Dou, W, Xu, X & Yu, S 2023, Intelligent Industrial Internet Systems, Springer Nature Singapore. View/Download from: Publisher's site
Wang, T, Li, B, Chen, M & Yu, S 2023, Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore. View/Download from: Publisher's site
Yu, S & Cui, L 2023, Security and Privacy in Federated Learning, Springer Nature Singapore. View/Download from: Publisher's site
Alam, SL & Gill, AQ 2023, 'Analyzing social interactions and conflicting goals: Australian government ecosystem context' in Digitalization and Sustainability, Edward Elgar Publishing, pp. 128-145. View/Download from: Publisher's site View description>>
There is an increasing use of social media platforms by public agencies for active interactions with community. In particular, the Australian public agencies are the major users of Facebook pages for actively interacting with the community. While active interactivity seems useful, however, this also marks the need for understanding and addressing tension and conflicts between government and community goals for effective interactions and mutual benefits. There is limited analysis and research into the use and impact of Facebook pages on the Australian government and community expectations. This chapter aims to address this gap and reports findings from the analysis of the Australian government Facebook pages. It is anticipated that findings from this analysis can be used by agencies to identity and address conflicting goals when redesigning their social media platforms such as Facebook pages for interactions with the community.
Alhussein, A, Kocaballi, B & Prasad, M 2023, 'Work from Home in Smart Home Technology During and After Covid-19 and Role of IOT' in Proceedings in Adaptation, Learning and Optimization, Springer Nature Switzerland, pp. 568-579. View/Download from: Publisher's site
Alzahrani, M, Karimi, F, Bharathy, G & Prasad, M 2023, 'Intention of MOOCs Adoption, Completion and Continued Use.' in Xie, H, Lai, C-L, Chen, W, Xu, G & Popescu, E (eds), Advances in Web-Based Learning - ICWL 2023 - 22nd International Conference, ICWL 2023, Sydney, NSW, Australia, November 26-28, 2023, Proceedings, Springer, pp. 3-12.
Daher, J, Correll, P, Kennedy, PJ & Drake, B 2023, 'Understanding the Impact of Patient Journey Patterns on Health Outcomes for Patients with Diabetes' in Data Driven Science for Clinically Actionable Knowledge in Diseases, Chapman and Hall/CRC, pp. 1-26. View/Download from: Publisher's site
Do, T-TN, Duong, NMH & Lin, C-T 2023, 'Integrated Sensing Devices for Brain-Computer Interfaces' in More-than-Moore Devices and Integration for Semiconductors, Springer International Publishing, pp. 241-258. View/Download from: Publisher's site View description>>
Brain-computer interfaces (BCI) help users to interact with machines via brain activity and without the use of muscles. Among the many components of BCI frameworks, sensor technology helps to make the systems highly efficient and robust. As a source of input for BCI systems, sensors also provide high-quality signals that can support downstream tasks, such as noise removal and feature extraction. This book chapter explores the fundamental concepts and configurations of the non-invasive sensor technology that are commonly used in BCI systems.
Hanna, B, Xu, G, Wang, X & Hossain, J 2023, 'Blockchain-Based Energy Efficient Supply Chain Management' in Blockchain in Supply Chain Digital Transformation, CRC Press, USA, pp. 195-218. View/Download from: Publisher's site View description>>
This book explores real-world supply chain use cases from a range of industries to demonstrate the digital transformative capabilities of blockchain and DLT technologies"--
Karetla, GR, Nguyen, QV, Simoff, SJ, Catchpoole, DR & Kennedy, PJ 2023, 'Feature-Ranking Methods for RNA Sequencing Data' in Data Driven Science for Clinically Actionable Knowledge in Diseases, Chapman and Hall/CRC, pp. 129-145. View/Download from: Publisher's site
Larpruenrudee, P, Paul, G, Saha, SC, Husain, S, Mortazavy Beni, H, Lawrence, C, He, X, Gu, Y & Islam, MS 2023, 'Ultrafine particle transport to the lower airways: airway diameter reduction effects' in Digital Human Modeling and Medicine, Elsevier, pp. 253-274. View/Download from: Publisher's site
Leon-Castro, E, Sahni, M, Blanco-Mesa, F, Alfaro-Garcia, V & Merigo, J 2023, 'Preface', p. xiii.
Marynowsky, W, Knowles, J, Bown, O & Ferguson, S 2023, 'Sonic Robotics: Musical Genres as Platforms for Understanding Robotic Performance as Cultural Events' in Springer Series on Cultural Computing, Springer International Publishing, Switzerland, pp. 219-235. View/Download from: Publisher's site View description>>
This edited collection approaches the field of social robotics from the perspective of a cultural ecology, fostering a deeper examination of the reach of robotic technology into the lived experience of diverse human populations, as well as ...
Nguyen, QV, Kennedy, PJ, Simoff, SJ & Catchpoole, DR 2023, 'Visual Communication and Trust in the Health Domain' in Data Driven Science for Clinically Actionable Knowledge in Diseases, Chapman and Hall/CRC, pp. 215-226. View/Download from: Publisher's site
Nguyen, QV, Qu, Z, Lau, CW, Tegegne, Y, Tran, J, Karetla, GR, Kennedy, PJ, Simoff, SJ & Catchpoole, DR 2023, 'Biomedical Data Analytics and Visualisation—A Methodological Framework' in Data Driven Science for Clinically Actionable Knowledge in Diseases, Chapman and Hall/CRC, pp. 174-196. View/Download from: Publisher's site
Qu, Z, Simoff, SJ, Kennedy, PJ, Catchpoole, DR & Nguyen, QV 2023, 'Visualisation for Explainable Machine Learning in Biomedical Data Analysis' in Data Driven Science for Clinically Actionable Knowledge in Diseases, Chapman and Hall/CRC, pp. 197-214. View/Download from: Publisher's site
Raza, MR, Hussain, W & Rajaeian, M 2023, 'Analysing Trust, Security and Cost of Cloud Consumer's Reviews using RNN, LSTM, and GRU' in Advances in Complex Decision Making, Chapman and Hall/CRC, pp. 52-65. View/Download from: Publisher's site
Sadaf, A, Mathieson, L & Musial, K 2023, 'Effects of Global and Local Network Structure on Number of Driver Nodes in Complex Networks' in Lecture Notes in Social Networks, Springer Nature Switzerland, pp. 81-98. View/Download from: Publisher's site View description>>
This book offers an excellent source of knowledge for readers who are interested in keeping up with the developments in the field of cyber security and social media analysis.
Sharma, R & Mehndiratta, S 2023, 'Recent Advancements in Biomedical Sciences and Their Healthcare Applications' in Biomedical Research, Medicine, and Disease, CRC Press, USA, pp. 3-8. View/Download from: Publisher's site View description>>
Today’s biomedical science is a product of consistency, multidiscipline collaborations of researchers to bring out the best in health services. While conventionally structured research laboratories and clinical studies are the basic and most essential components that led to breakthroughs, today’s biomedical field comprises of multi-dimensional approach with the use of artificial intelligence (AI). Technological developments have accelerated the speed of research in the biomedical field and have provided a new horizon to the concept of biomedical sciences. Use of AI to develop tools of bio-informatics and health-informatics helps in development, production, and testing of medical ailments and their treatment in a quick and affordable manner. Moreover, these advancements can enhance capacity to provide a link between early molecular or cellular detection of disease outcomes. This could provide a mechanistic understanding of human disease processes and a way to deal with them (e.g., development of AI-assisted neuron networks for studying the basic mechanisms of brain and bio-informatics-guided in-silico assays for drug testing to obsolete the need for animals). Considering the plethora of research being conducted in the field of biomedical sciences, this chapter will focus on biomedical technological advancements and its applications in health care such as signaling pathway-based approaches, human-on-a-chip, organoid models, and in-silico modeling via AI and machine learning tools.
Singh, AK & Lin, C-T 2023, 'Content Augmentation in Virtual Reality with Cognitive-Conflict-Based Brain-Computer Interface' in Handbook of Neuroengineering, Springer Nature Singapore, pp. 1901-1922. View/Download from: Publisher's site
Singh, AK & Zhu, H 2023, 'Human Computer Interface in Smart Agriculture' in Encyclopedia of Digital Agricultural Technologies, Springer International Publishing, pp. 605-613. View/Download from: Publisher's site View description>>
Our reference work takes full advantage of this feature, which allows for continuous improvement or revision of published content electronically. The Editorial BoardDr. Irwin R. Donis-Gonzalez, University of California Davis, Dept.
Singh, AK & Zhu, H 2023, 'Human Computer Interface in Smart Agriculture' in Encyclopedia of Smart Agriculture Technologies, Springer International Publishing, pp. 1-8. View/Download from: Publisher's site
Verma, H, Gupta, A, Singh Kirar, J, Prasad, M & Lin, CT 2023, 'Introduction to computational methods' in Computational Intelligence Aided Systems for Healthcare Domain, CRC Press, pp. 1-32. View/Download from: Publisher's site
Wang, T, Li, B, Chen, M & Yu, S 2023, 'Fundamentals of Machine Learning in Data Center Networks' in Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore, pp. 9-14. View/Download from: Publisher's site View description>>
In this chapter, we will briefly review the common learning paradigms of ML and some preliminary knowledge about data collection and processing. Furthermore, to better assess the strengths and weaknesses of the existing research work, we design a multi-dimensional and multi-perspective quality assessment criteria, called REBEL-3S.
Wang, T, Li, B, Chen, M & Yu, S 2023, 'Insights, Challenges and Opportunities' in Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore, pp. 101-108. View/Download from: Publisher's site View description>>
Through systematic research and analysis, we found that ML has been gradually introduced and applied to various fields of data center network, and has made certain achievements. However, the current researches are still in its infancy and need to be further improved in various areas.
Wang, T, Li, B, Chen, M & Yu, S 2023, 'Introduction' in Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore, pp. 1-8. View/Download from: Publisher's site View description>>
As the storage and computation progressively migrate to the cloud, the data center (DC) as the core infrastructure of cloud computing provides vital technical and platform support for enterprise and cloud services. However, with the rapid rise of the data center scale, the network optimization, resource management, operation and maintenance, and data center security have become more and more complicated and challenging.
Wang, T, Li, B, Chen, M & Yu, S 2023, 'Machine Learning Empowered Intelligent Data Center Networking' in Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore, pp. 15-99. View/Download from: Publisher's site View description>>
Machine learning has been widely studied and practiced in data center networks, and a large number of achievements have been made. In this chapter, we will review, compare, and discuss the existing work in the following research areas: flow prediction, flow classification, load balancing, resource management, energy management, routing optimization, congestion control, fault management, network security, and new intelligent networking concepts.
Yuan Zhu, H & Lin, C 2023, 'Virtual/Augmented/Mixed Reality Technologies for Enabling Metaverse' in Metaverse Communication and Computing Networks Applications, Technologies, and Approaches, Wiley, pp. 125-155. View/Download from: Publisher's site View description>>
Metaverse Communication and Computing Networks Understand the future of the Internet with this wide-ranging analysis “Metaverse” is the term for applications that allow users to assume digital avatars to interact with other humans and ...
Aboutorab, H, Hussain, OK, Saberi, M, Hussain, FK & Prior, D 2023, 'Reinforcement Learning-Based News Recommendation System', IEEE Transactions on Services Computing, vol. 16, no. 6, pp. 4493-4502. View/Download from: Publisher's site
Acharya, R, Aleiner, I, Allen, R, Andersen, TI, Ansmann, M, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Babbush, R, Bacon, D, Bardin, JC, Basso, J, Bengtsson, A, Boixo, S, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Y, Chen, Z, Chiaro, B, Cogan, J, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Debroy, DM, Del Toro Barba, A, Demura, S, Dunsworth, A, Eppens, D, Erickson, C, Faoro, L, Farhi, E, Fatemi, R, Flores Burgos, L, Forati, E, Fowler, AG, Foxen, B, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Grajales Dau, A, Gross, JA, Habegger, S, Hamilton, MC, Harrigan, MP, Harrington, SD, Higgott, O, Hilton, J, Hoffmann, M, Hong, S, Huang, T, Huff, A, Huggins, WJ, Ioffe, LB, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Kechedzhi, K, Kelly, J, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, A, Klimov, PV, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, K, Lester, BJ, Lill, A, Liu, W, Locharla, A, Lucero, E, Malone, FD, Marshall, J, Martin, O, McClean, JR, McCourt, T, McEwen, M, Megrant, A, Meurer Costa, B, Mi, X, Miao, KC, Mohseni, M, Montazeri, S, Morvan, A, Mount, E, Mruczkiewicz, W, Naaman, O, Neeley, M, Neill, C, Nersisyan, A, Neven, H, Newman, M, Ng, JH, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Opremcak, A, Platt, J, Petukhov, A, Potter, R, Pryadko, LP, Quintana, C, Roushan, P, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shvarts, V, Skruzny, J, Smelyanskiy, V, Smith, WC, Sterling, G, Strain, D, Szalay, M, Torres, A, Vidal, G, Villalonga, B, Vollgraff Heidweiller, C, White, T, Xing, C, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zalcman, A, Zhang, Y & Zhu, N 2023, 'Suppressing quantum errors by scaling a surface code logical qubit', Nature, vol. 614, no. 7949, pp. 676-681. View/Download from: Publisher's site View description>>
AbstractPractical quantum computing will require error rates well below those achievable with physical qubits. Quantum error correction1,2 offers a path to algorithmically relevant error rates by encoding logical qubits within many physical qubits, for which increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low for logical performance to improve with increasing code size. Here we report the measurement of logical qubit performance scaling across several code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find that our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, in terms of both logical error probability over 25 cycles and logical error per cycle ((2.914 ± 0.016)% compared to (3.028 ± 0.023)%). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a 1.7 × 10−6 logical error per cycle floor set by a single high-energy event (1.6 × 10−7 excluding this event). We accurately model our experiment, extracting error budgets that highlight the biggest challenges for future systems. These results mark an experimental demonstration in which quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation.
Adhikari, S, Thapa, S, Naseem, U, Lu, HY, Bharathy, G & Prasad, M 2023, 'Explainable hybrid word representations for sentiment analysis of financial news.', Neural Networks, vol. 164, pp. 115-123. View/Download from: Publisher's site View description>>
Due to the increasing interest of people in the stock and financial market, the sentiment analysis of news and texts related to the sector is of utmost importance. This helps the potential investors in deciding what company to invest in and what are their long-term benefits. However, it is challenging to analyze the sentiments of texts related to the financial domain, given the enormous amount of information available. The existing approaches are unable to capture complex attributes of language such as word usage, including semantics and syntax throughout the context, and polysemy in the context. Further, these approaches failed to interpret the models' predictability, which is obscure to humans. Models' interpretability to justify the predictions has remained largely unexplored and has become important to engender users' trust in the predictions by providing insight into the model prediction. Accordingly, in this paper, we present an explainable hybrid word representation that first augments the data to address the class imbalance issue and then integrates three embeddings to involve polysemy in context, semantics, and syntax in a context. We then fed our proposed word representation to a convolutional neural network (CNN) with attention to capture the sentiment. The experimental results show that our model outperforms several baselines of both classic classifiers and combinations of various word embedding models in the sentiment analysis of financial news. The experimental results also show that the proposed model outperforms several baselines of word embeddings and contextual embeddings when they are separately fed to a neural network model. Further, we show the explainability of the proposed method by presenting the visualization results to explain the reason for a prediction in the sentiment analysis of financial news.
Alabdali, SA, Pileggi, SF & Cetindamar, D 2023, 'Influential Factors, Enablers, and Barriers to Adopting Smart Technology in Rural Regions: A Literature Review', Sustainability, vol. 15, no. 10, pp. 7908-7908. View/Download from: Publisher's site View description>>
Smart Technology is a quickly and constantly evolving concept; it has different applications that cover a wide range of areas, such as healthcare, education, business, agriculture, and manufacturing. An effective application of these technologies increases productivity and performance within complex systems. On one side, trends show a lack of appeal for rural environments as people prefer to move to cities, looking for better opportunities and lifestyles. On the other side, recent studies and reports show that the attractiveness of rural areas as places with opportunities is increasing. Sustainable solutions are needed to enhance development in the rural context, and technological innovation is expected to lead and support the stability for people and organizations in rural regions. While Smart City is progressively becoming a reality and a successful model for integrating Smart Technology into different aspects of everyday life, its effective application in a rural context according to a Sustainable Development approach is not yet completely defined. This study adopts comparative and categorial content analysis to address the different applications and the specific characteristics of rural regions, which often present significant peculiarities depending on the country and the context. The main goal is to investigate and discuss how the Smart City model may be adopted and effectively applied within rural contexts, looking at major gaps and challenges. Additionally, because of the complexity of the topic, we provide an overview of the current adoption of Smart Technology in the different applications in rural areas, including farming, education, business, healthcare, and governance. The study highlights the huge difficulties in rural life and the potentiality of Smart Technology to enhance their Sustainable Development, which is still challenging. While the holistic analysis clearly points out a gap, there is no specific strategic roadmap to re-u...
Alalyan, MS, Jaafari, NA, Hussain, FK & Gill, AQ 2023, 'A systematic review of blockchain adoption in education institutions', International Journal of Web and Grid Services, vol. 19, no. 2, pp. 156-184. View/Download from: Publisher's site
Alalyan, MS, Jaafari, NA, Hussain, FK & Gill, AQ 2023, 'A systematic review of blockchain adoption in education institutions.', Int. J. Web Grid Serv., vol. 19, no. 2, pp. 156-184. View/Download from: Publisher's site
Aldini, S, Singh, AK, Leong, D, Wang, Y-K, Carmichael, MG, Liu, D & Lin, C-T 2023, 'Detection and Estimation of Cognitive Conflict During Physical Human–Robot Collaboration', IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 2, pp. 959-968. View/Download from: Publisher's site View description>>
Robots for physical Human-Robot Collaboration (pHRC) often need to adapt their admittance and how they operate due to several factors. As the admittance of the system becomes variable throughout the workspace, it is not always straightforward for the operator to predict the robot’s behaviour. Previous work demonstrated that cognitive conflicts can be detected during one-dimensional tasks. This work assesses whether cognitive conflicts can also be detected during 2D tasks in pHRC and a classification problem is formulated. Different robot admittance profiles anticipating the stimulus translated into different levels of cognitive conflict. Several commonly used classification algorithms for EEG signals were evaluated to classify different levels of cognitive conflict. Results demonstrate that cognitive conflict level is lower when the admittance smoothly decreases before unexpected events when compared to conditions in which the admittance abruptly decreases before the stimulus. Among the classification algorithms, Convolutional Neural Network has shown the best results to classify different levels of cognitive conflict. Results suggest the feasibility of adaptive approaches for future pHRC control systems that close the loop on users through EEG signals. The detected human cognitive state can also be used to assess and improve the predictability of Human-Robot teams in various pHRC applications.
Al-Doghman, F, Moustafa, N, Khalil, I, Sohrabi, N, Tari, Z & Zomaya, AY 2023, 'AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges', IEEE Transactions on Services Computing, vol. 16, no. 2, pp. 1485-1504. View/Download from: Publisher's site View description>>
The paradigm of edge computing has formed an innovative scope within the domain of IoT through expanding the services of the cloud to the network edge to design distributed architectures and securely enhance decision-making applications. Due to the heterogeneous of edge Computing, edge applications are required to be developed as a set of lightweight and interdependent modules. As this concept aligns with the objectives of microservice architecture, effective implementation of microservices-based edge applications within IoT networks has the prospective of fully leveraging edge nodes capabilities. Deploying microservices at IoT edge faces plenty of challenges associated with security and privacy. Advances in AI, and the easy access to resources with powerful computing providing opportunities for deriving precise models and developing different intelligent applications at the edge of network. In this study, an extensive survey is presented for securing edge computing-based AI Microservices to elucidate the challenges of IoT management and enable secure decision-making systems at the edge. We present recent research studies on edge AI and microservices orchestration and highlight key requirements as well as challenges of securing Microservices at IoT edge. We also propose a Microservices-based edge framework that provides secure edge AI algorithms as Microservices utilizing the containerization technology.
Alghamdi, AM, Pileggi, SF & Sohaib, O 2023, 'Social Media Analysis to Enhance Sustainable Knowledge Management: A Concise Literature Review', Sustainability, vol. 15, no. 13, pp. 9957-9957. View/Download from: Publisher's site View description>>
Although knowledge management relying on data from social networks has become an integral part of common practices, there needs to be a well-defined body of knowledge that explicitly addresses the process and the value generated. Sustainable knowledge management practices, which promote responsible and ethical knowledge sharing between different stakeholders, can also be facilitated through social media. This can foster a culture of continuous learning and innovation while considering the social implications of knowledge sharing. The main goal of this study is to critically and holistically discuss the impact of social media analysis in the knowledge management process holistically and maximize its value in a given context. More concretely, we conducted a systematic literature review (2012–2022) based on the PRISMA guidelines. We first approached the ideal phases of the knowledge management process and then discussed key issues and challenges from an application perspective. Overall, the study points out the positive impact of social network analysis on knowledge sharing, creativity and productivity, knowledge formulation, building trust, and cognitive capital. Additionally, value is provided in knowledge acquisition by simplifying and massively gathering information, reducing uncertainty and ambiguity, and organizing knowledge through storage, retrieval, and classification practices. At an application level, such knowledge may improve the quality of services and encourage creativity. Finally, this study analyzed specific domains, such as healthcare, marketing, politics, tourism, and event management, focusing on the potential and added value.
Alhosaini, H, Alharbi, S, Wang, X & Xu, G 2023, 'API Recommendation For Mashup Creation: A Comprehensive Survey', The Computer Journal. View/Download from: Publisher's site View description>>
AbstractMashups are web applications that expedite software development by reusing existing resources through integrating multiple application programming interfaces (APIs). Recommending the appropriate APIs plays a critical role in assisting developers in building such web applications easily and efficiently. The proliferation of publicly available APIs on the Internet has inspired the community to adopt various models to accomplish the recommendation task. Until present, considerable efforts have been made to recommend the optimal set of APIs, delivering fruitful results and achieving varying recommendation performance. This paper presents a timely review on the topic of API recommendations for mashup creation. Specifically, we investigate and compare not only traditional data mining approaches and recommendation techniques but also more recent approaches based on network representation learning and deep learning techniques. By analyzing the merits and pitfalls of existing approaches, we pinpoint a few promising directions to address the remaining challenges in the current research. This survey provides a timely comprehensive review of the API recommendation research and could be a useful reference for relevant researchers and practitioners.
Almadani, MS, Alotaibi, S, Alsobhi, H, Hussain, OK & Hussain, FK 2023, 'Blockchain-based multi-factor authentication: A systematic literature review', Internet of Things, vol. 23, pp. 100844-100844. View/Download from: Publisher's site
Al-Najjar, HAH, Pradhan, B, Beydoun, G, Sarkar, R, Park, H-J & Alamri, A 2023, 'A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset', Gondwana Research, vol. 123, pp. 107-124. View/Download from: Publisher's site View description>>
As artificial intelligence (AI) techniques are becoming more popular in landslide modeling, it is important to understand how decisions are made. Fairness, and transparency becomes ever more vital due to ethical concerns and trust. Despite the popularity of machine learning (ML) algorithms in landslide modeling, the explainability of these methods are often considered as black box. This paper aims to propose an explainable artificial intelligence (XAI) for landslide prediction using synthetic-aperture radar (SAR) time-series data, NDVI (normalized difference vegetation index) time-series data and other geo-environmental factors such as DEM (digital elevation model) derivatives. We employed a Shapley Additive Explanations (SHAP) approach to understand how and what decisions ML-based models are making. 37 features were extracted from various sources such as ALOS-PALSAR (ALOS Phased Array type L-band Synthetic Aperture Radar), ALOS-2 (SAR), Landsat-8, topographic maps, and DEM for landslide susceptibility mapping in a landslide prone area in Chukha, Bhutan as a test site. The result was then compared using two standard ML methods: random forest (RF) and support vector machine (SVM). As per results, the RF model outperformed (0.914) the SVM. Moreover, the higher reliability of the RF model was proved by the area under the curve (AUC) of 0.941. XAI results revealed, features like altitude, aspect, NDVI-2014, NDVI-2017, and NDVI-2018 were the most effective features for landslide prediction by both models. Interestingly, among those features, NDVI-2014, aspect, and NDVI-2017 negatively correlated with the landslide prediction; whereas positively correlated when SVM was utilized. This interpretation ability indicates the advantages of XAI over the conventional methods as it measures the impact, interaction and correlation of conditioning factors within a model. The current research finding can provide more transparency and explainability when working with MLs ...
