related tasks. In the end, this survey briefly discusses the commonly used ML- and DL-based classifiers, evaluation metrics, and the applications of these word embeddings in different NLP tasks.
Nguyen, T-D, Musial, K & Gabrys, B 2021, 'AutoWeka4MCPS-AVATAR: Accelerating automated machine learning pipeline composition and optimisation', Expert Systems with Applications, vol. 185, pp. 115643-115643.
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Automated machine learning pipeline (ML) composition and optimisation aim at automating the process of finding the most promising ML pipelines within allocated resources (i.e., time, CPU and memory). Existing methods, such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods frequently require a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid in the first place, and attempting to execute them is a waste of time and resources. To address this issue, we propose a novel method to evaluate the validity of ML pipelines, without their execution, using a surrogate model (AVATAR). The AVATAR generates a knowledge base by automatically learning the capabilities and effects of ML algorithms on datasets’ characteristics. This knowledge base is used for a simplified mapping from an original ML pipeline to a surrogate model which is a Petri net based pipeline. Instead of executing the original ML pipeline to evaluate its validity, the AVATAR evaluates its surrogate model constructed by capabilities and effects of the ML pipeline components and input/output simplified mappings. Evaluating this surrogate model is less resource-intensive than the execution of the original pipeline. As a result, the AVATAR enables the pipeline composition and optimisation methods to evaluate more pipelines by quickly rejecting invalid pipelines. We integrate the AVATAR into the sequential model-based algorithm configuration (SMAC). Our experiments show that when SMAC employs AVATAR, it finds better solutions than on its own. This is down to the fact that the AVATAR can evaluate more pipelines within the same time budget and allocated resources.
Niu, T, Wang, J, Lu, H, Yang, W & Du, P 2021, 'A Learning System Integrating Temporal Convolution and Deep Learning for Predictive Modeling of Crude Oil Price', IEEE Transactions on Industrial Informatics, vol. 17, no. 7, pp. 4602-4612.
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Nizami, S, McGregor AM, C & Green, JR 2021, 'Integrating Physiological Data Artifacts Detection With Clinical Decision Support Systems: Observational Study', JMIR Biomedical Engineering, vol. 6, no. 2, pp. e23495-e23495.
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Background Clinical decision support systems (CDSS) have the potential to lower the patient mortality and morbidity rates. However, signal artifacts present in physiological data affect the reliability and accuracy of the CDSS. Moreover, patient monitors and other medical devices generate false alarms while processing physiological data, further leading to alarm fatigue because of increased noise levels, staff disruption, and staff desensitization in busy critical care environments. This adversely affects the quality of care at the patient bedside. Hence, artifact detection (AD) algorithms play a crucial role in assessing the quality of physiological data and mitigating the impact of these artifacts. Objective The aim of this study is to evaluate a novel AD framework for integrating AD algorithms with CDSS. We designed the framework with features that support real-time implementation within critical care. In this study, we evaluated the framework and its features in a false alarm reduction study. We developed static framework component models, followed by dynamic framework compositions to formulate four CDSS. We evaluated these formulations using neonatal patient data and validated the six framework features: flexibility, reusability, signal quality indicator standardization, scalability, customizability, and real-time implementation support. Methods We developed four exemplar static AD components with standardized requirements and provisions interfaces that facilitate the interoperability of framework components. These AD components were mixed and matched into four different AD compositions to mitigate the artifacts’ effects. We developed a novel static clinical event det...
Oberst, S, Halkon, B, Ji, J & Brown, T 2021, 'Preface', Vibration Engineering for a Sustainable Future: Numerical and Analytical Methods to Study Dynamical Systems, Vol. 3, pp. v-vi.
Oberst, S, Halkon, B, Ji, J & Brown, T 2021, 'Preface', Vibration Engineering for a Sustainable Future: Experiments, Materials and Signal Processing, Vol. 2, vol. 2, pp. v-vi.
Olszak, CM, Zurada, JM & Cetindamar, D 2021, 'Business Intelligence & Big Data for Innovative and Sustainable Development of Organizations.', Inf. Syst. Manag., vol. 38, no. 4, pp. 268-269.
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Ortiz Marrero, C, Kieferová, M & Wiebe, N 2021, 'Entanglement-Induced Barren Plateaus', PRX Quantum, vol. 2, no. 4, p. 040316.
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Pan, Y, Tsang, IW, Lyu, Y, Singh, AK & Lin, C-T 2021, 'Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation', Neural Computation, vol. 33, no. 6, pp. 1616-1655.
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Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation.
Pang, G, Cao, L & Chen, L 2021, 'Homophily outlier detection in non-IID categorical data', Data Mining and Knowledge Discovery, vol. 35, no. 4, pp. 1163-1224.
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Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Independent and Identically Distributed (IID). This assumption does not hold in real-world applications where the outlierness of different entities is dependent on each other and/or taken from different probability distributions (non-IID). This may lead to the failure of detecting important outliers that are too subtle to be identified without considering the non-IID nature. The issue is even intensified in more challenging contexts, e.g., high-dimensional data with many noisy features. This work introduces a novel outlier detection framework and its two instances to identify outliers in categorical data by capturing non-IID outlier factors. Our approach first defines and incorporates distribution-sensitive outlier factors and their interdependence into a value-value graph-based representation. It then models an outlierness propagation process in the value graph to learn the outlierness of feature values. The learned value outlierness allows for either direct outlier detection or outlying feature selection. The graph representation and mining approach is employed here to well capture the rich non-IID characteristics. Our empirical results on 15 real-world data sets with different levels of data complexities show that (i) the proposed outlier detection methods significantly outperform five state-of-the-art methods at the 95%/99% confidence level, achieving 10–28% AUC improvement on the 10 most complex data sets; and (ii) the proposed feature selection methods significantly outperform three competing methods in enabling subsequent outlier detection of two different existing detectors.
Pare, S, Mittal, H, Sajid, M, Bansal, JC, Saxena, A, Jan, T, Pedrycz, W & Prasad, M 2021, 'Remote Sensing Imagery Segmentation: A Hybrid Approach', Remote Sensing, vol. 13, no. 22, pp. 4604-4604.
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In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.
Patel, OP, Bharill, N, Tiwari, A & Prasad, M 2021, 'A Novel Quantum-Inspired Fuzzy Based Neural Network for Data Classification', IEEE Transactions on Emerging Topics in Computing, vol. 9, no. 2, pp. 1031-1044.
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IEEE The performance of the neural network (NN) depends on the various parameters such as structure, initial weight, number of hidden layer neurons, and learning rate. The improvement in classification performance of NN without changing its structure is a challenging issue. This paper proposes a novel learning model called Quantum-inspired Fuzzy Based Neural Network (Q-FNN) to solve two-class classification problems. In the proposed model, NN architecture is formed constructively by adding neurons in the hidden layer and learning is performed using the concept of Fuzzy c-Means (FCM) clustering, where the fuzziness parameter (m) is evolved using the quantum computing concept. The quantum computing concept provides a large search space for a selection of m, which helps in finding the optimal weights and also optimizes the network architecture. This paper also proposes a modified step activation function for the formation of hidden layer neurons, which handles the overlapping samples belong to different class regions. The performance of the proposed Q-FNN model is superior and competitive with the state-of-the-art methods in terms of accuracy, sensitivity, and specificity on 15 real-world benchmark datasets.
Pietroni, N, Nuvoli, S, Alderighi, T, Cignoni, P & Tarini, M 2021, 'Reliable feature-line driven quad-remeshing.', ACM Trans. Graph., vol. 40, no. 4, pp. 155:1-155:1.
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We present a new algorithm for the semi-regular quadrangulation of an input surface, driven by its line features, such as sharp creases. We define a perfectly feature-aligned cross-field and a coarse layout of polygonal-shaped patches where we strictly ensure that all the feature-lines are represented as patch boundaries. To be able to consistently do so, we allow non-quadrilateral patches and T-junctions in the layout; the key is the ability to constrain the layout so that it still admits a globally consistent, T-junction-free, and pure-quad internal tessellation of its patches. This requires the insertion of additional irregular-vertices inside patches, but the regularity of the final-mesh is safeguarded by optimizing for both their number and for their reciprocal alignment. In total, our method guarantees the reproduction of feature-lines by construction, while still producing good quality, isometric, pure-quad, conforming meshes, making it an ideal candidate for CAD models. Moreover, the method is fully automatic, requiring no user intervention, and remarkably reliable, requiring little assumptions on the input mesh, as we demonstrate by batch processing the entire Thingi10K repository, with less than 0.5% of the attempted cases failing to produce a usable mesh.
Pinilla, A, Garcia, J, Raffe, W, Voigt-Antons, J-N, Spang, RP & Möller, S 2021, 'Affective Visualization in Virtual Reality: An Integrative Review', Frontiers in Virtual Reality, vol. 2, p. 630731.
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A cluster of research in Affective Computing suggests that it is possible to infer some characteristics of users’ affective states by analyzing their electrophysiological activity in real-time. However, it is not clear how to use the information extracted from electrophysiological signals to create visual representations of the affective states of Virtual Reality (VR) users. Visualization of users’ affective states in VR can lead to biofeedback therapies for mental health care. Understanding how to visualize affective states in VR requires an interdisciplinary approach that integrates psychology, electrophysiology, and audio-visual design. Therefore, this review aims to integrate previous studies from these fields to understand how to develop virtual environments that can automatically create visual representations of users’ affective states. The manuscript addresses this challenge in four sections: First, theories related to emotion and affect are summarized. Second, evidence suggesting that visual and sound cues tend to be associated with affective states are discussed. Third, some of the available methods for assessing affect are described. The fourth and final section contains five practical considerations for the development of virtual reality environments for affect visualization.
Qi, L, Song, H, Zhang, X, Srivastava, G, Xu, X & Yu, S 2021, 'Compatibility-Aware Web API Recommendation for Mashup Creation via Textual Description Mining', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 17, no. 1s, pp. 1-19.
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With the ever-increasing prosperity of web Application Programming Interface (API) sharing platforms, it is becoming an economic and efficient way for software developers to design their interested mashups through web API re-use. Generally, a software developer can browse, evaluate, and select his or her preferred web APIs from the API's sharing platforms to create various mashups with rich functionality. The big volume of candidate APIs places a heavy burden on software developers’ API selection decisions. This, in turn, calls for the support of intelligent API recommender systems. However, existing API recommender systems often face two challenges. First, they focus more on the functional accuracy of APIs while neglecting the APIs’ actual compatibility. This then creates incompatible mashups. Second, they often require software developers to input a set of keywords that can accurately describe the expected functions of the mashup to be developed. This second challenge tests partial developers who have little background knowledge in the fields. To tackle the above-mentioned challenges, in this article we propose a compatibility-aware and text description-driven web API recommendation approach (named WAR text ). WAR text guarantees the compatibility among the recommended APIs by utilizing the APIs’ composition records produced by historical mashup creations. Besides, WAR text entitles a software developer to type a simple text document that describes the expected mashup functions as input. Then through textual description mining, WAR ...
Qi, M, Zhao, F, Sun, T & Voinov, A 2021, 'Disentangling the relative influence of regeneration processes on marsh plant assembly with a stage-structured plant assembly model', Ecological Modelling, vol. 455, pp. 109646-109646.
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Qiao, M, Wang, D, Tuck, GN, Little, LR, Punt, AE & Gerner, M 2021, 'Deep learning methods applied to electronic monitoring data: automated catch event detection for longline fishing', ICES Journal of Marine Science, vol. 78, no. 1, pp. 25-35.
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Abstract Electronic monitoring (EM) systems have become functional and cost-effective tools for the conservation and sustainable harvesting of marine resources. EM is an alternative to on-board observers, which produces video segments that can subsequently be reviewed by analysts. It is currently used in a range of fisheries. There are two major challenges to the widespread adoption of EM. One is the large storage requirement for the video footage recorded and the other is the long time required by analysts to review the video footage. We propose an automated catch event detection framework to address these challenges. Our solution, based on deep learning techniques, automatically extracts video segments of catch events, which substantially reduces storage space and review time by analysts. Here, we demonstrate the framework using video footage from three longline fishing trips. The system recalled nearly 100% of the catch events across all trips.
Qiao, Y 2021, 'Enumerating alternating matrix spaces over finite fields with explicit coordinates', Discrete Mathematics, vol. 344, no. 11, pp. 112580-112580.
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We initiate the study of enumerating linear subspaces of alternating matrices over finite fields with explicit coordinates. We present q-analogues of Gilbert's formula for enumerating connected graphs (Gilbert (1956) [5]), and Read's formula for enumerating c-coloured graphs (Read (1960) [14]). We also develop an analogue of Riddell's formula relating the exponential generating function of graphs with that of connected graphs (Riddell's (1951) [15]), building on Eulerian generating functions developed by Srinivasan ((2006) [16]).
Qin, M, Sun, M & Li, J 2021, 'Impact of environmental regulation policy on ecological efficiency in four major urban agglomerations in eastern China', Ecological Indicators, vol. 130, pp. 108002-108002.
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Qu, Y, Yu, S, Zhou, W, Chen, S & Wu, J 2021, 'Customizable Reliable Privacy-Preserving Data Sharing in Cyber-Physical Social Networks', IEEE Transactions on Network Science and Engineering, vol. 8, no. 1, pp. 269-281.
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IEEE Privacy leakage becomes increasingly serious because massive volumes of data are constantly shared in diverse booming cyber-physical social networks (CPSN). Differential privacy is one of the dominating privacy-preserving methods, but most of its extensions assume all data users share the same privacy requirement, which fails to satisfy various privacy expectations in practice. To address this issue, customizable privacy preservation based on differential privacy is a potentially promising countermeasure. However, we found that customizable protection will trigger the composition mechanism of differential privacy and leads to unexpected correlations among injected noises that weakens privacy protection and reveal more sensitive inforamtion. As a result, customizable privacy protection is vulnerable to two primary attacks: background knowledge attack and collusion attack. To optimize the tradeoff between customizable privacy preservation and data utility, we propose a customizable reliable differential privacy model (CRDP), which provides customizable protection on each individual while being attack-proof. We define social distance as the shortest path between two nodes, which is used as an index to customize the privacy protection levels, followed by quantitatively modeling the attacks under the framework of differential privacy. We develop a modified Laplacian mechanism in which the noise generation complies with a Markov stochastic process. Consequently, the correlations of noises are properly de-coupled so that CRDP can simultaneously minimize background knowledge attacks and eliminate collusion attacks in this particular scenario. The evaluation results from real-world datasets show the feasibility and superiority of CRDP in terms of customizable privacy preservation and reliable attack resistance.
Quiroz, JC, Laranjo, L, Tufanaru, C, Kocaballi, AB, Rezazadegan, D, Berkovsky, S & Coiera, E 2021, 'Empirical analysis of Zipf’s law, power law, and lognormal distributions in medical discharge reports', International Journal of Medical Informatics, vol. 145, pp. 104324-104324.
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Raj, C, Agarwal, A, Bharathy, G, Narayan, B & Prasad, M 2021, 'Cyberbullying Detection: Hybrid Models Based on Machine Learning and Natural Language Processing Techniques', Electronics, vol. 10, no. 22, pp. 2810-2810.
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The rise in web and social media interactions has resulted in the efortless proliferation of offensive language and hate speech. Such online harassment, insults, and attacks are commonly termed cyberbullying. The sheer volume of user-generated content has made it challenging to identify such illicit content. Machine learning has wide applications in text classification, and researchers are shifting towards using deep neural networks in detecting cyberbullying due to the several advantages they have over traditional machine learning algorithms. This paper proposes a novel neural network framework with parameter optimization and an algorithmic comparative study of eleven classification methods: four traditional machine learning and seven shallow neural networks on two real world cyberbullying datasets. In addition, this paper also examines the effect of feature extraction and word-embedding-techniques-based natural language processing on algorithmic performance. Key observations from this study show that bidirectional neural networks and attention models provide high classification results. Logistic Regression was observed to be the best among the traditional machine learning classifiers used. Term Frequency-Inverse Document Frequency (TF-IDF) demonstrates consistently high accuracies with traditional machine learning techniques. Global Vectors (GloVe) perform better with neural network models. Bi-GRU and Bi-LSTM worked best amongst the neural networks used. The extensive experiments performed on the two datasets establish the importance of this work by comparing eleven classification methods and seven feature extraction techniques. Our proposed shallow neural networks outperform existing state-of-the-art approaches for cyberbullying detection, with accuracy and F1-scores as high as ~95% and ~98%, respectively.
Ranjbar, E, Menhaj, MB, Suratgar, AA, Andreu-Perez, J & Prasad, M 2021, 'Design of a fuzzy PID controller for a MEMS tunable capacitor for noise reduction in a voltage reference source', SN Applied Sciences, vol. 3, no. 6, pp. 1-17.
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Abstract This study presents a conventional Ziegler-Nichols (ZN) Proportional Integral Derivative (PID) controller, having reviewed the mathematical modeling of the Micro Electro Mechanical Systems (MEMS) Tunable Capacitors (TCs), and also proposes a fuzzy PID controller which demonstrates a better tracking performance in the presence of measurement noise, in comparison with conventional ZN-based PID controllers. Referring to importance and impact of this research, the proposed controller takes advantage of fuzzy control properties such as robustness against noise. TCs are responsible for regulating the reference voltage when integrated into Alternating Current (AC) Voltage Reference Sources (VRS). Capacitance regulation for tunable capacitors in VRS is carried out by modulating the distance of a movable plate. A successful modulation depends on maintaining the stability around the pull-in point. This distance regulation can be achieved by the proposed controller which guarantees the tracking performance of the movable plate in moving towards the pull-in point, and remaining in this critical position. The simulation results of the tracking performance and capacitance tuning are very promising, subjected to measurement noise. Article Highlights This article deals with MEMS tunable capacitor dynamics and modeling, considering measurement noise. It designs and applies fuzzy PID control system for regulating MEMS voltage reference output. <...
Renou, M-O, Trillo, D, Weilenmann, M, Le, TP, Tavakoli, A, Gisin, N, Acín, A & Navascués, M 2021, 'Quantum theory based on real numbers can be experimentally falsified', Nature, vol. 600, no. 7890, pp. 625-629.
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AbstractAlthough complex numbers are essential in mathematics, they are not needed to describe physical experiments, as those are expressed in terms of probabilities, hence real numbers. Physics, however, aims to explain, rather than describe, experiments through theories. Although most theories of physics are based on real numbers, quantum theory was the first to be formulated in terms of operators acting on complex Hilbert spaces1,2. This has puzzled countless physicists, including the fathers of the theory, for whom a real version of quantum theory, in terms of real operators, seemed much more natural3. In fact, previous studies have shown that such a ‘real quantum theory’ can reproduce the outcomes of any multipartite experiment, as long as the parts share arbitrary real quantum states4. Here we investigate whether complex numbers are actually needed in the quantum formalism. We show this to be case by proving that real and complex Hilbert-space formulations of quantum theory make different predictions in network scenarios comprising independent states and measurements. This allows us to devise a Bell-like experiment, the successful realization of which would disprove real quantum theory, in the same way as standard Bell experiments disproved local physics.
Roberts, AGK, Catchpoole, DR & Kennedy, PJ 2021, '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, pp. 1-14.
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AbstractThere 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 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 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, Zhang, X & Mobasheri, A 2021, 'Targeting mitochondrial dysfunction with small molecules in intervertebral disc aging and degeneration', GeroScience, vol. 43, no. 2, pp. 517-537.
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AbstractThe prevalence of rheumatic and musculoskeletal diseases (RMDs) including osteoarthritis (OA) and low back pain (LBP) in aging societies present significant cost burdens to health and social care systems. Intervertebral disc (IVD) degeneration, which is characterized by disc dehydration, anatomical alterations, and extensive changes in extracellular matrix (ECM) composition, is an important contributor to LBP. IVD cell homeostasis can be disrupted by mitochondrial dysfunction. Mitochondria are the main source of energy supply in IVD cells and a major contributor to the production of reactive oxygen species (ROS). Therefore, mitochondria represent a double-edged sword in IVD cells. Mitochondrial dysfunction results in oxidative stress, cell death, and premature cell senescence, which are all implicated in IVD degeneration. Considering the importance of optimal mitochondrial function for the preservation of IVD cell homeostasis, extensive studies have been done in recent years to evaluate the efficacy of small molecules targeting mitochondrial dysfunction. In this article, we review the pathogenesis of mitochondrial dysfunction, aiming to highlight the role of small molecules and a selected number of biological growth factors that regulate mitochondrial function and maintain IVD cell homeostasis. Furthermore, molecules that target mitochondria and their mechanisms of action and potential for IVD regeneration are identified. Finally, we discuss mitophagy as a key mediator of many cellular events and the small molecules regulating its function.
Saeed A Alqahtani, M & Erfani, E 2021, 'Exploring the Relationship Between Technology Adoption and Cyber Security Compliance', International Journal of Electronic Government Research, vol. 17, no. 4, pp. 40-62.
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IT infrastructure and systems are made up of technical and social systems that work together to ensure that organization's goals and objectives are met. Security controls and measures are developed and used to protect an organization's data and information systems. To improve cyber security, organizations focus most of their efforts on incorporating new technological approaches in products and processes, leaving out the most important and vulnerable factor. So this study intends to provide some practical implications to the technology developers and policymakers while identifying the factors that affect cyber security compliance in an organization or home environment for general users, HR, IT administrators, engineers, and others. It explored the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model and assessed the effect of its factors on cyber security compliance in organizations.
Sahonero-Alvarez, G, Singh, AK, Sayrafian, K, Bianchi, L & Roman-Gonzalez, A 2021, 'A Functional BCI Model by the P2731 Working Group: Transducer', Brain-Computer Interfaces, vol. 8, no. 3, pp. 92-107.
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A Brain-Computer Interface (BCI) can be considered as a technology that allows for alternative means of communication between humans and their environment using thoughts and intentions. The structure of this interface is composed of various stages, beginning with the acquisition of the brain signals, followed by several processing stages, and leading to the generation of feedback signals. The development of a BCI system involves a diverse set of expertise in order to produce a unique environment for continuous innovations. However, such diversity in technical background and expertise may lead to confusion in the terminology used by the community. As such, the IEEE P2731 WG has been tasked with the development of a functional model to facilitate the understanding of a BCI system. In this paper, we focus on the description of the functional elements that belong to the transducer stage of a BCI.
Sajid, M, Singh, J, Haidri, RA, Prasad, M, Varadarajan, V, Kotecha, K & Garg, D 2021, 'A Novel Algorithm for Capacitated Vehicle Routing Problem for Smart Cities', Symmetry, vol. 13, no. 10, pp. 1923-1923.
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Smart logistics is an indispensable building block in smart cities development that requires solving the challenge of efficiently serving the demands of geographically distributed customers by a fleet of vehicles. It consists of a very well-known NP-hard complex optimization problem, which is known as the capacitated vehicle routing problem (CVRP). The CVRP has widespread real-life applications such as delivery in smart logistics, the pharmaceutical distribution of vacancies, disaster relief efforts, and others. In this work, a novel giant tour best cost crossover (GTBCX) operator is proposed which works stochastically to search for the optimal solutions of the CVRP. An NSGA-II-based routing algorithm employing GTBCX is also proposed to solve the CVRP to minimize the total distance traveled as well as to minimize the longest route length. The simulated study is performed on 88 benchmark CVRP instances to validate the success of our proposed GTBCX operator against the nearest neighbor crossover (NNX) and edge assembly crossover (EAX) operators. The rigorous simulation study shows that the GTBCX is a powerful operator and helps to find results that are superior in terms of the overall distance traveled, length of the longest route, quality, and number of Pareto solutions. This work employs a multi-objective optimization algorithm to solve the capacitated vehicle routing problem (CVRP), where the CVRP is represented in the form of a two-dimensional graph. To compute the values’ objective functions, the distance between two nodes in the graph is considered symmetric. This indicates that the genetic algorithm complex optimization algorithm is employed to solve CVRP, which is a symmetry distance-based graph.
Sarker, PC, Guo, Y, Lu, HY & Zhu, JG 2021, 'Measurement and Modeling of Rotational Core Loss of Fe-Based Amorphous Magnetic Material Under 2-D Magnetic Excitation', IEEE Transactions on Magnetics, vol. 57, no. 11, pp. 1-8.
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Fe-based amorphous magnetic materials are recently attracting strong interests for constructing high-power density and high-efficiency rotating electrical machines due to their attractive properties, such as low core loss and high magnetic saturation. Accurate measurement and modeling of the rotational core losses of the core magnetic materials, and the corresponding patterns of rotating magnetic flux density ( $B$ ) and magnetic field strength ( $H$ ) are important for the analysis and design of electrical machines. This article presents the measurement of rotational core loss of a Fe-based amorphous magnetic material (amorphous 1k101), and its corresponding modelings under two-dimensional (2-D) circularly and elliptically rotating magnetic fields. In addition, an improved and simplified analogical model of rotational hysteresis loss is proposed for such magnetic materials. The circular and elliptical $B$ loci and the corresponding $H$ loci have been investigated to acquire the perception of anisotropy and permeability of the amorphous materials. The proposed theory and models are experimentally verified.
Shaham, S, Ding, M, Liu, B, Dang, S, Lin, Z & Li, J 2021, 'Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model', IEEE Transactions on Mobile Computing, vol. 20, no. 10, pp. 3006-3019.
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Shaham, S, Ding, M, Liu, B, Dang, S, Lin, Z & Li, J 2021, 'Privacy Preserving Location Data Publishing: A Machine Learning Approach', IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 9, pp. 3270-3283.