Alsobhi, HA, Alakhtar, RA, Ubaid, A, Hussain, OK & Hussain, FK 2023, 'Blockchain-based micro-credentialing system in higher education institutions: Systematic literature review', Knowledge-Based Systems, vol. 265, pp. 110238-110238. View/Download from: Publisher's site View description>>
A micro-credential is a proof of the student's knowledge, skills, and experience that can be used to progress towards a larger credential or degree that focuses on a particular field of study in the shortest amount of time. Micro-credentials are a new area in the education sector that has expanded significantly over recent years and have become a popular idea in the higher education sector. Since the Covid-19 pandemic, micro-credentials are the most recent innovation in online education, gaining traction in public and private universities throughout the world. This has resulted in many universities developing strategies to offer micro-credential-driven courses. Higher education institutions (HEIs) need to validate micro-credentials, but the validation is a long-drawn-out and cumbersome process, so blockchain technology can be used to easily validate the detailed information on each students’ micro-credentials. Unfortunately, to date, only scant scholarly research has been conducted on blockchain-based micro-credentialing systems in HEIs. This study provides a detailed overview of the state-of-the-art in the field of managing micro-credentials using blockchain technology. We start by outlining the various requirements that need to be met in a blockchain-based micro-credentialing system. We then use a systematic literature review (SLR) to retrieve relevant studies published between 2016–2022 and compare them to the defined requirements. We also analyse the relevant studies to determine the research gaps. This review will offer insight into micro-credentialing systems that have been proposed for HEIs over recent years.
Alsoibi, I, Agarwal, R, Bharathy, G, Samarawickrama, M, Unhelkar, B & Prasad, M 2023, 'A Systematic Review and Taxonomy of Data Analytics in Non-profit Organizations', Asia Pacific Journal of Information Systems (APJIS), vol. 33, no. 1, pp. 33-68. View description>>
Nonprofit organisations (NPOs) use data analytics and corresponding visualisations to discover and interpretpatterns of donations and donor behaviours, predict future funds, and analyse time series to undertake decisionsand resolve issues. Further detailed understanding of these activities in the context of NPOs is required forefficient and effective utilisation of data analytics. This article reports a systematic review of available literatureon data analytics applications in NPOs to answer three research questions: (1) What are the proposed approachesand frameworks for adopting and applying data analytics in NPOs? (2) What aspects of data analytics are usedfor NPO activities and missions? (3) What challenges and barriers face NPOs regarding the adoption and applicationof data analytics for their missions? We answered the three research questions by collecting and examiningdata and using it to develop a new taxonomy. The results show the utilisation of data analytics applicationsby NPOs has not been examined in depth, indicating the need for further research. This study contributesto the literature by providing insights on the existing use of data analytics applications in various domains,and their benefits and drawbacks for NPOs. This study also presents future research directions.
Alsolbi, I, Agarwal, R, Bharathy, G, Samarawickrama, M, Unhelkar, B & Prasad, M 2023, 'A Systematic Review and Taxonomy of Data Analytics in Nonprofit Organisations', Asia Pacific Journal of Information Systems, vol. 33, no. 1, pp. 39-68. View/Download from: Publisher's site
Alsolbi, I, Shavaki, FH, Agarwal, R, Bharathy, GK, Prakash, S & Prasad, M 2023, 'Big data optimisation and management in supply chain management: a systematic literature review', Artificial Intelligence Review, vol. 56, no. S1, pp. 253-284. View/Download from: Publisher's site View description>>
AbstractThe increasing interest from technology enthusiasts and organisational practitioners in big data applications in the supply chain has encouraged us to review recent research development. This paper proposes a systematic literature review to explore the available peer-reviewed literature on how big data is widely optimised and managed within the supply chain management context. Although big data applications in supply chain management appear to be often studied and reported in the literature, different angles of big data optimisation and management technologies in the supply chain are not clearly identified. This paper adopts the explanatory literature review involving bibliometric analysis as the primary research method to answer two research questions, namely: (1) How to optimise big data in supply chain management? and (2) What tools are most used to manage big data in supply chain management? A total of thirty-seven related papers are reviewed to answer the two research questions using the content analysis method. The paper also reveals some research gaps that lead to prospective future research directions.
Alzoubi, YI & Aljaafreh, A 2023, 'Blockchain-Fog Computing Integration Applications: A Systematic Review', Cybernetics and Information Technologies, vol. 23, no. 1, pp. 3-37. View/Download from: Publisher's site View description>>
AbstractThe Fog computing concept has been introduced to aid in the data processing of Internet of things applications using Cloud computing. Due to the profitable benefits of this combination, several papers have lately been published proposing the deployment of Blockchain alongside Fog computing in a variety of fields. A comprehensive evaluation and synthesis of the literature on Blockchain-Fog computing integration applications that have emerged in recent years is required. Although there have been several articles on the integration of Blockchain with Fog computing, the applications connected with this combination are still fragmented and require further exploration. Hence, in this paper, the applications of Blockchain-Fog computing integration are identified using a systematic literature review technique and tailored search criteria generated from the study objectives. This article found and evaluated 144 relevant papers. The findings of this article can be used as a resource for future Fog computing research and designs.
Anaissi, A, Zandavi, SM, Suleiman, B, Naji, M & Braytee, A 2023, 'Multi-objective variational autoencoder: an application for smart infrastructure maintenance', Applied Intelligence, vol. 53, no. 10, pp. 12047-12062. View/Download from: Publisher's site View description>>
AbstractMulti-way data analysis has become an essential tool for capturing underlying structures in higher-order data sets where standard two-way analysis techniques often fail to discover the hidden correlations between variables in multi-way data. We propose a multi-objective variational autoencoder (MO-VAE) method for smart infrastructure damage detection and diagnosis in multi-way sensing data based on the reconstruction probability of autoencoder deep neural network (ADNN). Our method fuses data from multiple sensors in one ADNN at which informative features are being extracted and utilized for damage identification. It generates probabilistic anomaly scores to detect damage, asses its severity and further localize it via a new localization layer introduced in the ADNN. We evaluated our method on multi-way laboratory-based and real-life structural datasets in the area of structural health monitoring for damage diagnosis purposes. The data was collected from our deployed data acquisition system on a cable-stayed bridge in Western Sydney, a reinforced concrete cantilever beam which replicates one of the major structural components on the Sydney Harbour Bridge and a laboratory based building structure obtained from Los Alamos National Laboratory (LANL). Experimental results show that the proposed method can accurately detect structural damage. It was also able to estimate the different levels of damage severity, and capture damage locations in an unsupervised aspect. Compared to the state-of-the-art approaches, our proposed method shows better performance in terms of damage detection and localization.
Andersen, TI, Lensky, YD, Kechedzhi, K, Drozdov, IK, Bengtsson, A, Hong, S, Morvan, A, Mi, X, Opremcak, A, Acharya, R, Allen, R, Ansmann, M, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Babbush, R, Bacon, D, Bardin, JC, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Z, Chiaro, B, Chik, D, Chou, C, Cogan, J, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Debroy, DM, Del Toro Barba, A, Demura, S, Dunsworth, A, Eppens, D, Erickson, C, Faoro, L, Farhi, E, Fatemi, R, Ferreira, VS, Burgos, LF, Forati, E, Fowler, AG, Foxen, B, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Gosula, R, Dau, AG, Gross, JA, Habegger, S, Hamilton, MC, Hansen, M, Harrigan, MP, Harrington, SD, Heu, P, Hilton, J, Hoffmann, MR, Huang, T, Huff, A, Huggins, WJ, Ioffe, LB, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, A, Klimov, PV, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, KW, Lester, BJ, Lill, AT, Liu, W, Locharla, A, Lucero, E, Malone, FD, Martin, O, McClean, JR, McCourt, T, McEwen, M, Miao, KC, Mieszala, A, Mohseni, M, Montazeri, S, Mount, E, Movassagh, R, Mruczkiewicz, W, Naaman, O, Neeley, M, Neill, C, Nersisyan, A, Newman, M, Ng, JH, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Omonije, S, Petukhov, A, Potter, R, Pryadko, LP, Quintana, C, Rocque, C, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shutty, N, Shvarts, V, Skruzny, J, Smith, WC, Somma, R, Sterling, G, Strain, D, Szalay, M, Torres, A, Vidal, G, Villalonga, B, Heidweiller, CV, White, T, Woo, BWK, Xing, C, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zalcman, A, Zhang, Y, Zhu, N, Zobrist, N, Neven, H, Boixo, S, Megrant, A, Kelly, J, Chen, Y, Smelyanskiy, V, Kim, E-A, Aleiner, I & Roushan, P 2023, 'Non-Abelian braiding of graph vertices in a superconducting processor', Nature, vol. 618, no. 7964, pp. 264-269. View/Download from: Publisher's site View description>>
AbstractIndistinguishability of particles is a fundamental principle of quantum mechanics1. For all elementary and quasiparticles observed to date—including fermions, bosons and Abelian anyons—this principle guarantees that the braiding of identical particles leaves the system unchanged2,3. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions4–8. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well-developed mathematical description of non-Abelian anyons and numerous theoretical proposals9–22, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. Whereas efforts on conventional solid-state platforms typically involve Hamiltonian dynamics of quasiparticles, superconducting quantum processors allow for directly manipulating the many-body wavefunction by means of unitary gates. Building on predictions that stabilizer codes can host projective non-Abelian Ising anyons9,10, we implement a generalized stabilizer code and unitary protocol23to create and braid them. This allows us to experimentally verify the fusion rules of the anyons and braid them to realize their statistics. We then study the prospect of using the anyons for quantum computation and use braiding to create an entangled state of anyons encoding three logical qubits. Our work provides new insights about non-Abelian braiding and, through the future inclusion of error correction to achieve topological protection, could open a path towards fault-tolerant quantum computing.
Appiahene, P, Chaturvedi, K, Asare, JW, Donkoh, ET & Prasad, M 2023, 'CP-AnemiC: A conjunctival pallor dataset and benchmark for anemia detection in children', Medicine in Novel Technology and Devices, vol. 18, pp. 100244-100244. View/Download from: Publisher's site
Asadabadi, MR, Saberi, M, Sadghiani, NS, Zwikael, O & Chang, E 2023, 'Enhancing the analysis of online product reviews to support product improvement: integrating text mining with quality function deployment', Journal of Enterprise Information Management, vol. 36, no. 1, pp. 275-302. View/Download from: Publisher's site View description>>
PurposeThe purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews.Design/methodology/approachThis paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process.FindingsThe paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes.Originality/valueOnline product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.
Bahrami, H, Sichetti, F, Puppo, E, Vettori, L, Liu Chung Ming, C, Perry, S, Gentile, C & Pietroni, N 2023, 'Physically-based simulation of elastic-plastic fusion of 3D bioprinted spheroids', Biofabrication, vol. 15, no. 4, pp. 045021-045021. View/Download from: Publisher's site View description>>
AbstractSpheroids are microtissues containing cells organized in a spherical shape whose diameter is usually less than a millimetre. Depending on the properties of the environment they are placed in, some nearby spheroids spontaneously fuse and generate a tissue. Given their potential to mimic features typical of body parts and their ability to assemble by fusing in permissive hydrogels, they have been used as building blocks to 3D bioprint human tissue parts. Parameters controlling the shape and size of a bioprinted tissue using fusing spheroid cultures include cell composition, hydrogel properties, and their relative initial position. Hence, simulating, anticipating, and then controlling the spheroid fusion process is essential to control the shape and size of the bioprinted tissue. This study presents the first physically-based framework to simulate the fusion process of bioprinted spheroids. The simulation is based on elastic-plastic solid and fluid continuum mechanics models. Both models use the ‘smoothed particle hydrodynamics’ method, which is based on discretizing the continuous medium into a finite number of particles and solving the differential equations related to the physical properties (e.g. Navier–Stokes equation) using a smoothing kernel function. To further investigate the effects of such parameters on spheroid shape and geometry, we performed sensitivity and morphological analysis to validate our simulations with in-vitro spheroids. Through our in-silico simulations by changing the aforementioned parameters, we show that the proposed models appropriately simulate the range of the elastic-plastic behaviours of in-vitro fusing spheroids to generate tissues of desired shapes and sizes. Altogether, this study presented a physically-based simulation that can provide a framework for monitoring and controlling the ...
AbstractProblem structuring methods imply the involvement of stakeholders and aim to create a shared understanding of the problem and commitment among them. The process and outcomes of such interventions entirely depend on the stakeholder’s level of engagement and willingness to contribute to the discussion. Gamification, in its turn, has been extensively used to increase engagement in an activity and nudge certain behaviors. Several gamification frameworks exist for stakeholder engagement; however, none fully considers the context of the modeling workshops with stakeholders.In this paper, we focus on a specific method for problem structuring, called Participatory Modeling (PM), and aim to explore the essential components and steps to gamify the PM process. We look at the literature on gamification, stakeholder engagement, problem structuring methods and, specifically, PM. Based on this analysis, we propose a gamification framework for PM, which includes the steps commonly mentioned in other existing frameworks and more nuanced features within each step that are specific to the PM context. Emphasis is given to analyzing the context of the gamified activity, including such aspects as participants, group interaction, and modeling. In addition, consideration of ethical points and potential risks of gamification is suggested as a necessary step to prevent undesired side effects during the gamified PM process.The gamification framework for PM leads to a variety of ways in which gamified intervention can be designed and incorporated into the process. Further research on the appropriateness of gamification use, practical applications, their evaluation, and risks associated with gamified interventions can contribute to the extension and clarification of the proposed framework.
Beyhan, B, Akcomak, IS & Cetindamar, D 2023, 'How do technology-based accelerators build their legitimacy as new organizations in an emerging entrepreneurship ecosystem?', Journal of Entrepreneurship in Emerging Economies, pp. 1-37. View/Download from: Publisher's site View description>>
PurposeThis paper aims to understand technology-based accelerators’ legitimation efforts in an emerging entrepreneurship ecosystem.Design/methodology/approachThis research is based on qualitative inductive methodology using ten Turkish technology-based accelerators.FindingsThe analysis indicates that accelerators’ legitimation efforts are shaped around crafting a distinctive identity and mobilizing allies around this identity; and establishing new collaborations to enable collective action. Further, the authors observe two types of technology-based accelerators, namely, “deal flow makers” and “welfare stimulators” in Turkey. These variations among accelerators affect how they build their legitimacy. Different types of accelerators make alliances with different actors in the entrepreneurship ecosystem. Accelerators take collective action to build a collective identity and simultaneously imply how they are distinguished from other organizations in the same category and the ones in the old category.Originality/valueThis study presents a framework to understand how accelerators use strategies and actions to legitimize themselves as new organizations and advocate new norms, values and routines in an emerging entrepreneurship ecosystem. The framework also highlights how different accelerators support legitimacy building by managing the judgments of diverse audiences and increasing the variety of resources these audiences provide to the ecosystem.
Bui, HT, Hussain, OK, Prior, D, Hussain, FK & Saberi, M 2023, 'SIAEF/PoE: Accountability of Earnestness for encoding subjective information in Blockchain', Knowledge-Based Systems, vol. 269, pp. 110501-110501. View/Download from: Publisher's site
Cancino, CA, Merigó, JM, Urbano, D & Amorós, JE 2023, 'Evolution of the entrepreneurship and innovation research in Ibero-America between 1986 and 2015', Journal of Small Business Management, vol. 61, no. 2, pp. 322-352. View/Download from: Publisher's site View description>>
Cao, Z & Lin, C-T 2023, 'Reinforcement Learning From Hierarchical Critics', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 2, pp. 1066-1073. View/Download from: Publisher's site View description>>
In this study, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of reinforcement learning (RL) in competition tasks. Within the framework of actor-critic RL, we introduce multiple cooperative critics from two levels of a hierarchy and propose an RL from the hierarchical critics (RLHC) algorithm. In our approach, each agent receives value information from local and global critics regarding a competition task and accesses multiple cooperative critics in a top-down hierarchy. Thus, each agent not only receives low-level details, but also considers coordination from higher levels, thereby obtaining global information to improve the training performance. Then, we test the proposed RLHC algorithm against a benchmark algorithm, that is, proximal policy optimization (PPO), under four experimental scenarios consisting of tennis, soccer, banana collection, and crawler competitions within the Unity environment. The results show that RLHC outperforms the benchmark on these four competitive tasks.
Caruana, A, Bandara, M, Musial, K, Catchpoole, D & Kennedy, PJ 2023, 'Machine Learning for Administrative Health Records: A Systematic Review of Techniques and Applications', Artificial Intelligence in Medicine, vol. 144, pp. 102642-102642. View/Download from: Publisher's site View description>>
Machine learning provides many powerful and effective techniques foranalysing heterogeneous electronic health records (EHR). Administrative HealthRecords (AHR) are a subset of EHR collected for administrative purposes, andthe use of machine learning on AHRs is a growing subfield of EHR analytics.Existing reviews of EHR analytics emphasise that the data-modality of the EHRlimits the breadth of suitable machine learning techniques, and pursuablehealthcare applications. Despite emphasising the importance of data modality,the literature fails to analyse which techniques and applications are relevantto AHRs. AHRs contain uniquely well-structured, categorically encoded recordswhich are distinct from other data-modalities captured by EHRs, and they canprovide valuable information pertaining to how patients interact with thehealthcare system. This paper systematically reviews AHR-based research, analysing 70 relevantstudies and spanning multiple databases. We identify and analyse which machinelearning techniques are applied to AHRs and which health informaticsapplications are pursued in AHR-based research. We also analyse how thesetechniques are applied in pursuit of each application, and identify thelimitations of these approaches. We find that while AHR-based studies aredisconnected from each other, the use of AHRs in health informatics research issubstantial and accelerating. Our synthesis of these studies highlights theutility of AHRs for pursuing increasingly complex and diverse researchobjectives despite a number of pervading data- and technique-based limitations.Finally, through our findings, we propose a set of future research directionsthat can enhance the utility of AHR data and machine learning techniques forhealth informatics research.
Cetindamar, D & Phaal, R 2023, 'Technology Management in the Age of Digital Technologies', IEEE Transactions on Engineering Management, vol. 70, no. 7, pp. 2507-2515. View/Download from: Publisher's site
This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net discriminator. The proposed method extends the ability of the standard discriminators to learn not only the global data representations via classification (real/fake) but also learn semantic and structural information through pseudo labels created using the self-supervised task. The proposed method empowers the generator to create meaningful masks by forcing it to learn informative per-pixel and global image feedback from the discriminator. Our experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on the standard benchmark datasets.
Chauhan, R, Shighra, S, Madkhali, H, Nguyen, L & Prasad, M 2023, 'Efficient Future Waste Management: A Learning-Based Approach with Deep Neural Networks for Smart System (LADS)', Applied Sciences, vol. 13, no. 7, pp. 4140-4140. View/Download from: Publisher's site View description>>
Waste segregation, management, transportation, and disposal must be carefully managed to reduce the danger to patients, the public, and risks to the environment’s health and safety. The previous method of monitoring trash in strategically placed garbage bins is a time-consuming and inefficient method that wastes time, human effort, and money, and is also incompatible with smart city needs. So, the goal is to reduce individual decision-making and increase the productivity of the waste categorization process. Using a convolutional neural network (CNN), the study sought to create an image classifier that recognizes items and classifies trash material. This paper provides an overview of trash monitoring methods, garbage disposal strategies, and the technology used in establishing a waste management system. Finally, an efficient system and waste disposal approach is provided that may be employed in the future to improve performance and cost effectiveness. One of the most significant barriers to efficient waste management can now be overcome with the aid of a deep learning technique. The proposed method outperformed the alternative AlexNet, VGG16, and ResNet34 methods.
Chen, C, Liu, Y, Chen, L & Zhang, C 2023, 'Bidirectional Spatial-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 6913-6925. View/Download from: Publisher's site View description>>
Urban traffic forecasting is the cornerstone of the intelligent transportation system (ITS). Existing methods focus on spatial-temporal dependency modeling, while two intrinsic properties of the traffic forecasting problem are overlooked. First, the complexity of diverse forecasting tasks is nonuniformly distributed across various spaces (e.g., suburb versus downtown) and times (e.g., rush hour versus off-peak). Second, the recollection of past traffic conditions is beneficial to the prediction of future traffic conditions. Based on these properties, we propose a bidirectional spatial-temporal adaptive transformer (Bi-STAT) for accurate traffic forecasting. Bi-STAT adopts an encoder-decoder architecture, where both the encoder and the decoder maintain a spatial-adaptive transformer and a temporal-adaptive transformer structure. Inspired by the first property, each transformer is designed to dynamically process the traffic streams according to their task complexities. Specifically, we realize this by the recurrent mechanism with a novel dynamic halting module (DHM). Each transformer performs iterative computation with shared parameters until DHM emits a stopping signal. Motivated by the second property, Bi-STAT utilizes one decoder to perform the present → past recollection task and the other decoder to perform the present → future prediction task. The recollection task supplies complementary information to assist and regularize the prediction task for a better generalization. Through extensive experiments, we show the effectiveness of each module in Bi-STAT and demonstrate the superiority of Bi-STAT over the state-of-the-art baselines on four benchmark datasets. The code is available at https://github.com/chenchl19941118/Bi-STAT.git.
Chen, S, Zhang, P, Xie, G-S, Peng, Q, Cao, Z, Yuan, W & You, X 2023, 'Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 2, pp. 824-837. View/Download from: Publisher's site
Chen, X, Yao, L, McAuley, J, Zhou, G & Wang, X 2023, 'Deep reinforcement learning in recommender systems: A survey and new perspectives', Knowledge-Based Systems, vol. 264, pp. 110335-110335. View/Download from: Publisher's site
Chen, X, Yao, L, Wang, X, Sun, A & Sheng, QZ 2023, 'Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 10, pp. 9878-9889. View/Download from: Publisher's site
Cheng, Z, Zhu, T, Zhu, C, Ye, D, Zhou, W & Yu, PS 2023, 'Privacy and evolutionary cooperation in neural-network-based game theory', Knowledge-Based Systems, vol. 282, pp. 111076-111076. View/Download from: Publisher's site
Chugh, D, Mittal, H, Saxena, A, Chauhan, R, Yafi, E & Prasad, M 2023, 'Augmentation of Densest Subgraph Finding Unsupervised Feature Selection Using Shared Nearest Neighbor Clustering', Algorithms, vol. 16, no. 1, pp. 28-28. View/Download from: Publisher's site View description>>
Determining the optimal feature set is a challenging problem, especially in an unsupervised domain. To mitigate the same, this paper presents a new unsupervised feature selection method, termed as densest feature graph augmentation with disjoint feature clusters. The proposed method works in two phases. The first phase focuses on finding the maximally non-redundant feature subset and disjoint features are added to the feature set in the second phase. To experimentally validate, the efficiency of the proposed method has been compared against five existing unsupervised feature selection methods on five UCI datasets in terms of three performance criteria, namely clustering accuracy, normalized mutual information, and classification accuracy. The experimental analyses have shown that the proposed method outperforms the considered methods.