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Shashi, S, Centobelli, P, Cerchione, R & Merigo, JM 2021, 'MAPPING KNOWLEDGE MANAGEMENT RESEARCH: A BIBLIOMETRIC OVERVIEW', Technological and Economic Development of Economy, vol. 28, no. 1, pp. 239-267.
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In recent years, knowledge management (KM) has consistently attained considerably growing research attention. Consequently, several literature reviews have been performed addressing different topic areas of KM. This paper seeks to present a comprehensive bibliometric and network analysis on KM to understand its development from the perspective of academic communities. Subsequently, it seeks to identify the structure of associations between prior and current themes, predict emerging trends and offer a longitudinal perspective on KM research. This study used web of science database and the initial sample was trimmed down by considering only the articles contributing to KM literature, and further 8,721 KM papers published in the last 30 years were systematically evaluated. The descriptive statistics and science mapping methods employing co-citation analysis were performed with VOSviewer software. In the descriptive analysis, we have analysed publication trends over time, geographical localization of the contributing institutions, journals, most prolific authors, top-performing institutions and most cited articles. Science mapping analysis is based on co-word analysis and co-citations analysis, namely articles’ co-citations and authors’ co-citations. The main findings of this paper will help researchers and academicians to develop knowledge in a specific sub-field by analysing the research outcomes of the papers included in the body of literature.
Shi, K, Wang, Y, Lu, H, Zhu, Y & Niu, Z 2021, 'EKGTF: A knowledge-enhanced model for optimizing social network-based meteorological briefings', Information Processing & Management, vol. 58, no. 4, pp. 102564-102564.
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Shi, Y, Lin, C-T, Chang, Y-C, Ding, W, Shi, Y & Yao, X 2021, 'Consensus Learning for Distributed Fuzzy Neural Network in Big Data Environment', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 5, no. 1, pp. 29-41.
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IEEE Uncertainty and distributed nature inherently exist in big data environment. Distributed fuzzy neural network (D-FNN) that not only employs fuzzy logics to alleviate the uncertainty problem but also deal with data in a distributed manner, is effective and crucial for big data. Existing D-FNNs always avoided consensus for their antecedent layer due to computational difficulty. Hence such D-FNNs are not really distributed since a single model can not be agreed by multiple agents. This article proposes a true D-FNN model to handle the uncertainty and distributed challenges in the big data environment. The proposed D-FNN model considers consensus for both the antecedent and consequent layers. A novel consensus learning, which involves a distributed structure learning and a distributed parameter learning, is proposed to handle the D-FNN model. The proposed consensus learning algorithm is built on the well-known alternating direction method of multipliers, which does not exchange local data among agents. The major contribution of this paper is to propose the true D-FNN model for the big data and the novel consensus learning algorithm for this D-FNN model. Simulation results on popular datasets demonstrate the superiority and effectiveness of the proposed D-FNN model and consensus learning algorithm.
Shi, Y, Tuan, HD, Savkin, AV, Lin, C-T, Zhu, JG & Poor, HV 2021, 'Distributed model predictive control for joint coordination of demand response and optimal power flow with renewables in smart grid', Applied Energy, vol. 290, pp. 116701-116701.
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Shirvani, F, Beydoun, G, Perez, P, Scott, W & Campbell, P 2021, 'An Architecture Framework Approach for Complex Transport Projects.', Inf. Syst. Frontiers, vol. 23, no. 3, pp. 575-595.
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Shirvani, F, Beydoun, G, Perez, P, Scott, W & Campbell, P 2021, 'Correction to: An Architecture Framework Approach for Complex Transport Projects.', Inf. Syst. Frontiers, vol. 23, no. 3, pp. 597-597.
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Sick, N, Merigó, JM, Krätzig, O & List, J 2021, 'Forty years of World Patent Information: A bibliometric overview', World Patent Information, vol. 64, pp. 102011-102011.
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Singanamalla, SKR & Lin, C-T 2021, 'Spiking Neural Network for Augmenting Electroencephalographic Data for Brain Computer Interfaces', Frontiers in Neuroscience, vol. 15, p. 651762.
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With the advent of advanced machine learning methods, the performance of brain–computer interfaces (BCIs) has improved unprecedentedly. However, electroencephalography (EEG), a commonly used brain imaging method for BCI, is characterized by a tedious experimental setup, frequent data loss due to artifacts, and is time consuming for bulk trial recordings to take advantage of the capabilities of deep learning classifiers. Some studies have tried to address this issue by generating artificial EEG signals. However, a few of these methods are limited in retaining the prominent features or biomarker of the signal. And, other deep learning-based generative methods require a huge number of samples for training, and a majority of these models can handle data augmentation of one category or class of data at any training session. Therefore, there exists a necessity for a generative model that can generate synthetic EEG samples with as few available trials as possible and generate multi-class while retaining the biomarker of the signal. Since EEG signal represents an accumulation of action potentials from neuronal populations beneath the scalp surface and as spiking neural network (SNN), a biologically closer artificial neural network, communicates via spiking behavior, we propose an SNN-based approach using surrogate-gradient descent learning to reconstruct and generate multi-class artificial EEG signals from just a few original samples. The network was employed for augmenting motor imagery (MI) and steady-state visually evoked potential (SSVEP) data. These artificial data are further validated through classification and correlation metrics to assess its resemblance with original data and in-turn enhanced the MI classification performance.
Singh, AK, Gramann, K, Chen, H-T & Lin, C-T 2021, 'The impact of hand movement velocity on cognitive conflict processing in a 3D object selection task in virtual reality', NeuroImage, vol. 226, pp. 117578-117578.
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Detecting and correcting incorrect body movements is an essential part of everyday interaction with one's environment. The human brain provides a monitoring system that constantly controls and adjusts our actions according to our surroundings. However, when our brain's predictions about a planned action do not match the sensory inputs resulting from that action, cognitive conflict occurs. Much is known about cognitive conflict in 1D/2D environments; however, less is known about the role of movement characteristics associated with cognitive conflict in 3D environment. Hence, we devised an object selection task in a virtual reality (VR) environment to test how the velocity of hand movements impacts human brain responses. From a series of analyses of EEG recordings synchronized with motion capture, we found that the velocity of the participants' hand movements modulated the brain's response to proprioceptive feedback during the task and induced a prediction error negativity (PEN). Additionally, the PEN originates in the anterior cingulate cortex and is itself modulated by the ballistic phase of the hand's movement. These findings suggest that velocity is an essential component of integrating hand movements with visual and proprioceptive information during interactions with real and virtual objects.
Singh, AK, Sahonero-Alvarez, G, Mahmud, M & Bianchi, L 2021, 'Towards Bridging the Gap Between Computational Intelligence and Neuroscience in Brain-Computer Interfaces With a Common Description of Systems and Data', Frontiers in Neuroinformatics, vol. 15, p. 699840.
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Skarding, J, Gabrys, B & Musial, K 2021, 'Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey', IEEE Access, vol. 9, pp. 79143-79168.
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Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also temporal patterns. However, as dynamic network literature stems from diverse fields and makes use of inconsistent terminology, it is challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a lot of attention in recent years for their ability to perform well on a range of network science tasks, such as link prediction and node classification. Despite the popularity of graph neural networks and the proven benefits of dynamic network models, there has been little focus on graph neural networks for dynamic networks. To address the challenges resulting from the fact that this research crosses diverse fields as well as to survey dynamic graph neural networks, this work is split into two main parts. First, to address the ambiguity of the dynamic network terminology we establish a foundation of dynamic networks with consistent, detailed terminology and notation. Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminology.
Sood, K, Karmakar, KK, Varadharajen, V, Kumar, N, Xiang, Y & Yu, S 2021, 'Plug-in over Plug-in Evaluation in Heterogeneous 5G Enabled Networks and Beyond', IEEE Network, vol. 35, no. 2, pp. 34-39.
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With the cool upcoming wave of 5G, currently, the networking and telecommunication industries are facing various digital transformations, which are changing the very fundamental nature of the existing network management infrastructure. Besides the Internet of Things (IoT) domain, we also notice that the 5G network in itself is composed of millions of heterogeneous physical entities and nodes, multiple domains, complex protocols and technologies, different gateways, and so on. This heterogeneity imposes critical impacts on the application specific quality of service (QoS) requirements, performance and utilization of network resources, and data and user security. In order to alleviate the above impacts, researchers propose to use different technologies such as software-defined networking, network function virtualization, blockchain, and artificial intelligence in 5G-enabled IoT networking. We notice that the layers over layers (of protocols and technologies) act like a plug-in over plug-in (PoP) in the network in order to accomplish various aims, including meeting QoS demands, enhancing security, load balancing, and so on. On one hand, we agree that this integration of different technologies in 5G networks bring numerous advantages, but on the other hand, we realize that this has posed a lot of unique critical issues in modern 5G network management. In this article, we point out that this straightforward approach of PoP is eventually not a healthy approach for network transformation. In this regard, using open source MANO (OSM), we provide a proof of concept (PoC) to show that at varying degrees of heterogeneity, PoP adds the delay in the VNF deployment process and further impacts the VIM CPU performance. This eventually affects the QoS requirements of IoT nodes or applications. Following this, we propose a high-level holistic approach that helps to alleviate the PoP issue. Finally, in this context, we also discuss the associated challenges and research opportunities.
Sood, K, Yu, S, Nguyen, DDN, Xiang, Y, Feng, B & Zhang, X 2021, 'A Tutorial on Next Generation Heterogeneous IoT Networks and Node Authentication', IEEE Internet of Things Magazine, vol. 4, no. 4, pp. 120-126.
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Sood, S & Woodside, AG 2021, 'Entrepreneurial orientation vignettes into open innovation of the internet of things: advancing into the age of service dominant reasoning', International Journal of Services Technology and Management, vol. 27, no. 4/5/6, pp. 324-324.
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Soomro, AM, Bharathy, G, Biloria, N & Prasad, M 2021, 'A review on motivational nudges for enhancing building energy conservation behavior', Journal of Smart Environments and Green Computing, vol. 1, no. 1, pp. 3-20.
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Srinivas, S, Gill, AQ & Roach, T 2021, 'Can Business Architecture Modeling be Adaptive?', IT Prof., vol. 23, no. 2, pp. 81-88.
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Businesses find the need to adapt to changes in the dynamic and competitive environment. However, they are confronted by static business architecture (BA) modeling and artefacts, which are less likely to adapt and, thus, quickly become obsolete. This article proposes a dynamic analytics-enabled adaptive BA modeling framework to address this concern. This research is performed using an action design research (ADR) method in collaboration with an Australian organization. The proposed approach has been implemented and evaluated using a banking organization as a case study.
Stender, M, Tiedemann, M, Spieler, D, Schoepflin, D, Hoffmann, N & Oberst, S 2021, 'Deep learning for brake squeal: Brake noise detection, characterization and prediction', Mechanical Systems and Signal Processing, vol. 149, pp. 107181-107181.
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Despite significant advances in modeling of friction-induced vibrations and brake squeal, the majority of industrial research and design is still conducted experimentally, since many aspects of squeal and its mechanisms involved remain unknown. In practice, measurement data is available in large amounts. We report here for the first time on novel strategies for handling data-intensive vibration testings to gain better insights into friction brake system vibrations and noise generation mechanisms. Machine learning-based methods to detect and characterize vibrations, to understand sensitivities and to predict brake squeal are applied with the aim to illustrate how interdisciplinary approaches can leverage the potential of data science techniques for classical mechanical engineering challenges.
In the first part, a deep learning brake squeal detector is developed to identify several classes of typical friction noise recordings. The detection method is rooted in recent computer vision techniques for object detection based on convolutional neural networks (CNN). It allows to overcome limitations of classical approaches that solely rely on instantaneous spectral properties of the recorded noise. Results indicate superior detection and characterization quality when compared to a state-of-the-art brake squeal detector. In the second part, a recurrent neural network (RNN) is employed to learn the parametric patterns that determine the dynamic stability of an operating brake system. Given a set of multivariate loading conditions, the RNN learns to predict the noise generation of the structure. The validated RNN represents a virtual twin model for the squeal behavior of a specific brake system. It is found that this model can predict the occurrence and the onset of brake squeal with high accuracy and that it can identify the complicated patterns and temporal dependencies in the loading conditions that drive the dynamical structure into regimes of instability. Large data se...
Strauss, H, Schutte, D & Fawcett, T 2021, 'An evaluation of the legislative and policy response of tax authorities to the digitalisation of the economy', South African Journal of Accounting Research, vol. 35, no. 3, pp. 239-262.
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Sulimani, H, Alghamdi, WY, Jan, T, Bharathy, G & Prasad, M 2021, 'Sustainability of Load Balancing Techniques in Fog Computing Environment: Review.', FNC/MobiSPC, vol. 191, pp. 93-101.
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The extreme workloads on the fog layer caused a misalignment in some fog nodes that affect its efficiency and degenerate fog technology's primary goal. Therefore, creating a balanced computing environment via the offloading process is the key. However, there are many obstacles to balance computing nodes in the fog environment, such as offloading strategy and its consequences due to the extreme offloading processes, and when need to offload and where. These obstacles are vital concerns among researchers. Thus, several studies have been conducted to enhance the fog system performance to increase the entire system's throughput. This paper explores the recent articles to determine the possible research gaps and opportunities to implement an efficient solution for load balancing in fog environments after analyzing and assessing the existing solutions. While most of the proposed solutions involve short-term solution, this literature review reveals the need to find out a sustainable resolution for load balancing to avoid listed obstacles by using the offloading technique with maintaining the bandwidth of the network.
Suwanwiwat, H, Das, A, Saqib, M & Pal, U 2021, 'Benchmarked multi-script Thai scene text dataset and its multi-class detection solution', Multimedia Tools and Applications, vol. 80, no. 8, pp. 11843-11863.
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Detecting text portion from scene images can be found to be one of the prevalent research topics. Text detection is considered challenging and non-interoperable since there could be multiple scripts in a scene image. Each of these scripts can have different properties, therefore, it is crucial to research the scene text detection based on the geographical location owing to different scripts. As no work on large-scale multi-script Thai scene text detection is found in the literature, the work conducted in this study focuses on multi-script text that includes Thai, English (Roman), Chinese or Chinese-like script, and Arabic. These scripts can generally be seen around Thailand. Thai script contains more consonants, vowels, and has numerals when compared to the Roman/ English script. Furthermore, the placement of letters, intonation marks, as well as vowels, are different from English or Chinese-like script. Hence, it could be considered challenging to detect and recognise the Thai text. This study proposed a multi-script dataset which includes the aforementioned scripts and numerals, along with a benchmarking employing Single Shot Multi-Box Detector (SSD) and Faster Regions with Convolutional Neural Networks (F-RCNN). The proposed dataset contains scene images which were recorded in Thailand. The dataset consists of 600 images, together with their manual detection annotation. This study also proposed a detection technique hypothesising a multiscript scene text detection problem as a multi-class detection problem which found to work more effective than legacy approaches. The experimental results from employing the proposed technique with the dataset achieved encouraging precision and recall rates when compared with such methods. The proposed dataset is available upon email request to the corresponding authors.
Taghikhah, F, Voinov, A, Shukla, N & Filatova, T 2021, 'Shifts in consumer behavior towards organic products: Theory-driven data analytics', Journal of Retailing and Consumer Services, vol. 61, pp. 102516-102516.
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Taghikhah, F, Voinov, A, Shukla, N, Filatova, T & Anufriev, M 2021, 'Integrated modeling of extended agro-food supply chains: A systems approach', European Journal of Operational Research, vol. 288, no. 3, pp. 852-868.
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Tian, H, Xu, X, Qi, L, Zhang, X, Dou, W, Yu, S & Ni, Q 2021, 'CoPace: Edge Computation Offloading and Caching for Self-Driving With Deep Reinforcement Learning', IEEE Transactions on Vehicular Technology, vol. 70, no. 12, pp. 13281-13293.
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Currently, self-driving, emerging as a key automatic application, has brought a huge potential for the provision of in-vehicle services (e.g., automatic path planning) to mitigate urban traffic congestion and enhance travel safety. To provide high-quality vehicular services with stringent delay constraints, edge computing (EC) enables resource-hungry self-driving vehicles (SDVs) to offload computation-intensive tasks to the edge servers (ESs). In addition, caching highly reusable contents decreases the redundant transmission time and improves the quality of services (QoS) of SDVs, which is envisioned as a supplement to the computation offloading. However, the high mobility and time-varying requests of SDVs make it challenging to provide reliable offloading decisions while guaranteeing the resource utilization of content caching. To this end, in this paper we propose a collaborative computation offloading and content caching method, named CoPace, by leveraging deep reinforcement learning (DRL) in EC for self-driving system. Specifically, we first introduce OSTP to predict the future time-varying content popularity, taking into account the temporal-spatial attributes of requests. Moreover, a DRL-based algorithm is developed to jointly optimize the offloading and caching decisions, as well as the resource allocation (i.e., computing and communication resources) strategies. Extensive experiments with real-world datasets in Shanghai, China, are conducted to evaluate the performance, which demonstrates that CoPace is both effective and well-performed.
Tong, HL, Quiroz, JC, Kocaballi, AB, Fat, SCM, Dao, KP, Gehringer, H, Chow, CK & Laranjo, L 2021, 'Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression', Preventive Medicine, vol. 148, pp. 106532-106532.
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Given that the one-size-fits-all approach to mobile health interventions have limited effects, a personalized approach might be necessary to promote healthy behaviors and prevent chronic conditions. Our systematic review aims to evaluate the effectiveness of personalized mobile interventions on lifestyle behaviors (i.e., physical activity, diet, smoking and alcohol consumption), and identify the effective key features of such interventions. We included any experimental trials that tested a personalized mobile app or fitness tracker and reported any lifestyle behavior measures. We conducted a narrative synthesis for all studies, and a meta-analysis of randomized controlled trials. Thirty-nine articles describing 31 interventions were included (n = 77,243, 64% women). All interventions personalized content and rarely personalized other features. Source of data included system-captured (12 interventions), user-reported (11 interventions) or both (8 interventions). The meta-analysis showed a moderate positive effect on lifestyle behavior outcomes (standardized difference in means [SDM] 0.663, 95% CI 0.228 to 1.10). A meta-regression model including source of data found that interventions that used system-captured data for personalization were associated with higher effectiveness than those that used user-reported data (SDM 1.48, 95% CI 0.76 to 2.19). In summary, the field is in its infancy, with preliminary evidence of the potential efficacy of personalization in improving lifestyle behaviors. Source of data for personalization might be important in determining intervention effectiveness. To fully exploit the potential of personalization, future high-quality studies should investigate the integration of multiple data from different sources and include personalized features other than content.
Ubaid, A, Hussain, F & Saqib, M 2021, 'Container Shipment Demand Forecasting in the Australian Shipping Industry: A Case Study of Asia–Oceania Trade Lane', Journal of Marine Science and Engineering, vol. 9, no. 9, pp. 968-968.
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Demand forecasting has a pivotal role in making informed business decisions by predicting future sales using historical data. Traditionally, demand forecasting has been widely used in the management of production, staffing and warehousing for sales and marketing data. However, the use of demand forecasting has little been studied in the container shipping industry. Improved visibility into the demand for container shipments has been a long-held objective of industry stakeholders. This paper addresses the shortcomings of both short-term and long-term shipment demand forecasting for the Australian container shipping industry. In this study, we compare three forecasting models, namely, the seasonal auto-regressive integrated moving average (SARIMA), Holt–Winters’ seasonal method and Facebook’s Prophet, to find the best fitting model for short-term and long-term import demand forecasting in the Australian shipping industry. Demand data from three years, i.e., 2016–2018, is used for the Asia–Oceania trade lane. The mean absolute percentage error (MAPE), root mean squared error (RMSE) and 2-fold walk-forward cross-validation are used for the model evaluation. The experiment results observed from the selected metrics suggest that Prophet outperforms the other models in its comparison for container shipment demand forecasting.
Ullah Khan, H, Kamel Alomari, M, Khan, S, Nazir, S, Qumer Gill, A, Ali Al-Maadid, A, Khalid Abu-Shawish, Z & Kamal Hassan, M 2021, 'Systematic Analysis of Safety and Security Risks in Smart Homes', Computers, Materials & Continua, vol. 68, no. 1, pp. 1409-1428.
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van den Hoven, E, Orth, D & Zijlema, A 2021, 'Possessions and memories', Current Opinion in Psychology, vol. 39, pp. 94-99.
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People often acquire souvenirs and photographs to facilitate remembering, but possessions and memories can relate to each other in a variety of ways. This review paper presents four different connection types found between meaningful things in our everyday lives and our personal memories. Each connection type either focuses on possessions or memories and the connection between the two is either active or lost. These perspectives will be detailed through examples of studies and design cases from different fields and research areas. More studies have been found focusing on existing connections between possessions and memories, such as in human-computer interaction, design, material culture, psychology and marketing, than those lost, which were specifically focused around ageing, forgetting, heirlooms, identity and hoarding behaviour. Our review of connections between possessions and memories accumulate to suggest the attachment people ascribe to certain possessions is mirrored by the ability of objects to fulfil people's desire to preserve, embody, showcase and recollect certain memories.
Verma, R & Merigó, JM 2021, 'On Sharma-Mittal’s Entropy under Intuitionistic Fuzzy Environment', Cybernetics and Systems, vol. 52, no. 6, pp. 498-521.
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Verma, R, Lobos-Ossandón, V, Merigó, JM, Cancino, C & Sienz, J 2021, 'Forty years of applied mathematical modelling: A bibliometric study', Applied Mathematical Modelling, vol. 89, pp. 1177-1197.
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Vo, NNY, Liu, S, Li, X & Xu, G 2021, 'Leveraging unstructured call log data for customer churn prediction', Knowledge-Based Systems, vol. 212, pp. 106586-106586.
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Wang, C, Huang, Y, Ding, W & Cao, Z 2021, 'Attribute reduction with fuzzy rough self-information measures', Information Sciences, vol. 549, pp. 68-86.
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The fuzzy rough set is one of the most effective methods for dealing with the fuzziness and uncertainty of data. However, in most cases this model only considers the information provided by the lower approximation of a decision when it is used to attribute reduction. In a realistic environment, the uncertainty of information is related to lower approximation as well as upper approximation. In this study, we construct four kinds of uncertainty measures by combining fuzzy rough approximations with the concept of self-information. These uncertainty measures can be employed to evaluate the classification ability of attribute subsets. The relationships between these measures are discussed in detail. It is proven that the fourth measure, called relative decision self-information, is better for attribute reduction than the other measures because it considers both the lower and upper approximations of a fuzzy decision. The proposed measures are generalizations of classical measures based on fuzzy rough sets. Finally, we have designed a greedy algorithm for attribute reduction. We validate the effectiveness of the proposed method by comparing the experimental results for efficiency and accuracy with those of three other algorithms using fundamental data.
Wang, C-H, Jin, Z, Zhang, W, Zowghi, D, Zhao, H-Y & Jiao, W-P 2021, 'Activity Diagram Synthesis Using Labelled Graphs and the Genetic Algorithm', Journal of Computer Science and Technology, vol. 36, no. 6, pp. 1388-1406.
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Many applications need to meet diverse requirements of a large-scale distributed user group. That challenges the current requirements engineering techniques. Crowd-based requirements engineering was proposed as an umbrella term for dealing with the requirements development in the context of large-scale user group. However, there are still many issues. Among others, a key issue is when a set of requirements descriptions from different participants are received, how to merge these requirements to produce the synthesized requirements description. Appropriate techniques are needed for supporting the requirements synthesis. Diagrams are widely used in industry to represent requirements. This paper chooses the activity diagrams and proposes a novel approach for the activity diagram synthesis which adopts genetic algorithm to repeatedly modify a population of individual solutions toward an optimal solution. As a result, it can automatically generate a resulting diagram which combines the commonalities as many as possible while leveraging the variabilities of a set of input diagrams. The approach is featured by (1) the labelled graph is proposed as the representation of the candidate solutions during the iterative evolution; (2) the generalized entropy is proposed and defined as the measurement of the solutions; (3) the genetic algorithm is designed for sorting out the high-quality solution. Four cases of different scales are used to evaluate the effectiveness of the approach. The experimental results show that not only the approach gets high precision and recall but also the resulting diagram satisfies the properties of minimization and information preservation and can support the requirements traceability
Wang, D, Wang, X, Xiang, Z, Yu, D, Deng, S & Xu, G 2021, 'Attentive sequential model based on graph neural network for next poi recommendation', World Wide Web, vol. 24, no. 6, pp. 2161-2184.
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Wang, D, Zhang, X, Yu, D, Xu, G & Deng, S 2021, 'CAME: Content- and Context-Aware Music Embedding for Recommendation', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 3, pp. 1375-1388.
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Wang, F, Zhu, M, Wang, M, Khosravi, MR, Ni, Q, Yu, S & Qi, L 2021, '6G-Enabled Short-Term Forecasting for Large-Scale Traffic Flow in Massive IoT Based on Time-Aware Locality-Sensitive Hashing', IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5321-5331.
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With the advent of the Internet of Things (IoT) and the increasing popularity of the intelligent transportation system, a large number of sensing devices are installed on the road for monitoring traffic dynamics in real time. These sensors can collect streaming traffic data distributed across different traffic sites, which constitute the main source of big traffic data. Analyzing and mining such big traffic data in massive IoT can help traffic administrations to make scientific and reasonable traffic scheduling decisions, so as to avoid prospective traffic congestions in the future. However, the above traffic decision making often requires frequent and massive data transmissions between distributed sensors and centralized cloud computing centers, which calls for lightweight data integrations and accurate data analyses based on large-scale traffic data. In view of this challenge, a big data-driven and nonparametric model aided by 6G is proposed in this article to extract similar traffic patterns over time for accurate and efficient short-term traffic flow prediction in massive IoT, which is mainly based on time-aware locality-sensitive hashing (LSH). We design a wide range of experiments based on a real-world big traffic data set to validate the feasibility of our proposal. Experimental reports demonstrate that the prediction accuracy and efficiency of our proposal are increased by 32.6% and 97.3%, respectively, compared with the other two competitive approaches.