Cui, L, Ma, J, Zhou, Y & Yu, S 2023, 'Boosting Accuracy of Differentially Private Federated Learning in Industrial IoT With Sparse Responses', IEEE Transactions on Industrial Informatics, vol. 19, no. 1, pp. 910-920. View/Download from: Publisher's site View description>>
Empowered by 5G, it has been extensively explored by existing works on the deployment of differentially private federated learning (DPFL) in the Industrial Internet of Things (IIoT). Through federated learning, decentralized IIoT devices can collaboratively train a machine learning model by merely exchanging model gradients with a parameter server (PS) for multiple global iterations. Differentially private (DP) mechanisms will be incorporated by IIoT devices (also called clients) to prevent the leakage of privacy due to the exposure of gradients because original gradients will be distorted DP noises. Yet, learning with distorted gradients can seriously deteriorate model accuracy, making DPFL unusable in reality. To address this problem, we propose a novel DPFL with sparse responses (DPFL-SR) algorithm, which applies the sparse vector technique to reduce the privacy budget consumption in each global iteration. Specifically, DPFL-SR evaluates the value of each gradient, and only distorts and uploads significant gradients to the PS because significant gradients are more essential for model training. Since insignificant gradients are not disclosed, the reserved privacy budget can be used to return significant gradients for more iterations so that DPFL-SR can achieve higher model accuracy without lowering the privacy protection level. Extensive experiments are conducted with the MNIST and Fashion-MNIST datasets to demonstrate the practicability and superiority of DPFL-SR in IIoT systems.
de Couvreur, LA, Cobo, MJ, Kennedy, PJ & Ellis, JT 2023, 'Bibliometric analysis of parasite vaccine research from 1990 to 2019', Vaccine, vol. 41, no. 44, pp. 6468-6477. View/Download from: Publisher's site
Devitt, SJ 2023, 'How do we quantify the utility of quantum algorithms?', Research Directions: Quantum Technologies, vol. 1, pp. 1-4. View/Download from: Publisher's site View description>>
Quantum computing advantage emerges not from brute force power, but from subtle differences in information processing that can occur for key bottleneck subroutines. In 2019, the Google Quantum AI team performed a landmark experiment demonstrating quantum computational supremacy (Arute et al., 2019) where they performed a quantum computation that, at the time, could not be done on a classical supercomputer. This was remarkable because it was achieved by a processor with only 53 qubits, an observation that emerged from theoretical work which identified that quantum computers could have a massive advantage for certain specially designed benchmarking tasks (Boixo et al., 2018; Bremner et al., 2016).
Ding, W, Liu, J, Lin, C & Mrozek, D 2023, 'Special issue on Recent Advances in Fuzzy Deep Learning for Uncertain Medicine Data', Information Sciences, vol. 642, pp. 118997-118997. View/Download from: Publisher's site
Ding, W, Wang, H, Huang, J, Ju, H, Geng, Y, Lin, C-T & Pedrycz, W 2023, 'FTransCNN: Fusing Transformer and a CNN based on fuzzy logic for uncertain medical image segmentation', Information Fusion, vol. 99, pp. 101880-101880. View/Download from: Publisher's site
Ding, Z, Chen, X, Dong, Y, Yu, S & Herrera, F 2023, 'Consensus Convergence Speed in Social Network DeGroot Model: The Effects of the Agents With High Self-Confidence Levels', IEEE Transactions on Computational Social Systems, vol. 10, no. 5, pp. 2882-2892. View/Download from: Publisher's site View description>>
In group decision making (GDM), opinion dynamics is a useful tool to investigate consensus formation. Notably, consensus convergence speed is of key importance to manage the consensus formation in GDM with opinion dynamics. Recently, social network DeGroot (SNDG) model has been widely used in opinion dynamics. Based on this, this article dedicates to study how agents’ high self-confidence levels affect the consensus convergence speed in SNDG model. Interestingly, using theoretical analysis, we prove that: 1) the speed of consensus reaching is subject to the largest self-confidence level of opinion followers and 2) the speed of consensus reaching is also subject to the top two self-confidence levels of opinion leaders. Furthermore, through extensive simulation’, we find that the theoretical results are robust to the topological structure and the size of social networks.
Do, PMT, Zhang, Q, Zhang, G & Lu, J 2023, 'meta-GRS: A Graph Neural Network for Cross-Domain Recommender System via Meta-Learning', Procedia Computer Science, vol. 225, pp. 2536-2545. View/Download from: Publisher's site
Dong, M, Yao, L, Wang, X, Xu, X & Zhu, L 2023, 'Adversarial dual autoencoders for trust-aware recommendation', Neural Computing and Applications, vol. 35, no. 18, pp. 13065-13075. View/Download from: Publisher's site View description>>
Recommender systems face longstanding challenges in gaining users’ trust due to the unreliable information caused by profile injection or human misbehavior. Traditional solutions to those challenges focus on leveraging users’ social relationships for inferring the user preference, i.e., recommending items according to the preference by user’s trusted friends; or adding random noise to the input to improve the robustness of the recommender systems. However, such approaches cannot defend the real-world noises like fake ratings. The recommender model is generally built upon all the user-item interactions, which incorporates the information from fake ratings or spammer groups, that neglects the reliability of the ratings. To address the above challenges, we propose an adversarial training approach in this work. In details, our approach includes two components: a predictor that infers the user preference; and a discriminator that enforces cohort rating patterns. In particular, the predictor applies an encoder-decoder structure to learn the shared latent information from sparse users’ ratings and trust relationships; the discriminator enforces the predictor to provide ratings as coherent with the cohort rating patterns. Our extensive experiments on three real-world datasets show the advantages of our approach over several competitive baselines.
Dong, Q, Zheng, X, Fu, A, Su, M, Zhou, L & Yu, S 2023, 'DMRA: Model Usability Detection Scheme Against Model-Reuse Attacks in the Internet of Things', IEEE Internet of Things Journal, vol. 10, no. 19, pp. 16907-16916. View/Download from: Publisher's site
Dong, Y, Ran, Q, Chao, X, Li, C & Yu, S 2023, 'Personalized Individual Semantics Learning to Support a Large-Scale Linguistic Consensus Process', ACM Transactions on Internet Technology, vol. 23, no. 2, pp. 1-27. View/Download from: Publisher's site View description>>
When making decisions, individuals often express their preferences linguistically. The computing with words methodology is a key basis for supporting linguistic decision making, and the words in that methodology may mean different things to different individuals. Thus, in this article, we propose a continual personalized individual semantics learning model to support a consensus-reaching process in large-scale linguistic group decision making. Specifically, we first derive personalized numerical scales from the data of linguistic preference relations. We then perform a clustering ensemble method to divide large-scale group and conduct consensus management. Finally, we present a case study of intelligent route optimization in shared mobility to illustrate the usability of our proposed model. We also demonstrate its effectiveness and feasibility through a comparative analysis.
Duan, Q, Huang, J, Hu, S, Deng, R, Lu, Z & Yu, S 2023, 'Combining Federated Learning and Edge Computing Toward Ubiquitous Intelligence in 6G Network: Challenges, Recent Advances, and Future Directions', IEEE Communications Surveys & Tutorials, vol. 25, no. 4, pp. 2892-2950. View/Download from: Publisher's site
Duan, Y, Lu, Y, Shen, S, Yu, S, Zhang, P, Zhang, W & Igorevich, KK 2023, 'NFLCS: An Service Function Chain Path Optimization Strategy Based on Network-Functional Layout Clustering', IEEE Transactions on Vehicular Technology, vol. 72, no. 8, pp. 10813-10825. View/Download from: Publisher's site
Duan, Y, Wang, Z, Li, Y, Tang, J, Wang, Y-K & Lin, C-T 2023, 'Cross task neural architecture search for EEG signal recognition', Neurocomputing, vol. 545, pp. 126260-126260. View/Download from: Publisher's site
El Majzoub, A, Rabhi, FA & Hussain, W 2023, 'Evaluating interpretable machine learning predictions for cryptocurrencies', Intelligent Systems in Accounting, Finance and Management, vol. 30, no. 3, pp. 137-149. View/Download from: Publisher's site View description>>
SummaryThis study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N‐BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.
Elgharabawy, A, Prasad, M & Lin, C-T 2023, 'Preference Neural Network', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 5, pp. 1362-1376. View/Download from: Publisher's site View description>>
This paper proposes a novel label ranker network to learn the relationship between labels to solve ranking and classification problems. The Preference Neural Network (PNN) uses spearman correlation gradient ascent and two new activation functions, positive smooth staircase (PSS), and smooth staircase (SS) that accelerate the ranking by creating almost deterministic preference values. PNN is proposed in two forms, fully connected simple Three layers and Preference Net (PN), where the latter is the deep ranking form of PNN to learning feature selection using ranking to solve images classification problem. PN uses a new type of ranker kernel to generate a feature map. PNN outperforms five previously proposed methods for label ranking, obtaining state-of-the-art results on label ranking, and PN achieves promising results on CFAR-100 with high computational efficiency.
Faisal, SN, Do, T-TN, Torzo, T, Leong, D, Pradeepkumar, A, Lin, C-T & Iacopi, F 2023, 'Noninvasive Sensors for Brain–Machine Interfaces Based on Micropatterned Epitaxial Graphene', ACS Applied Nano Materials, vol. 6, no. 7, pp. 5440-5447. View/Download from: Publisher's site
Fan, C, Zhang, X, Zhao, Y, Liu, Y & Yu, S 2023, 'Self-Adaptive Gradient Quantization for Geo-Distributed Machine Learning Over Heterogeneous and Dynamic Networks', IEEE Transactions on Cloud Computing, vol. 11, no. 4, pp. 3483-3496. View/Download from: Publisher's site
Fan, W, Xiao, F, Cai, H, Chen, X & Yu, S 2023, 'Disjoint Paths Construction and Fault-Tolerant Routing in BCube of Data Center Networks', IEEE Transactions on Computers, vol. 72, no. 9, pp. 2467-2481. View/Download from: Publisher's site
Fan, X, Li, Y, Chen, L, Li, B & Sisson, SA 2023, 'Hawkes Processes With Stochastic Exogenous Effects for Continuous-Time Interaction Modelling', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 2, pp. 1848-1861. View/Download from: Publisher's site View description>>
Continuous-time interaction data is usually generated under time-evolving environment. Hawkes processes (HP) are commonly used mechanisms for the analysis of such data. However, typical model implementations (such as e.g. stochastic block models) assume that the exogenous (background) interaction rate is constant, and so they are limited in their ability to adequately describe any complex time-evolution in the background rate of a process. In this paper, we introduce a stochastic exogenous rate Hawkes process (SE-HP) which is able to learn time variations in the exogenous rate. The model affiliates each node with a piecewise-constant membership distribution with an unknown number of changepoint locations, and allows these distributions to be related to the membership distributions of interacting nodes. The time-varying background rate function is derived through combinations of these membership functions. We introduce a stochastic gradient MCMC algorithm for efficient, scalable inference. The performance of the SE-HP is explored on real world, continuous-time interaction datasets, where we demonstrate that the SE-HP strongly outperforms comparable state-of-the-art methods. We introduce a stochastic gradient MCMC algorithm for efficient, scalable inference. The performance of the SE-HP is explored on real world, continuous-time interaction datasets, where we demonstrate that the SE-HP strongly outperforms comparable state-of-the-art methods.
Fang, Z, Lu, J, Liu, F & Zhang, G 2023, 'Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 1087-1105. View/Download from: Publisher's site View description>>
Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for the target domain, in which only unlabeled and a small number of labeled data are available. This is done by leveraging knowledge acquired from a heterogeneous source domain. From algorithmic perspectives, several methods have been proposed to solve the SsHeDA problem; yet there is still no theoretical foundation to explain the nature of the SsHeDA problem or to guide new and better solutions. Motivated by compatibility condition in semi-supervised probably approximately correct (PAC) theory, we explain the SsHeDA problem by proving its generalization error that is, why labeled heterogeneous source data and unlabeled target data help to reduce the target risk. Guided by our theory, we devise two algorithms as proof of concept. One, kernel heterogeneous domain alignment (KHDA), is a kernel-based algorithm; the other, joint mean embedding alignment (JMEA), is a neural network-based algorithm. When a dataset is small, KHDA's training time is less than JMEA's. When a dataset is large, JMEA is more accurate in the target domain. Comprehensive experiments with image/text classification tasks show KHDA to be the most accurate among all non-neural network baselines, and JMEA to be the most accurate among all baselines.
Feng, X, Zhang, Y, Meng, MH, Li, Y, Joe, CE, Wang, Z & Bai, G 2023, 'Detecting contradictions from IoT protocol specification documents based on neural generated knowledge graph', ISA Transactions, vol. 141, pp. 10-19. View/Download from: Publisher's site
Fraile Navarro, D, Kocaballi, AB, Dras, M & Berkovsky, S 2023, 'Collaboration, not Confrontation: Understanding General Practitioners’ Attitudes Towards Natural Language and Text Automation in Clinical Practice', ACM Transactions on Computer-Human Interaction, vol. 30, no. 2, pp. 1-34. View/Download from: Publisher's site View description>>
General Practitioners are among the primary users and curators of textual electronic health records, highlighting the need for technologies supporting record access and administration. Recent advancements in natural language processing facilitate the development of clinical systems, automating some time-consuming record-keeping tasks. However, it remains unclear what automation tasks would benefit clinicians most, what features such automation should exhibit, and how clinicians will interact with the automation. We conducted semi-structured interviews with General Practitioners uncovering their views and attitudes toward text automation. The main emerging theme was doctor-AI collaboration, addressing a reciprocal clinician-technology relationship that does not threaten to substitute clinicians, but rather establishes a constructive synergistic relationship. Other themes included: (i) desired features for clinical text automation; (ii) concerns around clinical text automation; and (iii) the consultation of the future. Our findings will inform the design of future natural language processing systems, to be implemented in general practice.
Gao, H, Dai, B, Miao, H, Yang, X, Barroso, RJD & Walayat, H 2023, 'A Novel GAPG Approach to Automatic Property Generation for Formal Verification: The GAN Perspective', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 19, no. 1, pp. 1-22. View/Download from: Publisher's site View description>>
Formal methods have been widely used to support software testing to guarantee correctness and reliability. For example, model checking technology attempts to ensure that the verification property of a specific formal model is satisfactory for discovering bugs or abnormal behavior from the perspective of temporal logic. However, because automatic approaches are lacking, a software developer/tester must manually specify verification properties. A generative adversarial network (GAN) learns features from input training data and outputs new data with similar or coincident features. GANs have been successfully used in the image processing and text processing fields and achieved interesting and automatic results. Inspired by the power of GANs, in this article, we propose a GAN-based automatic property generation (GAPG) approach to generate verification properties supporting model checking. First, the verification properties in the form of computational tree logic (CTL) are encoded and used as input to the GAN. Second, we introduce regular expressions as grammar rules to check the correctness of the generated properties. These rules work to detect and filter meaningless properties that occur because the GAN learning process is uncontrollable and may generate unsuitable properties in real applications. Third, the learning network is further trained by using labeled information associated with the input properties. These are intended to guide the training process to generate additional new properties, particularly those that map to corresponding formal models. Finally, a series of comprehensive experiments demonstrate that the proposed GAPG method can obtain new verification properties from two aspects: (1) using only CTL formulas and (2) using CTL formulas combined with Kripke structures.
Gao, H, Fang, D, Xiao, J, Hussain, W & Kim, JY 2023, 'CAMRL: A Joint Method of Channel Attention and Multidimensional Regression Loss for 3D Object Detection in Automated Vehicles', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 8, pp. 8831-8845. View/Download from: Publisher's site View description>>
Fully automated vehicles collect information about their road environments to adjust their driving actions, such as braking and slowing down. The development of artificial intelligence (AI) and the Internet of Things (IoT) has improved the cognitive abilities of vehicles, allowing them to detect traffic signs, pedestrians, and obstacles for increasing the intelligence of these transportation systems. Three-dimensional (3D) object detection in front-view images taken by vehicle cameras is important for both object detection and depth estimation. In this paper, a joint channel attention and multidimensional regression loss method for 3D object detection in automated vehicles (called CAMRL) is proposed to improve the average precision of 3D object detection by focusing on the model’s ability to infer the locations and sizes of objects. First, channel attention is introduced to effectively learn the yaw angles from the road images captured by vehicle cameras. Second, a multidimensional regression loss algorithm is designed to further optimize the size and position parameters during the training process. Third, the intrinsic parameters of the camera and the depth estimate of the model are combined to reduce the object depth computation error, allowing us to calculate the distance between an object and the camera after the object’s size is confirmed. As a result, objects are detected, and their depth estimations are validated. Then, the vehicle can determine when and how to stop if an object is nearby. Finally, experiments conducted on the KITTI dataset demonstrate that our method is effective and performs better than other baseline methods, especially in terms of 3D object detection and bird’s-eye view (BEV) evaluation.
Gao, H, Hussain, W, Durán Barroso, RJ, Arshad, J & Yin, Y 2023, 'Guest Editorial: Machine learning applied to quality and security in software systems', IET Software, vol. 17, no. 4, pp. 345-347. View/Download from: Publisher's site
Gao, H, Luo, B, Barroso, RJD & Hussain, W 2023, 'Guest Editorial Special Issue on Computational Intelligence to Edge AI for Ubiquitous IoT Systems', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 1, pp. 36-38. View/Download from: Publisher's site
Gao, H, Qiu, B, Barroso, RJD, Hussain, W, Xu, Y & Wang, X 2023, 'TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented Autoencoder', IEEE Transactions on Network Science and Engineering, vol. 10, no. 5, pp. 2978-2990. View/Download from: Publisher's site View description>>
With the development of the Internet of Things, it has been widely studied and deployed in industrial manufacturing, intelligent transportation, and healthcare systems. The time-series feature of the IoT makes the data density and the data dimension higher, where anomaly detection is important to ensure hardware and software security. However, the traditional anomaly detection algorithm has difficulty meeting this demand, not only in complexity but also accuracy. Sometimes the anomaly can be well reconstructed, resulting in a low reconstruction error. In this paper, we propose a memory-augmented autoencoder approach for detecting anomalies in IoT data, which aims to use reconstruction errors to determine data anomalies. First, a memory mechanism is introduced to suppress the generalization ability of the model, and a memory-augmented autoencoder TSMAE is designed for time-series data anomaly detection. Second, by adding penalties and derivable rectifier functions to loss to make the addressing vector sparse, memory modules are encouraged to extract typical normal patterns, thus inhibiting model generalization ability. Finally, through experiments on ECG and Wafer datasets, the validity of TSMAE is verified, and the rationality of hyperparameter setting is discussed through visualizing the memory module addressing vector.
Gao, S, Wang, R, Wang, X, Yu, S, Dong, Y, Yao, S & Zhou, W 2023, 'Detecting Adversarial Examples on Deep Neural Networks With Mutual Information Neural Estimation', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 6, pp. 5168-5181. View/Download from: Publisher's site
Ghannam, S & Hussain, F 2023, 'Comparison of deep learning approaches for forecasting urban short-term water demand a Greater Sydney Region case study', Knowledge-Based Systems, vol. 275, pp. 110660-110660. View/Download from: Publisher's site
Gill, AQ 2023, 'The digital ecosystem information framework: Insights from action design research', Journal of Information Science. View/Download from: Publisher's site View description>>
Digital ecosystem (DE) is a dynamic configuration of informational organisms, individual and organisational actors, which interact in the digitally networked and federated environment. Traditional approaches are challenged by the need for handling information in complex DE where information flows beyond the boundary of a single actor. This article presents the informational organism-interaction centric digital ecosystem information (DEi) framework for information operations, management, and governance. The DEi framework emerged based on the insights obtained through the application of well-known thematic network analysis and abstraction, reflection and learning techniques to 15 action design research projects across nine different industry partners in Australia. The DEi framework includes 27 topics that are organised into nine key knowledge and three focus areas. The DEi framework can be used by researchers and practitioners as a resource for designing digital information capabilities as appropriate to their context.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2023, 'A guide to current methodology and usage of reverse vaccinology towardsin silicovaccine discovery', FEMS Microbiology Reviews, vol. 47, no. 2, p. fuad004. View/Download from: Publisher's site View description>>
AbstractReverse vaccinology (RV) was described at its inception in 2000 as an in silico process that starts from the genomic sequence of the pathogen and ends with a list of potential protein and/or peptide candidates to be experimentally validated for vaccine development. Twenty-two years later, this process has evolved from a few steps entailing a handful of bioinformatics tools to a multitude of steps with a plethora of tools. Other in silico related processes with overlapping workflow steps have also emerged with terms such as subtractive proteomics, computational vaccinology, and immunoinformatics. From the perspective of a new RV practitioner, determining the appropriate workflow steps and bioinformatics tools can be a time consuming and overwhelming task, given the number of choices. This review presents the current understanding of RV and its usage in the research community as determined by a comprehensive survey of scientific papers published in the last seven years. We believe the current mainstream workflow steps and tools presented here will be a valuable guideline for all researchers wanting to apply an up-to-date in silico vaccine discovery process.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2023, 'A state-of-the-art methodology for high-throughput in silico vaccine discovery against protozoan parasites and exemplified with discovered candidates for Toxoplasma gondii', Scientific Reports, vol. 13, no. 1, p. 8243. View/Download from: Publisher's site View description>>
AbstractVaccine discovery against eukaryotic parasites is not trivial as highlighted by the limited number of known vaccines compared to the number of protozoal diseases that need one. Only three of 17 priority diseases have commercial vaccines. Live and attenuated vaccines have proved to be more effective than subunit vaccines but adversely pose more unacceptable risks. One promising approach for subunit vaccines is in silico vaccine discovery, which predicts protein vaccine candidates given thousands of target organism protein sequences. This approach, nonetheless, is an overarching concept with no standardised guidebook on implementation. No known subunit vaccines against protozoan parasites exist as a result of this approach, and consequently none to emulate. The study goal was to combine current in silico discovery knowledge specific to protozoan parasites and develop a workflow representing a state-of-the-art approach. This approach reflectively integrates a parasite’s biology, a host's immune system defences, and importantly, bioinformatics programs needed to predict vaccine candidates. To demonstrate the workflow effectiveness, every Toxoplasma gondii protein was ranked in its capacity to provide long-term protective immunity. Although testing in animal models is required to validate these predictions, most of the top ranked candidates are supported by publications reinforcing our confidence in the approach.
Grochow, J & Qiao, Y 2023, 'On the Complexity of Isomorphism Problems for Tensors, Groups, and Polynomials I: Tensor Isomorphism-Completeness', SIAM Journal on Computing, vol. 52, no. 2, pp. 568-617. View/Download from: Publisher's site
Guan, J, Pan, L, Wang, C, Yu, S, Gao, L & Zheng, X 2023, 'Trustworthy Sensor Fusion Against Inaudible Command Attacks in Advanced Driver-Assistance Systems', IEEE Internet of Things Journal, vol. 10, no. 19, pp. 17254-17264. View/Download from: Publisher's site
Guan, S, Lu, H, Zhu, L & Fang, G 2023, 'PoseGU: 3D human pose estimation with novel human pose generator and unbiased learning', Computer Vision and Image Understanding, vol. 233, pp. 103715-103715. View/Download from: Publisher's site
Guo, K, Wu, M, Soo, Z, Yang, Y, Zhang, Y, Zhang, Q, Lin, H, Grosser, M, Venter, D, Zhang, G & Lu, J 2023, 'Artificial intelligence-driven biomedical genomics', Knowledge-Based Systems, vol. 279, pp. 110937-110937. View/Download from: Publisher's site
Guo, Y, Liu, L, Ba, X, Lu, H, Lei, G, Yin, W & Zhu, J 2023, 'Measurement and Modeling of Magnetic Materials under 3D Vectorial Magnetization for Electrical Machine Design and Analysis', Energies, vol. 16, no. 1, pp. 417-417. View/Download from: Publisher's site View description>>
The magnetic properties of magnetic cores are essential for the design of electrical machines, and consequently appropriate mathematical modeling is needed. Usually, the design and analysis of electrical machines consider only the one-dimensional (1D) magnetic properties of core materials, i.e., the relationship of magnetic flux density (B) versus magnetic field strength (H), and their associated power loss under 1D magnetization, in which the B and H are constrained in the same orientation. Some studies have also been performed with the two-dimensional (2D) magnetizations in which the B and H are vectorial, rotating on a plane, and they may not be in the same direction. It has been discovered that the 2D rotational property is very different from its 1D alternating counterpart. However, the magnetic fields in an electrical machine, in particular claw pole and transverse flux machines, are naturally three-dimensional (3D), and the B and H vectors are rotational and may not lie on the same plane. It can be expected that the 3D vectorial property might be different from its 2D or 1D counterpart, and hence it should be investigated for the interests of both academic research and engineering application. This paper targets at a general summary about the magnetic material characterization with 3D vectorial magnetization, and their application prospect in electrical machine design and analysis.