Wang, J, Li, H, Wang, Y & Lu, H 2021, 'A hesitant fuzzy wind speed forecasting system with novel defuzzification method and multi-objective optimization algorithm', Expert Systems with Applications, vol. 168, pp. 114364-114364.
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Owing to the nondeterministic nature of wind speed, the conventional fuzzy time series forecasting model has difficulty in establishing a common membership level. Therefore, in this study, the fuzzy series forecasting model was improved based on hesitant fuzzy sets. A hesitant fuzzy wind speed forecasting system with a novel defuzzification method and multiobjective optimization algorithm was developed. First, an advanced decomposition model is employed to extract the effective feature and remove the noise component from the raw wind speed series. Then, the universe of discourse is partitioned into equal and unequal intervals by multifuzzification methods and merged by aggregating hesitant information. A multiobjective intelligent optimization algorithm is applied to determine the optimal weights of different intervals accurately and stably. Furthermore, a novel defuzzification model based on an ordered weighted averaging operator and a regular increasing monotone quantifier is proposed to calculate the final forecasting results. The crucial strengths of the developed system are verifying the possibility of enhancing the performance of wind speed forecasting models by improving conventional fuzzy time series forecasting models and integrating them with decomposition models and artificial-intelligence models. Typical wind speed series datasets with different resolutions were selected to evaluate the performance of the proposed system, and experimental results prove that the proposed system outperforms other comparison models with high forecasting accuracy and computing efficiency.
Wang, P, Li, L, Wang, R, Xu, G & Zhang, J 2021, 'Socially-driven multi-interaction attentive group representation learning for group recommendation', Pattern Recognition Letters, vol. 145, pp. 74-80.
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Wang, S, Wang, J, Lu, H & Zhao, W 2021, 'A novel combined model for wind speed prediction – Combination of linear model, shallow neural networks, and deep learning approaches', Energy, vol. 234, pp. 121275-121275.
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Accurate wind speed forecasting is increasingly essential for improving the operating efficiency of electric power systems. Numerous models have been proposed to obtain the accurate and stable wind speed forecasting results. However, previous proposed models are limited by single predictive model or cannot deal with complex nonlinear data characteristic, which resulted in poor and unstable prediction results. In this paper, a novel forecasting model that combines noise processing, statistical approaches, deep learning frameworks and multi-objective optimization algorithm is proposed. Multi-objective optimization algorithms can take advantage of the merits of benchmark prediction models to address nonlinear characteristics of wind speed series. The 10-min real wind speed data from three Sites in China are adopted for verifying the effectiveness of this proposed model. The experimental results of multi-step prediction show that the model achieves MAPE1-step = 2.2109%, MAPE2-step = 3.0309%, and MAPE3-step = 4.2536% at Site 1; MAPE1-step = 2.4586%, MAPE2-step = 3.2034%, and MAPE3-step = 4.6843% at Site 2; MAPE1-step = 2.3180%, MAPE2-step = 3.0846%, and MAPE3-step = 4.4193% at Site 3. Therefore, the forecasting performance of this model is excellent, and it is beneficial to the dispatching and planning of power grid.
Wang, W, Feng, S, Chen, L, Wang, D & Zhang, Y 2021, 'Learning to improve persona consistency in conversation generation with information augmentation', Knowledge-Based Systems, vol. 228, pp. 107246-107246.
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Wang, Z, Han, F, Jiang, Y, Yu, S, Ji, Y & Cai, W 2021, 'Conceptual design and assessment of a novel energy management system for LNG fueled ships with air separation', Thermal Science and Engineering Progress, vol. 26, pp. 101111-101111.
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Wang, Z, Zhao, J, Hu, J, Zhu, T, Wang, Q, Ren, J & Li, C 2021, 'Towards Personalized Task-Oriented Worker Recruitment in Mobile Crowdsensing', IEEE Transactions on Mobile Computing, vol. 20, no. 5, pp. 2080-2093.
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Worker recruitment in mobile crowdsensing systems aims to recruit the most suitable users to perform tasks with high quality and in real-time. Many worker recruitment or task matching mechanisms have been proposed, especially for crowdsourcing platforms, where content information of tasks from the implicit feedback of workers' attendance is extensively exploited to help workers find preferred tasks efficiently. Different from traditional crowdsourcing systems, tasks in mobile crowdsensing systems are usually time-sensitive and location-dependent which also play a crucial role in worker recruitment. However, these context information have not been effectively explored for user recruitment in mobile crowdsensing systems. In this paper, we propose a novel personalized task-oriented worker recruitment mechanism for mobile crowdsensing systems based on a careful characterization of workers' preference. In particular, we fully exploit the content information (e.g., task category, task description) together with the context information (e.g., task time, task location) from the implicit feedback of workers' attendance to accurately model workers' preference on tasks. Moreover, we regard the task-worker fitness prediction as a binary classification problem and utilize the Logit model to integrate the heterogeneous factors into a single framework to predict the matching probability of each task-worker pair. Finally, the workers with the highest matching probability are recruited proactively for each new task. Extensive experiments on real-world datasets demonstrate that the proposed mechanism achieves better performance than the benchmarks.
Wu, M, Kozanoglu, DC, Min, C & Zhang, Y 2021, 'Unraveling the capabilities that enable digital transformation: A data-driven methodology and the case of artificial intelligence', Advanced Engineering Informatics, vol. 50, pp. 101368-101368.
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Digital transformation (DT) is prevalent in businesses today. However, current studies to guide DT are mostly qualitative, resulting in a strong call for quantitative evidence of exactly what DT is and the capabilities needed to enable it successfully. With the aim of filling the gaps, this paper presents a novel bibliometric framework that unearths clues from scientific articles and patents. The framework incorporates the scientific evolutionary pathways and hierarchical topic tree to quantitatively identify the DT research topics’ evolutionary patterns and hierarchies at play in DT research. Our results include a comprehensive definition of DT from the perspective of bibliometrics and a systematic categorization of the capabilities required to enable DT, distilled from over 10,179 academic papers on DT. To further yield practical insights on technological capabilities, the paper also includes a case study of 9,454 patents focusing on one of the emerging technologies - artificial intelligence (AI). We summarized the outcomes with a four-level AI capabilities model. The paper ends with a discussion on its contributions: presenting a quantitative account of the DT research, introducing a process based understanding of DT, offering a list of major capabilities enabling DT, and drawing the attention of managers to be aware of capabilities needed when undertaking their DT journey.
Wu, Z, Li, G, Shen, S, Lian, X, Chen, E & Xu, G 2021, 'Constructing dummy query sequences to protect location privacy and query privacy in location-based services', World Wide Web, vol. 24, no. 1, pp. 25-49.
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Location-based services (LBS) have become an important part of people’s daily life. However, while providing great convenience for mobile users, LBS result in a serious problem on personal privacy, i.e., location privacy and query privacy. However, existing privacy methods for LBS generally take into consideration only location privacy or query privacy, without considering the problem of protecting both of them simultaneously. In this paper, we propose to construct a group of dummy query sequences, to cover up the query locations and query attributes of mobile users and thus protect users’ privacy in LBS. First, we present a client-based framework for user privacy protection in LBS, which requires not only no change to the existing LBS algorithm on the server-side, but also no compromise to the accuracy of a LBS query. Second, based on the framework, we introduce a privacy model to formulate the constraints that ideal dummy query sequences should satisfy: (1) the similarity of feature distribution, which measures the effectiveness of the dummy query sequences to hide a true user query sequence; and (2) the exposure degree of user privacy, which measures the effectiveness of the dummy query sequences to cover up the location privacy and query privacy of a mobile user. Finally, we present an implementation algorithm to well meet the privacy model. Besides, both theoretical analysis and experimental evaluation demonstrate the effectiveness of our proposed approach, which show that the location privacy and attribute privacy behind LBS queries can be effectively protected by the dummy queries generated by our approach.
Xiang, Y, Liu, K, Su, T, Li, J, Ouyang, S, Mao, SS & Geimer, M 2021, 'An Extension of BIM Using AI: A Multi Working-Machines Pathfinding Solution', IEEE Access, vol. 9, pp. 124583-124599.
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Xiao, F, Aritsugi, M, Abawajy, JH, Cao, Z, Al-Hmouz, R & Liu, P 2021, 'Editorial on Special Issue: “Applications of Intelligent and Fuzzy Theory in Data Science”', International Journal of Fuzzy Systems, vol. 23, no. 2, pp. 492-493.
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Xiao, F, Cao, Z & Jolfaei, A 2021, 'A Novel Conflict Measurement in Decision-Making and Its Application in Fault Diagnosis', IEEE Transactions on Fuzzy Systems, vol. 29, no. 1, pp. 186-197.
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Dempster-Shafer evidence (DSE) theory, which allows combining pieces of evidence from different data sources to derive a degree of belief function that is a type of fuzzy measure, is a general framework for reasoning with uncertainty. In this framework, how to optimally manage the conflicts of multiple pieces of evidence in DSE remains an open issue to support decision making. The existing conflict measurement approaches can achieve acceptable outcomes but do not fully consider the optimization at the decision-making level using the novel measurement of conflicts. In this article, we propose a novel evidential correlation coefficient (ECC) for belief functions by measuring the conflict between two pieces of evidence in decision making. Then, we investigate the properties of our proposed evidential correlation and conflict coefficients, which are all proven to satisfy the desirable properties for conflict measurement, including nonnegativity, symmetry, boundedness, extreme consistency, and insensitivity to refinement. We also present several examples and comparisons to demonstrate the superiority of our proposed ECC method. Finally, we apply the proposed ECC in a decision-making application of motor rotor fault diagnosis, which verifies the practicability and effectiveness of our proposed novel measurement.
Xiao, Y, Xiao, L, Lu, X, Zhang, H, Yu, S & Poor, HV 2021, 'Deep-Reinforcement-Learning-Based User Profile Perturbation for Privacy-Aware Recommendation', IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4560-4568.
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User profile perturbation protects privacy in the release of user profiles to receive recommendation services, in which the privacy budget as a privacy parameter can be controlled to effect a tradeoff between the recommendation quality and privacy protection against inference attacks. In this article, we propose a deep reinforcement learning (RL)-based user profile perturbation scheme for recommendation systems. This scheme applies differential privacy to protect user privacy and uses deep RL to choose the privacy budget against inference attackers. Based on an evaluated neural network (NN) and a target NN, this scheme enables a user device to optimize the privacy budget over time based on the sensitivity level of the clicked item, the similarities among the recommended items, and the estimated privacy loss. We provide an upper bound on the privacy protection performance of this scheme in the recommendation game and evaluate its computational complexity. Simulation results for a movie recommendation system show that this scheme increases the user privacy protection level for a given recommendation quality compared with benchmark schemes.
Xie, X, Niu, J, Liu, X, Chen, Z, Tang, S & Yu, S 2021, 'A survey on incorporating domain knowledge into deep learning for medical image analysis', Medical Image Analysis, vol. 69, pp. 101985-101985.
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Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area. To address this problem, researchers have started looking for external information beyond current available medical datasets. Traditional approaches generally leverage the information from natural images via transfer learning. More recent works utilize the domain knowledge from medical doctors, to create networks that resemble how medical doctors are trained, mimic their diagnostic patterns, or focus on the features or areas they pay particular attention to. In this survey, we summarize the current progress on integrating medical domain knowledge into deep learning models for various tasks, such as disease diagnosis, lesion, organ and abnormality detection, lesion and organ segmentation. For each task, we systematically categorize different kinds of medical domain knowledge that have been utilized and their corresponding integrating methods. We also provide current challenges and directions for future research.
Xiong, J, Xie, H, Liu, B, Li, B & Gui, L 2021, 'Cooperative Caching Services on High-Speed Train by Reverse Auction', IEEE Transactions on Vehicular Technology, vol. 70, no. 9, pp. 9437-9449.
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Xu, C, Zhang, T, Kuang, X, Zhou, Z & Yu, S 2021, 'Context-Aware Adaptive Route Mutation Scheme: A Reinforcement Learning Approach', IEEE Internet of Things Journal, vol. 8, no. 17, pp. 13528-13541.
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Moving Target Defense (MTD) is an emerging proactive defense technology, which can reduce the risk of vulnerabilities exploited by attacker. As a crucial component of MTD, route mutation (RM) faces a few fundamental problems defending against sophisticated Distributed Denial of Service (DDoS) attacks: 1) It’s unable to make optimal mutation selection due to insufficient learning in attack behaviors. 2) Because network situation is time-varying, RM also lacks self-adaptation in mutation parameters. In this paper, we propose a context-aware Q-learning algorithm for RM (CQ-RM) that can learn attack strategies to optimize the selection of mutated routes. We firstly integrate four representative attack strategies into a unified mathematical model and formalize multiple network constraints. Then, taking above network constraints into considerations, we model RM process as a Markov decision process (MDP). To look for the optimal policy of MDP, we develop a context estimation mechanism and further propose the CQ-RM scheme, which can adjust learning rate and mutation period adaptively. Correspondingly, the optimal convergence of CQ-RM is proved theoretically. Finally, extensive experimental results highlight the effectiveness of our method compared to representative solutions.
Xu, C, Zhu, L, Liu, Y, Guan, J & Yu, S 2021, 'DP-LTOD: Differential Privacy Latent Trajectory Community Discovering Services over Location-Based Social Networks', IEEE Transactions on Services Computing, vol. 14, no. 4, pp. 1068-1083.
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IEEE Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks. In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory sequences for trajectory privacy-preserving, and discovers latent trajectory communities through clustering the uploaded trajectory sequences. Different with traditional trajectory privacy-preserving methods, we first partition original trajectory sequence into different segments. Then, the suitable locations and segments are selected to constitute obfuscated trajectory sequence. Specifically, we formulate the trajectory obfuscation problem to select an optimal trajectory sequence which has the smallest difference with original trajectory sequence. In order to prevent privacy leakage, we add Laplace noise and exponential noise to the outputs during the stages of location obfuscation matrix generation and trajectory sequence function generation, respectively. Through formal privacy analysis,we prove that DP-LTOD scheme can guarantee \epsilon-differential private. Moreover, we develop a trajectory clustering algorithm to classify the trajectories into different kinds of clusters according to semantic distance and geographical distance. Extensive experiments on two real-world datasets illustrate that our DP-LTOD scheme can not only discover latent trajectory communities, but also protect user privacy from leaking.
Xu, Q, Su, Z, Zhang, K & Yu, S 2021, 'Fast Containment of Infectious Diseases With E-Healthcare Mobile Social Internet of Things', IEEE Internet of Things Journal, vol. 8, no. 22, pp. 16473-16485.
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Infectious disease presents great hazards to public health, due to their high infectivities and potential lethalities. One of the effective methods to hinder the spread of infectious disease is vaccination. However, due to the limitation of resource and the medical budget, vaccinating all people is not feasible in practice. Besides, the vaccinating effects are difficult to be timely observed through traditional ways, such as outpatient services. To tackle above problem, we propose an e-healthcare mobile social internet of things (MSIoTs) based targeted vaccination scheme to fast contain the spread of the infectious disease. Specifically, we first develop an e-healthcare MSIoT architecture by integrating the e-healthcare system and MSIoTs, whereby the spread status of the infectious disease is timely collected. Furthermore, a graph coloring and spreading centrality-based optional candidate searching algorithm is devised to hunt for the candidates that are powerfully capable of preventing infectious disease. Especially, in order to reduce the vaccination cost, we design an optimal vaccinated target selection algorithm to choose a minimum number of targets whose locations are differentially distributed. Extensive simulations demonstrate that the proposed scheme can effectively prevent infectious disease as compared to conventional schemes.
Yang, C-S, Liu, J, Singh, AK, Huang, K-C & Lin, C-T 2021, 'Brain Dynamics of Spatial Reference Frame Proclivity in Active Navigation', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1701-1710.
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Recent research into navigation strategy of different spatial reference frames (self-centered egocentric reference frame and environment-centered allocentric reference frame) has revealed that the parietal cortex plays an important role in processing allocentric information to provide a translation function between egocentric and allocentric spatial reference frames. However, most studies merely focused on a passive experimental environment, which is not truly representative of our daily spatial learning/navigation tasks. This study investigated the factor associated with brain dynamics that causes people to switch their preferred spatial strategy in both active and passive navigations to bridge the gap. Virtual reality (VR) technique and Omni treadmill are applied to realize actively walking for active navigation, and for passive navigation, participants were sitting while conducting the same task. Electroencephalography (EEG) signals were recorded to monitor spectral perturbations on transitions between egocentric and allocentric frames during a path integration task. Forty-one right-handed male participants from authors' university participated this study. Our brain dynamics results showed navigation involved areas including the parietal cortex with modulation in the alpha band, the occipital cortex with beta and low gamma band perturbations, and the frontal cortex with theta perturbation. Differences were found between two different turning-angle paths in the alpha band in parietal cluster event-related spectral perturbations (ERSPs). In small turning-angle paths, allocentric participants showed stronger alpha desynchronization than egocentric participants; in large turning-angle paths, participants for two reference frames had a smaller difference in the alpha frequency band. Behavior results of homing errors also corresponded to brain dynamic results, indicating that a larger angle path caused the allocentric to have a higher tendency to become eg...
Yang, J, Wang, Y-K, Yao, X & Lin, C-T 2021, 'Adaptive Initialization Method for K-Means Algorithm', Frontiers in Artificial Intelligence, vol. 4, p. 740817.
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The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. In this research, we propose an adaptive initialization method for the K-means algorithm (AIMK) which can adapt to the various characteristics in different datasets and obtain better clustering performance with stable results. For larger or higher-dimensional datasets, we even leverage random sampling in AIMK (name as AIMK-RS) to reduce the time complexity. 22 real-world datasets were applied for performance comparisons. The experimental results show AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Specifically, AIMK-RS can significantly reduce the time complexity to O (n). Moreover, we exploit AIMK to initialize K-medoids and spectral clustering, and better performance is also explored. The above results demonstrate superior performance and good scalability by AIMK or AIMK-RS. In the future, we would like to apply AIMK to more partition-based clustering algorithms to solve real-life practical problems.
Yang, X, Liu, W, Liu, W & Tao, D 2021, 'A Survey on Canonical Correlation Analysis', IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 6, pp. 2349-2368.
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Yang, X, Liu, W, Zhang, S, Liu, W & Tao, D 2021, 'Targeted Attention Attack on Deep Learning Models in Road Sign Recognition', IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4980-4990.
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Yang, Z, Garg, H, Li, J, Srivastava, G & Cao, Z 2021, 'Investigation of multiple heterogeneous relationships using a q-rung orthopair fuzzy multi-criteria decision algorithm', Neural Computing and Applications.
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© 2020, Springer-Verlag London Ltd., part of Springer Nature. Q-rung orthopair fuzzy (q-ROF) set is one of the powerful tools for handling the uncertain multi-criteria decision-making (MCDM) problems, various MCDM methods under q-ROF environment have been developed in recent years. However, most existing studies merely concerned about the relationship between the criteria but they have not investigated the interactions between membership function and non-membership function. To explore the multiple heterogeneous relationships among membership functions and criteria, we propose a novel decision algorithm based on q-ROF set to deal with these using interactive operators and Maclaurin symmetric mean (MSM) operators. Specifically, the new interaction laws in the membership pairs of q-ROF sets are explained, and their properties are analyzed as the initial stage. Then, taking into account the influence of two or more factors on decision analysis, a q-ROF interaction Maclaurin symmetry mean (q-ROFIMSM) operator is formed based on the proposed interaction law to identify these factors’ interrelationship. Thirdly, based on the proposed operator with q-ROF information, a MCDM algorithm is developed and illustrated by numerical examples. An analysis of the feasibility, sensitivity, and superiority of the proposed framework is provided to validate our proposed method.
Yao, L, Wang, X, Sheng, QZ, Benatallah, B & Huang, C 2021, 'Mashup Recommendation by Regularizing Matrix Factorization with API Co-Invocations', IEEE Transactions on Services Computing, vol. 14, no. 2, pp. 502-515.
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IEEE Mashup is a dominant approach for building data-centric applications, especially mobile applications, in recent years. Since mashups are predominantly based on public data sources and existing APIs, it requires no sophisticated programming knowledge of people to develop mashup applications. The recent prevalence of open APIs and open data sources in the Big Data era has provided new opportunities for mashup development, but at the same time increase the difficulty of selecting the right services for a given mashup task. The API recommendation for mashup differs from traditional service recommendation tasks in lacking the specific QoS information and formal semantic specification of the APIs, which limits the adoption of many existing methods. Although there are a significant number of service recommendation approaches, most of them focus on improving the recommendation accuracy and few work pays attention to the diversity of the recommendation results. Another challenge comes from the existence of both explicit and implicit correlations among the different APIs generally neglected by existing recommendation methods. In this paper, we address the above deficiencies of existing approaches by exploring API recommendation for mashups in the reusable composition context, with the goal of helping developers identify the most appropriate APIs for composition task
Ye, D, Zhu, T, Shen, S & Zhou, W 2021, 'A Differentially Private Game Theoretic Approach for Deceiving Cyber Adversaries', IEEE Transactions on Information Forensics and Security, vol. 16, pp. 569-584.
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Yu, J, Xue, H, Liu, B, Wang, Y, Zhu, S & Ding, M 2021, 'GAN-Based Differential Private Image Privacy Protection Framework for the Internet of Multimedia Things', Sensors, vol. 21, no. 1, pp. 58-58.
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With the development of the Internet of Multimedia Things (IoMT), an increasing amount of image data is collected by various multimedia devices, such as smartphones, cameras, and drones. This massive number of images are widely used in each field of IoMT, which presents substantial challenges for privacy preservation. In this paper, we propose a new image privacy protection framework in an effort to protect the sensitive personal information contained in images collected by IoMT devices. We aim to use deep neural network techniques to identify the privacy-sensitive content in images, and then protect it with the synthetic content generated by generative adversarial networks (GANs) with differential privacy (DP). Our experiment results show that the proposed framework can effectively protect users’ privacy while maintaining image utility.
Yu, S, Zuo, H, Xu, X, Ning, N, Yu, B, Zhang, L & Tian, M 2021, 'Self-Healable Silicone Elastomer Based on the Synergistic Effect of the Coordination and Ionic Bonds', ACS Applied Polymer Materials, vol. 3, no. 5, pp. 2667-2677.
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Yue, Z, Ding, S, Zhao, L, Zhang, Y, Cao, Z, Tanveer, M, Jolfaei, A & Zheng, X 2021, 'Privacy-preserving Time-series Medical Images Analysis Using a Hybrid Deep Learning Framework', ACM Transactions on Internet Technology, vol. 21, no. 3, pp. 1-21.
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Time-series medical images are an important type of medical data that contain rich temporal and spatial information. As a state-of-the-art, computer-aided diagnosis (CAD) algorithms are usually used on these image sequences to improve analysis accuracy. However, such CAD algorithms are often required to upload medical images to honest-but-curious servers, which introduces severe privacy concerns. To preserve privacy, the existing CAD algorithms support analysis on each encrypted image but not on the whole encrypted image sequences, which leads to the loss of important temporal information among frames. To meet this challenge, a convolutional-LSTM network, named HE-CLSTM, is proposed for analyzing time-series medical images encrypted by a fully homomorphic encryption mechanism. Specifically, several convolutional blocks are constructed to extract discriminative spatial features, and LSTM-based sequence analysis layers (HE-LSTM) are leveraged to encode temporal information from the encrypted image sequences. Moreover, a weighted unit and a sequence voting layer are designed to incorporate both spatial and temporal features with different weights to improve performance while reducing the missed diagnosis rate. The experimental results on two challenging benchmarks (a Cervigram dataset and the BreaKHis public dataset) provide strong evidence that our framework can encode visual representations and sequential dynamics from encrypted medical image sequences; our method achieved AUCs above 0.94 both on the Cervigram and BreaKHis datasets, constituting a significant margin of statistical improvement compared with several competing methods.
Zhang, C, Zhang, Q, Yu, S, Yu, JJQ & Song, X 2021, 'Complicating the Social Networks for Better Storytelling: An Empirical Study of Chinese Historical Text and Novel', IEEE Transactions on Computational Social Systems, vol. 8, no. 3, pp. 754-767.
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Digital humanities is an important subject because it enables developments in history, literature, and films. In this article, we perform an empirical study of a Chinese historical text, Records of the Three Kingdoms (Records), and a historical novel of the same story, Romance of the Three Kingdoms (Romance). We employ deep-learning-based natural language processing (NLP) techniques to extract characters and their relationships. The adopted NLP approach can extract 93% and 91% characters that appeared in the two books, respectively. Then, we characterize the social networks and sentiments of the main characters in the historical text and the historical novel. We find that the social network in Romance is more complex and dynamic than that of Records, and the influence of the main characters differs. These findings shed light on the different styles of storytelling in the two literary genres and how the historical novel complicates the social networks of characters to enrich the literariness of the story.
Zhang, C, Zhang, S, Yu, JJQ & Yu, S 2021, 'FASTGNN: A Topological Information Protected Federated Learning Approach for Traffic Speed Forecasting', IEEE Transactions on Industrial Informatics, vol. 17, no. 12, pp. 8464-8474.
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Federated learning has been applied to various tasks in intelligent transportation systems to protect data privacy through decentralized training schemes. The majority of the state-of-the-art models in ITS are graph neural networks (GNN)-based for spatial information learning. When applying federated learning to the ITS tasks with GNN-based models, the existing architectures can only protect the data privacy; however, ignore the one of topological information of transportation networks. In this work, we propose a novel federated learning architecture to tackle this problem. Specifically, we introduce a differential privacy-based adjacency matrix preserving approach for protecting the topological information. We also propose an adjacency matrix aggregation approach to allow local GNN-based models to access the global network for a better training effect. Furthermore, we propose a GNN-based model named Attention-based Spatial-Temporal Graph Neural Networks (ASTGNN) for traffic speed forecasting. We integrate the proposed federated learning architecture and ASTGNN as FASTGNN for traffic speed forecasting. Extensive case studies on a real-world dataset demonstrate that FASTGNN can develop accurate forecasting under the privacy preservation constraint.
Zhang, G, Wang, B, Wei, F, Shi, K, Wang, Y, Sui, X & Zhu, M 2021, 'Source camera identification for re-compressed images: A model perspective based on tri-transfer learning', Computers & Security, vol. 100, pp. 102076-102076.
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Zhang, J, Chen, B, Cheng, X, Binh, HTT & Yu, S 2021, 'PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems', IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3310-3322.
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Edge computing is a key-enabling technology that meets continuously increasing requirements for the intelligent Internet-of-Things (IoT) applications. To cope with the increasing privacy leakages of machine learning while benefiting from unbalanced data distributions, federated learning has been wildly adopted as a novel intelligent edge computing framework with a localized training mechanism. However, recent studies found that the federated learning framework exhibits inherent vulnerabilities on active attacks, and poisoning attack is one of the most powerful and secluded attacks where the functionalities of the global model could be damaged through attacker's well-crafted local updates. In this article, we give a comprehensive exploration of the poisoning attack mechanisms in the context of federated learning. We first present a poison data generation method, named Data_Gen, based on the generative adversarial networks (GANs). This method mainly relies upon the iteratively updated global model parameters to regenerate samples of interested victims. Second, we further propose a novel generative poisoning attack model, named PoisonGAN, against the federated learning framework. This model utilizes the designed Data_Gen method to efficiently reduce the attack assumptions and make attacks feasible in practice. We finally evaluate our data generation and attack models by implementing two types of typical poisoning attack strategies, label flipping and backdoor, on a federated learning prototype. The experimental results demonstrate that these two attack models are effective in federated learning.
Zhang, J, Wei, B, Wu, F, Dong, L, Hu, W, Kanhere, SS, Luo, C, Yu, S & Cheng, J 2021, 'Gate-ID: WiFi-Based Human Identification Irrespective of Walking Directions in Smart Home', IEEE Internet of Things Journal, vol. 8, no. 9, pp. 7610-7624.
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Research has shown the potential of device-free WiFi sensing for human identification. Each and every human has a unique gait and prior works suggest WiFi devices are able to capture the unique signature of a person's gait. In this article, we show for the first time that the monitored gait could be inconsistent and have mirror-like perturbations when individuals walk through WiFi devices in different directions, provided that the WiFi antenna array is horizontal to the walking path. Such inconsistent mirrored patterns are to negatively affect the uniqueness of gait and accuracy of human identification. Therefore, we propose a system called Gate-ID for accurately identifying individuals' identities irrespective of different walking directions. Gate-ID employs theoretical communication model and real measurements to demonstrate that antenna array orientations and walking directions contribute to the mirror-like patterns in WiFi signals. A novel heuristic algorithm is proposed to infer individual's walking directions. A set of methods are employed to extract and augment the representative spatial-temporal features of gait and enable the system performing irrespective of walking directions. We further propose a novel attention-based deep learning model that fuses various weighted features and ignores ineffective noises to uniquely identify individuals. We implement Gate-ID on commercial off-the-shelf devices. Extensive experiments demonstrate that our system can uniquely identify people with average accuracy of 90.7%-75.7% from a group of 6-20 people, respectively, and improve the accuracy by 12.5%-43.5% compared with baselines.
Zhang, K, Shi, W, Wang, C, Li, Y, Liu, Z, Liu, T, Li, J, Yan, X, Wang, Q, Cao, Z & Wang, G 2021, 'Reliability of EEG microstate analysis at different electrode densities during propofol-induced transitions of brain states', NeuroImage, vol. 231, pp. 117861-117861.
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Electroencephalogram (EEG) microstate analysis is a promising and effective spatio-temporal method that can segment signals into several quasi-stable classes, providing a great opportunity to investigate short-range and long-range neural dynamics. However, there are still many controversies in terms of reproducibility and reliability when selecting different parameters or datatypes. In this study, five electrode configurations (91, 64, 32, 19, and 8 channels) were used to measure the reliability of microstate analysis at different electrode densities during propofol-induced sedation. First, the microstate topography and parameters at five different electrode densities were compared in the baseline (BS) condition and the moderate sedation (MD) condition, respectively. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were introduced to quantify the consistency of the microstate parameters. Second, statistical analysis and classification between BS and MD were performed to determine whether the microstate differences between different conditions remained stable at different electrode densities, and ICC was also calculated between the different conditions to measure the consistency of the results in a single condition. The results showed that in both the BS or MD condition, respectively, there were few significant differences in the microstate parameters among the 91-, 64-, and 32-channel configurations, with most of the differences observed between the 19- or 8-channel configurations and the other configurations. The ICC and CV data also showed that the consistency among the 91-, 64-, and 32-channel configurations was better than that among all five electrode configurations after including the 19- and 8-channel configurations. Furthermore, the significant differences between the conditions in the 91-channel configuration remained stable at the 64- and 32-channel resolutions, but disappeared at the 19- and 8-channel resolutions...
Zhang, L, Shi, Y, Chang, Y-C & Lin, C-T 2021, 'Hierarchical Fuzzy Neural Networks With Privacy Preservation for Heterogeneous Big Data', IEEE Transactions on Fuzzy Systems, vol. 29, no. 1, pp. 46-58.
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© 1993-2012 IEEE. Heterogeneous big data poses many challenges in machine learning. Its enormous scale, high dimensionality, and inherent uncertainty make almost every aspect of machine learning difficult, from providing enough processing power to maintaining model accuracy to protecting privacy. However, perhaps the most imposing problem is that big data is often interspersed with sensitive personal data. Hence, we propose a privacy-preserving hierarchical fuzzy neural network to address these technical challenges while also alleviating privacy concerns. The network is trained with a two-stage optimization algorithm, and the parameters at low levels of the hierarchy are learned with a scheme based on the well-known alternating direction method of multipliers, which does not reveal local data to other agents. Coordination at high levels of the hierarchy is handled by the alternating optimization method, which converges very quickly. The entire training procedure is scalable, fast, and does not suffer from gradient vanishing problems like the methods based on backpropagation. Comprehensive simulations conducted on both regression and classification tasks demonstrate the effectiveness of the proposed model. Our code is available online.1
Zhang, W-W, Sanders, YR & Sanders, BC 2021, 'Channel discord and distortion', New Journal of Physics, vol. 23, no. 8, pp. 083025-083025.
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Abstract Discord, originally notable as a signature of bipartite quantum correlation, in fact can be nonzero classically, i.e. arising from noisy measurements by one of the two parties. Here we redefine classical discord to quantify channel distortion, in contrast to the previous restriction of classical discord to a state, and we then show a monotonic relationship between classical (channel) discord and channel distortion. We show that classical discord is equivalent to (doubly stochastic) channel distortion by numerically discovering a monotonic relation between discord and total-variation distance for a bipartite protocol with one party having a noiseless channel and the other party having a noisy channel. Our numerical method includes randomly generating doubly stochastic matrices for noisy channels and averaging over a uniform measure of input messages. Connecting discord with distortion establishes discord as a signature of classical, not quantum, channel distortion.
Zhang, X, Wu, D, Ding, L, Luo, H, Lin, C-T, Jung, T-P & Chavarriaga, R 2021, 'Tiny noise, big mistakes: adversarial perturbations induce errors in brain–computer interface spellers', National Science Review, vol. 8, no. 4.
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Abstract An electroencephalogram (EEG)-based brain–computer interface (BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a computer. Most studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations, which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.
Zhang, X, Yao, L, Wang, X, Monaghan, J, McAlpine, D & Zhang, Y 2021, 'A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers', Journal of Neural Engineering, vol. 18, no. 3, pp. 031002-031002.
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Abstract Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.
Zhang, Y, Bai, G, Zhong, M, Li, X & Ko, RKL 2021, 'Differentially Private Collaborative Coupling Learning for Recommender Systems', IEEE Intelligent Systems, vol. 36, no. 1, pp. 16-24.
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Zhang, Y, Li, B, Liu, B, Hu, Y & Zheng, H 2021, 'A Privacy-Aware PUFs-Based Multiserver Authentication Protocol in Cloud-Edge IoT Systems Using Blockchain', IEEE Internet of Things Journal, vol. 8, no. 18, pp. 13958-13974.
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The combination of the Internet of Things (IoT) and Cloud-Edge (CE) paradigm promises to be an efficient system to aggregate and further process huge volumes of data from IoT nodes. Physical Unclonable Functions (PUFs) emerge as a prospective primitive to provide IoT nodes with lightweight physical identities for authentication. However, when integrating PUFs into multi-server authentication protocols to improve security, the following problems occur: 1) the challenge-response pairs (CRPs) of PUFs generated by devices need to be explicitly stored by each edge-server. This will cause the privacy leakage of CRPs; 2) the reliability is reduced resulting from the single point failure; 3) existing PUFs-based authentication protocols would need to put great efforts into synchronizing CRPs, to ensure consistency in multi-server systems. To overcome these problems, in this paper, we propose a privacy-aware authentication protocol for the multi-server CE-IoT systems by combining PUFs and the blockchain technique. The real correlations of CRPs are double-encoded into mapping correlations (MCs) by a one-time physical identity and the keyed-hash function. The blockchain is leveraged to store MCs, synchronize them efficiently, and incorporate the multi-receiver encryption to share the physical identity securely. The security of our protocol is formally proved by a random oracle model, and security features are discussed to show that our protocol resists various attacks. Moreover, a prototype was implemented to prove the efficiency of the protocol, and the comparison results present that our protocol accommodates CE-IoT systems. Finally, the simulation of the smart contract evaluates the scalability of our protocol.
Zhang, Y, Tsang, IW, Li, J, Liu, P, Lu, X & Yu, X 2021, 'Face Hallucination With Finishing Touches', IEEE Transactions on Image Processing, vol. 30, pp. 1728-1743.
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Obtaining a high-quality frontal face image from a low-resolution (LR) non-frontal face image is primarily important for many facial analysis applications. However, mainstreams either focus on super-resolving near-frontal LR faces or frontalizing non-frontal high-resolution (HR) faces. It is desirable to perform both tasks seamlessly for daily-life unconstrained face images. In this paper, we present a novel Vivid Face Hallucination Generative Adversarial Network (VividGAN) for simultaneously super-resolving and frontalizing tiny non-frontal face images. VividGAN consists of coarse-level and fine-level Face Hallucination Networks (FHnet) and two discriminators, i.e., Coarse-D and Fine-D. The coarse-level FHnet generates a frontal coarse HR face and then the fine-level FHnet makes use of the facial component appearance prior, i.e., fine-grained facial components, to attain a frontal HR face image with authentic details. In the fine-level FHnet, we also design a facial component-aware module that adopts the facial geometry guidance as clues to accurately align and merge the frontal coarse HR face and prior information. Meanwhile, two-level discriminators are designed to capture both the global outline of a face image as well as detailed facial characteristics. The Coarse-D enforces the coarsely hallucinated faces to be upright and complete while the Fine-D focuses on the fine hallucinated ones for sharper details. Extensive experiments demonstrate that our VividGAN achieves photo-realistic frontal HR faces, reaching superior performance in downstream tasks, i.e., face recognition and expression classification, compared with other state-of-the-art methods.
Zhao, J, Yang, S, Huo, H, Sun, Q & Geng, X 2021, 'TBTF: an effective time-varying bias tensor factorization algorithm for recommender system', Applied Intelligence, vol. 51, no. 7, pp. 4933-4944.
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Context-aware processing is a research hotspot in the recommendation area, which achieves better recommendation accuracy by considering more context information such as time, location and etc. besides the information of the users, items and ratings. Tensor factorization is an effective algorithm in context-aware recommendation and current approaches show that adding bias to the tensor factorization model can improve the accuracy. However, users’ rating preferences fluctuate greatly over time, which makes bias fluctuate with time too. Current context-aware recommendation algorithms ignore this problem, and usually use the same bias for a user or an item in different time. Aiming at this problem, this paper first considers the time-varying effect on user bias and item bias in context-aware recommendation, and proposes a time-varying bias tensor factorization recommendation algorithm based on the bias tensor factorization model (BiasTF). We experiment on two real datasets, and the experimental results show that the proposed algorithms get better accuracy than other algorithms.
Zhao, J, Zhang, Q, Sun, Q, Huo, H, Xiao, Y & Gong, M 2021, 'FolkRank++: An Optimization of FolkRank Tag Recommendation Algorithm Integrating User and Item Information', KSII Transactions on Internet and Information Systems, vol. 15, no. 1, pp. 1-19.
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The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance.
Zheng, C, Tao, D, Wang, J, Cui, L, Ruan, W & Yu, S 2021, 'Memory Augmented Hierarchical Attention Network for Next Point-of-Interest Recommendation', IEEE Transactions on Computational Social Systems, vol. 8, no. 2, pp. 489-499.
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IEEE Next point-of-interest (POI) recommendation has been an important task for location-based intelligent services. However, the application of such promising technique is still limited due to the following three challenges: 1) the difficulty of capturing complicated spatiotemporal patterns of user movements; 2) the hardness of modeling fine-grained long-term preferences of users; and 3) the effective learning of interaction between long- and short-term preferences. Motivated by this, we propose a memory augmented hierarchical attention network (MAHAN), which considers both short-term check-in sequences and long-term memories. To capture the complicated interest tendencies of users within a short-term period, we design a spatiotemporal self-attention network (ST-SAN). For long-term preferences modeling, we employ a memory network to maintain fine-grained preferences of users and dynamically operate them based on users' constantly updated check-ins. Moreover, we first employ a coattention network/mechanism to integrate the proposed ST-SAN and memory network, which can fully learn the dynamic interaction between long- and short-term preferences. Our extensive experiments on two publicly available data sets demonstrate the effectiveness of MAHAN.
Zhou, L, Fu, A, Mu, Y, Wang, H, Yu, S & Sun, Y 2021, 'Multicopy provable data possession scheme supporting data dynamics for cloud-based Electronic Medical Record system', Information Sciences, vol. 545, pp. 254-276.
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In the era of big data, there are several insuperable research challenges in establishing Electronic Medical Record (EMR) for updating massive data with traditional methods. It is an attractive option to create the cloud-based EMR system, since cloud provides elastic and affordable data storage and management services. However, once the medical records are uploaded into the cloud, the owner will lose the control over the data, and sensitive contents might be accessed or modified by unauthorized entities. To address this issue, we propose a multicopy provable data possession for cloud-based EMR systems, which ensures the integrity and privacy of EMR data. In particular, to achieve data updates, we design a novel dynamic structure that improves the Merkle Hash Tree for multicopy storage, which achieves full dynamics efficiently and safely. Moreover, a random masking technique is employed in our proposal to generate distinguishable replica blocks of one block. Our construction prevents a verifier from obtaining medical records from challenge responses, but also eliminates exposing the content to unauthorized entities. Our security analysis shows that our scheme is provably secure. Evaluation experiments demonstrate that the proposal has lower communication and computation costs in comparison with the existing schemes.
Zhou, L, Luo, E, Wang, G & Yu, S 2021, 'Secure fine-grained friend-making scheme based on hierarchical management in mobile social networks', Information Sciences, vol. 554, pp. 15-32.
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With the rapid development of mobile social networks (MSNs) and smart mobile devices, mobile users can easily make new social interactions with others via their smartphones. Unfortunately, users enjoy these conveniences at the cost of revealing their personal data. The inevitable release of information conflicts with increasing privacy concerns. To address this problem, we propose a hierarchical management scheme for friend matching using attributes, which aims to facilitate secure friend discovery in MSNs. The scheme involves the establishment of several attribute centers, which perform fine-grained management based on various user attributes. The user attributes are used to generate attribute sub-keys. After meeting the conditions set by the friend-making initiator, the friend-making requester combines the sub-keys into a complete decryption key and decrypts the user data file in the friend-making center. By introducing hierarchical and sorting management of attribute sub-keys, we prevent the crypto key disclosure and single point failure, which usually happens because single-authority management centers are vulnerable to attack. The security and performance of this system were analyzed and evaluated via simulations, and the results indicate that the proposed scheme is CPA-safe.
Zhou, Y, Cheng, G & Yu, S 2021, 'An SDN-Enabled Proactive Defense Framework for DDoS Mitigation in IoT Networks', IEEE Transactions on Information Forensics and Security, vol. 16, pp. 5366-5380.
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Zhou, Y, Shen, X, Zhang, S, Yu, D & Xu, G 2021, 'DFIAM: deep factorization integrated attention mechanism for smart TV recommendation', World Wide Web, vol. 24, no. 5, pp. 1465-1481.
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Users are frequently overwhelmed by their uninterested programs due to the development of smart TV and the excessive number of programs. For addressing this issue, various recommendation methods have been introduced to TV fields. In TV content recommendation, auxiliary information, such as users’ personality traits and program features, greatly influences their program preferences. However, existing methods always fail to take auxiliary information into account. In this paper, aiming at personality program recommendation on smart TV platforms, we propose a novel Deep Factorization Integrated Attention Mechanism (DFIAM) model, which fully takes advantage of users’ personality traits, program and interaction features to construct users’ preference representations. DFIAM consists of two components, FNN component and DMF component. By suitably exploiting auxiliary information, FNN component devises a feature-interaction layer to capture the low- and higher-order feature interactions, while DMF component has a field-interaction layer to acquire higher-order field interactions. The embedding layer is divided into two layers , including feature embedding layer and field embedding layer. The two components share the feature embedding layer to profile latent representations of user and program features to reduce learning parameters and computational complexity. And the field embedding layer calculated by feature embedding layer is the input of DMF component. Besides, hierarchical attention networks are applied to self-adapt the influence of each feature and effectively capture more important feature interactions. To evaluate the performance of the DFIAM model, extensive experiments are conducted on two real-world datasets from different scenarios. The results of our proposed model have outperformed the mainstream neural network-based recommendation models in terms of RMSE, MAE and R-square.
Zhu, A, Ma, M, Guo, S, Yu, S & Yi, L 2021, 'Adaptive Multi-Access Algorithm for Multi-Service Edge Users in 5G Ultra-Dense Heterogeneous Networks', IEEE Transactions on Vehicular Technology, vol. 70, no. 3, pp. 2807-2821.
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In the 5G ultra-dense wireless heterogeneous network system, it is a crucial issue to implement an effective network selection strategy to satisfy the demands of massive edge users and novel 5G services. In this paper, we model the network selection problem of edge users requesting different services as a bipartite graph, and propose a network selection algorithm based on weighted bipartite graph matching for 5G ultra-dense heterogeneous networks, named BGMNS. The proposed algorithm combines Analytic Hierarchy Process (AHP) and Grey Relation Analysis (GRA) to analyze the preferences of multiple services for different network attributes and obtain the Quality of Experience (QoE) of different edge users for each network. Moreover, in order to realize the fair allocation of network resources, we comprehensively consider the importance of the requested services and the obtained QoE by edge users to construct system fairness index. The BGMNS algorithm can optimize the overall QoE of users under the premise of ensuring the system fairness. Simulation results show that compared to the existing network selection algorithms, the proposed BGMNS algorithm can not only provide stable access to users when network status fluctuates randomly, but also effectively reduce user blocking probability as well as total packet loss rate, and significantly improve user average energy efficiency.
Zhu, C, Qin, B, Xiao, F, Cao, Z & Pandey, HM 2021, 'A fuzzy preference-based Dempster-Shafer evidence theory for decision fusion', Information Sciences, vol. 570, pp. 306-322.
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Dempster-Shafer evidence theory (D-S) is an effective instrument for merging the collected pieces of basic probability assignment (BPA), and it exhibits superiority in achieving robustness of soft computing and decision making in an uncertain and imprecise environment. However, the determination of BPA is still uncertain, and merely applying evidence theory can sometimes lead to counterintuitive results when lines of evidence conflict. In this paper, a novel BPA generation method for binary problems called as the base algorithm is designed based on the kernel density estimation to construct the probability density function models, using the pairwise learning method to establish binary classification pairs. By means of the new BPA generation method, a new decision-making algorithm based on D-S evidence theory, fuzzy preference relation and nondominance criterion is effectively designed. The strength of the proposed method is presented in applying pairwise learning, which transforms the original complex problem into simple subproblems. With this process, the complexity of the problem to be solved is greatly reduced, which increases the feasibility for industrial applications. Furthermore, the fuzzy computing technique is used to aggregate the output for each single subproblem, and the nondominance degree of each class is determined from the fuzzy preference relation matrix, which can be directly used for the determination of the input instance. Based on several industrial-based classification experiments, the proposed BPA generation method and decision-making algorithm present the effectiveness and improvement in terms of precision and Cohen's kappa.
Zhu, R, Li, S, Wang, P, Xu, M & Yu, S 2021, 'Energy-Efficient Deep Reinforced Traffic Grooming in Elastic Optical Networks for Cloud–Fog Computing', IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12410-12421.
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Cloud-fog computing emerges to satisfy the low latency and high computation requirements of Internet of Things (IoTs) services. Elastic Optical Networks (EONs) are excellent substrate communication networks between fog datacenters and cloud datacenters. However, the uneven traffic of massive cloud-fog services incurs many spectrum fragments, leading to high extra energy consumption. To solve this problem, we propose an Energy-efficient Deep Reinforced Traffic Grooming (EDTG) algorithm based on deep reinforcement learning. Unlike existing manually network features extracting methods, we convert the traditional network modal and the service routing path into colored network images to represent their states, and extract the features automatically by MobilenetV3 according to these images. With the extracted features, we implement an Advantage Actor-Critic (A2C) algorithm, whose actor module and critic module share an Artificial Neural Network (ANN) to get optimal grooming actions. Additionally, after repeated attempts and experiments, we set up an objective reward and punishment mechanism to evaluate the grooming actions. We conduct extensive simulations for performance evaluation, and the results have shown that EDTG can significantly reduce energy consumption compared with two well-performed traffic grooming algorithms.
Zhu, R, Samuel, A, Wang, P, Li, S, Oun, BK, Li, L, Lv, P, Xu, M & Yu, S 2021, 'Protected Resource Allocation in Space Division Multiplexing-Elastic Optical Networks with Fluctuating Traffic', Journal of Network and Computer Applications, vol. 174, pp. 102887-102887.
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In this paper, we introduce a Protected Routing, Modulation, Spectrum, and Core Allocation (RMSCA) algorithms with fluctuating traffic in Space Division Multiplexing-Elastic Optical Networks (SDM-EONs). This RMSCA algorithm is called Traffic Awareness Cross-talk Interference Avoidance (TACIA-RMSCA). We investigate the efficiency of efficient allocation of spectrum resources to minimize the impact of crosstalk (XT), and blocking probability. We first assess the impact inter-core crosstalk in Multi-Core Fiber (MCF) with the peak load of the fluctuating traffic. This is usually caused by signal leaks generated by the adjacent cores. It can occur whenever the transmitted optical signals overlap their spectrum segments. We design the Triangular Iterative Core Assignment (TICA) strategy to overcome XT, and to minimize the blocking probability. We evaluate the performance of TACIA-RMSCA with the benchmark algorithm (GAK-RMSCA). Experimental results demonstrate that the proposed algorithm can achieve promising performance with the peak load of the fluctuating traffic.
Zhu, T, Ye, D, Wang, W, Zhou, W & Yu, P 2021, 'More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 6, pp. 1-1.
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CCBY Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violations, security issues and model fairness. Differential privacy, as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. For this reason, differential privacy has been broadly applied in AI but to date, no study has documented which differential privacy mechanisms can or have been leveraged to overcome its issues or the properties that make this possible. In this paper, we show that differential privacy can do more than just preserve privacy. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI. With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving AI performance with differential privacy techniques.
Zhu, Y, Lin, Q, Lu, H, Shi, K, Qiu, P & Niu, Z 2021, 'Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks', Knowledge-Based Systems, vol. 215, pp. 106744-106744.
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Zurita, G, Merigó, JM, Lobos-Ossandón, V & Mulet-Forteza, C 2021, 'Leading countries in computer science: A bibliometric overview', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1957-1970.
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This paper presents a current overview of the main productive and influential countries around the world in the computer science field. Research in the computer science field has experienced significant growth in recent years. This study develops a bibliometric overview of all journals that have been indexed in the Web of Science (WoS) database over the past 25 years (1995–2019), according to several bibliometric indicators in the seven categories of computer science research. The study shows that United States is the leading country in the computer science field. Other countries, such as the United Kingdom, China, Canada and Germany, also obtain high positions in the ranking. The average country that performs research in computer science is European, has English-speaking researchers, is highly developed and has a high income. However, there is a wide range of countries that perform research in computer science, including South American and Arabic countries, meaning that computer science traverses many countries and cultures.
Abd Majid, ES, Garcia, J, Alias, H, Nordin, A & Raffe, W 1970, 'Heuristic Evaluation for Virtual Reality for Paediatric Cancer Patient: Perceptions of Healthcare Professionals', 2021 IEEE 9th International Conference on Serious Games and Applications for Health(SeGAH), 2021 IEEE 9th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, IEEE.