Gupta, A, Kumar, D, Verma, H, Tanveer, M, Javier, AP, Lin, C-T & Prasad, M 2023, 'Recognition of multi-cognitive tasks from EEG signals using EMD methods', Neural Computing and Applications, vol. 35, no. 31, pp. 22989-23006. View/Download from: Publisher's site View description>>
AbstractMental task classification (MTC), based on the electroencephalography (EEG) signals is a demanding brain–computer interface (BCI). It is independent of all types of muscular activity. MTC-based BCI systems are capable to identify cognitive activity of human. The success of BCI system depends upon the efficient feature representation from raw EEG signals for classification of mental activities. This paper mainly presents on a novel feature representation (formation of most informative features) of the EEG signal for the both, binary as well as multi MTC, using a combination of some statistical, uncertainty and memory- based coefficient. In this work, the feature formation is carried out in the two stages. In the first stage, the signal is split into different oscillatory functions with the help of three well-known empirical mode decomposition (EMD) algorithms, and a new set of eight parameters (features) are calculated from the oscillatory function in the second stage of feature vector construction. Support vector machine (SVM) is used to classify the feature vectors obtained corresponding to the different mental tasks. This study consists the problem formulation of two variants of MTC; two-class and multi-class MTC. The suggested scheme outperforms the existing work for the both types of mental tasks classification.
Halkon, BJ, Darwish, A, Rothberg, S, Mohammadi, M & Oberst, S 2023, 'Correction of scanning laser Doppler vibrometer measurements when subjected to six degree-of-freedom base motion', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A74-A74. View/Download from: Publisher's site View description>>
Scanning laser Doppler vibrometer (SLDV) measurements are affected by sensor head vibrations as though they are vibrations of the target surface itself. Previous work has established a fully general theoretical analysis which shows that the only measurement required for measurement correction is of the vibration velocity at the incident point on the final steering mirror in the direction of the outgoing laser beam. Two practical—but not quite perfect—options for measurement correction were presented (one more suitable to manufacturer implementation, one more applicable to the vibration engineer end user). In both cases, placement of the correction transducer is critical with correction working for totally arbitrary instrument vibration and scan angle. Experimental validation, employing frequency-domain based processing, has been completed for one degree-of-freedom, on-axis vibration. Simultaneously, advancements in the data processing approach have realised improved correction in practice, especially for lower frequencies and for transient, as opposed to statistically stationary, vibration. In this paper, extension of the experimental validation to six degree-of-freedom instrument vibration is presented for the first time. In combination with the latest data processing approaches, reductions in the measurement error of 29.4 and 28.2 dB for the frequency- and time-domain processing techniques, respectively, are realised.
Han, Z, Xu, C, Ma, S, Hu, Y, Zhao, G & Yu, S 2023, 'DTE-RR: Dynamic Topology Evolution-Based Reliable Routing in VANET', IEEE Wireless Communications Letters, vol. 12, no. 6, pp. 1061-1065. View/Download from: Publisher's site
Han, Z, Xu, C, Zhao, G, Wang, S, Cheng, K & Yu, S 2023, 'Time-Varying Topology Model for Dynamic Routing in LEO Satellite Constellation Networks', IEEE Transactions on Vehicular Technology, vol. 72, no. 3, pp. 3440-3454. View/Download from: Publisher's site View description>>
With the characteristics of low-latency, seamless coverage and high bandwidth, the Low Earth Orbit (LEO) satellite network has been the promising technology for the sixth-generation mobile communication (6G) networks, especially, the inter-satellite link can improve the flexibility of inter-satellite networking and routing. However, in the existing works, since the impact of link attributes on the satellite topology has not been well investigated, it is difficult to avoid the loss of topology information for the transmission path selection, which may aggravate the unreliability of routing path. In this paper, we propose a novel time-varying topology model for the LEO satellite network, to improve the adaptability of the satellite routing. Firstly, the weighted time-space evolution graph based on the link attributes is established to construct the time-varying topology model of LEO satellite networks. Then, the utility function of the link attributes is modelled and the multi-attribute decision-making is introduced to calculate the weight of each link attribute for the quantification of the link utility. Finally, based on the topology model, the inter-satellite link utility-based dynamic routing algorithm is proposed to improve the adaptability of satellite routing. The simulation results demonstrate that the proposed routing algorithm outperforms the existing routing algorithms in terms of packet drop rate, end-to-end delay and throughput.
He, L, Shi, K, Wang, D, Wang, X & Xu, G 2023, 'A topic‐controllable keywords‐to‐text generator with knowledge base network', CAAI Transactions on Intelligence Technology. View/Download from: Publisher's site View description>>
AbstractWith the introduction of more recent deep learning models such as encoder‐decoder, text generation frameworks have gained a lot of popularity. In Natural Language Generation (NLG), controlling the information and style of the output produced is a crucial and challenging task. The purpose of this paper is to develop informative and controllable text using social media language by incorporating topic knowledge into a keyword‐to‐text framework. A novel Topic‐Controllable Key‐to‐Text (TC‐K2T) generator that focuses on the issues of ignoring unordered keywords and utilising subject‐controlled information from previous research is presented. TC‐K2T is built on the framework of conditional language encoders. In order to guide the model to produce an informative and controllable language, the generator first inputs unordered keywords and uses subjects to simulate prior human knowledge. Using an additional probability term, the model increases the likelihood of topic words appearing in the generated text to bias the overall distribution. The proposed TC‐K2T can produce more informative and controllable senescence, outperforming state‐of‐the‐art models, according to empirical research on automatic evaluation metrics and human annotations.
He, L, Xu, G, Jameel, S, Wang, X & Chen, H 2023, 'Graph-Aware Deep Fusion Networks for Online Spam Review Detection', IEEE Transactions on Computational Social Systems, vol. 10, no. 5, pp. 2557-2565. View/Download from: Publisher's site
Hertrampf, T, Oberst, S & Sepehrirahnama, S 2023, 'Recurrence rate spectrograms for impact localization in wood', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A142-A142. View/Download from: Publisher's site View description>>
Characteristics like cellular grain structure, inhomogeneous density, aging, and altering environmental conditions (moisture, temperature) give wood highly anisotropic viscoelastic properties and non-linear vibrational wave propagation properties. Nonlinearity limits the use of linear methods, such as modal analysis and parameter identification via transfer functions. Acoustic localization of natural damage to wood, like crack growth, is of general interest in structural health monitoring of timber structures. Time-difference of arrival or energy attenuation is commonly used for localization, which are prone to boundary reflections or require the frequency response function. Recent advancements in machine learning-based classification of non-linear signals can achieve a much higher accuracy when recurrence rate-based spectrograms are used compared relative to conventional short-time Fourier transforms, especially in the presence of noise. Hence, in this work, multi-sensor measurements of impulse induced vibration in wood beams are classified by their distance to the excitation, based on their time series, avoiding a priori knowledge of a transfer function for the localization. The machine learning model is trained across various widths and thicknesses of samples, giving a localization estimate independent of beam dimensions. This research will contribute to early detection of damage in the field of vibration-based structural health monitoring of wood.
Hoke, JC, Ippoliti, M, Rosenberg, E, Abanin, D, Acharya, R, Andersen, TI, Ansmann, M, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Bardin, JC, Bengtsson, A, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Z, Chiaro, B, Chik, D, Cogan, J, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Dau, AG, Debroy, DM, Del Toro Barba, A, Demura, S, Di Paolo, A, Drozdov, IK, Dunsworth, A, Eppens, D, Erickson, C, Farhi, E, Fatemi, R, Ferreira, VS, Burgos, LF, Forati, E, Fowler, AG, Foxen, B, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Gosula, R, Gross, JA, Habegger, S, Hamilton, MC, Hansen, M, Harrigan, MP, Harrington, SD, Heu, P, Hoffmann, MR, Hong, S, Huang, T, Huff, A, Huggins, WJ, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Kechedzhi, K, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, A, Klimov, PV, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, KW, Lensky, YD, Lester, BJ, Lill, AT, Liu, W, Locharla, A, Martin, O, McClean, JR, McEwen, M, Miao, KC, Mieszala, A, Montazeri, S, Morvan, A, Movassagh, R, Mruczkiewicz, W, Neeley, M, Neill, C, Nersisyan, A, Newman, M, Ng, JH, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Omonije, S, Opremcak, A, Petukhov, A, Potter, R, Pryadko, LP, Quintana, C, Rocque, C, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shutty, N, Shvarts, V, Skruzny, J, Smith, WC, Somma, R, Sterling, G, Strain, D, Szalay, M, Torres, A, Vidal, G, Villalonga, B, Heidweiller, CV, White, T, Woo, BWK, Xing, C, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zalcman, A, Zhang, Y, Zhu, N, Zobrist, N, Neven, H, Babbush, R, Bacon, D, Boixo, S, Hilton, J, Lucero, E, Megrant, A, Kelly, J, Chen, Y, Smelyanskiy, V, Mi, X, Khemani, V & Roushan, P 2023, 'Measurement-induced entanglement and teleportation on a noisy quantum processor', Nature, vol. 622, no. 7983, pp. 481-486. View/Download from: Publisher's site View description>>
AbstractMeasurement has a special role in quantum theory1: by collapsing the wavefunction, it can enable phenomena such as teleportation2 and thereby alter the ‘arrow of time’ that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space–time3–10 that go beyond the established paradigms for characterizing phases, either in or out of equilibrium11–13. For present-day noisy intermediate-scale quantum (NISQ) processors14, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping9,15–17 to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling3,4 to measurement-induced teleportation18. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.
Hu, X, Zhu, T, Zhai, X, Zhou, W & Zhao, W 2023, 'Privacy Data Propagation and Preservation in Social Media: A Real-World Case Study', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 4137-4150. View/Download from: Publisher's site View description>>
Social media has become a ubiquitous tool for spreading news, messages, and generally allowing for communication between individuals. Hence, studying how our private information might also spread across social media is important research. To date, many studies have used information diffusion models to simulate and then examine how information flows through social networks. But these models are theoretical, and newsworthy information may not behave in the same way as private information, raising the question: Are the observed phenomena indicative of real privacy propagation To explore this question, we assembled a dataset from Twitter comprising propagated information flows for both private and normal information. We then built a graph convolutional network to trace and classify differences in the way each type of information spreads throughout the platform. The results reveal that there are indeed key differences in the diffusion processes of the two types of information. More importantly, we design privacy-preserving methods to reduce the privacy propagation in social media.
Huang, J, Song, X, Xiao, F, Cao, Z & Lin, C-T 2023, 'Belief f-divergence for EEG complexity evaluation', Information Sciences, vol. 643, pp. 119189-119189. View/Download from: Publisher's site
Huang, W, Zhuo, M, Zhu, T, Zhou, S & Liao, Y 2023, 'Differential privacy: Review of improving utility through cryptography‐based technologies', Concurrency and Computation: Practice and Experience, vol. 35, no. 5. View/Download from: Publisher's site View description>>
SummaryDue to successful applications of data analysis technologies in many fields, various institutions have accumulated a large amount of data to improve their services. As the speed of data collection has increased dramatically over the last few years, an increasing number of users are growing concerned about their personal information. Therefore, privacy preservation has become an urgent problem to be solved. Differential privacy as a strong privacy preservation tool has attracted significant attention. In this review, we focus on improving data utility of differentially private mechanisms through technologies related to cryptography. In particular, we first focus on how to improve data utility through anonymous communication. Then, we summarize how to improve data utility by combining differentially private mechanisms with homomorphic encryption schemes. Next, we summarize hardness results of what is impossible to achieve for differentially private mechanisms' data utility from the view of cryptography. Differential privacy borrowed intuitions from cryptography and still benefits from the progress of cryptography. To summarize the state‐of‐the‐art and to benefit future researches, we are motivated to provide this review.
Huang, Y, Xiao, F, Cao, Z & Lin, C-T 2023, 'Fractal Belief Rényi Divergence with Its Applications in Pattern Classification', IEEE Transactions on Knowledge and Data Engineering, pp. 1-16. View/Download from: Publisher's site
Huang, Y, Xiao, F, Cao, Z & Lin, C-T 2023, 'Higher Order Fractal Belief Rényi Divergence With Its Applications in Pattern Classification', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12, pp. 14709-14726. View/Download from: Publisher's site
Hussain, W & Merigo, JM 2023, 'Onsite/offsite social commerce adoption for SMEs using fuzzy linguistic decision making in complex framework', Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 9, pp. 12875-12894. View/Download from: Publisher's site View description>>
AbstractThere has been a growing social commerce adoption trend among SMEs for few years. However, it is often a challenging strategic task for SMEs to choose the right type of social commerce. SMEs usually have a limited budget, technical skills and resources and want to maximise productivity with those limited resources. There is much literature that discusses the social commerce adoption strategy for SMEs. However, there is no work to enable SMEs to choose social commerce—onsite/offsite or hybrid strategy. Moreover, very few studies allow the decision-makers to handle uncertain, complex nonlinear relationships of social commerce adoption factors. The paper proposes a fuzzy linguistic multi-criteria group decision-making in a complex framework for onsite, offsite social commerce adoption to address the problem. The proposed approach uses a novel hybrid approach by combining FAHP, FOWA and selection criteria of the technological–organisation–environment (TOE) framework. Unlike previous methods, the proposed approach uses the decision maker's attitudinal characteristics and recommends intelligently using the OWA operator. The approach further demonstrates the decision behaviour of the decision-makers with Fuzzy Minimum (FMin), Fuzzy Maximum (FMax), Laplace criteria, Hurwicz criteria, FWA, FOWA and FPOWA. The framework enables the SMEs to choose the right type of social commerce considering TOE factors that help them build a stronger relationship with current and potential customers. The approach's applicability is demonstrated using a case study of three SMEs seeking to adopt a social commerce type. The analysis results indicate the proposed approach's effectiveness in handling uncertain, complex nonlinear decisions in social commerce adoption.
Hussain, W, Merigó, JM, Gil-Lafuente, J & Gao, H 2023, 'Complex nonlinear neural network prediction with IOWA layer', Soft Computing, vol. 27, no. 8, pp. 4853-4863. View/Download from: Publisher's site View description>>
AbstractNeural network methods are widely used in business problems for prediction, clustering, and risk management to improving customer satisfaction and business outcome. The ability of a neural network to learn complex nonlinear relationship is due to its architecture that uses weight parameters to transform input data within the hidden layers. Such methods perform well in many situations where the ordering of inputs is simple. However, for a complex reordering of a decision-maker, the process is not enough to get an optimal prediction result. Moreover, existing machine learning algorithms cannot reduce computational complexity by reducing data size without losing any information. This paper proposes an induced ordered weighted averaging (IOWA) operator for the artificial neural network IOWA-ANN. The operator reorders the data according to the order-inducing variable. The proposed sorting mechanism in the neural network can handle a complex nonlinear relationship of a dataset, which results in reduced computational complexities. The proposed approach deals with the complexity of the neuron, collects the data and allows a degree of customisation of the structure. The application further extended to IGOWA and Quasi-IOWA operators. We present a numerical example in a financial decision-making process to demonstrate the approach's effectiveness in handling complex situations. This paper opens a new research area for various complex nonlinear predictions where the dataset is big enough, such as cloud QoS and IoT sensors data. The approach can be used with different machine learning, neural networks or hybrid fuzzy neural methods with other extensions of the OWA operator.
Ibrar, I, Yadav, S, Altaee, A, Braytee, A, Samal, AK, Zaid, SMJ & Hawari, AH 2023, 'A machine learning approach for prediction of reverse solute flux in forward osmosis', Journal of Water Process Engineering, vol. 54, pp. 103956-103956. View/Download from: Publisher's site
Islam, MM, Ramezani, F, Lu, HY & Naderpour, M 2023, 'Optimal placement of applications in the fog environment: A systematic literature review', Journal of Parallel and Distributed Computing, vol. 174, pp. 46-69. View/Download from: Publisher's site
Jia, M, Gabrys, B & Musial, K 2023, 'A Network Science Perspective of Graph Convolutional Networks: A Survey', IEEE Access, vol. 11, pp. 39083-39122. View/Download from: Publisher's site
Jiang, L, Li, F, Chen, Z, Zhu, B, Yi, C, Li, Y, Zhang, T, Peng, Y, Si, Y, Cao, Z, Chen, A, Yao, D, Chen, X & Xu, P 2023, 'Information transmission velocity-based dynamic hierarchical brain networks', NeuroImage, vol. 270, pp. 119997-119997. View/Download from: Publisher's site
Jiang, Z, Li, C, Chang, X, Chen, L, Zhu, J & Yang, Y 2023, 'Dynamic Slimmable Denoising Network', IEEE Transactions on Image Processing, vol. 32, pp. 1583-1598. View/Download from: Publisher's site
Khatri, I, Choudhry, A, Rao, A, Tyagi, A, Vishwakarma, DK & Prasad, M 2023, 'Influence Maximization in social networks using discretized Harris’ Hawks Optimization algorithm', Applied Soft Computing, vol. 149, pp. 111037-111037. View/Download from: Publisher's site
Kong, Y, Liu, L, Qiao, M, Wang, Z & Tao, D 2023, 'Trust-Region Adaptive Frequency for Online Continual Learning', International Journal of Computer Vision, vol. 131, no. 7, pp. 1825-1839. View/Download from: Publisher's site View description>>
AbstractIn the paradigm of online continual learning, one neural network is exposed to a sequence of tasks, where the data arrive in an online fashion and previously seen data are not accessible. Such online fashion causes insufficient learning and severe forgetting on past tasks issues, preventing a good stability-plasticity trade-off, where ideally the network is expected to have high plasticity to adapt to new tasks well and have the stability to prevent forgetting on old tasks simultaneously. To solve these issues, we propose a trust-region adaptive frequency approach, which alternates between standard-process and intra-process updates. Specifically, the standard-process replays data stored in a coreset and interleaves the data with current data, and the intra-process updates the network parameters based on the coreset. Furthermore, to improve the unsatisfactory performance stemming from online fashion, the frequency of the intra-process is adjusted based on a trust region, which is measured by the confidence score of current data. During the intra-process, we distill the dark knowledge to retain useful learned knowledge. Moreover, to store more representative data in the coreset, a confidence-based coreset selection is presented in an online manner. The experimental results on standard benchmarks show that the proposed method significantly outperforms state-of-art continual learning algorithms.
Kozanoglu, DC, Daim, TU & Contreras-Cruz, A 2023, 'Unraveling the Dynamics of Immigrant Engineers’ Full-Utilization in Australia', IEEE Transactions on Engineering Management, vol. 70, no. 11, pp. 3776-3791. View/Download from: Publisher's site View description>>
The study aims to improve our understanding of the full-utilization of immigrant engineers by answering three research questions: (1) what are the economic and social costs of the under-utilization of immigrant engineers, (2) what factors determine immigrant engineers’ employment, and (3) what might be potential solutions to tackle with their under-utilization? We adopt the intersectionality theory to observe a rich set of social factors influential in immigrant engineers’ under-utilization by using 188 surveys and 14 interviews of immigrant engineers living in Australia. The paper concludes with the findings’ theoretical and policy implications, followed by suggestions for future studies.
Kronowetter, F, Pretsch, L, Chiang, YK, Melnikov, A, Sepehrirahnama, S, Oberst, S, Powell, DA & Marburg, S 2023, 'Sound attenuation enhancement of acoustic meta-atoms via coupling', The Journal of the Acoustical Society of America, vol. 154, no. 2, pp. 842-851. View/Download from: Publisher's site View description>>
Arrangements of acoustic meta-atoms, better known as acoustic metamaterials, are commonly applied in acoustic cloaking, for the attenuation of acoustic fields or for acoustic focusing. A precise design of single meta-atoms is required for these purposes. Understanding the details of their interaction allows improvement of the collective performance of the meta-atoms as a system, for example, in sound attenuation. Destructive interference of their scattered fields, for example, can be mitigated by adjusting the coupling or tuning of individual meta-atoms. Comprehensive numerical studies of various configurations of a resonator pair show that the coupling can lead to degenerate modes at periodic distances between the resonators. We show how the resonators' separation and relative orientation influence the coupling and thereby tunes the sound attenuation. The simulation results are supported by experiments using a two-dimensional parallel-plate waveguide. It is shown that coupling parameters like distance, orientation, detuning, and radiation loss provide additional degrees of freedom for efficient acoustic meta-atom tuning to achieve unprecedented interactions with excellent sound attenuation properties.
Video compression technology for Ultra-High Definition (UHD) and 8K UHD video has been established and is being widely adopted by major broadcasting companies and video content providers, allowing them to produce high-quality videos that meet the demands of today’s consumers. However, high-resolution video content broadcasting is not an easy problem to be resolved in the near future due to limited resources in network bandwidth and data storage. An alternative solution to overcome the challenges of broadcasting high-resolution video content is to downsample UHD or 8K video at the transmission side using existing infrastructure, and then utilizing Video Super-Resolution (VSR) technology at the receiving end to recover the original quality of the video content. Current deep learning-based methods for Video Super-Resolution (VSR) fail to consider the fact that the delivered video to viewers goes through a compression and decompression process, which can introduce additional distortion and loss of information. Therefore, it is crucial to develop VSR methods that are specifically designed to work with the compression–decompression pipeline. In general, various information in the compressed video is not utilized enough to realize the VSR model. This research proposes a highly efficient VSR network making use of data from decompressed video such as frame type, Group of Pictures (GOP), macroblock type and motion vector. The proposed Convolutional Neural Network (CNN)-based lightweight VSR model is suitable for real-time video services. The performance of the model is extensively evaluated through a series of experiments, demonstrating its effectiveness and applicability in practical scenarios.
Lei, Y, Ye, D, Shen, S, Sui, Y, Zhu, T & Zhou, W 2023, 'New challenges in reinforcement learning: a survey of security and privacy', Artificial Intelligence Review, vol. 56, no. 7, pp. 7195-7236. View/Download from: Publisher's site View description>>
Reinforcement learning is one of the most important branches of AI. Due to its capacity for self-adaption and decision-making in dynamic environments, reinforcement learning has been widely applied in multiple areas, such as healthcare, data markets, autonomous driving, and robotics. However, some of these applications and systems have been shown to be vulnerable to security or privacy attacks, resulting in unreliable or unstable services. A large number of studies have focused on these security and privacy problems in reinforcement learning. However, few surveys have provided a systematic review and comparison of existing problems and state-of-the-art solutions to keep up with the pace of emerging threats. Accordingly, we herein present such a comprehensive review to explain and summarize the challenges associated with security and privacy in reinforcement learning from a new perspective, namely that of the Markov Decision Process (MDP). In this survey, we first introduce the key concepts related to this area. Next, we cover the security and privacy issues linked to the state, action, environment, and reward function of the MDP process, respectively. We further highlight the special characteristics of security and privacy methodologies related to reinforcement learning. Finally, we discuss the possible future research directions within this area.
Leong, D, Do, TT-T & Lin, C-T 2023, 'Ventral and Dorsal Stream EEG Channels: Key Features for EEG-Based Object Recognition and Identification', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 4862-4870. View/Download from: Publisher's site
Li, A, Yang, B, Huo, H, Chen, H, Xu, G & Wang, Z 2023, 'Hyperbolic Neural Collaborative Recommender', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 9114-9127. View/Download from: Publisher's site
Li, G, Wu, Y, Wang, C, Peng, S, Niu, J & Yu, S 2023, 'The SRVM: A Similarity-Based Relevance Vector Machine for Remaining Useful Lifetime Prediction in the Industrial Internet of Things', IEEE Intelligent Systems, vol. 38, no. 5, pp. 45-55. View/Download from: Publisher's site
Li, J, Zhu, T, Ren, W & Raymond, K-K 2023, 'Improve individual fairness in federated learning via adversarial training', Computers & Security, vol. 132, pp. 103336-103336. View/Download from: Publisher's site
Li, K, Lu, J, Zuo, H & Zhang, G 2023, 'Multi-Source Domain Adaptation with Incomplete Source Label Spaces', Procedia Computer Science, vol. 225, pp. 2343-2350. View/Download from: Publisher's site
Li, K, Lu, J, Zuo, H & Zhang, G 2023, 'Source-Free Multidomain Adaptation With Fuzzy Rule-Based Deep Neural Networks', IEEE Transactions on Fuzzy Systems, vol. 31, no. 12, pp. 4180-4194. View/Download from: Publisher's site
Li, K, Niu, Z, Shi, K & Qiu, P 2023, 'Paper Recommendation Based on Academic Knowledge Graph and Subject Feature Embedding', Data Analysis and Knowledge Discovery, vol. 7, no. 5, pp. 48-59. View/Download from: Publisher's site View description>>
[Objective] This paper proposes a new model that integrates multiple features to provide accurate paper recommendation services for researchers. [Methods] First, we designed a feature extraction framework to extract and fuse entity relation features and topic features from the knowledge graph and the content of academic papers, respectively. Then, we proposed a paper recommendation method based on the knowledge embedding-based encoding-decoding model, which improved the learning effect of high-dimensional fusion features. [Results] We examined our new model on the DBLP-v11 dataset. The proposed method improved the Recall and MRR scores by 8.9% and 2.9%, respectively, compared with the suboptimal model. [Limitations] The proposed graph feature learning method does not consider the weight of entities in the real environment. [Conclusions] The new paper recommendation method could effectively learn high-dimensional features, which provide guidance for subsequent research.