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Adak, C & Tao, X 1970, 'BigyaPAn: Deep Analysis of Old Paper Advertisement', 2021 International Joint Conference on Neural Networks (IJCNN), 2021 International Joint Conference on Neural Networks (IJCNN), IEEE.
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Adak, C, Chaudhuri, BB, Lin, C-T & Blumenstein, M 1970, 'Text-line-up: Don’t Worry About the Caret', Document Analysis and Recognition – ICDAR 2021, International Conference on Document Analysis and Recognition, Springer International Publishing, Lausanne, Switzerland, pp. 207-222.
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In a freestyle handwritten text-line, sometimes words are inserted using a caret symbol (∧ ) for corrections/annotations. Such insertions create fluctuations in the reading sequence of words. In this paper, we aim to line-up the words of a text-line, so that it can assist the OCR engine. Previous text-line segmentation techniques in the literature have scarcely addressed this issue. Here, the task undertaken is formulated as a path planning problem, and a novel multi-agent hierarchical reinforcement learning-based architecture solution is proposed. As a matter of fact, no linguistic knowledge is used here. Experimentation of the proposed solution architecture has been conducted on English and Bengali offline handwriting, which yielded some interesting results.
Adhikari, S, Thapa, S, Singh, P, Huo, H, Bharathy, G & Prasad, M 1970, 'A Comparative Study of Machine Learning and NLP Techniques for Uses of Stop Words by Patients in Diagnosis of Alzheimer's Disease.', IJCNN, International Joint Conference on Neural Networks, IEEE, Shenzhen, China, pp. 1-8.
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Alzheimer's Disease (AD) is one of the most common forms of neuropsychological disorder in elderly people. It is a slow progressive disease affecting the brain cells. This affects the cognitive abilities of people and their daily activities. During the course of the disease, memory gets brutally affected too. Working as well as long-term declarative memory deteriorates in AD patients. Due to this deterioration of the memory, AD patients tend to show a decline in their communicative skills as well. This decline is reflected in their speech. AD patients usually have poor grammar along with very low coherent ideas. Also, they tend to repeat the words very often and hence become unclear on the message they are trying to convey. As the disease progresses, the speech is completely impaired, and the patients are left to sing or utter words that are totally out of context. Stopwords are the words that are most commonly used in language and it is often hypothesized that AD patients use them much often as compared to Control Normal (CN) subjects. It is seen that due to the degeneration of brain cells in AD patients, they have a tendency to use a lot of stopwords to fill their perplexities in their statements. In this paper, the usefulness of the stopwords in capturing the linguistic information of the patients suffering from AD are discussed. Learning algorithms are evaluated by including stopwords and dropping stopwords at preprocessing to draw comparisons.
Akbarzadeh, M, Oberst, S, Sepehrirahnama, S & Halkon, B 1970, 'Modulated acoustic radiation force in a carrier standing wave', Acoustofluidics, Acoustofluidics, USWNet (online, virtual conference).
Aldini, S, Singh, AK, Carmichael, M, Wang, Y-K, Liu, D & Lin, C-T 1970, 'Prediction-Error Negativity to Assess Singularity Avoidance Strategies in Physical Human-Robot Collaboration', 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Xi'an, China, pp. 3241-3247.
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In physical human-robot collaboration (pHRC), singularity avoidance strategies are often critical to obtain stable interaction dynamics. It is hypothesised a predictable singularity avoidance strategy is preferred in pHRC as humans tend to maximise predictability when using complex systems. By using an electroencephalogram (EEG), it is possible to assess the predictability of a task through a feature found in event-related potentials (ERP) and called prediction-error negativity (PEN). In this paper, two research questions are addressed. Can a complex pHRC singularity avoidance strategy generate a detectable PEN? Are PEN and human preferences related when comparing different control settings in a singularity avoidance strategy? Fourteen participants compared two different sets of parameters (modes) in a singularity avoidance strategy based on the exponentially damped least-squared (EDLS) method. ERP results are presented in terms of power spectral density (PSD). ERP results were then compared with human preferences to see whether they are related. Results show that the mode that causes PEN is also the one that participants did not like, suggesting that a lack of predictability might have an impact on human preference.
Almansor, EH & Hussain, FK 1970, 'Fuzzy Prediction Model to Measure Chatbot Quality of Service', 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Luxembourg, Luxembourg.
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Detecting breakdown is a common phenomenon in the conversational system, which is referred to when the system fails to provide appropriate responses to the user. Existing studies are detect breakdown using different features such as word similarity, topic transition, and clustering. In this paper, we focus on the different important feature, which is human thinking and reasoning. We use this feature to model chatbot quality of services (CQoS) based on detecting the breakdown. Thus we introduce the fuzzy prediction rule-based framework to measure chatbot quality of service by detecting the breakdown utterance considering end-user and chatbot points of view. Inputs utilized in the proposed fuzzy logic-based model are multiple useful features extracted from utterances. The outputs are the degrees of relevance for each utterance to the quality of services. Several fuzzy rules are designed, and the defuzzification method is used in order to achieve desired CQoS results. Based on the outputs from the fuzzy model, the handover mechanism will activate. We evaluate the proposed formwork with other state-of-the-art models.
Alnefaie, A, Singh, S, Kocaballi, AB & Prasad, M 1970, 'Factors Influencing Artificial Intelligence Conversational Agents Usage in the E-commerce Field: A Systematic Literature Review', ACIS 2021 - Australasian Conference on Information Systems, Proceedings.
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Artificial intelligence conversational agents have become an essential strategy for business, both as an online shopping application and as a customer support solution, where they provide interactive communication for online customers. To ensure the effective usage and successful implementation of the conversational agents, the factors influencing customers' attitudes and acceptance towards conversational agents need to be explored. This paper presents a systematic literature review of conversational agents in the field of e-commerce to identify the variables that influence conversational agents' usage and to present the state-of-the-art in this research area. Twenty-four relevant papers are reviewed, and many significant factors are identified that positively influence customers' acceptance, satisfaction, and trust towards conversational agents’ technology.
Alnefaie, A, Singh, S, Kocaballi, B & Prasad, M 1970, 'An Overview of Conversational Agent: Applications, Challenges and Future Directions', Proceedings of the 17th International Conference on Web Information Systems and Technologies, 17th International Conference on Web Information Systems and Technologies, SCITEPRESS - Science and Technology Publications, pp. 388-396.
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Alqahtani, A, Hawryszkiewycz, I & Erfani, E 1970, 'Capturing community needs through an open innovation process', Proceedings of the European Conference on Knowledge Management, ECKM, pp. 29-35.
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Public online open innovation platforms allow a local community to be part of an open innovation process by sharing their innovative ideas about a specific problem. However, capturing community needs before problems are posted is an essential phase that is ambiguous in open innovation literature. In previous studies, open innovation on a community was applied by seeking innovative solutions without exploring ways to capture community needs as an initial phase before designing a product or service. This study explores this missing step in the literature and asks how community needs are captured before selecting a specific problem to be posted on the platform. Semi-structured interviews area carried out with two different open innovation platforms teams in two countries. Thematic analysis is utilised to highlight the procedures used to capture community needs through an open innovation process. The study develops a revised open innovation process that can be followed to capture community needs in the context of open innovation.
Alsufyani, N & Gill, AQ 1970, 'A Review of Digital Maturity Models from Adaptive Enterprise Architecture Perspective: Digital by Design.', CBI (1), IEEE Conference on Business Informatics, IEEE, Bolzano, Italy, pp. 121-130.
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There is a growing interest among organisations to assess and improve digital capabilities. Thus, a digital maturity model can assist organisations in planning and navigating their digital transformation. The challenge is that there are several maturity models to choose from. This paper aims to review the most recent digital maturity models from an enterprise architecture design perspective to understand, tailor, and adopt the appropriate model. This paper presents a systematic review of 30 selected maturity models across 36 papers. Further, the review results were synthesised and analysed using the adaptive enterprise architecture as a theoretical lens. This review reveals that digital maturity models still lack the ability to capture a holistic picture of digital maturity from an enterprise design perspective. The results of this review can be further casted into developing digital maturity principles and metamodel.
Altulayan, MS, Huang, C, Yao, L, Wang, X & Kanhere, S 1970, 'Contextual Bandit Learning for Activity-Aware Things-of-Interest Recommendation in an Assisted Living Environment', 32th Australasian Database Conference, 32th Australasian Database Conference, Springer International Publishing, Dunedin, New Zealand, pp. 37-49.
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Recommendation systems are crucial for providing services to the elderly with Alzheimer’s disease in IoT-based smart home environments. Therefore, we present a Reminder Care System to help Alzheimer patients live safely and independently in their homes. The proposed recommendation system is formulated based on a contextual bandit approach to tackle dynamicity in human activity patterns for accurate recommendations meeting user needs without their feedback. Our experiment results demonstrate the feasibility and effectiveness of the proposed Reminder Care System in real-world IoT-based smart home applications.
Bakhanova, E, Garcia Marin, J, Raffe, W & Voinov, A 1970, 'Side effects of gamification in the context of participatory modeling', 31st European Conference on Operational Research, 31st European Conference on Operational Research, https://www.euro-online.org/conf/euro31/treat_abstract?paperid=2700, Athens.
Bandara, M, Catchpoole, D & Kennedy, PJ 1970, 'Message from the AI-CLRA 2021 Workshop Chairs', 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), IEEE, p. XV.
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Bandara, M, Catchpoole, D & Kennedy, PJ 1970, 'PriSEM 2021 Workshop Organizing Committee', 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), IEEE, p. XXVI.
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Bano, M, Arora, C, Zowghi, D & Ferrari, A 1970, 'The Rise and Fall of COVID-19 Contact-Tracing Apps: when NFRs Collide with Pandemic.', RE, 2021 IEEE 29th International Requirements Engineering Conference, IEEE, Notre Dame, IN, USA, pp. 106-116.
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To complement the manual contact-tracing methods, a flood of coronavirus-related apps was launched in the first half of 2020. Despite the incredible promises made by the governments, contact-tracing apps did not live up to expectations. We provide a contextual perspective of the government commissioned contact-tracing apps from four countries to understand the non-functional requirements (NFRs) and socio-technical factors that hindered the success of these apps. We collected the user reviews from the app stores for iOS and Android versions and identified top news articles related to each app. Our analysis revealed that the dominant factors behind the negligible success of these apps are complex and entangled with the cultural and political dimensions rather than being just technical. The multilayer diversity of the target users also impacted the design and development of contact-tracing apps in an extremely challenging situation. This perspective paper brings into light important elements, such as politics and socio-cultural aspects that should be studied in the design of contact-tracing apps, and public apps in general.
Beydoun, G & Shen, J 1970, 'Preface', ACM International Conference Proceeding Series, p. VII.
Biddle, R, Rybiński, M, Li, Q, Paris, C & Xu, G 1970, 'Harnessing Privileged Information for Hyperbole Detection', ALTA 2021 - Proceedings of the 19th Workshop of the Australasian Language Technology Association, Workshop of the Australasian Language Technology Association, Australasian Language Technology Association, Online, pp. 58-67.
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The detection of hyperbole is an important stepping stone to understanding the intentions of a hyperbolic utterance. We propose a model that combines pre-trained language models with privileged information for the task of hyperbole detection. We also introduce a suite of behavioural tests to probe the capabilities of hyperbole detection models across a range of hyperbole types. Our experiments show that our model improves upon baseline models on an existing hyperbole detection dataset. Probing experiments combined with analysis using local linear approximations (LIME) show that our model excels at detecting one particular type of hyperbole. Further, we discover that our experiments highlight annotation artifacts introduced through the process of literal paraphrasing of hyperbole. These annotation artifacts are likely to be a roadblock to further improvements in hyperbole detection.
Brandhofer, S, Devitt, S & Polian, I 1970, 'ArsoNISQ: Analyzing Quantum Algorithms on Near-Term Architectures', 2021 IEEE European Test Symposium (ETS), 2021 IEEE European Test Symposium (ETS), IEEE, Bruges, Belgium.
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While scalable, fully error corrected quantum computing is years or even decades away, there is considerable interest in noisy intermediate-scale quantum computing (NISQ). In this paper, we introduce the ArsoNISQ framework that determines the tolerable error rate of a given quantum algorithm computation, i.e. quantum circuits, and the success probability of the computation given a success criterion and a NISQ computer. ArsoNISQ is based on simulations of quantum circuits subject to errors according to the Pauli error model.ArsoNISQ was evaluated on a set of quantum algorithms that can incur a quantum speedup or are otherwise relevant to NISQ computing. Despite optimistic expectations in recent literature, we did not observe quantum algorithms with intrinsic robustness, i.e. algorithms that tolerate one error on average, in this evaluation. The evaluation demonstrated, however, that the quantum circuit size sets an upper bound for its tolerable error rate and quantified the difference in tolerate error rates for quantum circuits of similar sizes. Thus, the framework can assist quantum algorithm developers in improving their implementation and selecting a suitable NISQ computing platform. Extrapolating the results into the quantum advantage regime suggests that the error rate of larger quantum computers must decrease substantially or active quantum error correction will need to be deployed for most of the evaluated algorithms.
Brandhofer, S, Devitt, S & Polian, I 1970, 'Error Analysis of the Variational Quantum Eigensolver Algorithm', 2021 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), 2021 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), IEEE, AB, Canada.
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Variational quantum algorithms have been one of the most intensively studied applications for near-term quantum computing applications. The noisy intermediate-scale quantum (NISQ) regime, where small enough algorithms can be run successfully on noisy quantum computers expected during the next 5 years, is driving both a large amount of research work and a significant amount of private sector funding. Therefore, it is important to understand whether variational algorithms are effective at successfully converging to the correct answer in presence of noise. We perform a comprehensive study of the variational quantum eigensolver (VQE) and its individual quantum subroutines. Building on asymptotic bounds, we show through explicit simulation that the VQE algorithm effectively collapses already when single errors occur during a quantum processing call. We discuss the significant implications of this result in the context of being able to run any variational type algorithm without resource expensive error correction protocols.
Brandhofer, S, Devitt, S, Wellens, T & Polian, I 1970, 'Special Session: Noisy Intermediate-Scale Quantum (NISQ) Computers—How They Work, How They Fail, How to Test Them?', 2021 IEEE 39th VLSI Test Symposium (VTS), 2021 IEEE 39th VLSI Test Symposium (VTS), IEEE.
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Braytee, A & Liu, W 1970, 'Learning Discriminative Features Using Multi-label Dual Space', In Proceedings of the 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2021)., 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Springer International Publishing, Delhi, India, pp. 233-245.
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Multi-label learning handles instances associated with multiple class labels. The original label space is a logical matrix with entries from the Boolean domain ∈ {0, 1 }. Logical labels are not able to show the relative importance of each semantic label to the instances. The vast majority of existing methods map the input features to the label space using linear projections with taking into consideration the label dependencies using logical label matrix. However, the discriminative features are learned using one-way projection from the feature representation of an instance into a logical label space. Given that there is no manifold in the learning space of logical labels, which limits the potential of learned models. In this work, inspired from a real-world example in image annotation to reconstruct an image from the label importance and feature weights. We propose a novel method in multi-label learning to learn the projection matrix from the feature space to semantic label space and projects it back to the original feature space using encoder-decoder deep learning architecture. The key intuition which guides our method is that the discriminative features are identified due to map the features back and forth using two linear projections. To the best of our knowledge, this is one of the first attempts to study the ability to reconstruct the original features from the label manifold in multi-label learning. We show that the learned projection matrix identifies a subset of discriminative features across multiple semantic labels. Extensive experiments on real-world datasets show the superiority of the proposed method.
Braytee, A, Naji, M, Anaissi, A, Chaturvedi, K & Prasad, M 1970, 'Zero-Shot Learning with Missing Attributes using Semantic Correlations', 2021 International Joint Conference on Neural Networks (IJCNN), 2021 International Joint Conference on Neural Networks (IJCNN), IEEE, Shenzhen, China.
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Zero-shot learning (ZSL) aims to recognize instances belonging to unseen categories which are not available at training time. Previous ZSL models learn a projection function from the visual feature space to a semantic space which contains a description of the categories. The semantic attributes are often correlated with each other at the semantic space and it is not appropriate to learn them independently. Existing ZSL methods are designed to work on complete descriptions of the semantic attributes. However, because these attributes are human-designed values, they might be incomplete or contains noisy values which may affect the recognition performance of many existing ZSL models. This paper proposes a novel zero-shot learning approach (ZSL-MSA) to handle missing and noisy semantic attributes during the training process. Significantly, the proposed method learns a supplementary attribute matrix by exploiting the attribute correlation. The proposed method also learns the relevant feature coefficients in the projection matrix to identify the correlated attribute space. Th proposed method also adopts l1 regularization norm to select the relevant sparse features. A constrained optimization function is formulated and solved using the accelerated proximal gradient method. Extensive experiments on three benchmark datasets using ZSL and generalized ZSL demonstrate the effectiveness of the proposed method.
Cao, J, Liu, B, Wen, Y, Xie, R & Song, L 1970, 'Personalized and Invertible Face De-identification by Disentangled Identity Information Manipulation', 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE.
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Chang, Y-C, Shi, Y, Dostovalova, A, Lin, C-T, Kim, J & Gibbons, D 1970, 'Fuzzy Logic Control for Multi-Robot Navigation and Arrival-Time Control in a Cluttered Environment', 2021 International Conference on Machine Learning and Cybernetics (ICMLC), 2021 International Conference on Machine Learning and Cybernetics (ICMLC), IEEE, Adelaide, Australia, pp. 33-38.
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Coordination in multi-robot systems has attracted much attention in recent years. This study presents a hierarchical fuzzy controller (HiFC) to navigate a robot team and coordinate their moving speed and path for arrival time control. The proposed HiFC consists of multilayered architecture to reduce the number of free parameters and improve the scalability. The multilayered architecture includes a lower-level rule-embedded controller serving as behaviour selector and a higher-level fuzzy-logic-based coordinator to adjust each robot's moving speed and direction in the team. The HiFC is trained with a multi-objective multi-swarm evolutionary PSO (MMEPSO). Simulation results demonstrate the effectiveness of this technique in reliably solving the proposed navigation problem
Chaturvedi, K, Braytee, A, Vishwakarma, DK, Saqib, M, Mery, D & Prasad, M 1970, 'Automated Threat Objects Detection with Synthetic Data for Real-Time X-ray Baggage Inspection', 2021 International Joint Conference on Neural Networks (IJCNN), 2021 International Joint Conference on Neural Networks (IJCNN), IEEE, Shenzhen, China.
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With the recent surge in threats to public safety, the security focus of several organizations has been moved towards enhanced intelligent screening systems. Conventional X-ray screening, which relies on the human operator is the best use of this technology, allowing for the more accurate identification of potential threats. This paper explores X-ray security imagery by introducing a novel approach that generates realistic synthesized data, which opens up the possibility of using different settings to simulate occlusion, radiopacity, varying textures, and distractors to generate cluttered scenes. The generated synthetic data is effective in the training of deep networks. It allows better generalization on training data to deal with domain adaptation in the real world. The extensive set of experiments in this paper provides evidence for the efficacy of synthetic datasets over human-annotated datasets for automated X-ray security screening. The proposed approach outperforms the state-of-the-art approach for a diverse threat object dataset on mean Average Precision (mAP) of region-based detectors and classification/regression-based detectors.
Chen, H, Li, Y, Sun, X, Xu, G & Yin, H 1970, 'Temporal Meta-path Guided Explainable Recommendation', Proceedings of the 14th ACM International Conference on Web Search and Data Mining, WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining, ACM, pp. 1056-1064.
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Chen, H, Wang, Z, Jiang, Y, Yu, S, Han, F & Ji, Y 1970, 'Study on Working Medium Selection of High and Low Temperature Coupled ORC Scheme for Waste Heat Recovery of Dual-Fuel Ship Engine', 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), IEEE.
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Chen, X, Yao, L, Sun, A, Wang, X, Xu, X & Zhu, L 1970, 'Generative Inverse Deep Reinforcement Learning for Online Recommendation', Proceedings of the 30th ACM International Conference on Information & Knowledge Management, CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, ACM, online.
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Chin Derix, E, Leong, TW & Prior, J 1970, '“It's The Same Conflict Every Time, On Repeat.”', Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI '21: CHI Conference on Human Factors in Computing Systems, ACM, Yokohama, Japan, pp. 1-6.
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Family technology use can create or amplify conflict in parents’ relationships – we found four key factors that contribute to this issue. We conducted a probe and interview study with eight sets of parents, to explore how and why technology use might cause conflict in their relationships. This paper presents data from two particular sets of parents to illustrate our findings. In doing so, it complements existing work that primarily focuses on parent-child relationships, and contributes to a more complete understanding of how family technology use can affect family dynamics. We also suggest directions for further work to address this issue of conflict between parents, associated with their family's use of technology.
Chin Derix, E, Wah Leong, T & Prior, J 1970, 'Family Technology Use: Sources of Conflict In Parents’ Relationships', 33rd Australian Conference on Human-Computer Interaction, OzCHI '21: 33rd Australian Conference on Human-Computer Interaction, ACM, pp. 75-85.
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The use of digital technologies, particularly mobile devices, play an increasingly critical role within everyday family life. However, recent research indicates that family technology use can create conflict in parents' relationships. In this paper, we present four sources of this conflict, discovered by conducting a probe and interview study with eight parent dyads. By providing an understanding of how family technology use can create conflict between parents, this research complements existing work that primarily focuses on parent-child relationships. Thus, we contribute to a more complete understanding of how technology use can affect family dynamics. Finally, we consider how designers might address these sources of conflict between parents, when creating future technologies that are destined for use in domestic settings.
Chu, Y, Li, L, Xie, Q & Xu, G 1970, 'C$$^2$$-Guard: A Cross-Correlation Gaining Framework for Urban Air Quality Prediction', Springer International Publishing, pp. 779-790.
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Desai, R & Carolyn McGregor, A 1970, 'Transfusion in Neonatal Care in relation to Heart Rate Variability (HRV) and Anemia: A Literature Review', 2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), 2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), IEEE, pp. 5-8.
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Anemia is the leading cause of transfusion in premature infants. Anemia can be caused by an excess loss of blood through laboratory testing and phlebotomy. Heart Rate Variability (HRV) has been known to be an indicator of distress within the body and research has been conducted showing association between HRV and transfusion. This paper presents the current state of knowledge regarding transfusion in the premature population and literature assessing what association of HRV and transfusion is known
Desai, R & McGregor, C 1970, 'Antenatal Care in Australia: Process Mapping to Visualise Resources and Care', 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, Mexico, pp. 2419-2422.
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Antenatal Care (ANC) in Australia is of a high standard internationally and is an important care model for mothers. ANC is able to help prevent preterm birth complications. Process mapping enables the visualization of the journal of care, however different functionality is available from different process mapping tools. This paper presents and critically analyses Lean VSM and PaJMa modelling for the ANC pathway in Australia.Clinical Relevance—This work can help inform discrepancies in perceived care and received care and can be used as a tool to help guide organizations in the decision-making for health services deployments for ANC services.
Dong, M, Yao, L, Wang, X, Xu, X & Zhu, L 1970, 'MetaGB: A Gradient Boosting Framework for Efficient Task Adaptive Meta Learning', 2021 IEEE International Conference on Data Mining (ICDM), 2021 IEEE International Conference on Data Mining (ICDM), IEEE, Auckland, New Zealand.
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Duong, TD, Li, Q & Xu, G 1970, 'Stochastic Intervention for Causal Effect Estimation', 2021 International Joint Conference on Neural Networks (IJCNN), 2021 International Joint Conference on Neural Networks (IJCNN), IEEE, Shenzhen, China, pp. 1-8.
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Causal inference methods are widely applied in various decision-making domains such as precision medicine, optimal policy and economics. Central to these applications is the treatment effect estimation of intervention strategies. Current estimation methods are mostly restricted to the deterministic treatment, which however, is unable to address the stochastic space treatment policies. Moreover, previous methods can only make binary yes-or-no decisions based on the treatment effect, lacking the capability of providing fine-grained effect estimation degree to explain the process of decision making. In our study, we therefore advance the causal inference research to estimate stochastic intervention effect by devising a new stochastic propensity score and stochastic intervention effect estimator (SIE). Meanwhile, we design a customized genetic algorithm specific to stochastic intervention effect (Ge-SIO) with the aim of providing causal evidence for decision making. We provide the theoretical analysis and conduct an empirical study to justify that our proposed measures and algorithms can achieve a significant performance lift in comparison with state-of-the-art baselines.
Erfani, E, Samy Helmy Hanna, A & Boroon, L 1970, 'Social Network Sites Use and Psychological distress: A Systematic Review', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii In.
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Farhood, H, Saberi, M & Najafi, M 1970, 'Improving Object Recognition in Crime Scenes via Local Interpretable Model-Agnostic Explanations', 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), IEEE.
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Fattoruso, V, Oberst, S, Lai, JCS & Evans, TA 1970, 'Exploring the effects of acoustic camouflage studying termite inquiline-host relationships', XI AISASP Student Meeting, XI AISASP Student Meeting, Italy.
G. Pillai, A, Baki Kocaballi, A, Wah Leong, T, A. Calvo, R, Parvin, N, Shilton, K, Waycott, J, Fiesler, C, C. Havens, J & Ahmadpour, N 1970, 'Co-designing Resources for Ethics Education in HCI', Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI '21: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-5.
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Due to the evolving nature of technology and its impact on individuals, communities and society, practitioners and designers in Human-Computer Interaction (HCI) are expected to consider ethics in their work. This role has inspired the development of a number of resources for practice, such as tools, frameworks and methods to tackle ethical issues in HCI. But these suffer from low adoption rate potentially because they are not yet part of the standard body of knowledge. To mitigate the issue, we argue that there is an urgent need for ethics education in HCI. Beyond defining ethics, an ethics curriculum must enable practitioners to reflect and allow consideration of intended and unintended consequences of the technologies they create from the ground up, rather than as a fix or an afterthought. In this co-design workshop, we aim to build upon existing practices and knowledge of ethics in HCI and work with the CHI community to enrich ethics curriculum. We will scaffold our collective understandings of the existing resources and create guidelines that support interactive educational experiences to support HCI ethics curriculum.
Geng, Z, He, Y, Wang, C, Xu, G, Xiao, K & Yu, S 1970, 'A Blockchain based Privacy-Preserving Reputation Scheme for Cloud Service', ICC 2021 - IEEE International Conference on Communications, ICC 2021 - IEEE International Conference on Communications, IEEE, Montreal, QC, Canada, pp. 1-6.
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Nowadays, the quality of cloud services offered by different service providers varies greatly. Reputation mechanism, as a better service evaluation method, can help standardize and regulate the cloud service market. However, existing reputation systems either rely on a trusted third party with security and privacy issues, or lack reliable evaluation. To address the above issues, we propose a blockchain based privacy-preserving dynamic reputation mechanism for cloud service and a reputation management smart contract (RM) is designed to implement trusted reputation computation. The reputation integrates conformance trust in the subjective view and recommendation trust in the objective view together to provide a comprehensive evaluation. Besides, a miner selection algorithm is designed to prevent miners from launching a collusion attack. Moreover, the Paillier homomorphic encryption algorithm (PHE) is introduced to encrypt the sensitive data of customers, ensuring the security of data stored on the blockchain. Experiment results reveal that the proposed model is feasible and the performance of encryption is acceptable.
Gill, AQ & Maheshwari, D 1970, 'Applying DevOps for Distributed Agile Development: A Case Study', Advances in Software Engineering, Education, and e-Learning, The 18th International Conference on Software Engineering Research and Practice, Springer International Publishing, Las Vegas, Nevada, USA, pp. 719-728.
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Gorman, C & Wang, Y-K 1970, 'A Closed-Loop AR-based BCI for Real-World System Control', 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 1-7.
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Gough, P, Kocaballi, AB, Naqshbandi, KZ, Cochrane, K, Mah, K, Pillai, AG, Yorulmaz, Y, Deny, AK & Ahmadpour, N 1970, 'Co-designing a Technology Probe with Experienced Designers', 33rd Australian Conference on Human-Computer Interaction, OzCHI '21: 33rd Australian Conference on Human-Computer Interaction, ACM.
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Grochow, JA & Qiao, Y 1970, 'On p-group isomorphism: Search-to-decision, counting-to-decision, and nilpotency class reductions via tensors', Leibniz International Proceedings in Informatics, LIPIcs.
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In this paper we study some classical complexity-theoretic questions regarding Group Isomorphism (GpI). We focus on p-groups (groups of prime power order) with odd p, which are believed to be a bottleneck case for GpI, and work in the model of matrix groups over finite fields. Our main results are as follows. Although search-to-decision and counting-to-decision reductions have been known for over four decades for Graph Isomorphism (GI), they had remained open for GpI, explicitly asked by Arvind & Torán (Bull. EATCS, 2005). Extending methods from Tensor Isomorphism (Grochow & Qiao, ITCS 2021), we show moderately exponential-time such reductions within p-groups of class 2 and exponent p. Despite the widely held belief that p-groups of class 2 and exponent p are the hardest cases of GpI, there was no reduction to these groups from any larger class of groups. Again using methods from Tensor Isomorphism (ibid.), we show the first such reduction, namely from isomorphism testing of p-groups of “small” class and exponent p to those of class two and exponent p. For the first results, our main innovation is to develop linear-algebraic analogues of classical graph coloring gadgets, a key technique in studying the structural complexity of GI. Unlike the graph coloring gadgets, which support restricting to various subgroups of the symmetric group, the problems we study require restricting to various subgroups of the general linear group, which entails significantly different and more complicated gadgets. The analysis of one of our gadgets relies on a classical result from group theory regarding random generation of classical groups (Kantor & Lubotzky, Geom. Dedicata, 1990). For the nilpotency class reduction, we combine a runtime analysis of the Lazard Correspondence with Tensor Isomorphism-completeness results (Grochow & Qiao, ibid.).
Grochow, JA & Qiao, Y 1970, 'On the complexity of isomorphism problems for tensors, groups, and polynomials I: Tensor isomorphism-completeness', Leibniz International Proceedings in Informatics, LIPIcs.
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We study the complexity of isomorphism problems for tensors, groups, and polynomials. These problems have been studied in multivariate cryptography, machine learning, quantum information, and computational group theory. We show that these problems are all polynomial-time equivalent, creating bridges between problems traditionally studied in myriad research areas. This prompts us to define the complexity class TI, namely problems that reduce to the Tensor Isomorphism (TI) problem in polynomial time. Our main technical result is a polynomial-time reduction from d-tensor isomorphism to 3-tensor isomorphism. In the context of quantum information, this result gives multipartite-to-tripartite entanglement transformation procedure, that preserves equivalence under stochastic local operations and classical communication (SLOCC).
Grochow, JA, Qiao, Y & Tang, G 1970, 'Average-case algorithms for testing isomorphism of polynomials, Algebras, and multilinear forms', Leibniz International Proceedings in Informatics, LIPIcs.
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We study the problems of testing isomorphism of polynomials, algebras, and multilinear forms. Our first main results are average-case algorithms for these problems. For example, we develop an algorithm that takes two cubic forms f, g ∈ Fq [x1,...,xn], and decides whether f and g are isomorphic in time qO(n) for most f. This average-case setting has direct practical implications, having been studied in multivariate cryptography since the 1990s. Our second result concerns the complexity of testing equivalence of alternating trilinear forms. This problem is of interest in both mathematics and cryptography. We show that this problem is polynomial-time equivalent to testing equivalence of symmetric trilinear forms, by showing that they are both Tensor Isomorphismcomplete (Grochow & Qiao, ITCS 2021), therefore is equivalent to testing isomorphism of cubic forms over most fields.
Guan, L, Abbasi, A & Ryan, MJ 1970, 'Prioritizing Project Interdependent Risks: A Network-based Approach', 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE.
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Guan, S, Lu, H, Zhu, L & Fang, G 1970, 'PoseGate-Former: Transformer Encoder with Trainable Gate for 3D Human Pose Estimation Using Weakly Supervised Learning', Springer International Publishing, pp. 266-274.
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Han, Z, Xu, C, Liu, K, Yu, L, Zhao, G & Yu, S 1970, 'A Novel Mobile Core Network Architecture for Satellite-Terrestrial Integrated Network', 2021 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2021 - 2021 IEEE Global Communications Conference, IEEE, Madrid, Spain.
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Loading the functions of base station on satellite can improve the flexibility of user access and expand the effective coverage of Satellite-Terrestrial Integrated Network (STIN). However, the functions of management and control that supports the satellite networking have not been well investigated. A large amount of signaling needs to be forwarded to the ground control center for processing, which increases the delay of network control and management. In this paper, we propose a novel mobile core network architecture that loads the mobility management function of core network on the satellite, which can improve the flexibility of management and control of STIN. Firstly, considering the on-board computing capacity and the dynamic characteristic of satellites, we design the lightweight Satellite Mobile Core Network (SMCN) to improve the flexibility of the Satellite next-generation NodeB (Sat-gNB) networking. Then, the interactive protocol for user access control, mobility control of Sat-gNBs and the coordinated control between ter-restrial network and satellite network are designed to support the mobility management. Finally, in order to optimize the delay of Sat-gNBs networking, we construct the mathematical model for the multi-SMCN deployment. The simulation results show that compared to the architecture of Non-Terrestrial Networks (NTN), the proposed mobile core network architecture can improve the flexibility of mobility management and optimize the network delay.
Han, Z, Zhao, G, Xing, Y, Sun, N, Xu, C & Yu, S 1970, 'Dynamic Routing for Software-Defined LEO Satellite Networks based on ISL Attributes', 2021 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2021 - 2021 IEEE Global Communications Conference, IEEE, Madrid, Spain.
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The software-defined Low Earth Orbit (LEO) satellite network can effectively improve the flexibility of the inter-satellite networking. However, the impact of Inter-Satellite Link (ISL) attributes on the network topology has not been well investigated in existed works, which leads to the loss of topology information and the unreliability of routing path. In this paper, we propose a dynamic routing algorithm based on ISL attributes to improve the adaptability and reliability of routing in LEO satellite network. Firstly, we construct the utility function of ISL attributes to quantify the link utility. Then, the maximum deviation algorithm is introduced to adaptively calculate the weight of attribute parameters in link utility function, which can quantify the impact of each attribute on link quality. Finally, the mathematical model of path utility is established, the routing path with highest path utility is selected as the optimal routing path. The simulation results show that the proposed scheme can improve the network performance by about 35%, 10% and 20% in terms of packet drop rate, end-to-end delay and throughput.
He, L, Chen, H, Wang, D, Jameel, S, Yu, P & Xu, G 1970, 'Click-Through Rate Prediction with Multi-Modal Hypergraphs', Proceedings of the 30th ACM International Conference on Information & Knowledge Management, CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, ACM, pp. 690-699.
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Advertising is critical to many online e-commerce platforms such as e-Bay and Amazon. One of the important signals that these platforms rely upon is the click-through rate (CTR) prediction. The recent popularity of multi-modal sharing platforms such as TikTok has led to an increased interest in online micro-videos. It is, therefore, useful to consider micro-videos to help a merchant target micro-video advertising better and find users' favourites to enhance user experience. Existing works on CTR prediction largely exploit unimodal content to learn item representations. A relatively minimal effort has been made to leverage multi-modal information exchange among users and items. We propose a model to exploit the temporal user-item interactions to guide the representation learning with multi-modal features, and further predict the user click rate of the micro-video item. We design a Hypergraph Click-Through Rate prediction framework (HyperCTR) built upon the hyperedge notion of hypergraph neural networks, which can yield modal-specific representations of users and micro-videos to better capture user preferences. We construct a time-aware user-item bipartite network with multi-modal information and enrich the representation of each user and item with the generated interests-based user hypergraph and item hypergraph. Through extensive experiments on three public datasets, we demonstrate that our proposed model significantly outperforms various state-of-the-art methods.
He, L, Wang, D, Wang, H, Chen, H & Xu, G 1970, 'TagPick', Proceedings of the 30th ACM International Conference on Information & Knowledge Management, CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, ACM, pp. 4721-4724.
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Hashtag, a product of user tagging behavior, which can well describe the semantics of the user-generated content personally over social network applications, e.g., the recently popular micro-videos. Hashtags have been widely used to facilitate various micro-video retrieval scenarios, such as search engine and categorization. In order to leverage hashtags on micro-media platform for effective e-commerce marketing campaign, there is a demand from e-commerce industry to develop a mapping algorithm bridging its categories and micro-video hashtags. In this demo paper, we therefore proposed a novel solution called TagPick that incorporates clues from all user behavior metadata (hashtags, interactions, multimedia information) as well as relational data (graph-based network) into a unified system to reveal the correlation between e-commerce categories and hashtags in industrial scenarios. In particular, we provide a tag-level popularity strategy to recommend the relevant hashtags for e-Commerce platform (e.g., eBay).
Inibhunu, C & McGregor, C 1970, 'Privacy Preserving Framework for Big Data Management in Smart Buildings', 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), IEEE, Kassel, Germany, pp. 667-673.
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There are many possibilities for smart buildings that have well provisioned and managed internet of things (IoT) frameworks where seamless data acquisition from sensors, processing to analytics can bring benefits to vast domains. Specifically, in building management, data captured from sensors, actuators and multiple devices within a building can be analyzed and utilized for resource planning as well as informed services provision while maintaining the security and privacy of IoT data sources and context. To facilitate such a process, a robust framework for acquisition, processing and analysis of data from vast IoT systems is necessary. This is an extremely complex procedure that requires augmentation of multiple computation layers for management of IoT data coupled with privacy preservation capabilities. In this paper, an overview of IoT and the data process flow from acquisition, collection, processing, integration and the technological enablers is presented. In particular this work examines methods proposed in the literature for management of IoT data from acquisition to analytical applications, highlights challenges and opportunities that are still open for research and proposes a privacy preserving computation framework that can be explored for robust processing of IoT data in a smart building ecosystem leading to effective provision of services while maintaining privacy of IoT data sources and context.
Inibhunu, C, McGregor, C & Pugh, JEV 1970, 'An Alert Notification Subsystem for AI Based Clinical Decision Support: A Protoype in NICU', 2021 IEEE International Conference on Big Data (Big Data), 2021 IEEE International Conference on Big Data (Big Data), IEEE, pp. 3511-3518.
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Inibhunu, C, Yeung, J, Gates, A, Chicoine, B & McGregor, C 1970, 'A Decoupled Data Pipeline and its Reliability Assessment: Case Study in Extreme Climatic Humans Studies', 2021 IEEE International Conference on Big Data (Big Data), 2021 IEEE International Conference on Big Data (Big Data), IEEE.
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Islam, MR, Zhang, J, Ashmafee, MH, Razzak, I, Zhou, J, Wang, X & Xu, G 1970, 'ExVis: Explainable Visual Decision Support System for Risk Management', 2021 8th International Conference on Behavioral and Social Computing (BESC), 2021 8th International Conference on Behavioral and Social Computing (BESC), IEEE, online.
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Jia, M, Gabrys, B & Musial, K 1970, 'Closure Coefficient in Complex Directed Networks', Complex Networks & Their Applications IX, Springer International Publishing, pp. 62-74.
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The 3-clique formation, a natural phenomenon in real-world networks, is typically measured by the local clustering coefficient, where the focal node serves as the centre-node in an open triad. The local closure coefficient provides a novel perspective, with the focal node serving as the end-node. It has shown some interesting properties in network analysis, yet it cannot be applied to complex directed networks. Here, we propose the directed closure coefficient as an extension of the closure coefficient in directed networks, and we extend it to weighted and signed networks. In order to better use it in network analysis, we introduce further the source closure coefficient and the target closure coefficient. Our experiments show that the proposed directed closure coefficient provides complementary information to the classic directed clustering coefficient. We also demonstrate that adding closure coefficients leads to better performance in link prediction task in most directed networks.
Jiang, C, Li, W, Wu, S & Bai, Q 1970, 'OMT: An Operate-Based Approach for Modelling Multi-topic Influence Diffusion in Online Social Networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 542-556.
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Influence diffusion modelling in online social networks has been widely studied and applied in public opinion management, viral marketing, and rumour detection. Most existing studies focus on the network topology and the complex user characteristics while ignoring the diverse topic features of the information, especially the cross-impact of multiple topics on the information propagation. In this paper, we propose the Operator-based Multi-Topic (OMT) model by considering user topic interest, topic penetration, and topic correlation to explain the topic effects on influence diffusion fully. Meanwhile, the operator-based approach inherits the advantages of the heat diffusion-based model and the agent-based model. Accordingly, the OMT is recognized as a user context-aware and topic-aware prediction model, which can improve the practicability, quality, and simulation of influence diffusion modelling in multi-topic social networks. In the experiments, real-world datasets are adopted to evaluate the performance of the proposed OMT. The experimental results demonstrate that the OMT performs effectively in diffusion simulation and influence maximization.
Kridalukmana, R, Rochim, AF & Ramezani, F 1970, 'IoT Microservice Architecture for IoTaaS Device Users', 2021 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), 2021 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), IEEE.
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Kwiatkowski, J, Ou, L, Chang, Y-C & Lin, C-T 1970, 'Explainable Hybrid CNN and FNN Approach Applied on Robotic Wall-Following Behaviour Learning', 2021 4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021: 2021 4th International Conference on Artificial Intelligence and Pattern Recognition, ACM, Xiamen, China, pp. 623-628.
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Fuzzy Neural Network (FNN) applied to robotic control tasks has proved to be effective by previous researchers. However, FNN has an inherent deficiency in dealing with inputs of large dimensions, such as images. Therefore, this research utilizes a Convolutional Neural Network (CNN) model to convert image into distance values and delivers these values to FNN based robot controller as inputs. The proposed hybrid CNN+FNN are tested with both a regression model and a multi-task model. Results show that the multi-task method performs better with less information loss from input images. This paper also proved that the proposed hybrid approach can be generalized into an unknown robotic simulation environment and performs better than its FNN counterpart. By utilizing state of the art explainable analysis method, both the CNN part and the FNN part of the hybrid approach can be explained in a human-understandable way.
Le, NTT, Zhu, HY & Chen, H-T 1970, 'Remote Visual Line-of-Sight: A Remote Platform for the Visualisation and Control of an Indoor Drone using Virtual Reality', Proceedings of the 27th ACM Symposium on Virtual Reality Software and Technology, VRST '21: 27th ACM Symposium on Virtual Reality Software and Technology, ACM.
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Li, G, Wang, X & Li, M 1970, 'A Review of Recent Trends and Industry Prospects for Artificial Intelligence Technologies', 2021 8th International Conference on Behavioral and Social Computing (BESC), 2021 8th International Conference on Behavioral and Social Computing (BESC), IEEE, online.
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Li, J, Liu, X, Jin, J & Yu, S 1970, 'Too Expensive to Attack: Enlarge the Attack Expense through Joint Defense at the Edge', 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), IEEE, Shenyang, China, pp. 524-531.
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The distributed denial of service (DDoS) attack is detrimental to businesses and individuals as people are heavily relying on the Internet. Due to remarkable profits, crackers favor DDoS as cybersecurity weapons to attack a victim. Even worse, edge servers are more vulnerable. Current solutions lack adequate consideration to the expense of attackers and inter-defender collaborations. Hence, we revisit the DDoS attack and defense, clarifying the advantages and disadvantages of both parties. We further propose a joint defense framework to defeat attackers by incurring a significant increment of required bots and enlarging attack expenses. The quantitative evaluation and experimental assessment showcase that such expense can surge up to thousands of times. The skyrocket of expenses leads to heavy loss to the cracker, which prevents further attacks.
Li, K, Lu, J, Zuo, H & Zhang, G 1970, 'Multi-Source Domain Adaptation with Fuzzy-Rule based Deep Neural Networks', 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Luxembourg, Luxembourg, pp. 1-6.
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Unsupervised domain adaptation provides a variety of methods to leverage the previously gained knowledge from a labeled source domain to help complete a task from a similar unlabeled target domain. Many existing methods focus on transferring knowledge across single source and single target domains, while few studies deal with multi-source domain adaptation, which is more realistic and challengeable. Existing multi-source domain adaptation methods rarely consider the uncertainty of the transformed knowledge resulting from limited information in target domain. A fuzzy system allows imprecision and ambiguity within transfer, thus it can deal with problems with uncertainty. This work proposes a multi-source domain adaptation method with fuzzy-rule based deep neural networks (MDAFuz). The proposed method first extracts multi-view adapted features and pre-trains source classifiers. Using the learned features and classifiers, training samples are then split into multiple clusters, hence fuzzy rules can be built to learn new classifiers. At the same time, the cluster discriminator is trained to define the membership. Finally, by measuring the similarities among source and target domains using the pseudo target labels and a domain discriminator, the target task is completed by combining all source classifiers with regard to the learned weights. The experiment results on real-world visual datasets show the superiority of the proposed method.
Li, Q, Duong, TD, Wang, Z, Liu, S, Wang, D & Xu, G 1970, 'Causal-Aware Generative Imputation for Automated Underwriting', Proceedings of the 30th ACM International Conference on Information & Knowledge Management, CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, ACM, pp. 3916-3924.
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Underwriting is an important process in insurance and is concerned with accepting individuals into insurance policy with tolerable claim risk. Underwriting is a tedious and labor intensive process relying on underwriters' domain knowledge and experience, thus is labor intensive and prone to error. Machine learning models are recently applied to automate the underwriting process and thus to ease the burden on the underwriters as well as improve underwriting accuracy. However, observational data used for underwriting modelling is high dimensional, sparse and incomplete, due to the dynamic evolving nature (e.g., upgrade) of business information systems. Simply applying traditional supervised learning methods e.g., logistic regression or Gradient boosting on such highly incomplete data usually leads to the unsatisfactory underwriting result, thus requiring practical data imputation for training quality improvement. In this paper, rather than choosing off-the-shelf solutions tackling the complex data missing problem, we propose an innovative Generative Adversarial Nets (GAN) framework that can capture the missing pattern from a causal perspective. Specifically, we design a structural causal model to learn the causal relations underlying the missing pattern of data. Then, we devise a Causality-aware Generative network (CaGen) using the learned causal relationship prior to generating missing values, and correct the imputed values via the adversarial learning. We also show that CaGen significantly improves the underwriting prediction in real-world insurance applications.
Li, Q, Wang, Z, Li, G, Pang, J & Xu, G 1970, 'Hilbert Sinkhorn Divergence for Optimal Transport', 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 3834-3843.
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Li, X, Cao, Z & Bai, Q 1970, 'A Novel Mountain Driving Unity Simulated Environment for Autonomous Vehicles', 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence, ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, ELECTR NETWORK, pp. 16075-16077.
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The simulated driving environment provides a low cost and time-saving platform to test the performance of the autonomous vehicle by linkage with existing machine learning approaches. However, most of existing simulated driving environments focus on building flat roads in urban areas. Still, they neglected to endeavour the tough steep, curvy hill roads, such as mountain paths around suburban areas. In this study, by deploying in Unity engine, we developed the first complex mountain driving simulated environment with characterizing continuous curves and up/downhill. Then, two state-of-art reinforcement learning (RL) algorithms are used to train a vehicle agent and test the performance of autonomous vehicles in our developed simulated environment. Also, we set 5 different levels of vehicle's speeds and observe the cumulative rewards during the vehicle agent training. Our demonstration presents the developed environment supports for complex mountain scenario configurations and RL-based autonomous vehicles, and our findings show that the vehicle agent could achieve high cumulative rewards during the training stage, suggesting that our work is a potential new simulation environment for autonomous vehicles research. The demonstration video can be viewed via the link: https://youtu.be/0wSqGeCn-NU.
Li, Y, Chen, H, Sun, X, Sun, Z, Li, L, Cui, L, Yu, PS & Xu, G 1970, 'Hyperbolic Hypergraphs for Sequential Recommendation', Proceedings of the 30th ACM International Conference on Information & Knowledge Management, CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, ACM, pp. 988-997.
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Hypergraphs have been becoming a popular choice to model complex, non-pairwise, and higher-order interactions for recommender systems. However, compared with traditional graph-based methods, the constructed hypergraphs are usually much sparser, which leads to a dilemma when balancing the benefits of hypergraphs and the modelling difficulty. Moreover, existing sequential hypergraph recommendation overlooks the temporal modelling among user relationships, which neglects rich social signals from the recommendation data. To tackle the above shortcomings of the existing hypergraph-based sequential recommendations, we propose a novel architecture named Hyperbolic Hypergraph representation learning method for Sequential Recommendation (H2SeqRec) with the pre-training phase. Specifically, we design three self-supervised tasks to obtain the pre-training item embeddings to feed or fuse into the following recommendation architecture (with two ways to use the pre-trained embeddings). In the recommendation phase, we learn multi-scale item embeddings via a hierarchical structure to capture multiple time-span information. To alleviate the negative impact of sparse hypergraphs, we utilize a hyperbolic space-based hypergraph convolutional neural network to learn the dynamic item embeddings. Also, we design an item enhancement module to capture dynamic social information at each timestamp to improve effectiveness. Extensive experiments are conducted on two real-world datasets to prove the effectiveness and high performance of the model.
Li, Y, Fan, X, Chen, L, Li, B & Sisson, SA 1970, 'Decoupling Sparsity and Smoothness in Dirichlet Belief Networks', Machine Learning and Knowledge Discovery in Databases. Research Track, Springer International Publishing, pp. 148-163.
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The Dirichlet Belief Network (DirBN) has been proposed as a promising deep generative model that uses Dirichlet distributions to form layer-wise connections and thereby construct a multi-stochastic layered deep architecture. However, the DirBN cannot simultaneously achieve both sparsity, whereby the generated latent distributions place weights on a subset of components, and smoothness, which requires that the posterior distribution should not be dominated by the data. To address this limitation we introduce the sparse and smooth Dirichlet Belief Network (ssDirBN) which can achieve both sparsity and smoothness simultaneously, thereby increasing modelling flexibility over the DirBN. This gain is achieved by introducing binary variables to indicate whether each entity’s latent distribution at each layer uses a particular component. As a result, each latent distribution may use only a subset of components in each layer, and smoothness is enforced on this subset. Extra efforts on modifying the models are also made to fix the issues which is caused by introducing these binary variables. Extensive experimental results on real-world data show significant performance improvements of ssDirBN over state-of-the-art models in terms of both enhanced model predictions and reduced model complexity.
Li, Y, Yin, J & Chen, L 1970, 'Unified Robust Training for Graph Neural Networks Against Label Noise', Springer International Publishing, pp. 528-540.
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Lin, J, Sun, G, Shen, J, Pritchard, D, Yu, P, Cui, T, Li, L & Beydoun, G 1970, 'MOOC Student Dropout Rate Prediction via Separating and Conquering Micro and Macro Information.', ICONIP (6), International Conference on Neural Information Processing, Springer, Sanur, Bali, Indonesia, pp. 459-467.