Li, Q, Wang, X, Wang, Z & Xu, G 2023, 'Be Causal: De-Biasing Social Network Confounding in Recommendation', ACM Transactions on Knowledge Discovery from Data, vol. 17, no. 1, pp. 1-23. View/Download from: Publisher's site View description>>
In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. A common practice to address MNAR is to treat missing entries from the so-called “exposure” perspective, i.e., modeling how an item is exposed (provided) to a user. Most of the existing approaches use heuristic models or re-weighting strategy on observed ratings to mimic the missing-at-random setting. However, little research has been done to reveal how the ratings are missing from a causal perspective. To bridge the gap, we propose an unbiased and robust method called DENC ( De-Bias Network Confounding in Recommendation ), inspired by confounder analysis in causal inference. In general, DENC provides a causal analysis on MNAR from both the inherent factors (e.g., latent user or item factors) and auxiliary network’s perspective. Particularly, the proposed exposure model in DENC can control the social network confounder meanwhile preserve the observed exposure information. We also develop a deconfounding model through the balanced representation learning to retain the primary user and item features, which enables DENC generalize well on the rating prediction. Extensive experiments on three datasets validate that our proposed model outperforms the state-of-the-art baselines.
Li, Q, Wang, Z, Liu, S, Li, G & Xu, G 2023, 'Causal Optimal Transport for Treatment Effect Estimation', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 8, pp. 4083-4095. View/Download from: Publisher's site View description>>
Treatment effect estimation helps answer questions, such as whether a specific treatment affects the outcome of interest. One fundamental issue in this research is to alleviate the treatment assignment bias among those treated units and controlled units. Classical causal inference methods resort to the propensity score estimation, which unfortunately tends to be misspecified when only limited overlapping exists between the treated and the controlled units. Moreover, existing supervised methods mainly consider the treatment assignment information underlying the factual space, and thus, their performance of counterfactual inference may be degraded due to overfitting of the factual results. To alleviate those issues, we build on the optimal transport theory and propose a novel causal optimal transport (CausalOT) model to estimate an individual treatment effect (ITE). With the proposed propensity measure, CausalOT can infer the counterfactual outcome by solving a novel regularized optimal transport problem, which allows the utilization of global information on observational covariates to alleviate the issue of limited overlapping. In addition, a novel counterfactual loss is designed for CausalOT to align the factual outcome distribution with the counterfactual outcome distribution. Most importantly, we prove the theoretical generalization bound for the counterfactual error of CausalOT. Empirical studies on benchmark datasets confirm that the proposed CausalOT outperforms state-of-the-art causal inference methods.
Li, W, Hu, Y, Jiang, C, Wu, S, Bai, Q & Lai, E 2023, 'ABEM: An adaptive agent-based evolutionary approach for influence maximization in dynamic social networks', Applied Soft Computing, vol. 136, pp. 110062-110062. View/Download from: Publisher's site
Li, X, Li, X, Jia, J, Li, L, Yuan, J, Gao, Y & Yu, S 2023, 'A High Accuracy and Adaptive Anomaly Detection Model With Dual-Domain Graph Convolutional Network for Insider Threat Detection', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 1638-1652. View/Download from: Publisher's site
Li, X, Liu, Q, Wu, S, Cao, Z & Bai, Q 2023, 'Game theory based compatible incentive mechanism design for non-cryptocurrency blockchain systems', Journal of Industrial Information Integration, vol. 31, pp. 100426-100426. View/Download from: Publisher's site
Li, Y, Chen, H, Li, Y, Li, L, Yu, PS & Xu, G 2023, 'Reinforcement Learning Based Path Exploration for Sequential Explainable Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11801-11814. View/Download from: Publisher's site
Li, Y, Shen, J & Cetindamar, D 2023, 'Think Tank Innovation-Driven Knowledge Service Ecosystems: A Conceptual Framework and Case Study Application', Sustainability, vol. 15, no. 10, pp. 8355-8355. View/Download from: Publisher's site View description>>
By drawing on ecosystem and innovation-driven development theories, the aim of this paper is to increase our understanding of their application to think tanks. The composition, structure, and features of the knowledge service ecosystem of think tanks are conceptualized via a literature review. The model developed from this was validated by analyzing the data collected from 25 think tanks in the United States (US). The model constructed provides a reference for the sustainable and healthy development of knowledge services in think tanks and an innovation-driven development perspective for researchers interested in their innovation ecosystem dynamics. The intake of talent forms a necessary part of think tank construction, but, more importantly, this continuous intake is a crucial driving force for their sustainable development. This paper suggests that an increasing focus on talents in knowledge service ecosystems can lead to and assist in establishing innovative think tanks in many countries.
Li, Y, Yin, J & Chen, L 2023, 'Informative pseudo-labeling for graph neural networks with few labels', Data Mining and Knowledge Discovery, vol. 37, no. 1, pp. 228-254. View/Download from: Publisher's site View description>>
AbstractGraph neural networks (GNNs) have achieved state-of-the-art results for semi-supervised node classification on graphs. Nevertheless, the challenge of how to effectively learn GNNs with very few labels is still under-explored. As one of the prevalent semi-supervised methods, pseudo-labeling has been proposed to explicitly address the label scarcity problem. It is the process of augmenting the training set with pseudo-labeled unlabeled nodes to retrain a model in a self-training cycle. However, the existing pseudo-labeling approaches often suffer from two major drawbacks. First, these methods conservatively expand the label set by selecting only high-confidence unlabeled nodes without assessing their informativeness. Second, these methods incorporate pseudo-labels to the same loss function with genuine labels, ignoring their distinct contributions to the classification task. In this paper, we propose a novel informative pseudo-labeling framework (InfoGNN) to facilitate learning of GNNs with very few labels. Our key idea is to pseudo-label the most informative nodes that can maximally represent the local neighborhoods via mutual information maximization. To mitigate the potential label noise and class-imbalance problem arising from pseudo-labeling, we also carefully devise a generalized cross entropy with a class-balanced regularization to incorporate pseudo-labels into model retraining. Extensive experiments on six real-world graph datasets validate that our proposed approach significantly outperforms state-of-the-art baselines and competitive self-supervised methods on graphs.
Li, Y, Zeng, D, Gu, L, Zhu, A, Chen, Q & Yu, S 2023, 'PASTO: Enabling Secure and Efficient Task Offloading in TrustZone-Enabled Edge Clouds', IEEE Transactions on Vehicular Technology, vol. 72, no. 6, pp. 8234-8238. View/Download from: Publisher's site
Li, Z, Gao, W, Kessissoglou, N, Oberst, S, Wang, MY & Luo, Z 2023, 'Multifunctional mechanical metamaterials with tunable double-negative isotropic properties', Materials & Design, vol. 232, pp. 112146-112146. View/Download from: Publisher's site
Liao, W, Zhang, Q, Yuan, B, Zhang, G & Lu, J 2023, 'Heterogeneous Multidomain Recommender System Through Adversarial Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 11, pp. 8965-8977. View/Download from: Publisher's site View description>>
To solve the user data sparsity problem, which is the main issue in generating user preference prediction, cross-domain recommender systems transfer knowledge from one source domain with dense data to assist recommendation tasks in the target domain with sparse data. However, data are usually sparsely scattered in multiple possible source domains, and in each domain (source/target) the data may be heterogeneous, thus it is difficult for existing cross-domain recommender systems to find one source domain with dense data from multiple domains. In this way, they fail to deal with data sparsity problems in the target domain and cannot provide an accurate recommendation. In this article, we propose a novel multidomain recommender system (called HMRec) to deal with two challenging issues: 1) how to exploit valuable information from multiple source domains when no single source domain is sufficient and 2) how to ensure positive transfer from heterogeneous data in source domains with different feature spaces. In HMRec, domain-shared and domain-specific features are extracted to enable the knowledge transfer between multiple heterogeneous source and target domains. To ensure positive transfer, the domain-shared subspaces from multiple domains are maximally matched by a multiclass domain discriminator in an adversarial learning process. The recommendation in the target domain is completed by a matrix factorization module with aligned latent features from both the user and the item side. Extensive experiments on four cross-domain recommendation tasks with real-world datasets demonstrate that HMRec can effectively transfer knowledge from multiple heterogeneous domains collaboratively to increase the rating prediction accuracy in the target domain and significantly outperforms six state-of-the-art non-transfer or cross-domain baselines.
Lin, C-T, Liu, J, Fang, C-N, Hsiao, S-Y, Chang, Y-C & Wang, Y-K 2023, 'Multistream 3-D Convolution Neural Network With Parameter Sharing for Human State Estimation', IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 1, pp. 261-271. View/Download from: Publisher's site
Lin, C-T, Wang, Y, Chen, S-F, Huang, K-C & Liao, L-D 2023, 'Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission', Medical & Biological Engineering & Computing, vol. 61, no. 11, pp. 3003-3019. View/Download from: Publisher's site
Liu, C & Zowghi, D 2023, 'Citizen involvement in digital transformation: a systematic review and a framework', Online Information Review, vol. 47, no. 4, pp. 644-660. View/Download from: Publisher's site View description>>
PurposeThe purpose of this paper is to improve the understanding of the factors influencing the success of digital transformation (DT) and problems/challenges in DT as well as the communication methods used to involve citizens, based on a systematic literature review of research articles about citizen involvement in DT published between January 2010 and May 2021.Design/methodology/approachAfter establishing inclusion and exclusion criteria, a systematic review of relevant studies was conducted. Out of a total of 547 articles, 33 met the paper selection criteria.FindingsThe analysis of the included 33 empirical studies reveals that the factors influencing the success of DT can be described as the opposite side from challenges and problems in DT. These factors and challenges/problems all influence DT and they can be grouped into organisational values, management capabilities, organisational infrastructure, and workforce capabilities. The communication methods for citizen involvement in DT include: (1) communication mediated by human, (2) communication mediated by computers, and (3) mixed communication methods.Originality/valueThe study identified specific factors that influence DT supported by citizen involvement, at a more fine-grained level. The findings concerning communication methods extend related studies for citizen involvement by adding town hall meetings and communication methods mediated by computers. Furthermore, this study links the research findings to develop a framework for citizen involvement in DT, assisting in better selecting communication methods to involve citizens for ...
Liu, C, Chen, H, Zhu, T, Zhang, J & Zhou, W 2023, 'Making DeepFakes More Spurious: Evading Deep Face Forgery Detection via Trace Removal Attack', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 6, pp. 5182-5196. View/Download from: Publisher's site
Liu, C, Zhu, T, Zhang, J & Zhou, W 2023, 'Privacy Intelligence: A Survey on Image Privacy in Online Social Networks', ACM Computing Surveys, vol. 55, no. 8, pp. 1-35. View/Download from: Publisher's site View description>>
Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also increased the risk of privacy invasion. An online image can reveal various types of sensitive information, prompting the public to rethink individual privacy needs in OSN image sharing critically. However, the interaction of images and OSN makes the privacy issues significantly complicated. The current real-world solutions for privacy management fail to provide adequate personalized, accurate, and flexible privacy protection. Constructing a more intelligent environment for privacy-friendly OSN image sharing is urgent in the near future. Meanwhile, given the dynamics in both users’ privacy needs and OSN context, a comprehensive understanding of OSN image privacy throughout the entire sharing process is preferable to any views from a single side, dimension, or level. To fill this gap, we contribute a survey of “privacy intelligence” that targets modern privacy issues in dynamic OSN image sharing from a user-centric perspective. Specifically, we present the important properties and a taxonomy of OSN image privacy, along with a high-level privacy analysis framework based on the lifecycle of OSN image sharing. The framework consists of three stages with different principles of privacy by design. At each stage, we identify typical user behaviors in OSN image sharing and their associated privacy issues. Then a systematic review of representative intelligent solutions to those privacy issues is conducted, also in a stage-based manner. The analysis results in an intelligent “privacy firewall” for closed-loop privacy management. Challenges and future directions in this area are also discussed.
Liu, T, Xia, J, Ling, Z, Fu, X, Yu, S & Chen, M 2023, 'Efficient Federated Learning for AIoT Applications Using Knowledge Distillation', IEEE Internet of Things Journal, vol. 10, no. 8, pp. 7229-7243. View/Download from: Publisher's site View description>>
As a promising distributed machine learning paradigm, Federated Learning (FL) trains a central model with decentralized data without compromising user privacy, which makes it widely used by Artificial Intelligence Internet of Things (AIoT) applications. However, the traditional FL suffers from model inaccuracy, since it trains local models only using hard labels of data while useful information of incorrect predictions with small probabilities is ignored. Although various solutions try to tackle the bottleneck of the traditional FL, most of them introduce significant communication overhead, making the deployment of large-scale AIoT devices a great challenge. To address the above problem, this paper presents a novel Distillation-based Federated Learning (DFL) method that enables efficient and accurate FL for AIoT applications. By using Knowledge Distillation (KD), in each round of FL training, our approach uploads both the soft targets and local model gradients to the cloud server for aggregation, where the aggregation results are then dispatched to AIoT devices for the next round of local training. During the DFL local training, in addition to hard labels, the model predictions approximate soft targets, which can improve model accuracy by leveraging the knowledge of soft targets. To further improve our DFL model performance, we design a dynamic adjustment strategy of loss function weights for tuning the ratio of KD and FL, which can maximize the synergy between soft targets and hard labels. Comprehensive experimental results on well-known benchmarks show that our approach can significantly improve the model accuracy of FL without introducing significant communication overhead.
Liu, Y, Lee, T-U, Koronaki, A, Pietroni, N & Xie, YM 2023, 'Reducing the number of different nodes in space frame structures through clustering and optimization', Engineering Structures, vol. 284, pp. 116016-116016. View/Download from: Publisher's site
Liu, Y, Zhang, X, Zhao, Y, He, Y, Yu, S & Zhu, K 2023, 'Chronos: Accelerating Federated Learning With Resource Aware Training Volume Tuning at Network Edges', IEEE Transactions on Vehicular Technology, vol. 72, no. 3, pp. 3889-3903. View/Download from: Publisher's site View description>>
Due to the limited resources and data privacy issue, last decade witnesses the fast development of Distributed Machine Learning (DML) at network edges. Among all the existing DML paradigms, Federated Learning (FL) would be a promising one, since in FL, each client trains its local model without sharing the raw data with others. A community of clients with the same interest can join together to derive a high-performance model by periodically synchronizing the parameters of their local models under the help of a coordination server. However, FL will encounter the straggler problem at network edges, and hence the synchronization among clients becomes inefficient. It slows down the convergence speed of learning process. To alleviate the straggler problem, we propose a method named Chronos that accelerates FL with training volume tuning in this paper. More specifically, Chronos is a resource aware method that adaptively adjusts the amount of data used by each client for training (i.e. training volume) in each iteration in order to eliminate the synchronization waiting time caused by the heterogeneous and dynamical computing and communication resources. In addition, we theoretically analyze the convergence of Chronos in a non-convex setting and utilize the results for the algorithm design of Chronos in return to guarantee the convergence. Extensive experiments show that compared with the benchmark algorithms (i.e BSP and SSP), Chronos significantly improves convergence speed by up to 6.4×.
Loengbudnark, W, Khalilpour, K, Bharathy, G, Voinov, A & Thomas, L 2023, 'Impact of occupant autonomy on satisfaction and building energy efficiency', Energy and Built Environment, vol. 4, no. 4, pp. 377-385. View/Download from: Publisher's site
Logan, J, Kennedy, PJ & Catchpoole, D 2023, 'A review of the machine learning datasets in mammography, their adherence to the FAIR principles and the outlook for the future', Scientific Data, vol. 10, no. 1, p. 595. View/Download from: Publisher's site View description>>
AbstractThe increasing rates of breast cancer, particularly in emerging economies, have led to interest in scalable deep learning-based solutions that improve the accuracy and cost-effectiveness of mammographic screening. However, such tools require large volumes of high-quality training data, which can be challenging to obtain. This paper combines the experience of an AI startup with an analysis of the FAIR principles of the eight available datasets. It demonstrates that the datasets vary considerably, particularly in their interoperability, as each dataset is skewed towards a particular clinical use-case. Additionally, the mix of digital captures and scanned film compounds the problem of variability, along with differences in licensing terms, ease of access, labelling reliability, and file formats. Improving interoperability through adherence to standards such as the BIRADS criteria for labelling and annotation, and a consistent file format, could markedly improve access and use of larger amounts of standardized data. This, in turn, could be increased further by GAN-based synthetic data generation, paving the way towards better health outcomes for breast cancer.
Long, G, Xie, M, Shen, T, Zhou, T, Wang, X & Jiang, J 2023, 'Multi-center federated learning: clients clustering for better personalization', World Wide Web, vol. 26, no. 1, pp. 481-500. View/Download from: Publisher's site View description>>
AbstractPersonalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises. FL can mitigate the data privacy risk of collaborative training since it merely collects local gradients from users without access to their data. However, FL is fragile in the presence of statistical heterogeneity that is commonly encountered in personalized decision making, e.g., non-IID data over different clients. Existing FL approaches usually update a single global model to capture the shared knowledge of all users by aggregating their gradients, regardless of the discrepancy between their data distributions. By comparison, a mixture of multiple global models could capture the heterogeneity across various clients if assigning the client to different global models (i.e., centers) in FL. To this end, we propose a novel multi-center aggregation mechanism to cluster clients using their models’ parameters. It learns multiple global models from data as the cluster centers, and simultaneously derives the optimal matching between users and centers. We then formulate it as an optimization problem that can be efficiently solved by a stochastic expectation maximization (EM) algorithm. Experiments on multiple benchmark datasets of FL show that our method outperforms several popular baseline methods. The experimental source codes are publicly available on the Github repository (GitHub repository: https://github.com/mingxuts/multi-center-fed-learning).
Lu, Q, Zhu, L, Xu, X, Whittle, J, Zowghi, D & Jacquet, A 2023, 'Operationalizing Responsible AI at Scale: CSIRO Data61's Pattern-Oriented Responsible AI Engineering Approach', Communications of the ACM, vol. 66, no. 7, pp. 64-66. View/Download from: Publisher's site
Lu, X, Xiao, L, Li, P, Ji, X, Xu, C, Yu, S & Zhuang, W 2023, 'Reinforcement Learning-Based Physical Cross-Layer Security and Privacy in 6G', IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 425-466. View/Download from: Publisher's site
Lv, M, Wang, J, Niu, X & Lu, H 2023, 'A newly combination model based on data denoising strategy and advanced optimization algorithm for short-term wind speed prediction', Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 7, pp. 8271-8290. View/Download from: Publisher's site
Lyu, C, Shi, Y, Sun, L & Lin, C-T 2023, 'Community Detection in Multiplex Networks Based on Evolutionary Multitask Optimization and Evolutionary Clustering Ensemble', IEEE Transactions on Evolutionary Computation, vol. 27, no. 3, pp. 728-742. View/Download from: Publisher's site View description>>
Community detection in multiplex networks is an emerging research topic in the field of network science. Existing methods usually ignore the similarities among component layers of a multiplex network when detecting its community structures, which decreases the detection efficiency. In this paper, we decompose the community detection in multiplex networks into two problems and propose a novel algorithm which can detect both the specific community partition for each component layer (layer-level community structure) and the composite community structure shared by all layers. Firstly, by specifying the modularity optimization on a network layer as an optimization task, we model the layer-level community detection as a multi-task optimization problem and employ an evolutionary multi-task optimization algorithm to solve it. In this way, the topology correlations among different layers can be utilized to facilitate the community detection. Secondly, we propose an evolutionary clustering ensemble method to find the composite community structure based on the layer-level community partitions and the multiplex network. The proposed method is tested on both synthetic and real-world benchmark networks and compared with classical and state-of-the-art algorithms. Experimental results show that the proposed algorithm has superior community detection performances on multiplex networks.
Ma, C, Li, J, Ding, M, Liu, B, Wei, K, Weng, J & Poor, HV 2023, 'RDP-GAN: A Rényi-Differential Privacy Based Generative Adversarial Network', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 6, pp. 4838-4852. View/Download from: Publisher's site
Ma, C, Li, J, Wei, K, Liu, B, Ding, M, Yuan, L, Han, Z & Vincent Poor, H 2023, 'Trusted AI in Multiagent Systems: An Overview of Privacy and Security for Distributed Learning', Proceedings of the IEEE, vol. 111, no. 9, pp. 1097-1132. View/Download from: Publisher's site
Ma, X, Xu, H, Gao, H, Bian, M & Hussain, W 2023, 'Real-Time Virtual Machine Scheduling in Industry IoT Network: A Reinforcement Learning Method', IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 2129-2139. View/Download from: Publisher's site
Mas-Tur, A, Roig-Tierno, N, Sarin, S, Haon, C, Sego, T, Belkhouja, M, Porter, A & Merigó, JM 2023, 'Corrigendum to ‘Co-citation, bibliographic coupling and leading authors, institutions and countries in the 50 years of technological forecasting and social change’ [Technol. Forecast. Soc. Change 165 (2021) 120487]', Technological Forecasting and Social Change, vol. 186, pp. 122157-122157. View/Download from: Publisher's site
Matin, A, Islam, MR, Wang, X, Huo, H & Xu, G 2023, 'AIoT for sustainable manufacturing: Overview, challenges, and opportunities', Internet of Things, vol. 24, pp. 100901-100901. View/Download from: Publisher's site
Meng, L, Jiang, X, Huang, J, Zeng, Z, Yu, S, Jung, T-P, Lin, C-T, Chavarriaga, R & Wu, D 2023, 'EEG-Based Brain–Computer Interfaces are Vulnerable to Backdoor Attacks', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 2224-2234. View/Download from: Publisher's site
Mirdad, A, Hussain, FK & Hussain, OK 2023, 'A systematic literature review on pharmaceutical supply chain: research gaps and future opportunities', International Journal of Web and Grid Services, vol. 19, no. 2, pp. 233-258. View/Download from: Publisher's site
Moradi, F, Biloria, N & Prasad, M 2023, 'Analyzing the age-friendliness of the urban environment using computer vision methods', Environment and Planning B: Urban Analytics and City Science, vol. 50, no. 8, pp. 2294-2308. View/Download from: Publisher's site View description>>
The accelerated growth of cities and urban populations over recent decades and the complexity and diversity of urban areas demands proficient spatial affordance assessment especially for the vulnerable sections of the society. Lately machine learning and computer vision models have become highly competent in analyzing urban images for assessing the built environment. This study harnesses the potential of computer vision techniques to assess the age-friendliness of urban areas. The developed machine learning model utilizes Google’s Street View images and is trained using lived experience-based image ratings provided by elderly participants. Newly assigned urban images are accordingly rated for their level of age-friendliness by the model with an accuracy of 85%. This paper elaborates upon the associated literature review, explains the data collection approach and the developed machine learning model. The success of the implementation is also demonstrated, confirming the validity of the proposed methodology.