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With the increasing availability of ubiquitous and intelligent mobile devices, MOOC (Massive Open Online Courses) has become a popular choice for people who want to learn in a more flexible manner. However, compared to traditional in-class face-to-face learning, the MOOC platforms always suffer from a high learner dropout rate. Hence, correctly predicting the dropout rate at the early stage of a learning activity is significant for the MOOC adaptors and developers, who can conduct effective intervention strategies to improve the online course’s quality and increase the retention rate. In this paper, we designed a double-tower-based framework for dropout rate prediction. The framework separately models the different types of information, namely the macro and the micro information. Our work also leads to the design of a Convolutional Neural Network (CNN)-based model for effectively mining time-series information from the learners’ successive activity records. The experimental results demonstrated that the proposed double-tower-based framework also clearly outperformed the various baselines.
Lin, J, Zhang, C & Yu, S 1970, 'Safeguard the Original Data in Federated Learning via Data Decomposition', 2021 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2021 - 2021 IEEE Global Communications Conference, IEEE, Madrid, Spain.
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In federated learning, more and more studies have discovered that attackers can recover the original data from the shared gradients of participants. However, existing defense models struggle to balance the privacy of participants and the effectiveness of federated learning in the face of cutting-edge attack models. Therefore, we propose a powerful defense model to protect the original data while ensuring the effect of classification. First, we get two featured datasets from original data based on Sparse Dictionary Learning (DL) or QR decomposition. In these two featured datasets, we select one dataset to replace the original data for federated training named co-trained data, and the other one is kept on the local client named left data. At this point, the adversary in federated learning can only obtain co-trained data, which cannot recover the original data due to the lack of left data. Following the completion of the federated learning, the participant requests a parameter from the server. Using this parameter, we can combine the aggregated global model over co-trained data with the offline-trained local model of an arbitrary participant to develop the final classification results. Some theories and a lot of experiments demonstrate the classification effectiveness of our model. It also can be a general solution to the original data leakage problems caused by gradient leakage.
Liu, Y, Lee, J, Zhu, L, Chen, L, Shi, H & Yang, Y 1970, 'A Multi-Mode Modulator for Multi-Domain Few-Shot Classification', 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Montreal, QC, Canada, pp. 8433-8442.
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Most existing few-shot classification methods only consider generalization on one dataset (i.e., single-domain), failing to transfer across various seen and unseen domains. In this paper, we consider the more realistic multi-domain few-shot classification problem to investigate the cross-domain generalization. Two challenges exist in this new setting: (1) how to efficiently generate multi-domain feature representation, and (2) how to explore domain correlations for better cross-domain generalization. We propose a parameter-efficient multi-mode modulator to address both challenges. First, the modulator is designed to maintain multiple modulation parameters (one for each domain) in a single network, thus achieving single-network multi-domain representation. Given a particular domain, domain-aware features can be efficiently generated with the well-devised separative selection module and cooperative query module. Second, we further divide the modulation parameters into the domain-specific set and the domain-cooperative set to explore the intra-domain information and inter-domain correlations, respectively. The intra-domain information describes each domain independently to prevent negative interference. The inter-domain correlations guide information sharing among relevant domains to enrich their own representation. Moreover, unseen domains can utilize the correlations to obtain an adaptive combination of seen domains for extrapolation. We demonstrate that the proposed multi-mode modulator achieves state-of-the-art results on the challenging META-DATASET benchmark, especially for unseen test domains.
Liu, Z, Li, Y, Yao, L, Wang, X & Long, G 1970, 'Task Aligned Generative Meta-learning for Zero-shot Learning', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), online, pp. 8723-8731.
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Zero-shot learning (ZSL) refers to the problem of learning to classify instances from novel classes (unseen) that are absent in the training set (seen). Most ZSL methods infer the correlation between visual features and attributes to train the classifier for unseen classes. They may have a strong bias towards seen classes during training. Meta-learning has been introduced to mitigate the basis, but meta-ZSL methods are inapplicable when tasks used for training are sampled from diverse distributions. In this regard, we propose a novel Task-aligned Generative Meta-learning model for Zero-shot learning (TGMZ), aiming to mitigate the potentially biased training and to enable meta-ZSL to accommodate real-world datasets that contain diverse distributions. Specifically, TGMZ incorporates an attribute-conditioned task-wise distribution alignment network that projects tasks into a unified distribution to deliver an unbiased model. Our experiments show TGMZ achieves a relative improvement of 2.1%, 3.0%, 2.5%, and 7.6% over state-of-the-art algorithms on AWA1, AWA2, CUB, and aPY datasets, respectively. Overall, TGMZ outperforms competitors by 3.6% in the generalized zero-shot learning (GZSL) setting and 7.9% in our proposed fusion-ZSL setting.
Lv, X, Ji, K, Chen, Z, Ma, K, Wu, J, Li, Y & Xu, G 1970, 'Expert Recommendations with Temporal Dynamics of User Interest in CQA', Springer International Publishing, pp. 645-652.
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Mahadewa, K, Zhang, Y, Bai, G, Bu, L, Zuo, Z, Fernando, D, Liang, Z & Dong, JS 1970, 'Identifying privacy weaknesses from multi-party trigger-action integration platforms', Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA '21: 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, ACM.
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McGregor, C 1970, 'A Platform for Real-Time Space Health Analytics as a Service Utilizing Space Data Relays', 2021 IEEE Aerospace Conference (50100), 2021 IEEE Aerospace Conference, IEEE, pp. 1-14.
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The health, wellness and adaptation response of astronauts during spaceflight is a key component for the success of any manned mission. Physiological and psychological responses of astronauts during spaceflight have been monitored from the first manned missions sixty years ago. However, limited communication networks to and within the spacecraft have limited methods to monitor the health, wellness and adaptation response of astronauts in real-time. This has resulted in a paradigm of astronaut monitoring as discontinuous samplings of physiological data that are captured on board the spacecraft and transported to Earth on storage devices for retrospective down sampled analysis. In 2009, as part of prior research, McGregor proposed a big data analytics framework and platform, that enables the capture and processing of physiological data and other clinical data in real-time for new approaches to real-time health monitoring. The platform, was named the Artemis platform after the Greek goddess of childbearing as the first domain it was used was neonatal intensive care. Its efficacy and reliability as a new approach for real-time health monitoring has been demonstrated in the critical care domain and specifically within the domain of neonatal intensive care. McGregor previously proposed the application of Artemis as an approach for autonomous health monitoring within the spacecraft to support missions within and beyond low Earth orbit. This would enable sophisticated realtime health, wellness and adaptation assessment that did not require the transmission of data beyond the spacecraft. Artemis Cloud has been proposed as a cloud-based approach to provide remote health monitoring. Artemis Cloud enables Health Analytics as a Service and has been demonstrated utilizing the Ontario Research and Innovation Optical Network (ORION) in Ontario and Artemis Cloud instances located at the Compute Ontario advanced research computing node within the Centre for Advanced Computing, Que...
McGregor, C, Dewey, C & Luan, R 1970, 'Big data and artificial intelligence in healthcare: Ethical and social implications of neonatology', 2021 IEEE International Symposium on Technology and Society (ISTAS), 2021 IEEE International Symposium on Technology and Society (ISTAS), IEEE, pp. 1-1.
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High-speed physiological data are proving to be one of the most untapped resources in healthcare today. Many medical devices produce data streams at frequencies of a reading a second or faster making the effective use of that data a Big Data challenge. A growing body of research studies are demonstrating common physiological patterns for a range of medical conditions at earlier stages in the condition progression paving the way for new artificial intelligence and machine learning based approaches that could also be more reliable. There is great potential for real-time assessment of this physiological data to improve patient outcomes and to do so on an individualized personalized level. Systemic use of Big Data and AI in Healthcare present many ethics and social implications. This keynote will demonstrate how Big Data and AI can be used systemically for new approaches in research and clinical care for differential diagnosis and condition management. Ethical and social implications will be considered within the context of the application of these approaches in neonatology.
Milton, J, Hall, M, Chiang, YK, Halkon, B, Oberst, S & Powell, D 1970, 'Exploring the effect of underwater burial on the resonant behaviour of simplified shell geometries', Annual Conference of the Australian Acoustical Society 2021: Making Waves, AAS 2021, Australian Acoustics Society - Acoustics 2021, Wollongong, pp. 15-22.
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Naval mines and unexploded ordnance litter large areas of the ocean floor resulting in many coastlines being abandoned due to their deadly and indiscriminate threat. Over time, many become buried within the seabed, becoming less visible and thereby presenting a challenge to existing image-based detection techniques. Alternative approaches might rely on acoustic scattering, and it is therefore necessary to understand how the signatures of such objects may change when burial occurs. In this paper, the scattering spectra of several simple shell geometries have been evaluated through complementary but independently developed numerical and analytical modelling techniques. Scenarios investigated include air and fluid-filled spherical targets surrounded either by seawater or saturated sand, representative of burial within the seabed. The results show how embedding the objects within saturated sand results in a decrease in frequency of the dominant scattering resonances. In general, these frequencies were reduced by a factor of between 1.2 and 1.4.
Naji, M, Anaissi, A, Braytee, A & Goyal, M 1970, 'Anomaly Detection in X-ray Security Imaging: a Tensor-Based Learning Approach', 2021 International Joint Conference on Neural Networks (IJCNN), 2021 International Joint Conference on Neural Networks (IJCNN), IEEE, Shenzhen, China.
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Anomaly detection in X-ray security screening systems has earned a lot of interests in recent years and has attracted many researchers working in the area of machine learning. With the advances in computing technology, it is becoming more feasible to develop an approach for automated anomaly detection in security screening systems based on images collected via Xray machines. Analyzing these X-ray images and constructing a detection model is considered as a challenging problem because of the lack or limited number of samples of anomalous objects. This paper presents a novel tensor based learning method for anomaly detection in X-ray security screening systems based on tensor analysis augmented with one-class classification model. Our method initially performs data fusion of multi-angle scanned images in a tensor data structure from where we extract the informative features. Further, it constructs a one-class support vector machine model using these features to detect anomalies. We evaluate this approach using two image-based datasets and one real X-ray security baggage data collected from Sydney airport. The results show that our tensor based learning method outperforms other state-of-the-art approaches.
Nerse, C, Schadeberg, R & Oberst, S 1970, 'Novel resonator geometry for easily manufactured tunable locally resonant metamaterial', Annual Conference of the Australian Acoustical Society 2021: Making Waves, AAS 2021, Annual Conference of the Australian Acoustical Society, AAS, Wollongong, Australia, pp. 68-72.
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Mechanical waves and sound waves have complex propagation characteristics that are manipulated by periodic structures such as elastic metamaterials and phononic crystals for the purposes of wave guiding, vibration isolation and sound absorption. System parameters are tuned to induce auxetic physical properties such as negative effective mass density and negative Poisson's ratio. Locally resonant metamaterial (LRM) uses Fano-type interference to manipulate elastic wave propagation from the host structure by formation of a band gap due to local resonance. Not restricted by the Bragg interference limit, such sub-wavelength structures are particularly effective in attenuation of the low frequency oscillations. Tunability of the lower and upper bounds of the band gap through simple geometrical and material variations has made the LRMs a strong candidate for the noise and vibration control of automotive and industrial applications. In this study, we demonstrate a tunable LRM design that can be fabricated by injection moulding and vacuum casting. The mould for the fabrication of the resonator features a cylindrical hollow section. Pins of different diameter can be inserted into the mould to vary the material distribution in the cavity, thereby changing the resonance. A numerical model using COMSOL Multiphysics has been developed to investigate the dispersion mechanism. A parametric study of the pin diameter with respect to target band gap frequency demonstrates the capability of broadband vibration attenuation while keeping the overall size of the resonator small and constant. These results are promising for practical implementation of LRMs.
Nguyen, T-D, Kedziora, DJ, Musial, K & Gabrys, B 1970, 'Exploring Opportunistic Meta-knowledge to Reduce Search Spaces for Automated Machine Learning', 2021 International Joint Conference on Neural Networks (IJCNN), 2021 International Joint Conference on Neural Networks (IJCNN), IEEE, Shenzhen, China.
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Machine learning (ML) pipeline composition and optimisation have been studied to seek multi-stage ML models' i.e. preprocessor-inclusive, that are both valid and well-performing. These processes typically require the design and traversal of complex configuration spaces consisting of not just individual ML components and their hyperparameters, but also higher-level pipeline structures that link these components together. Optimisation efficiency and resulting ML-model accuracy both suffer if this pipeline search space is unwieldy and excessively large; it becomes an appealing notion to avoid costly evaluations of poorly performing ML components ahead of time. Accordingly, this paper investigates whether, based on previous experience, a pool of available classifiers/regressors can be preemptively culled ahead of initiating a pipeline composition/opti-misation process for a new ML problem, i.e. dataset. The previous experience comes in the form of classifier/regressor accuracy rankings derived, with loose assumptions, from a substantial but non-exhaustive number of pipeline evaluations; this meta-knowledge is considered ‘opportunistic’. Numerous experiments with the AutoWeka4MCPS package, including ones leveraging similarities between datasets via the relative landmarking method, show that, despite its seeming unreliability, opportunistic meta-knowledge can improve ML outcomes. However, results also indicate that the culling of classifiers/regressors should not be too severe either. In effect, it is better to search through a ‘top tier’ of recommended predictors than to pin hopes onto one previously supreme performer.
Novakovic, A, Marshall, AH & McGregor, C 1970, 'Introducing a Conceptual Framework for Architecting Healthcare 4.0 Systems', Advances in Computer Vision and Computational Biology, Springer International Publishing, pp. 579-589.
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Novakovic, A, Marshall, AH & McGregor, C 1970, 'Knowledge Discovery of the Delays Experienced in Reporting COVID-19 Confirmed Positive Cases Using Time to Event Models', Discovery Science, Springer International Publishing, pp. 183-193.
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Pare, S, Prasad, M, Puthal, D, Gupta, D, Malik, A & Saxena, A 1970, 'Multilevel Color Image Segmentation using Modified Fuzzy Entropy and Cuckoo Search Algorithm', 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Luxembourg, Luxembourg.
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To handle the fuzziness and spatial uncertainties among pixels entailed in color images, this paper proposes a novel fuzzy entropy function for multi-threshold image segmentation based on the energy curve concept and minimum fuzzy entropy criterion. The proposed energy curve based new fuzzy entropy function (ECFE) considers intensity distribution and spatial contextual information among the pixels. To improve efficiency and threshold selection process of the method, cuckoo search algorithm is employed. For comparison, backtracking search algorithm, and Lévy flight based firefly algorithm included. Comparison with recent color image multilevel segmentation techniques presented to test the effectiveness of the proposed algorithm. The performance of the proposed technique is evaluated using different satellite and natural color images. Quantitative and qualitative results demonstrate that the proposed algorithm is highly accurate, robust, and efficient for color image multilevel segmentation.
Patibanda, R, Li, X, Chen, Y, Saini, A, Hill, CN, van den Hoven, E & Mueller, FF 1970, 'Actuating Myself: Designing Hand-Games Incorporating Electrical Muscle Stimulation', Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play, CHI PLAY '21: The Annual Symposium on Computer-Human Interaction in Play, ACM.
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Peng, S, Zeng, R, Liu, H, Chen, G, Wu, R, Yang, A & Yu, S 1970, 'Emotion Classification of Text Based on BERT and Broad Learning System', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 382-396.
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Emotion classification is one of the most important tasks of natural language processing (NLP). It focuses on identifying each kind of emotion expressed in text. However, most of the existing models are based on deep learning methods, which often suffer from long training time, difficulties in convergence and theoretical analysis. To solve the above problems, we propose a method for emotion classification of text based on bidirectional encoder representation from transformers (BERT) and broad learning system (BLS) in this paper. The texts are input into BERT pre-trained model to obtain context-related word embeddings and all word vectors are averaged to obtain sentence embedding. The feature nodes and enhancement nodes of BLS are used to extract the linear and nonlinear features of text, and three cascading structures of BLS are designed to transform input data to improve the ability of text feature extraction. The two groups of features are fused and input into the output layer to obtain the probability distribution of each kind of emotion, so as to achieve emotion classification. Extensive experiments are conducted on datasets from SemEval-2019 Task 3 and SMP2020-EWECT, and the experimental results show that our proposed method can better reduce the training time and improve the classification performance than that of the baseline methods.
Peng, Z, Gong, X, Lu, Z, Xu, X, Wei, B & Prasad, M 1970, 'A Novel Fabric Defect Detection Network Based on Attention Mechanism and Multi-Task Fusion', 2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), 2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), IEEE, Beijing, China, pp. 484-488.
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Fabric is an important material, which is applied in the entire process of textile manufacturing, such as spinning, weaving, dyeing, printing, and finishing, and garments manufacturing. As defects on the surface of the fabric are inevitable in the process of fabric production, the defect detection of fabric is significant for fabric manufacture. The current CNN-based defect detection methods face several challenges when tackling the fabric defect with a tiny shape, the low grayscale difference with background, and ambiguous defect type. To deal with the problem, we proposed a novel fabric defect detection network - AMTFNet based on the attention mechanism and multi-task fusion module in this paper. On one hand, the attention mechanism module forced networks to pay attention to defects. On the other hand, the multi-task fusion module helps AMTFNet to further improve the classification effect using feature concatenated. The experimental result indicates that the precision-score, recall-score, and F1-score of AMTFNet reach 0.980, 0.994, and 0.987, respectively. The proposed method can be successfully applied in the detection of industrial fabric material.
Prysyazhnyuk, A & McGregor, C 1970, 'A Wholistic Approach to Human-in-the-Loop Ecosystem', AIAA Scitech 2021 Forum, AIAA Scitech 2021 Forum, American Institute of Aeronautics and Astronautics, pp. 1-9.
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Space exploration is amongst the greatest endeavors of the humankind. It continues to fuel human curiosity and imagination, as the limits and boundaries continue to extend beyond the Low Earth Orbit to other destinations, such as Moon and Mars. Technological advancements and scientific discoveries continue to redefine the boundaries of the human body and mind, revealing remarkable resilience, cognitive, physical and psychological performance in austere environments. However, as human kind prepares to embark on deep-space missions, there are fundamentally new challenges and considerations that have to be addressed to ensure successful mission outcomes. Restricted space, increased communication delays and remoteness from Earth, with limited ability for emergency return, necessitate development of a comprehensive human-in-the-loop ecosystem, with increased autonomy and clinical decision-making capacity. The proposed research harnesses the potential of big data and streaming data analytics to support a paradigm shift from reactive to proactive health management in-flight. It demonstrates the potential to support prognostics, diagnostics and mitigation of medical contingencies in-flight through a meaningful and practical use of the acquired data to inform clinical decision making, significance of which is demonstrated within the context of adaption-based analytics in a ground-based study “Luna-2015”.
Prysyazhnyuk, A & McGregor, C 1970, 'Adaption-Based Analytics for Assessment of Human Deconditioning during Deep Space Exploration', 2021 IEEE Aerospace Conference (50100), 2021 IEEE Aerospace Conference, IEEE, pp. 1-10.
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Technological and scientific advancements continue to enable safe prolonged human presence in space, while extending the boundaries of manned exploration from low-Earth orbit into deep space. As humankind prepares to embark on exploration-class missions, to the Moon and Mars, mission objectives, risks and challenges become more complex and vastly different from the majority of human manned space exploration experience known to-date. The potential health risks associated with deep space exploration are expected to amplify, the mitigation of which would necessitate complex and autonomous in-flight medical capacity, which has not been available to-date. The logistics of medical care delivery in-flight have been significantly limited by impracticality of existing biomedical monitoring modalities and retrospective data analytics methods and techniques. Conventionally, physiological health monitoring has been discontinuous and extremely limited, hindering the usability and practicality of the acquired data to support clinical decision-making in-flight. This paper presents an integrated big data framework that utilizes stream computing to support real-time autonomous clinical-decision making in-flight. The proposed framework extends previous research known as the Artemis and Artemis Cloud platforms by integrating multi-source, multi-type data to provide in-depth adaption-based assessment and identify the activity of the various compensatory reactions of regulatory mechanisms, which have been known to impact human health in weightlessness. The instantiation of the proposed big data integrated framework is demonstrated within the context of a ground-based 5-day Dry Immersion study. More specifically, the paper demonstrates the potential to support adaption-based analytics-as-a-service within the context of space medicine. Further to that, adaption-based analytics are enhanced through the introduction of multimodal real-time analytics. The multimodal adaption-based analyti...
Rafe, AW, Garcia, JA & Raffe, WL 1970, 'Exploration Of Encoding And Decoding Methods For Spiking Neural Networks On The Cart Pole And Lunar Lander Problems Using Evolutionary Training', 2021 IEEE Congress on Evolutionary Computation (CEC), 2021 IEEE Congress on Evolutionary Computation (CEC), IEEE, ELECTR NETWORK, pp. 498-505.
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Raza, MR, Hussain, W & Merigo, JM 1970, 'Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU', 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), IEEE.
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Raza, MR, Hussain, W & Merigo, JM 1970, 'Long Short-Term Memory-based Sentiment Classification of Cloud Dataset', 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), IEEE.
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Raza, MR, Hussain, W, Tanyildizi, E & Varol, A 1970, 'Sentiment Analysis using Deep Learning in Cloud', 2021 9th International Symposium on Digital Forensics and Security (ISDFS), 2021 9th International Symposium on Digital Forensics and Security (ISDFS), IEEE.
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Raza, MR, Varol, A & Hussain, W 1970, 'Blockchain-based IoT: An Overview', 2021 9th International Symposium on Digital Forensics and Security (ISDFS), 2021 9th International Symposium on Digital Forensics and Security (ISDFS), IEEE.
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Reddy, A, Kocaballi, AB, Nicenboim, I, Søndergaard, MLJ, Lupetti, ML, Key, C, Speed, C, Lockton, D, Giaccardi, E, Grommé, F, Robbins, H, Primlani, N, Yurman, P, Sumartojo, S, Phan, T, Bedö, V & Strengers, Y 1970, 'Making Everyday Things Talk: Speculative Conversations into the Future of Voice Interfaces at Home', Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI '21: CHI Conference on Human Factors in Computing Systems, ACM.
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Reddy, TK, Wang, Y-K, Lin, C-T & Andreu-Perez, J 1970, 'JOINT APPROXIMATE DIAGONALIZATION DIVERGENCE BASED SCHEME FOR EEG DROWSINESS DETECTION BRAIN COMPUTER INTERFACES', 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Luxembourg, Luxembourg, pp. 1-6.
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Neurons usually converse through electrochemical signals and pooled neuronal firings feasibly be recorded on the scalp through the medium of electroencephalogram (EEG). EEG waveforms are recorded, analysed and categorized across directives concerning a Brain-Computer Interface (BCI). Deteriorated signal to noise ratio and non-stationarities stand as a paramount obstacle in steady decoding of EEG. Appearance of non-stationarities across EEG patterns notably upset the feature waveforms thus worsening the functioning of detection block and as a whole the Brain Computer Interface. Stationary Subspace schemes bring to light subspaces within which data distribution persists stably over time. Current work focuses on the development of a novel spatial transform based feature extraction scheme to address nonstationarity in EEG signals recorded against a drowsiness detection problem (a machine learning regression scenario). The presented approach: F-DIV-IT-JAD-WS derived features distinctly surpassed DivOVR-FuzzyCSP-WS based standard features across RMSE and CC performance criteria pair. We construe that the propounded feature derivation approach based on F-DIV-IT-JAD-WS will usher a significant attention in researchers who are developing algorithms for signal processing, specifically, for BCI regression scenarios.
Rudorf, M, Oberst, S, Stender, M & Hoffmann, N 1970, 'Bifurcation Analysis of a Doubly Curved Thin Shell Considering Inertial Effects', Vibration Engineering for a Sustainable Future, Asia-Pacific Vibration Conference, Springer International Publishing, Australia, pp. 51-57.
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Thin-elastic structures can be found in nature as well as in many technical applications, including plant material (leaves) or insect appendages (wings) and aircraft outer bodies, optical mirrors and membranes, solar panels or satellite antennas. Numerical modelling of those structures is commonly conducted using shell elements. Especially doubly curved shells have found much attention due to their applicability in thin shell or sandwich structures used in the automotive, aerospace and space industry. In the design process it is generally assumed that these structures behave linearly, however, considering their curvature and how thin they are, large deflections easily become an issue as shown experimentally. Yet, the numerical modelling does conventionally assume that inertia e ffects can be neglected. Here we derive the equations of motion of a simply supported configuration of a doubly curved shell with 9 degrees of freedom with and without inertial coupling terms. We show by conducting a bifurcation analysis that the additional inertia effects cannot be neglected and that care has to be taken when structures as such are being employed as appendages on real-life satellites.
Sadaf, A, Mathieson, L & Musial, K 1970, 'An insight into network structure measures and number of driver nodes', Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM '21: International Conference on Advances in Social Networks Analysis and Mining, ACM, pp. 471-478.
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Salama, U, Chen, X, Yao, L, Paik, H-Y & Wang, X 1970, 'Deep Multi-view Spatio-Temporal Network for Urban Crime Prediction', 32th Australasian Database Conference, 32th Australasian Database Conference, Springer International Publishing, Dunedin, New Zealand, pp. 50-61.
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Crimes sabotage various societal aspects, such as social stability, public safety, economic development, and individuals’ quality of life. To accurately predict crime occurrences can not only bring the peace of mind to individuals but also help distribute and manage police resources effectively by authorities. We aim to take into account plenty of environmental factors, such as data collected from Internet of Things (IoT) devices and social networks to predict crimes at city or a finer level. To this end, we propose a deep-learning-based spatio-temporal multi-view model, which explores the relationship between tweets, weather (a type of sensory data) and crime rate, for effective crime prediction. Our extensive experiments on a four-month crime dataset (covering 77 communities, 22 crime types, and 120 days) of Chicago city show that our model can achieve improvement over 19 out of 22 crime types (up to 6.7% in homicide). We also collect the corresponding weather information for different regions of Chicago city to support the crime prediction. Our experiments demonstrate that weather information can improve the performance of the proposed method.