Motahari, R, Alavifar, Z, Zareh Andaryan, A, Chipulu, M & Saberi, M 2023, 'A multi-objective linear programming model for scheduling part families and designing a group layout in cellular manufacturing systems', Computers & Operations Research, vol. 151, pp. 106090-106090. View/Download from: Publisher's site
Nerse, C, Mohapatra, AR, Oberst, S, Navarro-Payá, D, Etxeberria, J, Matus, JT, Bianco, L, Tucci, MR, Cumino, E, Casacci, LP & Barbero, F 2023, 'Model updating of flowering snapdragon (Antirrhinum litigiosum) biomechanical responses to vibro-acoustic stimuli', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A172-A172. View/Download from: Publisher's site View description>>
The combined variation in gene expression and environmental conditions during flower development can result in phenotypic differences in shape, size, and material composition. Biomechanical responses in flower organs due to external stimuli can be mechanically measured at various levels. Here, we investigate snapdragon (Antirrhinum litigiosum) response to vibro-acoustic stimuli by an interdisciplinary model updating framework. In a climate-controlled setup, sweep signals and artificial signals representative of plant pollinator species were given as excitation input through a loudspeaker to a set of plants; vibrations of the flower organs were measured by laser Doppler vibrometry. Geometric features of the plants were identified using LiDAR combined with photogrammetry, while the density distribution in the flower organs and internal dimensions were estimated using micro-computed tomography scans. A computer model using finite element method was used to identify material properties of the flower organs by combining time domain measurements and dimensional classification. Results demonstrate density and stiffness gradient in the corolla contributing to a modal activity that is adaptive to local conditions and pollinators, but resilient against external noise. The framework outlined herein may give clues to which pollinators induce early-plant responses. [The authors acknowledge the support of the Human Frontier Science Program (HFSP) grant RGP0003/2022.]
Nguyen, DDN, Sood, K, Xiang, Y, Gao, L, Chi, L & Yu, S 2023, 'Toward IoT Node Authentication Mechanism in Next Generation Networks', IEEE Internet of Things Journal, vol. 10, no. 15, pp. 13333-13341. View/Download from: Publisher's site
Nguyen, M, Zhu, H, Sun, H, Nguyen, V, Jin, C & Lin, C-T 2023, 'An evaluation of various spatial audio rendering and presentation techniques to enhance active navigation with sensory augmentation', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A196-A196. View/Download from: Publisher's site View description>>
Active navigation is essential in everyday life and refers to the combination of cognition (spatial mapping, path planning, and decision making) and motor-sensory execution (moving and sensing environment). For people who are blind or have low-vision, auditory and tactile sensory augmentation is critical to active navigation. In assistive technologies, binaural spatial audio rendering is widely adopted. However, the most effective methods to support fluent spatial navigation are still being studied. For example, in a previous study, we demonstrated the feasibility of using spatialized earcons to support a shorelining task. In this work, we use the same shorelining task to explore various forms of spatial earcon presentation with a focus on standardization and effectiveness. We also explore the development of an intuitive auditory grammar for spatial and contextual cues. We conduct psychophysical experiments and present experimental measures such as performance time and accuracy, heart-rate variability, and the NASA task load index.
Nimmy, SF, Hussain, OK, Chakrabortty, RK, Hussain, FK & Saberi, M 2023, 'An optimized Belief-Rule-Based (BRB) approach to ensure the trustworthiness of interpreted time-series decisions', Knowledge-Based Systems, vol. 271, pp. 110552-110552. View/Download from: Publisher's site
Nimmy, SF, Hussain, OK, Chakrabortty, RK, Hussain, FK & Saberi, M 2023, 'Interpreting the antecedents of a predicted output by capturing the interdependencies among the system features and their evolution over time', Engineering Applications of Artificial Intelligence, vol. 117, pp. 105596-105596. View/Download from: Publisher's site
Nirbhav, Malik, A, Maheshwar, Jan, T & Prasad, M 2023, 'Landslide Susceptibility Prediction based on Decision Tree and Feature Selection Methods', Journal of the Indian Society of Remote Sensing, vol. 51, no. 4, pp. 771-786. View/Download from: Publisher's site View description>>
Landslide hazards give rise to considerable demolition and losses to lives in hilly areas. To reduce the destruction in these endangered regions, the prediction of landslide incidents with good accuracy remains a key challenge. Over the years, machine learning models have been used to increase the accuracy and precision of landslide predictions. These machine learning models are sensitive to the data on which they are applied. Feature selection is a crucial task in applying machine learning as meticulously selected features can significantly improve the performance of the machine learning model. These selected features decrease the learning time of the model and increase comprehensibility. In this paper, we have considered three feature selection methods namely chi-squared, extra tree classifier and heat map. The paper substantiates that feature selection can significantly increase the performance of the model. The study was carried out on the landslide data of the Kullu to Rohtang Pass transport corridor in Himachal Pradesh, India. The classification score and receiver operating characteristics (ROC) curves were used to evaluate the model performance. Results exhibited that eliminating one or more features using different feature selection methods increased the comprehensibility of the model by reducing the dimensionality of the dataset. The model achieved an accuracy of 90.74% and an area under the ROC curve (AUROC) value of 0.979. Furthermore, it can be deduced that with a reduced number of features model learns faster without affecting the actual result.
Nirbhav, Malik, A, Maheshwar, Prasad, M, Saini, A & Long, NT 2023, 'A comparative study of different machine learning models for landslide susceptibility prediction: a case study of Kullu-to-Rohtang pass transport corridor, India', Environmental Earth Sciences, vol. 82, no. 7, p. 167. View/Download from: Publisher's site View description>>
Landslide susceptibility prediction can be considered a crucial step in landslide risk assessment. This prediction helps in planning the land use properly. The primary aim of the study is to investigate different machine learning methods and develop anatomy to train and validate the landslide susceptibility prediction models with the help of various statistical techniques. The Kullu–Rohtang pass transport corridor has been selected as the study area. Initially, a landslide inventory was prepared using different sources and nine landslide triggering features were used for further study. All landslide locations in the study area were arbitrarily divided into a ratio of 67:33 to train and test various landslide susceptibility prediction models. The best-triggering features were chosen with the help of the information gain ratio (IGR) defining the predictive capability of different triggering features. Afterwards, five landslide susceptibility prediction models were constructed using a decision tree, K-nearest neighbour (KNN), Gaussian Naïve Bayes, support vector machine (SVM) and multilayer perceptron (MLP). The comparison and validation study of different resulting models was done by applying the receiver operating characteristic (ROC) curve, the kappa index and other statistical methods. Results show that the different models have the outstanding predictive capability with the decision tree model (100%), the Gaussian Naïve Bayes model (100%), the SVM model (100%), and the MLP model (100%) and the KNN model (99.9%). The result indicates statistical differences among various models. The validation results demonstrate the perfect agreement between the expected and predicted landslides along the transport corridor.
Nosouhi, MR, Yu, S, Sood, K, Grobler, M, Jurdak, R, Dorri, A & Shen, S 2023, 'UCoin: An Efficient Privacy Preserving Scheme for Cryptocurrencies', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 1, pp. 242-255. View/Download from: Publisher's site View description>>
In cryptocurrencies, privacy of users is preserved using pseudonymity. However, it has been shown that pseudonymity does not result in anonymity if a users transactions are linkable. This makes cryptocurrencies vulnerable to deanonymization attacks. The current solutions proposed in the literature suffer from at least one of the following issues: (1) requiring a trusted thirdparty entity, (2) poor performance, and (3) incompatible with the standard structure of cryptocurrencies. In this paper, we propose Unlinkable Coin (UCoin), a secure mixbased approach to address these issues. In UCoin, the link between the input (payer) and output (payee) addresses in a transaction is broken. This is done by mixing the transactions of multiple users into a single aggregated transaction in which the output addresses have been secretly shuffled. In our protocol design, we first develop HDCnet, a secure shuffling protocol that enables a group of users to anonymously publish their data. Then, we deploy the proposed HDCnet protocol in the UCoin architecture (as a mixing unit) to generate the aggregate transactions. We show that UCoin (1) does not rely on a trusted thirdparty, (2) can mix 50 transactions in 6.3 seconds that is 18% faster than the current solutions, and (3) is fully compatible with the architecture of cryptocurrencies.
O’Brien, TE, Anselmetti, G, Gkritsis, F, Elfving, VE, Polla, S, Huggins, WJ, Oumarou, O, Kechedzhi, K, Abanin, D, Acharya, R, Aleiner, I, Allen, R, Andersen, TI, Anderson, K, Ansmann, M, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Bardin, JC, Bengtsson, A, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, B, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Campero, J, Chen, Z, Chiaro, B, Chik, D, Cogan, J, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Debroy, DM, Demura, S, Drozdov, I, Dunsworth, A, Erickson, C, Faoro, L, Farhi, E, Fatemi, R, Ferreira, VS, Flores Burgos, L, Forati, E, Fowler, AG, Foxen, B, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Gosula, R, Grajales Dau, A, Gross, JA, Habegger, S, Hamilton, MC, Hansen, M, Harrigan, MP, Harrington, SD, Heu, P, Hoffmann, MR, Hong, S, Huang, T, Huff, A, Ioffe, LB, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Klimov, PV, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, K, Lester, BJ, Lill, AT, Liu, W, Livingston, WP, Locharla, A, Malone, FD, Mandrà, S, Martin, O, Martin, S, McClean, JR, McCourt, T, McEwen, M, Mi, X, Mieszala, A, Miao, KC, Mohseni, M, Montazeri, S, Morvan, A, Movassagh, R, Mruczkiewicz, W, Naaman, O, Neeley, M, Neill, C, Nersisyan, A, Newman, M, Ng, JH, Nguyen, A, Nguyen, M, Niu, MY, Omonije, S, Opremcak, A, Petukhov, A, Potter, R, Pryadko, LP, Quintana, C, Rocque, C, Roushan, P, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shutty, N, Shvarts, V, Skruzny, J, Smith, WC, Somma, RD, Sterling, G, Strain, D, Szalay, M, Thor, D, Torres, A, Vidal, G, Villalonga, B, Vollgraff Heidweiller, C, White, T, Woo, BWK, Xing, C, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zalcman, A, Zhang, Y, Zhu, N, Zobrist, N, Bacon, D, Boixo, S, Chen, Y, Hilton, J, Kelly, J, Lucero, E, Megrant, A, Neven, H, Smelyanskiy, V, Gogolin, C, Babbush, R & Rubin, NC 2023, 'Purification-based quantum error mitigation of pair-correlated electron simulations', Nature Physics, vol. 19, no. 12, pp. 1787-1792. View/Download from: Publisher's site View description>>
AbstractAn important measure of the development of quantum computing platforms has been the simulation of increasingly complex physical systems. Before fault-tolerant quantum computing, robust error-mitigation strategies were necessary to continue this growth. Here, we validate recently introduced error-mitigation strategies that exploit the expectation that the ideal output of a quantum algorithm would be a pure state. We consider the task of simulating electron systems in the seniority-zero subspace where all electrons are paired with their opposite spin. This affords a computational stepping stone to a fully correlated model. We compare the performance of error mitigations on the basis of doubling quantum resources in time or in space on up to 20 qubits of a superconducting qubit quantum processor. We observe a reduction of error by one to two orders of magnitude below less sophisticated techniques such as postselection. We study how the gain from error mitigation scales with the system size and observe a polynomial suppression of error with increased resources. Extrapolation of our results indicates that substantial hardware improvements will be required for classically intractable variational chemistry simulations.
Oberst, S & Sepehrirahnama, S 2023, 'A case study on generative learning approaches in a studio and flipped class-room setting for increased learning outcomes', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A59-A59. View/Download from: Publisher's site View description>>
Teaching and learning during Covid-19 have been strongly affected by lockdowns, isolated online learning, and the sudden requirement to alternatively assess students while considering the effect internet-based information sources. Here, we present outcomes on the learning outcome of students returning from distance learning into the face-to-face mode, studying the subject “Embedded Mechatronic Systems” employing increasingly methods of Generative Learning Theory (GLT) in a flipped classroom environment, using studio and project-based learning approaches. By introducing a group project component, the formerly disconnected laboratory components become strongly connected with students being exposed to practical aspects and teamwork, generating reflected reports and videos of their practical work. To overcome the effects of Covi-d19, tighter assessments, and in-person engagements is emphasised. Viva-voces have been introduced and AI invigilated final exams have been altered to in-class room quizzes, while monitoring the cohort’s performance over 3 sessions. Our data indicate that face-to-face learning and hands-on practice with peers using self-testing and self-explaining strategies enacts higher outcomes, opposed to remote modes of teaching. Our results exemplify on how to move back into face-to-face teaching with future steps to increase learning outcomes using the flipped classroom, GLT, and a studio setting being discussed.
Olszak, CM, Zurada, J & Kozanoglu, DC 2023, 'Introduction to the Business Intelligence for Innovative, Collaborative and Sustainable Development of Organizations in Digital Era Mini-track', Proceedings of the Annual Hawaii International Conference on System Sciences, vol. 2023-January, pp. 257-258.
Patibanda, R, Hill, C, Saini, A, Li, X, Chen, Y, Matviienko, A, Knibbe, J, van den Hoven, E & Mueller, FF 2023, 'Auto-Paizo Games: Towards Understanding the Design of Games That Aim to Unify a Player’s Physical Body and the Virtual World', Proceedings of the ACM on Human-Computer Interaction, vol. 7, no. CHI PLAY, pp. 893-918. View/Download from: Publisher's site View description>>
Most digital bodily games focus on the body as they use movement as input. However, they also draw the player’s focus away from the body as the output occurs on visual displays, creating a divide between the physical body and the virtual world. We propose a novel approach – the 'Body as a Play Material' – where a player uses their body as both input and output to unify the physical body and the virtual world. To showcase this approach, we designed three games where a player uses one of their hands (input) to play against the other hand (output) by loaning control over its movements to an Electrical Muscle Stimulation (EMS) system. We conducted a thematic analysis on the data obtained from a field study with 12 participants to articulate four player experience themes. We discuss our results about how participants appreciated the engagement with the variety of bodily movements for play and the ambiguity of using their body as a play material. Ultimately, our work aims to unify the physical body and the virtual world.
Patibanda, R, Saini, A, Overdevest, N, Montoya, MF, Li, X, Chen, Y, Nisal, S, Andres, J, Knibbe, J, van den Hoven, E & Mueller, FF 2023, 'Fused Spectatorship: Designing Bodily Experiences Where Spectators Become Players', Proceedings of the ACM on Human-Computer Interaction, vol. 7, no. CHI PLAY, pp. 769-802. View/Download from: Publisher's site View description>>
Spectating digital games can be exciting. However, due to its vicarious nature, spectators often wish to engage in the gameplay beyond just watching and cheering. To blur the boundaries between spectators and players, we propose a novel approach called 'Fused Spectatorship', where spectators watch their hands play games by loaning bodily control to a computational Electrical Muscle Stimulation (EMS) system. To showcase this concept, we designed three games where spectators loan control over both their hands to the EMS system and watch them play these competitive and collaborative games. A study with 12 participants suggested that participants could not distinguish if they were watching their hands play, or if they were playing the games themselves. We used our results to articulate four spectator experience themes and four fused spectator types, the behaviours they elicited and offer one design consideration to support each of these behaviours. We also discuss the ethical design considerations of our approach to help game designers create future fused spectatorship experiences.
Pei, C, Qiu, Y, Li, F, Huang, X, Si, Y, Li, Y, Zhang, X, Chen, C, Liu, Q, Cao, Z, Ding, N, Gao, S, Alho, K, Yao, D & Xu, P 2023, 'The different brain areas occupied for integrating information of hierarchical linguistic units: a study based on EEG and TMS', Cerebral Cortex, vol. 33, no. 8, pp. 4740-4751. View/Download from: Publisher's site View description>>
AbstractHuman language units are hierarchical, and reading acquisition involves integrating multisensory information (typically from auditory and visual modalities) to access meaning. However, it is unclear how the brain processes and integrates language information at different linguistic units (words, phrases, and sentences) provided simultaneously in auditory and visual modalities. To address the issue, we presented participants with sequences of short Chinese sentences through auditory, visual, or combined audio-visual modalities while electroencephalographic responses were recorded. With a frequency tagging approach, we analyzed the neural representations of basic linguistic units (i.e. characters/monosyllabic words) and higher-level linguistic structures (i.e. phrases and sentences) across the 3 modalities separately. We found that audio-visual integration occurs in all linguistic units, and the brain areas involved in the integration varied across different linguistic levels. In particular, the integration of sentences activated the local left prefrontal area. Therefore, we used continuous theta-burst stimulation to verify that the left prefrontal cortex plays a vital role in the audio-visual integration of sentence information. Our findings suggest the advantage of bimodal language comprehension at hierarchical stages in language-related information processing and provide evidence for the causal role of the left prefrontal regions in processing information of audio-visual sentences.
Peng, P, Sun, H, Marcireau, A, Nguyen, M, Zhu, H, Lin, C-T & Jin, C 2023, 'Auditory sensory augmentation to support table tennis games for people with vision loss', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A197-A197. View/Download from: Publisher's site View description>>
People with vision loss often face limitations in regular sports games with standard rules and equipment. For example, in current blind table tennis, conventional rules are modified so that the ball rolls along the table instead of bouncing. In this work, we propose an auditory sensory augmentation system to support traditional table tennis in three dimensions. We capture the trajectory of the table tennis ball using two neuromorphic event cameras and sonify the path of the ball using loudspeakers mounted near the left and right edges of the playing table. The two event cameras capture rapid changes in brightness allowing fast and precise ball tracking. The ball's 3D trajectory is then sonified using four lines of loudspeakers mounted at two different heights near the left and right edges of the playing table. We present a preliminary implementation and investigation of the proposed sensory augmentation system with a focus on the technical and perceptual challenges.
Pérez-Romero, ME, Alfaro-García, VG, Merigó, JM & Flores-Romero, MB 2023, 'Covariance Logarithmic Aggregation Operators in Decision-Making Processes', Cybernetics and Systems, vol. 54, no. 2, pp. 220-238. View/Download from: Publisher's site
Phillips, L, Oberst, S & Sepehrirahnama, S 2023, 'Sensor fusion for simultaneous measurement of micro-vibrations', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A141-A142. View/Download from: Publisher's site View description>>
Low-amplitude micro-vibrations are common in nature and engineering, throughout structural applications, and biology. The ability to accurately measure and analyse these vibrations in the presence of noise (unwanted signal content) has far reaching consequences across many fields of acoustics. Methodologies for the enhancement and improvement of such signals are, therefore, sought. We explore the capability of sensor fusion in combination with Kalman filtering (KF), using pairs of accelerometers to improve measurement of low-amplitude micro-vibrations. Prior research of pairing sensor fusion with machine learning approaches like KF, support vector machines, or coherence analysis reported up to 90% reductions in “ghost” detections. This research attempts to extend this success to micro-vibrations where broadband noise, and external perturbations can have dramatic impacts on measurability. A pair of accelerometers has been placed both parallel and perpendicular to the axis of an excitation in pine timber planks of varying dimensions and cuts. Simultaneous measurement of ∼5 N excitations with an automated hammer at varying distances are recorded and patterns observed in the time domain through preliminary analysis in MATLAB. Features identifiable from this data become clearer as compared to conventional approaches and have potential applications in non-invasive early predictive analysis of structures for timber pest control.
Pinilla, A, Voigt-Antons, J-N, Garcia, J, Raffe, W & Möller, S 2023, 'Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals', Frontiers in Virtual Reality, vol. 3, p. 964754. View/Download from: Publisher's site View description>>
This manuscript explores the development of a technique for detecting the affective states of Virtual Reality (VR) users in real-time. The technique was tested with data from an experiment where 18 participants observed 16 videos with emotional content inside a VR home theater, while their electroencephalography (EEG) signals were recorded. Participants evaluated their affective response toward the videos in terms of a three-dimensional model of affect. Two variants of the technique were analyzed. The difference between both variants was the method used for feature selection. In the first variant, features extracted from the EEG signals were selected using Linear Mixed-Effects (LME) models. In the second variant, features were selected using Recursive Feature Elimination with Cross Validation (RFECV). Random forest was used in both variants to build the classification models. Accuracy, precision, recall and F1 scores were obtained by cross-validation. An ANOVA was conducted to compare the accuracy of the models built in each variant. The results indicate that the feature selection method does not have a significant effect on the accuracy of the classification models. Therefore, both variations (LME and RFECV) seem equally reliable for detecting affective states of VR users. The mean accuracy of the classification models was between 87% and 93%.
Power, T, Kennedy, P, Chen, H, Martinez-Maldonado, R, McGregor, C, Johnson, A, Townsend, L & Hayes, C 2023, 'Learning to Manage De-escalation Through Simulation: An Exploratory Study', Clinical Simulation in Nursing, vol. 77, pp. 23-29. View/Download from: Publisher's site
Pratt, L, Johnston, A & Pietroni, N 2023, 'Bending the light: Next generation anamorphic sculptures.', Comput. Graph., vol. 114, pp. 210-218. View/Download from: Publisher's site
Prior, J & Leaney, J 2023, 'Software Development Practice as Baradian Entanglement', Human Organization, vol. 82, no. 1, pp. 25-35. View/Download from: Publisher's site View description>>
Software development practice is a messy, complicated, and constantly shifting human endeavor. Barad’s concept of “entanglement” helps to theorize complex sociotechnical systems. We are testing the application of this theory to understand and explain software development practices, as our work appears to be the only ethnographic research using Barad in any technology industry. Our continual aim is to understand large-scale, collaborative software development more deeply in practice and to discover appropriate theories that describe our observations and insights. Both authors are experienced software engineers and researchers. Through an ongoing longitudinal ethnographic study at a large Australian software development company, we explore, support, and improve the lived experience and practice of the software developers that work there. Ethnographic insights and an appreciation of the mutual constitution of situated phenomena have expanded over several years into an elaboration of entanglement as a more insightful explanation of software development practice. This research is having a significant impact on the participant developers and organization, including changes in measurement practices, mentoring, knowledge management, and innovation.
Guided by the connections between hypergraphs and exterior algebras, we study Turán and Ramsey type problems for alternating multilinear maps. This study lies at the intersection of combinatorics, group theory, and algebraic geometry, and has origins in the works of Lovász (Proc. Sixth British Combinatorial Conf., 1977), Buhler, Gupta, and Harris (J. Algebra, 1987), and Feldman and Propp (Adv. Math., 1992). Our main result is a Ramsey theorem for alternating bilinear maps. Given s, t ∈ N, s, t ≥ 2, and an alternating bilinear map ϕ: V × V → U with dim(V) ≥ s ·t4, we show that there exists either a dimension-s subspaceW ≤ V such that dim(span(ϕ(W,W))) = 0, or a dimension-t subspaceW ≤ V such that dim(span(ϕ(W,W))) = (t2). This result has natural group-theoretic (for finite p-groups) and geometric (for Grassmannians) implications, and leads to new Ramsey-type questions for varieties of groups and Grassmannians.
Qu, Y, Ma, L, Ye, W, Zhai, X, Yu, S, Li, Y & Smith, D 2023, 'Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence', Big Data Mining and Analytics, vol. 6, no. 4, pp. 443-464. View/Download from: Publisher's site
Rajawat, AS, Goyal, SB, Chauhan, C, Bedi, P, Prasad, M & Jan, T 2023, 'Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm', Electronics, vol. 12, no. 1, pp. 217-217. View/Download from: Publisher's site View description>>
Agile product development cycles and re-configurable Industrial Internet of Things (IIoT) allow more flexible and resilient industrial production systems that can handle a broader range of challenges and improve their productivity. Reinforcement Learning (RL) was shown to be able to support industrial production systems to be flexible and resilient to respond to changes in real time. This study examines the use of RL in a wide range of adaptive cognitive systems with IIoT-edges in manufacturing processes. We propose a cognitive adaptive system using IIoT with RL (CAS-IIoT-RL) and our experimental analysis showed that the proposed model showed improvements with adaptive and dynamic decision controls in challenging industrial environments.
Rehman, A, Razzak, I & Xu, G 2023, 'Federated Learning for Privacy Preservation of Healthcare Data From Smartphone-Based Side-Channel Attacks', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 2, pp. 684-690. View/Download from: Publisher's site View description>>
Federated learning has recently emerged as a striking framework for allowing machine and deep learning models with thousands of participants to have distributed training to preserve the privacy of users' data. Federated learning comes with the pros of allowing all participants the possibility of creating robust models even in the absence of sufficient training data. Meanwhile, the participants are allowed to stay anonymous in the process. Recently, Smartphone usage has increased on a huge scale due to its portability and ability to perform many daily life tasks. Typing on a smartphone's soft keyboard generates vibrations that could be abused to distinguish the typed keys, aiding side-channel attacks. This data can be in the form of clinical notes, medical information, username, and passwords. The attackers can steal this data using smartphone hardware sensors. This study proposes a novel framework based on federated learning for side-channel attack detection to secure this information. We collected a dataset from 10 Android smartphone users who were asked to type on the smartphone soft keyboard. We convert this dataset into two windows of five users to make two clients train local models. The federated learning-based framework aggregates model updates contributed by two clients and trains the DNN model individually on the dataset. To reduce the over-fitting factor, each client examines the findings three times. Experiments reveal that the DNN model has a higher accuracy of 80.09\%, showing that the proposed framework can efficiently detect side-channel attacks.
Reinhartz-Berger, I, Zdravkovic, J & Gill, A 2023, 'Guest editorial for EMMSAD’2021 special section', Software and Systems Modeling, vol. 22, no. 1, pp. 9-11. View/Download from: Publisher's site
Rigi, F, Saberi, M & Ebrahimi, M 2023, 'Improved drought tolerance in Festuca ovina L. using plant growth promoting bacteria', Journal of Arid Land, vol. 15, no. 6, pp. 740-755. View/Download from: Publisher's site
Saberi, Z, K. Hussain, O & Saberi, M 2023, 'Data-driven personalized assortment optimization by considering customers’ value and their risk of churning: Case of online grocery shopping', Computers & Industrial Engineering, vol. 182, pp. 109328-109328. View/Download from: Publisher's site
Sanderson, B, Field, JD, Kocaballi, AB, Estcourt, LJ, Magrabi, F, Wood, EM & Coiera, EW 2023, 'Multicenter, multidisciplinary user‐centered design of a clinical decision‐support and simulation system for massive transfusion', Transfusion, vol. 63, no. 5, pp. 993-1004. View/Download from: Publisher's site View description>>
AbstractBackgroundManaging critical bleeding with massive transfusion (MT) requires a multidisciplinary team, often physically separated, to perform several simultaneous tasks at short notice. This places a significant cognitive load on team members, who must maintain situational awareness in rapidly changing scenarios. Similar resuscitation scenarios have benefited from the use of clinical decision support (CDS) tools.Study Design and MethodsA multicenter, multidisciplinary, user‐centered design (UCD) study was conducted to design a computerized CDS for MT. This study included analysis of the problem context with a cognitive walkthrough, development of a user requirement statement, and co‐design with users of prototypes for testing. The final prototype was evaluated using qualitative assessment and the System Usability Scale (SUS).ResultsEighteen participants were recruited across four institutions. The first UCD cycle resulted in the development of four prototype interfaces that addressed the user requirements and context of implementation. Of these, the preferred interface was further developed in the second UCD cycle to create a high‐fidelity web‐based CDS for MT. This prototype was evaluated by 15 participants using a simulated bleeding scenario and demonstrated an average SUS of 69.3 (above average, SD 16) and a clear interface with easy‐to‐follow blood product tracking.DiscussionWe used a UCD process to explore a highly complex clinical scenario and develop a prototype CDS for MT that incorporates distributive situational awareness, supports multiple user roles, and allows simulated MT training. Evaluation of the impact of this prototype on the efficacy and efficiency of managing MT is currentl...
Sanderson, BJ, Field, JD, Kocaballi, AB, Estcourt, LJ, Magrabi, F, Wood, EM & Coiera, E 2023, 'Clinical decision support versus a paper‐based protocol for massive transfusion: Impact on decision outcomes in a simulation study', Transfusion, vol. 63, no. 12, pp. 2225-2233. View/Download from: Publisher's site View description>>
AbstractBackgroundManagement of major hemorrhage frequently requires massive transfusion (MT) support, which should be delivered effectively and efficiently. We have previously developed a clinical decision support system (CDS) for MT using a multicenter multidisciplinary user‐centered design study. Here we examine its impact when administering a MT.Study Design and MethodsWe conducted a randomized simulation trial to compare a CDS for MT with a paper‐based MT protocol for the management of simulated hemorrhage. A total of 44 specialist physicians, trainees (residents), and nurses were recruited across critical care to participate in two 20‐min simulated bleeding scenarios. The primary outcome was the decision velocity (correct decisions per hour) and overall task completion. Secondary outcomes included cognitive workload and System Usability Scale (SUS).ResultsThere was a statistically significant increase in decision velocity for CDS‐based management (mean 8.5 decisions per hour) compared to paper based (mean 6.9 decisions per hour; p .003, 95% CI 0.6–2.6). There was no significant difference in the overall task completion using CDS‐based management (mean 13.3) compared to paper‐based (mean 13.2; p .92, 95% CI ‐1.2–1.3). Cognitive workload was statistically significantly lower using the CDS compared to the paper protocol (mean 57.1 vs. mean 64.5, p .005, 95% CI 2.4–12.5). CDS usability was assessed as a SUS score of 82.5 (IQR 75–87.5).DiscussionCompared to paper‐based management, CDS‐based MT supports more time‐efficient decision‐making by users with limited CDS training and achieves similar overall task completion whil...
Scriven, A, Kedziora, DJ, Musial, K & Gabrys, B 2023, 'The Technological Emergence of AutoML: A Survey of Performant Software and Applications in the Context of Industry', Foundations and Trends® in Information Systems, vol. 7, no. 1-2, pp. 1-252. View/Download from: Publisher's site
Sepehrirahnama, S, Oberst, S, Croft, BE & Hanson, D 2023, 'Investigating sound absorption in rail tunnels using wave decomposition', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A180-A180. View/Download from: Publisher's site View description>>
Acoustic noise in trains is a more prevalent problem in tunnels as compared to open track scenarios. This is mainly due to less acoustic radiation relative to increasing contributions of reflections and reverberation. Sound absorbing panels on a tunnel wall and in the track four-foot perform better above 1 kHz with absorption coefficients larger than 0.8. To investigate sound absorption below 1 kHz, we employ wave decomposition into incidence, reflection and absorption components for a section of a given underground tunnel design. A Finite Element (FE) 2D model of a carriage and tunnel is developed, representing a tangent portion of a rail track and including the noise power spectra from the rail-wheel interactions for three different roughness scenarios. The FE model, compared to a Ray Tracing one, provides precisely imposed boundary conditions and the pressure field of the entire tunnel interior. Our results can identify the performance of current panels, absorbing significantly less noise power in the lower frequency range, especially within the 0.3–0.5 kHz interval. The insights from wave decomposition analysis can lead to solutions to increase absorption by changing the reflection pattern below 1 kHz band, improving the passenger comfort during a longer train ride.
Shamsi, A, Asgharnezhad, H, Bouchani, Z, Jahanian, K, Saberi, M, Wang, X, Razzak, I, Alizadehsani, R, Mohammadi, A & Alinejad-Rokny, H 2023, 'A novel uncertainty-aware deep learning technique with an application on skin cancer diagnosis', Neural Computing and Applications, vol. 35, no. 30, pp. 22179-22188. View/Download from: Publisher's site View description>>
AbstractSkin cancer, primarily resulting from the abnormal growth of skin cells, is among the most common cancer types. In recent decades, the incidence of skin cancer cases worldwide has risen significantly (one in every three newly diagnosed cancer cases is a skin cancer). Such an increase can be attributed to changes in our social and lifestyle habits coupled with devastating man-made alterations to the global ecosystem. Despite such a notable increase, diagnosis of skin cancer is still challenging, which becomes critical as its early detection is crucial for increasing the overall survival rate. This calls for advancements of innovative computer-aided systems to assist medical experts with their decision making. In this context, there has been a recent surge of interest in machine learning (ML), in particular, deep neural networks (DNNs), to provide complementary assistance to expert physicians. While DNNs have a high processing capacity far beyond that of human experts, their outputs are deterministic, i.e., providing estimates without prediction confidence. Therefore, it is of paramount importance to develop DNNs with uncertainty-awareness to provide confidence in their predictions. Monte Carlo dropout (MCD) is vastly used for uncertainty quantification; however, MCD suffers from overconfidence and being miss calibrated. In this paper, we use MCD algorithm to develop an uncertainty-aware DNN that assigns high predictive entropy to erroneous predictions and enable the model to optimize the hyper-parameters during training, which leads to more accurate uncertainty quantification. We use two synthetic (two moons and blobs) and a real dataset (skin cancer) to validate our algorithm. Our experiments on these datasets prove effectiveness of our approach in quantifying reliable uncertainty. Our method achieved 85.65 ± 0.18 prediction accuracy, 83.03 ± 0.25 uncertainty accuracy, and 1.93 ± 0.3 expected calibration error ou...
Sharma, M, Joshi, S, Prasad, M & Bartwal, S 2023, 'Overcoming barriers to circular economy implementation in the oil & gas industry: Environmental and social implications', Journal of Cleaner Production, vol. 391, pp. 136133-136133. View/Download from: Publisher's site View description>>
This anticipated consumer demand has put unprecedented pressure on natural resources. Being the highest contributor in the energy transition, Oil & gas (O&G) industry needs to lessen the negative impact of climate change and natural disasters. To combat the impact of emissions and a move towards circularity, O&G industry has undertaken numerous initiatives including energy efficiency, process fuel improvements, and technological transformation etc. But due to certain barriers O&G industry is unable to embrace Circular Economy (CE) implementation in the firms. Therefore, this study has proposed a model to examine the existing critical barriers and suggest strategies to overcome the barriers. The current study has employed an extensive analysis using a hybrid methodology of Fuzzy-DEMATEL (F-DEMATEL) and Best Worst Method (BWM) for assessing the barriers and ranking the strategies. The results showed that ‘knowledge barriers’ are the most critical in the O&G industry that hampers the implementation of CE currently. Further, the strategies ‘Developing collaborative model’ and ‘Internal research and development, innovation’ are the two most significant strategies that may help to reduce the barriers to a minimum. The findings, social and environmental implications are beneficial for the stakeholders and policy-makers to support the transition to CE.
Sharma, R, Goel, T, Tanveer, M, Lin, CT & Murugan, R 2023, 'Deep-Learning-Based Diagnosis and Prognosis of Alzheimer’s Disease: A Comprehensive Review', IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 3, pp. 1123-1138. View/Download from: Publisher's site
Sharma, RK, Bharathy, G, Karimi, F, Mishra, AV & Prasad, M 2023, 'Thematic Analysis of Big Data in Financial Institutions Using NLP Techniques with a Cloud Computing Perspective: A Systematic Literature Review.', Inf., vol. 14, no. 10, pp. 577-577. View/Download from: Publisher's site View description>>
This literature review explores the existing work and practices in applying thematic analysis natural language processing techniques to financial data in cloud environments. This work aims to improve two of the five Vs of the big data system. We used the PRISMA approach (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for the review. We analyzed the research papers published over the last 10 years about the topic in question using a keyword-based search and bibliometric analysis. The systematic literature review was conducted in multiple phases, and filters were applied to exclude papers based on the title and abstract initially, then based on the methodology/conclusion, and, finally, after reading the full text. The remaining papers were then considered and are discussed here. We found that automated data discovery methods can be augmented by applying an NLP-based thematic analysis on the financial data in cloud environments. This can help identify the correct classification/categorization and measure data quality for a sentiment analysis.
Shen, M, Ye, K, Liu, X, Zhu, L, Kang, J, Yu, S, Li, Q & Xu, K 2023, 'Machine Learning-Powered Encrypted Network Traffic Analysis: A Comprehensive Survey', IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 791-824. View/Download from: Publisher's site
Shen, S, Wu, X, Sun, P, Zhou, H, Wu, Z & Yu, S 2023, 'Optimal privacy preservation strategies with signaling Q-learning for edge-computing-based IoT resource grant systems', Expert Systems with Applications, vol. 225, pp. 120192-120192. View/Download from: Publisher's site
Shen, S, Xie, L, Zhang, Y, Wu, G, Zhang, H & Yu, S 2023, 'Joint Differential Game and Double Deep Q-Networks for Suppressing Malware Spread in Industrial Internet of Things', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 5302-5315. View/Download from: Publisher's site
Shi, K, Peng, X, Lu, H, Zhu, Y & Niu, Z 2023, 'Application of Social Sensors in Natural Disasters Emergency Management: A Review', IEEE Transactions on Computational Social Systems, vol. 10, no. 6, pp. 3143-3158. View/Download from: Publisher's site View description>>
Natural disasters are public emergencies characterized by suddenness, universality, and nonconventionality. Realizing the early warning, monitoring, and intervention of natural disasters and their derivative social impacts is significant for reducing the disasters’ damage and benefits the maintenance of social stability. Social sensors are ubiquitous sensors based on social network platforms. It uses the concepts and methods of physical space to mine social signals that integrate human perception and intelligence in cyberspace. Compared with traditional physical sensors, social sensors represent a crucial data acquisition channel in the emergency management of natural disasters and have the advantages of real time, comprehensive coverage, low cost, and flexible deployment. This article reviews the application of social sensors in natural disasters emergency management. We summarize the application functions of social sensors into three categories: natural disaster situation awareness and event detection, disaster information dissemination and communication, and disaster sentiment analysis and public opinion mining. Based on the above functions, this article analyzes the research status, data, technical methods, and application systems. Finally, this article proposes a research trend of applying social sensors in natural disaster emergency management according to the requirements of real scenarios.
Smith, H & Hussain, W 2023, 'Is it time we changed the way we manage melanoma in situ of the trunk and limbs?', British Journal of Dermatology, vol. 188, no. 5, pp. 685-687. View/Download from: Publisher's site View description>>
There is little evidence on the optimal clinical and histological margins required to reduce local recurrence in melanoma in situ (MIS). Our aim was to identify the number of lesions on the trunk and limbs with histological clearance > 1 mm after initial narrow-margin excision. In our cohort 93.6% were considered clear after initial exclusion with no residual MIS seen when further wide local excision was carried out.
Song, L, Wang, H, Zhang, G & Yu, S 2023, 'FedInf: Social influence prediction with federated learning', Neurocomputing, vol. 548, pp. 126407-126407. View/Download from: Publisher's site
Song, Y, Lu, J, Lu, H & Zhang, G 2023, 'Learning Data Streams With Changing Distributions and Temporal Dependency', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 8, pp. 3952-3965. View/Download from: Publisher's site View description>>
In a data stream, concept drift refers to unpredictable distribution changes over time, which violates the identical-distribution assumption required by conventional machine learning methods. Current concept drift adaptation techniques mostly focus on a data stream with changing distributions. However, since each variable of a data stream is a time series, these variables normally have temporal dependency problems in the real world. How to solve concept drift and temporal dependency problems at the same time is rarely discussed in the concept-drift literature. To solve this situation, this article proves and validates that the testing error decreases faster if a predictor is trained on a temporally reconstructed space when drift occurs. Based on this theory, a novel drift adaptation regression (DAR) framework is designed to predict the label variable for data streams with concept drift and temporal dependency. A new statistic called local drift degree (LDD⁺) is proposed and used as a drift adaptation technique in the DAR framework to discard outdated instances in a timely way, thereby guaranteeing that the most relevant instances will be selected during the training process. The performance of DAR is demonstrated by a set of experimental evaluations on both synthetic data and real-world data streams.
Sood, K, Nguyen, DDN, Qu, Y, Cui, L, Karmakar, KK & Yu, S 2023, 'Security Challenges and Potential Solutions in Aerial-Terrestrial Wireless Networking', IEEE Internet of Things Magazine, vol. 6, no. 4, pp. 118-123. View/Download from: Publisher's site
Sood, K, Nosouhi, MR, Kumar, N, Gaddam, A, Feng, B & Yu, S 2023, 'Accurate Detection of IoT Sensor Behaviors in Legitimate, Faulty and Compromised Scenarios', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 1, pp. 288-300. View/Download from: Publisher's site View description>>
In smart farming sector, Internet of Things (IoT) based smart sensing systems are vulnerable to failure, malfunction, and malicious attacks. Also, sensors are deployed often in an alien and harsh environment. Here, the conditions are not well supportive which either causes the sensor to fail prematurely or gives unusual and erroneous readings, known as outliers. This effects the smart networks performance and decision-making ability in many ways. Therefore, it is important to accurately detect the IoT sensor behaviour in legitimate, faulty, and compromised or attack scenarios. To distinguish the sensor behaviour in different scenarios we have proposed a feasible approach using spatial correlation theory which is validated using Morans I index tool. We have used Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) models to test our approach. For real-time anomaly detection we have used an edge computing technology. We have compared the proposed approach, using Forest Fire real dataset, with the three existing recent works. Our results are promising in terms of accurate detection of IoT sensor behaviours in real-time. This will assist the precision farming industry in making better decisions to securely manage IoT field network, increase productivity, and improves operational efficiency.
Sood, S & Pattinson, H 2023, 'Marketing Education Renaissance Through Big Data Curriculum: Developing Marketing Expertise Using AI Large Language Models', International Journal of Innovation and Economic Development, vol. 8, no. 6, pp. 23-40. View/Download from: Publisher's site View description>>
Utilising big data sources and artificial intelligence (AI) tools with marketing activities and analysis contrast with questionnaires and small n observations, essentially creating a renaissance in marketing education. As a result, marketing education keeps pace with AI developments and ensures learners (or students) prepare for the demands of the modern marketing landscape 2025-30. The authors advocate a central focus on a big data-driven marketing curriculum for marketing education. Such a curriculum places AI and machine learning center stage to help understand, analyze and utilize large and complex marketing datasets for predictive marketing. In doing so, the potential exists for practitioners to link marketing strategy directly with marketing execution, allowing learners to use big data and AI for upstream strategy design and marketing plan development while downstream predicting the results of marketing campaigns, programs, and initiatives But necessary changes in pedagogy are creating adaptive learning experiences breaking free from traditional assessments In our model of learning educators enable the development of practical marketing expertise using the techniques and tools of micro-testing to nudge learners using Python data science notebooks. Overall, a renaissance in marketing education is made possible with a focus on a big data AI tools-driven curriculum. Such attention ensures learners prepare for the demands of the modern marketing landscape, moving well beyond marketing analytics using the AI technologies of Large Language Models, further expanding the use of big data Learners use role play, witnessing firsthand experiences fulfilling new hitherto emerging marketing roles By 2025, Educators fostering a big data AI-focused marketing education curriculum ensure the next generation of AI marketers will eagerly shape the future of marketing practice and behavior with new roles combining human work with AI.
Soomro, WA, Guo, Y, Lu, H, Jin, J, Shen, B & Zhu, J 2023, 'AC Loss in High-Temperature Superconducting Bulks Subjected to Alternating and Rotating Magnetic Fields', Materials, vol. 16, no. 2, pp. 633-633. View/Download from: Publisher's site View description>>
High-temperature superconductor (HTS) bulks have demonstrated extremely intriguing potential for industrial and commercial applications due to their capability to trap significantly larger magnetic fields than conventional permanent magnets. The magnetic field in electrical rotating machines is a combination of alternating and rotational fields. In contrast, all previous research on the characterization of electromagnetic properties of HTS have solely engrossed on the alternating AC magnetic fields and the associated AC loss. This research paper gives a thorough examination of the AC loss measurement under various conditions. The obtained results are compared to the finite element-based H-formulation. The AC loss is measured at various amplitudes of circular flux density patterns and compared with the AC loss under one-dimensional alternating flux density. The loss variation has also been studied at other frequencies. The findings in this research paper provide more insights into material characterization, which will be useful in the design of future large-scale HTS applications.
Sulimani, H, Sajjad, AM, Alghamdi, WY, Kaiwartya, O, Jan, T, Simoff, S & Prasad, M 2023, 'Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment', Transactions on Emerging Telecommunications Technologies, vol. 34, no. 11. View/Download from: Publisher's site View description>>
AbstractA decentralized service placement policy plays a key role in distributed systems, such as fog computing, where sharing workloads fairly among active computing nodes is critical. A decentralized policy is an inherent feature of the service placement process that may improve load balancing among computers and can reduce the latency in many real‐time Internet of Things (IoT) applications. This article proposes reinforcement optimization for a decentralized service placement policy, which attempts to mitigate some of the drawbacks of existing service placement policies. Matching task size with node specifications and the allocation of less popular but time‐sensitive applications in the fog layer are the primary contributions of this study. Extensive experimental comparisons are made between the proposed algorithm and other well‐known algorithms over service latency, network usage, and computing usage using the iFogSim simulator. A microservice‐based application with varying sizes of computing requests are tested experimentally and show that the proposed algorithm effectively serves computing instances that are closer to users, reducing service latency and network usage. Compared to the existing models, the proposed modified algorithm reduces service latency by 24.1%, network usage by 4%, and computing usage by 20%, thus highlighting positive outcomes when using the proposed algorithm for fog analytics in future real‐time IoT applications.
Sun, L, Chang, Y-C, Lyu, C, Shi, Y, Shi, Y & Lin, C-T 2023, 'Toward multi-target self-organizing pursuit in a partially observable Markov game', Information Sciences, vol. 648, pp. 119475-119475. View/Download from: Publisher's site
Sun, X, Cheng, H, Liu, B, Li, J, Chen, H, Xu, G & Yin, H 2023, 'Self-Supervised Hypergraph Representation Learning for Sociological Analysis', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11860-11871. View/Download from: Publisher's site
T., Y & D., C 2023, 'A Typology of Competitive Strategies for Social Enterprises', Journal of Social Entrepreneurship, pp. 1-27. View/Download from: Publisher's site View description>>
This article tackles the limited theorising on social enterprises’ (SEs) decisions on the product or service mix, quality, pricing, and the targeted beneficiaries by proposing a typology of competitive strategies for them. The paper empirically observes how SEs react to the challenges faced by the marketisation of their fields. The context of this study is the supplementary education of the disabled in Turkey, a field where increased state coverage led to the entrance of many profit-focused counterparts. Based on a Grounded Theory methodology and a longitudinal dataset including ten cases, the study developed a unique typology comprising three competitive strategies, i.e., innovator, enforcer, and includer. The findings illustrate various strategic responses to heightened competition from incumbent SEs. However, deviation of these strategic responses from the typology appeared to be detrimental in the long-term. By shedding light on the intricacies of the hybrid nature of SEs and considering changes in their competitive environment over time, this study concludes with a summary of contributions to theory, practice, and policy.
Tan, J, Goyal, SB, Singh Rajawat, A, Jan, T, Azizi, N & Prasad, M 2023, 'Anti-Counterfeiting and Traceability Consensus Algorithm Based on Weightage to Contributors in a Food Supply Chain of Industry 4.0', Sustainability, vol. 15, no. 10, pp. 7855-7855. View/Download from: Publisher's site View description>>
Supply chain management can significantly benefit from contemporary technologies. Among these technologies, blockchain is considered suitable for anti-counterfeiting and traceability applications due to its openness, decentralization, anonymity, and other characteristics. This article introduces different types of blockchains and standard algorithms used in blockchain technology and discusses their advantages and disadvantages. To improve the work efficiency of anti-counterfeiting traceability systems in supply chains and reduce their energy consumption, this paper proposes a model based on the practical Byzantine fault tolerance (PBFT) algorithm of alliance chains. This model uses a credit evaluation system to select the primary node and integrates the weightage to contributors (WtC) algorithm based on the consensus mechanism. This model can reduce the decline in the algorithm success rate while increasing the number of malicious transaction nodes, thereby reducing the computing cost. Additionally, the throughput of the algorithmic system increases rapidly, reaching approximately 680 transactions per second (TPS) in about 120 min after the malicious nodes are eliminated. The throughput rapidly increases as the blacklist mechanism reduces the number of malicious nodes, which improves the system’s fault tolerance. To validate the effectiveness of the proposed model, a case study was conducted using data from the anti-counterfeiting traceability system of the real-life supply chain of a food company. The analysis results show that after a period of stable operation of the WtCPBFT algorithm in the proposed model, the overall communication cost of the system was reduced, the throughput and stability were improved, and the fault-tolerant performance of the system was improved. In conclusion, this paper presents a novel model that utilizes the PBFT algorithm of alliance chains and the WtC algorithm to improve the efficiency and security of anti-counte...