Sepehrirahnama, S, Oberst, S, Chiang, YK & Powell, D 1970, 'Effects of shape and Willis coupling on acoustic radiation force and torque, Acoustic traps revisited!', Acoustofluidics, USWNet (online, virtual conference).
Shan, M, Xiong, J, Liu, B, Shi, Z, Li, X & Miao, N 1970, 'UAV Resource Cooperation Based on Reinforcement Learning', 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE.
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Shen, H, Liao, K, Liao, Z, Doornberg, J, Qiao, M, van den Hengel, A & Verjans, JW 1970, 'Human-AI Interactive and Continuous Sensemaking: A Case Study of Image Classification using Scribble Attention Maps', Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI '21: CHI Conference on Human Factors in Computing Systems, ACM.
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Shi, J, Lin, Y, Zhang, Z & Yu, S 1970, 'A Hybrid Intrusion Detection System Based on Machine Learning under Differential Privacy Protection', 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), IEEE.
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Singh, AK, Liu, J, Tirado Cortes, CA & Lin, C-T 1970, 'Virtual Global Landmark: An Augmented Reality Technique to Improve Spatial Navigation Learning', Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI '21: CHI Conference on Human Factors in Computing Systems, ACM.
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Singh, AK, Liu, J, Tirado Cortes, CA & Lin, C-T 1970, 'Virtual Global Landmark: An Augmented Reality Technique to Improve Spatial Navigation Learning', Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI '21: CHI Conference on Human Factors in Computing Systems, ACM, Yokohama, Japan, pp. 1-6.
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Navigation is a multifaceted human ability involving complex cognitive functions. It allows the active exploration of unknown environments without becoming lost while enabling us to move efficiently across well-known spaces. However, the increasing reliance on navigation assistance systems reduces surrounding environment processing and decreases spatial knowledge acquisition and thus orienting ability. To prevent such a skill loss induced by current navigation support systems like Google Maps, we propose a novel landmark technique in augmented reality (AR): the virtual global landmark (VGL). This technique seeks to help navigation and promote spatial learning. We conducted a pilot study with five participants to compare the directional arrows with VGL. Our result suggests that the participants learned more about the environment while navigation using VGL than directional arrows without any significant mental workload increase. The results have a substantial impact on the future of our navigation system.
Soomro, WA, Guo, Y, Lu, HY, Zhu, JG, Jin, JX & Shen, B 1970, 'Numerical Investigation of AC Loss in HTS Bulks Subjected to Rotating Magnetic Fields', 2021 31st Australasian Universities Power Engineering Conference (AUPEC), 2021 31st Australasian Universities Power Engineering Conference (AUPEC), IEEE, Perth, Australia.
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High-temperature superconductor (HTS) bulks have shown very promising potential for industrial applications due to their highly attractive superconducting characteristics. The practical application, however, has been handicapped by the AC losses. In rotating electrical machines, the magnetic field is a combination of alternating and rotating fields. All the AC loss studies presented in the literature so far have only focused on the alternating AC loss due to the unavailability of experimental techniques and analytical models. This paper presents a numerical investigation of AC loss by using the H-formulation under various two-dimensional rotating magnetizations with the magnetic flux density vectors rotating in clockwise and anti-clockwise directions in the XOY, XOZ, and YOZ planes. The modeling results show that the rotational AC loss of HTS bulk material is significantly higher than the alternating AC loss.
Sun, L, Lyu, C, Shi, Y & Lin, C-T 1970, 'Multiple-Preys Pursuit based on Biquadratic Assignment Problem', 2021 IEEE Congress on Evolutionary Computation (CEC), 2021 IEEE Congress on Evolutionary Computation (CEC), IEEE, Kraków, Poland, pp. 1585-1592.
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The multiple-preys pursuit (MPP) is the adversarial game between predators and preys. If the capture of a prey is defined as that it cannot move anymore due to the surrounding of predators, there are two kinds of task allocations. One is about assigning which prey to which group of predators so that all preys can be captured. The other is about assigning which capturing position to which predator to encircle the prey simultaneously. In this paper, the MPP is modeled as a dynamic optimization problem and each its time step is solved in two stages. Firstly, the first kind of task allocation problem is modeled as the biquadratic assignment problem (BiQAP) and a MPP fitness function is proposed for the evaluation of such BiQAP task allocations. In this way, the MPP is transformed to several single-prey pursuit (SPP) problems. Secondly, for each SPP, we extend the coordinated SPP strategy CCPSO-R (cooperative coevolutionary particle swarm optimization for robots) to its parallel version as PCCPSO-R to enable the parallel implicit capturing position allocating by parallel observation, decision making, and moving of predators. Through experiments of the current BiQAP solvers on the task allocation, we improve the best one of them in statistic based on the domain knowledge. Moreover, the advantages of PCCPSO-R in the capturing efficiency over CCPSO-R is testified in the MPP experiments.
Tan, S, Tian, Z, Zhong, X, Yu, S, Zhang, W & Dong, G 1970, 'A Novel Android Malware Detection Method Based on Visible User Interface', 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), IEEE, Shenyang, China, pp. 659-666.
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Machine learning has been increasingly adopted to detect Android malwares. Most existing studies depend on features in code space such as information flows and API calls. Malware variants would engage these models in a never-ending war. Inspired by the observation that some variants share similar or even identical user interfaces (UIs), this paper explores employing visible UI screenshot as the indicator to build a novel Android malware detection method. To achieve this vision, we built the first Android Application Screenshot Dataset (AnASD) consisting of more than twenty thousand UI screenshots produced by both benign applications and malwares. A thorough analysis was conducted to characterize the dataset, especially the UI difference between benign applications and malwares. Then a set of state of the art deep learning classifiers on AnASD were trained and evaluated. The results of both sim-ilarity measurement and classification performance proved the feasibility to detect Android malwares based on user interfaces. To facilitate the research community, the dataset is free available at https://doi.org/10.6084/m9.figshare.14445768.
Tavares Vasconcelos Oliveira, F, Gay, V & Garcia Marin, J 1970, 'GAMES FOR THE COGNITIVE ASSESSMENT OF OLDER ADULTS', https://gsgs.ch/wp-content/uploads/2021/11/gsgs21-web.pdf, 6th International Conference on Gamification and Serious Games, Lausanne, Switzerland.
Tian, Y, Minton, AG, Zhu, HY, Notaro, G, Galvan, R, Wang, Y-K, Chen, H-T, Allen, J, Ziegler, MD & Lin, C-T 1970, 'A Comparison of Common Video Game versus Real-World Heads-Up-Display Designs for the Purpose of Target Localization and Identification', 2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), 2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), IEEE, Bari, Italy, pp. 228-233.
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This paper presents the findings of an investigation into the user ergonomics and performance for industry-inspired and traditional video game-inspired Heads-Up-Display (HUD) designs for target localization and identification in a 3D real-world environment. Our online user study (N = 85) compared one industry-inspired design (Ellipse) to three common video game HUD designs (Radar, Radar Indicator, and Compass). Participants interacted and evaluated each HUD design through our novel web-based game. The game involved a target localization and identification task where we recorded and analyzed their performance results as a quantitative metric. Afterwards, participants were asked to provide qualitative responses for specific aspects of each HUD design and comparatively rate the designs. Our findings show that not only do common video game HUDs provide comparable performance to the real-world inspired HUD, participants tended to prefer the designs they had experience with, these being video game designs.
Tran, HV, Tuan Ngo, A & Kennedy, PJ 1970, 'Transfer learning approaches to recognize X-ray Covid–19 images', 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), IEEE, pp. 9-17.
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van Rheden, V, Grah, T, Meschtscherjakov, A, Patibanda, R, Liu, W, Daiber, F, van den Hoven, E & Mueller, FF 1970, 'Out of Your Mind!? Embodied Interaction in Sports', Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI '21: CHI Conference on Human Factors in Computing Systems, ACM.
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Verma, R, Singh, M & Merigó, JM 1970, 'A Generalized Solution Approach to Matrix Games with 2-Tuple Linguistic Payoffs', Springer Singapore, pp. 531-543.
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Wang, S, Chen, L, Hussain, F & Zhang, C 1970, 'Semi-supervised Variational Multi-view Anomaly Detection', Web and Big Data, Asia-Pacific Web and Web-Age Information Management Joint International Conference on Web and Big Data, Springer International Publishing, Guangzhou, China, pp. 125-133.
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Multi-view anomaly detection (Multi-view AD) is a challenging problem due to the inconsistent behaviors across multiple views. Meanwhile, learning useful representations with little or no supervision has attracted much attention in machine learning. There are a large amount of recent advances in representation learning focusing on deep generative models, such as Variational Auto Encoder (VAE). In this study, by utilizing the representation learning ability of VAE and manipulating the latent variables properly, we propose a novel Bayesian generative model as a semi-supervised multi-view anomaly detector, called MultiVAE. We conduct experiments to evaluate the performance of MultiVAE on multi-view data. The experimental results demonstrate that MultiVAE outperforms the state-of-the-art competitors across popular datasets for semi-supervised multi-view AD. As far as we know, this is the first work that applies VAE-based deep models on multi-view AD.
Wang, T, Mao, J, Chen, M, Liu, G, Di, J & Yu, S 1970, 'ICE: Intelligent Caching at the Edge', 2021 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2021 - 2021 IEEE Global Communications Conference, IEEE, Madrid, Spain.
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The unprecedented growth of mobile data traffic brings unique challenges for network bandwidth and server resources to meet the diverse QoE (Quality of Experience). Caching becomes a promising way to alleviate these issues by storing a subset of data at the network edge, for which caching policy becomes critical. To this end, various caching schemes have been put forward, however, these schemes are either not intelligent lacking the ability of self-learning and self-decision-making, or inefficient with low data hit rate. Based on these observations, in this paper, we propose a novel Intelligent Caching framework at the Edge, named ICE, via deep reinforcement learning to capture certain valued information of the requested data. Notably, in our approach, the popularity of the data to be cached will be explored and considered. A Markov decision model is further developed to determine whether the data should be cached. The evaluation shows that ICE greatly improves the hit rate in comparison with the state-of-the-art approaches, and reduces the energy consumption for data transmission. Furthermore, based on ICE, the users' QoE is greatly improved. In conclusion, both theoretical analysis and experimental results prove the effectiveness and high performance of ICE compared with conventional strategies.
Wang, W, Gao, W, Feng, S, Chen, L & Wang, D 1970, 'Adaptive Posterior Knowledge Selection for Improving Knowledge-Grounded Dialogue Generation', Proceedings of the 30th ACM International Conference on Information & Knowledge Management, CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, ACM.
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Wang, X, Yao, L, Wang, X, Nie, F & Benatallah, B 1970, 'NP-PROV: Neural Processes with Position-Relevant-Only Variances', International conference on web information systems engineering, International conference on web information systems engineering, Springer International Publishing, Melbourne, Australia, pp. 129-142.
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Wang, X, Yao, L, Wang, X, Paik, H-Y & Wang, S 1970, 'Global Convolutional Neural Processes', 2021 IEEE International Conference on Data Mining (ICDM), 2021 IEEE International Conference on Data Mining (ICDM), IEEE, Auckland, New Zealand.
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Wen, Y, Liu, B, Xie, R, Cao, J & Song, L 1970, 'Deep Motion Flow Aided Face Video De-identification', 2021 International Conference on Visual Communications and Image Processing (VCIP), 2021 International Conference on Visual Communications and Image Processing (VCIP), IEEE.
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Wu, F, Cui, L, Feng, J, Wu, L, Yao, S & Yu, S 1970, 'Data Privacy Protection based on Feature Dilution in Cloud Services', 2021 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2021 - 2021 IEEE Global Communications Conference, IEEE, Madrid, Spain.
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Machine learning as a service (MLaaS) brings many benefits to people's daily life. However, the service mode of MLaaS will increase the risk of users' privacy leakage. Existing works focusing on privacy-preserving based on encryption, differential privacy, and distributed framework require high computing resources or cannot be applied in MLaaS. In this paper, we propose feature dilution (FD), a noise-based desensitization algorithm to remove sensitive information in raw data. In particular, FD continuously adds raw data features to the random noise until it meets the minimum amount for an effective query, and we call this noise weak-feature noise (WFN). By fine-tuning the MLaaS architecture, we have realized that users can utilize WFN to get normal services without exposing their local private data. Meanwhile, noise addition technology is introduced by us to reduce the risk of privacy leakage caused by 'weak features'. Extensive experiments have demonstrated that users can use FD to obtain effective services without exposing their private data. Finally, we conducted practical tests on weak-feature noises and found that these noises are difficult to use by malicious service providers.
Wu, S, Bai, Q & Li, W 1970, 'Learning policies for effective incentive allocation in unknown social networks', Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, pp. 1689-1691.
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Most existing incentive allocation approaches rely on sufficient information about users' attributes, such as their preferences, followers in the social network, and activities, to customize effective incentives. However, this may lead to failure when such knowledge is unavailable. In this light, we propose an end-to-end reinforcement learning-based framework, named Geometric Actor-Critic (GAC), to discover effective incentive allocation policies towards users in a social network. More specifically, given a limited budget, the proposed approach can extract information from a high-level network representation for learning effective incentive allocation policies. The proposed GAC only requires the topology of the social network and does not rely on any prior information about users' attributes.We use three real-world social network datasets to evaluate the performance of the proposed GAC. The experimental results demonstrate the effectiveness of the proposed approach.
Wu, Y, Cao, J, Xu, G & Tan, Y 1970, 'TFROM', Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM.
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Xiang, Y, Wang, S, Su, T, Li, J, Mao, SS & Geimer, M 1970, 'KIT Bus: A Shuttle Model for CARLA Simulator', 2021 IEEE Industrial Electronics and Applications Conference (IEACon), 2021 IEEE Industrial Electronics and Applications Conference (IEACon), IEEE, Penang, Malaysia, pp. 7-12.
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With the continuous development of science and technology, self-driving vehicles will surely change the nature of transportation and realize the automotive industry's transformation in the future. Compared with self-driving cars, self-driving buses are more efficient in carrying passengers and more environmentally friendly in terms of energy consumption. Therefore, it is speculated that in the future, self-driving buses will become more and more important. As a simulator for autonomous driving research, the CARLA simulator can help people accumulate experience in autonomous driving technology faster and safer. However, a shortcoming is that there is no modern bus model in the CARLA simulator. Consequently, people cannot simulate autonomous driving on buses or the scenarios interacting with buses. Therefore, we built a bus model in 3ds Max software and imported it into the CARLA to fill this gap. Our model, namely KIT bus, is proven to work in the CARLA by testing it with the autopilot simulation. The video demo is shown on our Youtube.
Xu, Y, Fang, M, Chen, L, Du, Y & Zhang, C 1970, 'Generalization in Text-based Games via Hierarchical Reinforcement Learning', Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021, pp. 1343-1353.
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Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents. However, the generalization still remains a big challenge as the agents depend critically on the complexity and variety of training tasks. In this paper, we address this problem by introducing a hierarchical framework built upon the knowledge graph-based RL agent. In the high level, a meta-policy is executed to decompose the whole game into a set of subtasks specified by textual goals, and select one of them based on the KG. Then a subpolicy in the low level is executed to conduct goal-conditioned reinforcement learning. We carry out experiments on games with various difficulty levels and show that the proposed method enjoys favorable generalizability.
Yang, H, Chen, H, Li, L, Yu, PS & Xu, G 1970, 'Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation', 2021 IEEE International Conference on Data Mining (ICDM), 2021 IEEE International Conference on Data Mining (ICDM), IEEE, pp. 787-796.
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User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. Various methods have been proposed to address it via leveraging the advantages of graph neural networks (GNNs) or multi-task learning. However, most existing works do not take the complex dependencies among different behaviors of users into consideration. They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user's behavioral patterns which will be used in downstream recommendation tasks. To tackle the challenge, in this paper, we first propose the concept of hyper meta-path to construct hyper meta-paths or hyper meta-graphs to explicitly illustrate the dependencies among different behaviors of a user. How to obtain a unified embedding for a user from hyper meta-paths and avoid the previously mentioned limitations simultaneously is critical. Thanks to the recent success of graph contrastive learning, we leverage it to learn embeddings of user behavior patterns adaptively instead of assigning a fixed scheme to understand the dependencies among different behaviors. A new graph contrastive learning based framework is proposed by coupling with hyper meta-paths, namely HMG-CR, which consistently and significantly outperforms all baselines in extensive comparison experiments.
Yang, X, Liu, W, Tao, D & Liu, W 1970, 'BESA: BERT-based Simulated Annealing for Adversarial Text Attacks', Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}, International Joint Conferences on Artificial Intelligence Organization, Montreal, Canada, pp. 3293-3299.
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Modern Natural Language Processing (NLP) models are known immensely brittle towards text adversarial examples. Recent attack algorithms usually adopt word-level substitution strategies following a pre-computed word replacement mechanism. However, their resultant adversarial examples are still imperfect in achieving grammar correctness and semantic similarities, which is largely because of their unsuitable candidate word selections and static optimization methods. In this research, we propose BESA, a BERT-based Simulated Annealing algorithm, to address these two problems. Firstly, we leverage the BERT Masked Language Model (MLM) to generate contextual-aware candidate words to produce fluent adversarial text and avoid grammar errors. Secondly, we employ Simulated Annealing (SA) to adaptively determine the word substitution order. The SA provides sufficient word replacement options via internal simulations, with an objective to obtain both a high attack success rate and a low word substitution rate. Besides, our algorithm is able to jump out of local optima with a controlled probability, making it closer to achieve the best possible attack (i.e., the global optima). Experiments on five popular datasets manifest the superiority of BESA compared with existing methods, including TextFooler, BAE, BERT-Attack, PWWS, and PSO.
Yeung, J & McGregor, C 1970, 'An Individualized Countermeasure Assessment Framework for Astronauts in Space', 2021 IEEE Aerospace Conference (50100), 2021 IEEE Aerospace Conference, IEEE, pp. 1-9.
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Preventive measures and countermeasure exercises play integral roles in maintaining the physiological health, wellbeing, and performance of astronauts in current day four to 11 month space missions to the ISS. While these activities help the body adapt to the adverse effects of spaceflight and minimize risks of physical deconditioning associated with weightlessness, these activities are inadequate for longer trips such as a 2-3 year mission to and from Mars. Furthermore, physical reconditioning and other issues such as visual impairment intracranial pressure (VIIP) syndrome remain for some astronauts returning from space missions. Combined with the effects of isolation and confinement in space, an astronaut's condition and performance can be compromised to a high degree with long-term impacts to their health and wellness upon returning to Earth if the appropriate interventions are not performed at appropriate times. Research has shown that representation of the relevant data to the active user during their activities has been proven effective in allowing them to perform the appropriate intervention to mitigate projected health risks. The physiological data and countermeasure equipment data currently located on the ISS has the potential to be correlated with the respective activities performed per astronaut for an individualized physiological monitoring approach for real-time health assessments. This paper presents a correlation method to enable individualized countermeasure assessments using big data collected during a simulated extreme environment workshop for firefighters as an analog. The online health analytics platform, created by McGregor, known as Artemis demonstrates this method using its capabilities in temporal abstraction for knowledge discovery, mechanisms for early detection of illnesses, and continuous real-time monitoring.
Zamani, K, Zowghi, D & Arora, C 1970, 'Machine Learning in Requirements Engineering: A Mapping Study', 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), IEEE.
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Zhan, Y, Li, L, Qian, Y, Ma, C, Ding, M & Liu, B 1970, 'A Hybrid Spatiotemporal Attack in Continuous LBS Queries', 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), IEEE.
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Zhang, S, Chen, H, Ming, X, Cui, L, Yin, H & Xu, G 1970, 'Where are we in embedding spaces?', Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 2223-2231.
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Hyperbolic space and hyperbolic embeddings are becoming a popular research field for recommender systems. However, it is not clear under what circumstances the hyperbolic space should be considered. To fill this gap, This paper provides theoretical analysis and empirical results on when and where to use hyperbolic space and hyperbolic embeddings in recommender systems. Specifically, we answer the questions that which type of models and datasets are more suited for hyperbolic space, as well as which latent size to choose. We evaluate our answers by comparing the performance of Euclidean space and hyperbolic space on different latent space models in both general item recommendation domain and social recommendation domain, with 6 widely used datasets and different latent sizes. Additionally, we propose a new metric learning based recommendation method called SCML and its hyperbolic version HSCML. We evaluate our conclusions regarding hyperbolic space on SCML and show the state-of-the-art performance of hyperbolic space by comparing HSCML with other baseline methods.
Zhang, Y, Bai, G, Li, X, Curtis, C, Chen, C & Ko, RKL 1970, 'Privacy-Preserving Gradient Descent for Distributed Genome-Wide Analysis', Springer International Publishing, pp. 395-416.
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Zhang, Z, Oberst, S & Lai, JCS 1970, 'Towards Overcoming the Challenges of the Prediction of Brake Squeal Propensity', Springer International Publishing, Switzerland, pp. 47-53.
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Despite significant efforts made in the last two decades for the
analysis and prediction of brake squeal propensity, brake squeal remains a major source of customer dissatisfaction and warranty-related costs. Brake squeal is a fugitive nonlinear self excitation phenomenon induced by friction. Traditional linear complex eigenvalue analysis (CEA) combined with noise dynamometer tests has achieved some success in the analysis of brake squeal and in designing countermeasures in suppressing brake squeal. However, prediction of brake squeal propensity without experimental testings is generally unreliable because of three main challenges. Firstly, most conventional analysis methods used are linear but brake squeal could be caused by nonlinearities. Secondly, while non-linear time-domain simulations have achieved some success, they require substantial high-performance computing resources and are too time consuming for practical applications. Thirdly, even if the second challenge can be overcome, there are many determining and interacting factors that are not known exactly such as material properties of pad and disc, operating conditions (brake pad pressure, temperature, speed), contact conditions between pad and
disc, non-linear boundary conditions and modelling of friction. In this paper,
examples are given to illustrate the success and limitations of the CEA. To address difficulties in predicting brakes squeal posed by nonlinearity, uncertain contact conditions and friction modelling, examples will be given to illustrate how a stochastic approach using the traditional linear eigenvalue analysis could be used to improve the prediction of brake squeal by identifying unstable vibration modes that are not too sensitive to these uncertainties.
Zhou, Z, Xu, C, Wang, M, Ma, T & Yu, S 1970, 'Augmented Dual-Shuffle-based Moving Target Defense to Ensure CIA-triad in Federated Learning', 2021 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2021 - 2021 IEEE Global Communications Conference, IEEE, Madrid, Spain.
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In today's 'Internet of Everything (IoE)' era, the collaboration from massive participants significantly boosts the performance and efficiency of model training. This trend also un-avoidably stirs up considerable concerns about multi-dimensional security problems. Under the circumstances, federated learning (FL) is enthusiastically adopted, as it protects privacy to a certain extent by only processing personal data locally. Nevertheless, FL's characteristics of concealment also pave the way for sev-eral emerging attacks during the training process, i.e., model inversion, poisoning, and backdoor. Currently, although partially mitigating attack effects, existing countermeasures against those threats are studied separately and orthogonal. This separation makes those defense methods mutually exclusive and restrictive in real-world application scenarios, far from satisfying. In this paper, we extensively model different attack paradigms into three types based on CIA-triad, the well-known information security primitive, and propose a novel dual-shuffle method to thwart aforementioned threats jointly. Concretely speaking, our primary model shuffling mechanism provides the confidentiality guarantee based on the information-theoretic notion of identifiability; then, an augmented client shuffling mechanism purges the user group of adversaries proactively without any compromise of anonymous constraints. By conducting a series of experiments on bench-mark datasets, we demonstrate that our method could achieve significant security and convergence performance against three state-of-the-art attacks.
Zhu, HY, Chen, H-T & Lin, C-T 1970, 'The Effects of a Stressful Physical Environment During Virtual Reality Height Exposure', 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE, Lisbon, Portugal, pp. 468-469.
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Virtual reality height exposure is a reliable method of inducing stress with low variance across age and demographics. As the virtual environment's quality of rendering fidelity increases dramatically, it leads to the neglect or simplification of the physical environment. This paper presents an experiment that explored the effects of an elevated physical platform with a virtually heightened environment to induce stress. Fifteen participants experienced four different conditions of varying physical and virtual heights. Participants reported significantly higher stress level when physically elevated regardless of the virtual height, which suggests that the inherent elevation will induce more stress within participants.
Zhu, HY, Magsino, EM, Hamim, SM, Lin, C-T & Chen, H-T 1970, 'A Drone Nearly Hit Me! A Reflection on the Human Factors of Drone Collisions', Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI '21: CHI Conference on Human Factors in Computing Systems, ACM, Yokohama Japan, pp. 1-6.
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The use of multi-rotor drones has grown exponentially as a consumer product and in the commercial sector. The inescapable reality is that drones will become a ubiquitous part of society. One major obstacle to the mainstream acceptance of drones is the public perception of drones being dangerous or a safety hazard. This paper presents an investigation into the human factors toward potential drone collisions. The study included twenty participants who underwent a controlled drone collision exposure and a post-exposure interview. We propose a novel drone collision exposure involving a novel experimental setup simulating drone to human collisions safely. We found that all participants identified the drone’s propellers as their primary concern, with the propeller’s sound being the most threatening. Based on the participant feedback, we identified some concerns on a drone’s unregulated aspects and outline common participant recommendations on drone regulations.
Zogan, H, Razzak, I, Jameel, S & Xu, G 1970, 'DepressionNet: Learning Multi-modalities with User Post Summarization for Depression Detection on Social Media', Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 133-142.
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