Tan, S, Liu, W, Dong, Q, Chan, S, Yu, S, Zhong, X & He, D 2023, 'Hitting Moving Targets: Intelligent Prevention of IoT Intrusions on the Fly', IEEE Internet of Things Journal, vol. 10, no. 23, pp. 21000-21012. View/Download from: Publisher's site
Tang, X-W, Huang, Y, Shi, Y, Huang, X-L & Yu, S 2023, 'UAV Placement for VR Reconstruction: A Tradeoff Between Resolution and Delay', IEEE Communications Letters, vol. 27, no. 5, pp. 1382-1386. View/Download from: Publisher's site
Tanveer, M, Ganaie, MA, Beheshti, I, Goel, T, Ahmad, N, Lai, K-T, Huang, K, Zhang, Y-D, Del Ser, J & Lin, C-T 2023, 'Deep learning for brain age estimation: A systematic review', Information Fusion, vol. 96, pp. 130-143. View/Download from: Publisher's site
Tanveer, M, Lin, C-T & Singh, AK 2023, 'Guest Editorial Advanced Machine Learning Algorithms for Biomedical Data and Imaging—Part II', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 1, pp. 188-189. View/Download from: Publisher's site
Tao, X, Adak, C, Chun, P-J, Yan, S & Liu, H 2023, 'ViTALnet: Anomaly on Industrial Textured Surfaces With Hybrid Transformer', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-13. View/Download from: Publisher's site
Tayari, S, Taghikhah, F, Bharathy, G & Voinov, A 2023, 'Designing a conceptual framework for strategic selection of Bushfire mitigation approaches', Journal of Environmental Management, vol. 344, pp. 118486-118486. View/Download from: Publisher's site
Tian, A, Feng, B, Zhou, H, Huang, Y, Sood, K, Yu, S & Zhang, H 2023, 'Efficient Federated DRL-Based Cooperative Caching for Mobile Edge Networks', IEEE Transactions on Network and Service Management, vol. 20, no. 1, pp. 246-260. View/Download from: Publisher's site View description>>
Edge caching has been regarded as a promising technique for low-latency, high-rate data delivery in future networks, and there is an increasing interest to leverage Machine Learning (ML) for better content placement instead of traditional optimization-based methods due to its self-adaptive ability under complex environments. Despite many efforts on ML-based cooperative caching, there are still several key issues that need to be addressed, especially to reduce computation complexity and communication costs under the optimization of cache efficiency. To this end, in this paper, we propose an efficient cooperative caching (FDDL) framework to address the issues in mobile edge networks. Particularly, we propose a DRL-CA algorithm for cache admission, which extracts a boarder set of attributes from massive requests to improve the cache efficiency. Then, we present an lightweight eviction algorithm for fine-grained replacements of unpopular contents. Moreover, we present a Federated Learning-based parameter sharing mechanism to reduce the signaling overheads in collaborations. We implement an emulation system and evaluate the caching performance of the proposed FDDL. Emulation results show that the proposed FDDL can achieve a higher cache hit ratio and traffic offloading rate than several conventional caching policies and DRL-based caching algorithms, and effectively reduce communication costs and training time.
Tian, H, Liu, B, Zhu, T, Zhou, W & Yu, PS 2023, 'CIFair: Constructing continuous domains of invariant features for image fair classifications', Knowledge-Based Systems, vol. 268, pp. 110417-110417. View/Download from: Publisher's site
Tian, Z, Cui, L, Liang, J & Yu, S 2023, 'A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning', ACM Computing Surveys, vol. 55, no. 8, pp. 1-35. View/Download from: Publisher's site View description>>
The prosperity of machine learning has been accompanied by increasing attacks on the training process. Among them, poisoning attacks have become an emerging threat during model training. Poisoning attacks have profound impacts on the target models, e.g., making them unable to converge or manipulating their prediction results. Moreover, the rapid development of recent distributed learning frameworks, especially federated learning, has further stimulated the development of poisoning attacks. Defending against poisoning attacks is challenging and urgent. However, the systematic review from a unified perspective remains blank. This survey provides an in-depth and up-to-date overview of poisoning attacks and corresponding countermeasures in both centralized and federated learning. We firstly categorize attack methods based on their goals. Secondly, we offer detailed analysis of the differences and connections among the attack techniques. Furthermore, we present countermeasures in different learning framework and highlight their advantages and disadvantages. Finally, we discuss the reasons for the feasibility of poisoning attacks and address the potential research directions from attacks and defenses perspectives, separately.
The characterization of orthotropic materials has challenged the vibration and acoustics community for quite some time. Complex composite materials such as wooden structures require attention to factors including moisture, grain boundaries in addition to macroscopic features. Here we devise a basic model developed by measuring the vibrational response in two separate axes to determine the material characteristics of a timber dowel. A proposed benchtop procedure utilises vibrometers and accelerometers to gather data before the updating process, for which, FEMtools was used. Based on the input material parameters, uncovered by previous studies, provide a starting point for the model updating procedure where experimental mode shapes and frequency responses are correlated to the finite element model. With the focus on radiata pine, the results show radial and tangential values converge similar to previous literature but with variation in the longitudinal direction and shear planes. Overall, this study provides a solid foundation to the characterization process of orthotropic materials like timber which can be further expanded into fields of structural health monitoring, damage detection and potential use in digital twins. The authors acknowledge the support of the Australian Research Council Linkage Project LP200301196.
Uddin, A, Cetindamar, D, Hawryszkiewycz, I & Sohaib, O 2023, 'The Role of Dynamic Cloud Capability in Improving SME’s Strategic Agility and Resource Flexibility: An Empirical Study', Sustainability, vol. 15, no. 11, pp. 8467-8467. View/Download from: Publisher's site View description>>
This research explores how the cloud’s technological capability helps small and medium enterprises (SMEs) adapt to challenging business environments, providing long-term sustainability and strategic agility. The article uses a theoretical and quantitative empirical approach, known as the positivist research paradigm, in offering a unique capability called dynamic cloud capability that leverages the cloud’s technological capabilities. Based on the quantitative analysis of 222 Australian Information and Communication Technology (ICT) SMEs, dynamic cloud capability favourably improves the flexible allocation of resources (resource fluidity) and the ability to adapt business models (strategic agility). Additionally, because of the successful mediating effect of resource fluidity, it is inferred that dynamic cloud capability allows for the flexible allocation of resources leading to improved strategic agility. Hence, adopting dynamic cloud capability in an organisation’s strategy would be particularly appealing to ICT SMEs as it has been verified to enhance adaptability to a challenging business environment and flexible allocation of resources.
Valenzuela-Fernández, L, Munoz Quezada, I & Merigo, JM 2023, 'Mapping the most competitive journals in advertising research. A bibliometric analysis in a 25-year period', Journal of Global Scholars of Marketing Science, vol. 33, no. 3, pp. 349-381. View/Download from: Publisher's site
Vizuete-Luciano, E, Güzel, O & Merigó, JM 2023, 'Bibliometric research of the Pay-What-You-Want Topic', Journal of Revenue and Pricing Management, vol. 22, no. 5, pp. 413-426. View/Download from: Publisher's site View description>>
AbstractPay-What-You-Want (PWYW), is a pricing strategy increasingly applied in many different industries, both profitable and not. This study aims to identify influential cited works in PWYW research, determine the current status, and indicate the extent to which influential works have shaped the field addressing this concern, a set of bibliometric analyses conducted in this paper. The analysis was carried out on 136 research papers published between 2009 and 2022 have been analyzed based on Web of Science Core Collection (WoS) results. In order to identify the most cited authors and works, the co-citation analysis was applied. To scrutinize the intellectual structure of the field, bibliometric coupling was applied, to show the network structure of the themes, co-word analysis was applied. Building upon the results, this study suggests future research paths.
Wang, G, Wu, N, Tao, Y, Lee, WH, Cao, Z, Yan, X & Wang, G 2023, 'The Diagnosis of Major Depressive Disorder Through Wearable fNIRS by Using Wavelet Transform and Parallel-CNN Feature Fusion', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-11. View/Download from: Publisher's site
Wang, J, Wang, K, Li, Z, Lu, H & Jiang, H 2023, 'Short-term power load forecasting system based on rough set, information granule and multi-objective optimization', Applied Soft Computing, vol. 146, pp. 110692-110692. View/Download from: Publisher's site
Wang, K, Lu, J, Liu, A & Zhang, G 2023, 'TCR-M: A Topic Change Recognition-based Method for Data Stream Learning', Procedia Computer Science, vol. 225, pp. 3001-3010. View/Download from: Publisher's site
Wang, K, Lu, J, Liu, A, Zhang, G & Xiong, L 2023, 'Evolving Gradient Boost: A Pruning Scheme Based on Loss Improvement Ratio for Learning Under Concept Drift', IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2110-2123. View/Download from: Publisher's site View description>>
In nonstationary environments, data distributions can change over time. This phenomenon is known as concept drift, and the related models need to adapt if they are to remain accurate. With gradient boosting (GB) ensemble models, selecting which weak learners to keep/prune to maintain model accuracy under concept drift is nontrivial research. Unlike existing models such as AdaBoost, which can directly compare weak learners' performance by their accuracy (a metric between [0, 1]), in GB, weak learners' performance is measured with different scales. To address the performance measurement scaling issue, we propose a novel criterion to evaluate weak learners in GB models, called the loss improvement ratio (LIR). Based on LIR, we develop two pruning strategies: 1) naive pruning (NP), which simply deletes all learners with increasing loss and 2) statistical pruning (SP), which removes learners if their loss increase meets a significance threshold. We also devise a scheme to dynamically switch between NP and SP to achieve the best performance. We implement the scheme as a concept drift learning algorithm, called evolving gradient boost (LIR-eGB). On average, LIR-eGB delivered the best performance against state-of-the-art methods on both stationary and nonstationary data.
Wang, L, Deng, X, Gui, J, Zhang, H & Yu, S 2023, 'Computation Placement Orchestrator for Mobile Edge Computing in Heterogeneous Vehicular Networks', IEEE Internet of Things Journal, vol. 10, no. 24, pp. 1-1. View/Download from: Publisher's site
Wang, M, Li, W, Shi, J, Wu, S & Bai, Q 2023, 'DOR: a novel dual-observation-based approach for recommendation systems', Applied Intelligence, vol. 53, no. 23, pp. 29109-29127. View/Download from: Publisher's site View description>>
AbstractAs online social media platforms continue to proliferate, users are faced with an overwhelming amount of information, making it challenging to filter and locate relevant information. While personalized recommendation algorithms have been developed to help, most existing models primarily rely on user behavior observations such as viewing history, often overlooking the intricate connection between the reading content and the user’s prior knowledge and interest. This disconnect can consequently lead to a paucity of diverse and personalized recommendations. In this paper, we propose a novel approach to tackle the multifaceted issue of recommendation. We introduce the Dual-Observation-based approach for the Recommendation (DOR) system, a novel model leveraging dual observation mechanisms integrated into a deep neural network. Our approach is designed to identify both the core theme of an article and the user’s unique engagement with the article, considering the user’s belief network, i.e., a reflection of their personal interests and biases. Extensive experiments have been conducted using real-world datasets, in which the DOR model was compared against a number of state-of-the-art baselines. The experimental results explicitly demonstrate the reliability and effectiveness of the DOR model, highlighting its superior performance in news recommendation tasks.
Wang, M, Zhu, T, Zuo, X, Yang, M, Yu, S & Zhou, W 2023, 'Differentially Private Crowdsourcing With the Public and Private Blockchain', IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8918-8930. View/Download from: Publisher's site View description>>
As a result of the rapid development of IoT systems, an increasing number of academics are focusing on finding new applications for IoT systems. For Internet of Things (IoT) systems, crowdsourcing is a prevalent practise. Due to the large number of deployed devices in IoT networks, more research is still required on the privacy and trust issues that arise when utilising crowdsourcing. As a result of the characteristics of social computing, the crowdsourcing network poses issues in terms of confidentiality and reliability. To consolidate and create this industry, we have built a differentially private crowdsourcing system that integrates public and private blockchains to address the privacy and trust issues of conventional crowdsourcing systems. Our proposed solution enables varying levels of privacy protection to protect the user’s identity and location. Moreover, the installation of blockchain networks might potentially ensure the data’s integrity. In the conclusion of this article, the possibility of deploying a crowdsourcing system with blockchain in IoE networks is examined.
Wang, T, Li, B, Chen, M & Yu, S 2023, 'Preface', SpringerBriefs in Computer Science, p. v.
Wang, W, Karimi, F, Khalilpour, K, Green, DG & Varvarigos, M 2023, 'Robustness analysis of electricity networks against failure or attack: The case of the Australian National Electricity Market (NEM).', Int. J. Crit. Infrastructure Prot., vol. 41, pp. 100600-100600. View/Download from: Publisher's site
Wang, X, Chen, H-T, Wang, Y-K & Lin, C-T 2023, 'Implicit Robot Control Using Error-Related Potential-Based Brain–Computer Interface', IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 1, pp. 198-209. View/Download from: Publisher's site View description>>
This paper investigates the application of using error-related potential (ErrP) based brain-computer interface (BCI) paradigm to control robot movements with implicit commands. ErrP is a neural signal that is automatically evoked when the machine’s behavior deviates from the observer’s expectations. By continuously monitoring the presence of ErrP, the system infers the observer’s reaction toward robot movements and automatically translates them into control commands, allowing the implicit control of robot movements without interfering the observer’s other tasks. However, ErrP-based BCI has a major limitation: the ErrP is evoked after the robot has committed an error, which might be costly or dangerous in contexts such as assembly line or autonomous driving. To address these limitations, we propose a novel robotic design for ErrP-based BCI that allows humans to continuously evaluate the robot’s intentions and intervene earlier, if necessary before the robot commits an error. We evaluate the proposed robotic design and BCI system via an experiment where a ground robot performs a binary target-reaching task. The high classification accuracy (77.57%) demonstrated that the proposed ErrP-based BCI is feasible for human-robot intention communication before the robot commits an error and has the potential to broaden the range of applications for ErrP-based BCIs.
Wang, X, Li, Q, Yu, D, Cui, P, Wang, Z & Xu, G 2023, 'Causal Disentanglement for Semantic-Aware Intent Learning in Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 10, pp. 9836-9849. View/Download from: Publisher's site View description>>
Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users true intent and thus deteriorate the recommendation effectiveness. Existing methods tracks this problem as eliminating bias for the robust recommendation, e.g., by re-weighting training samples or learning disentangled representation. The disentangled representation methods as the state-of-the-art eliminate bias through revealing cause-effect of the bias generation. However, how to design the semantics-aware and unbiased representation for users true intents is largely unexplored. To bridge the gap, we are the first to propose an unbiased and semantics-aware disentanglement learning called CaDSI (Causal Disentanglement for Semantics-Aware Intent Learning) from a causal perspective. Particularly, CaDSI explicitly models the causal relations underlying recommendation task, and thus produces semantics-aware representations via disentangling users true intents aware of specific item context. Moreover, the causal intervention mechanism is designed to eliminate confounding bias stemmed from context information, which further to align the semantics-aware representation with users true intent. Extensive experiments and case studies both validate the robustness and interpretability of our proposed model.
Wang, X, Yao, L, Wang, X, Paik, H-Y & Wang, S 2023, 'Uncertainty Estimation With Neural Processes for Meta-Continual Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 6887-6897. View/Download from: Publisher's site View description>>
The ability to evaluate uncertainties in evolving data streams has become equally, if not more, crucial than building a static predictor. For instance, during the pandemic, a model should consider possible uncertainties such as governmental policies, meteorological features, and vaccination schedules. Neural process families (NPFs) have recently shone a light on predicting such uncertainties by bridging Gaussian processes (GPs) and neural networks (NNs). Their abilities to output average predictions and the acceptable variances, i.e., uncertainties, made them suitable for predictions with insufficient data, such as meta-learning or few-shot learning. However, existing models have not addressed continual learning which imposes a stricter constraint on the data access. Regarding this, we introduce a member meta-continual learning with neural process (MCLNP) for uncertainty estimation. We enable two levels of uncertainty estimations: the local uncertainty on certain points and the global uncertainty p(z) that represents the function evolution in dynamic environments. To facilitate continual learning, we hypothesize that the previous knowledge can be applied to the current task, hence adopt a coreset as a memory buffer to alleviate catastrophic forgetting. The relationships between the degree of global uncertainties with the intratask diversity and model complexity are discussed. We have estimated prediction uncertainties with multiple evolving types including abrupt/gradual/recurrent shifts. The applications encompass meta-continual learning in the 1-D, 2-D datasets, and a novel spatial-temporal COVID dataset. The results show that our method outperforms the baselines on the likelihood and can rebound quickly even for heavily evolved data streams.
Wang, X, Zhu, T, Ren, W, Zhang, D & Xiong, P 2023, 'Migrating federated learning to centralized learning with the leverage of unlabeled data', Knowledge and Information Systems, vol. 65, no. 9, pp. 3725-3752. View/Download from: Publisher's site
Wang, Y, Zhang, A, Wu, S & Yu, S 2023, 'VOSA: Verifiable and Oblivious Secure Aggregation for Privacy-Preserving Federated Learning', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 5, pp. 3601-3616. View/Download from: Publisher's site View description>>
Federated learning has emerged as a promising paradigm by collaboratively training a global model through sharing local gradients without exposing raw data. However, the shared gradients pose a threat to privacy leakage of local data. The central server may forge the aggregated results. Besides, it is common that resource-constrained devices drop out in federated learning. To solve these problems, the existing solutions consider either only efficiency, or privacy preservation. It is still a challenge to design a verifiable and lightweight secure aggregation with drop-out resilience for large-scale federated learning. In this paper, we propose VOSA, an efficient verifiable and oblivious secure aggregation protocol for privacy-preserving federated learning. We exploit aggregator oblivious encryption to efficiently mask users' local gradients. The central server performs aggregation on the obscured gradients without revealing the privacy of local data. Meanwhile, each user can efficiently verify the correctness of the aggregated results. Moreover, VOSA adopts a dynamic group management mechanism to tolerate users' dropping out with no impact on their participation in future learning process. Security analysis shows that the VOSA can guarantee the security requirements of privacy-preserving federated learning. The extensive experimental evaluations conducted on real-world datasets demonstrate the practical performance of the proposed VOSA with high efficiency.
Wang, Z, Li, J, Wang, Y, Su, Z, Yu, S & Meng, W 2023, 'Optimal Repair Strategy Against Advanced Persistent Threats Under Time-Varying Networks', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 5964-5979. View/Download from: Publisher's site
Wen, J, Gabrys, B & Musial, K 2023, 'Review and Assessment of Digital Twin–Oriented Social Network Simulators', IEEE Access, vol. 11, pp. 97503-97521. View/Download from: Publisher's site
White, T, Opremcak, A, Sterling, G, Korotkov, A, Sank, D, Acharya, R, Ansmann, M, Arute, F, Arya, K, Bardin, JC, Bengtsson, A, Bourassa, A, Bovaird, J, Brill, L, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Z, Chiaro, B, Cogan, J, Collins, R, Crook, AL, Curtin, B, Demura, S, Dunsworth, A, Erickson, C, Fatemi, R, Burgos, LF, Forati, E, Foxen, B, Giang, W, Giustina, M, Grajales Dau, A, Hamilton, MC, Harrington, SD, Hilton, J, Hoffmann, M, Hong, S, Huang, T, Huff, A, Iveland, J, Jeffrey, E, Kieferová, M, Kim, S, Klimov, PV, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Laws, L, Lee, K, Lester, BJ, Lill, A, Liu, W, Locharla, A, Lucero, E, McCourt, T, McEwen, M, Mi, X, Miao, KC, Montazeri, S, Morvan, A, Neeley, M, Neill, C, Nersisyan, A, Ng, JH, Nguyen, A, Nguyen, M, Potter, R, Quintana, C, Roushan, P, Sankaragomathi, K, Satzinger, KJ, Schuster, C, Shearn, MJ, Shorter, A, Shvarts, V, Skruzny, J, Smith, WC, Szalay, M, Torres, A, Woo, BWK, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zhu, N, Zobrist, N, Chen, Y, Megrant, A, Kelly, J & Naaman, O 2023, 'Readout of a quantum processor with high dynamic range Josephson parametric amplifiers', Applied Physics Letters, vol. 122, no. 1, pp. 014001-014001. View/Download from: Publisher's site View description>>
We demonstrate a high dynamic range Josephson parametric amplifier (JPA) in which the active nonlinear element is implemented using an array of rf-SQUIDs. The device is matched to the 50 Ω environment with a Klopfenstein-taper impedance transformer and achieves a bandwidth of 250–300 MHz with input saturation powers up to −95 dBm at 20 dB gain. A 54-qubit Sycamore processor was used to benchmark these devices, providing a calibration for readout power, an estimation of amplifier added noise, and a platform for comparison against standard impedance matched parametric amplifiers with a single dc-SQUID. We find that the high power rf-SQUID array design has no adverse effect on system noise, readout fidelity, or qubit dephasing, and we estimate an upper bound on amplifier added noise at 1.6 times the quantum limit. Finally, amplifiers with this design show no degradation in readout fidelity due to gain compression, which can occur in multi-tone multiplexed readout with traditional JPAs.
Wilkins-Caruana, A, Bandara, M, Musial, K, Catchpoole, D & Kennedy, PJ 2023, 'Inferring Actual Treatment Pathways from Patient Records', J Biomed Inform. 2023 Nov 22:104554. Epub ahead of print. PMID: 38000767. View description>>
Treatment pathways are step-by-step plans outlining the recommended medicalcare for specific diseases; they get revised when different treatments arefound to improve patient outcomes. Examining health records is an importantpart of this revision process, but inferring patients' actual treatments fromhealth data is challenging due to complex event-coding schemes and the absenceof pathway-related annotations. This study aims to infer the actual treatmentsteps for a particular patient group from administrative health records (AHR) -a common form of tabular healthcare data - and address several technique- andmethodology-based gaps in treatment pathway-inference research. We introduceDefrag, a method for examining AHRs to infer the real-world treatment steps fora particular patient group. Defrag learns the semantic and temporal meaning ofhealthcare event sequences, allowing it to reliably infer treatment steps fromcomplex healthcare data. To our knowledge, Defrag is the firstpathway-inference method to utilise a neural network (NN), an approach madepossible by a novel, self-supervised learning objective. We also developed atesting and validation framework for pathway inference, which we use tocharacterise and evaluate Defrag's pathway inference ability and compareagainst baselines. We demonstrate Defrag's effectiveness by identifyingbest-practice pathway fragments for breast cancer, lung cancer, and melanoma inpublic healthcare records. Additionally, we use synthetic data experiments todemonstrate the characteristics of the Defrag method, and to compare Defrag toseveral baselines where it significantly outperforms non-NN-based methods.Defrag significantly outperforms several existing pathway-inference methods andoffers an innovative and effective approach for inferring treatment pathwaysfrom AHRs. Open-source code is provided to encourage further research in thisarea.
OBJECTIVE: Treatment pathways are step-by-step plans outlining the recommended medical care for specific diseases; they get revised when different treatments are found to improve patient outcomes. Examining health records is an important part of this revision process, but inferring patients' actual treatments from health data is challenging due to complex event-coding schemes and the absence of pathway-related annotations. The objective of this study is to develop a method for inferring actual treatment steps for a particular patient group from administrative health records - a common form of tabular healthcare data - and address several technique- and methodology-based gaps in treatment pathway-inference research. METHODS: We introduce Defrag, a method for examining health records to infer the real-world treatment steps for a particular patient group. Defrag learns the semantic and temporal meaning of healthcare event sequences, allowing it to reliably infer treatment steps from complex healthcare data. To our knowledge, Defrag is the first pathway-inference method to utilise a neural network (NN), an approach made possible by a novel, self-supervised learning objective. We also developed a testing and validation framework for pathway inference, which we use to characterise and evaluate Defrag's pathway inference ability, establish benchmarks, and compare against baselines. RESULTS: We demonstrate Defrag's effectiveness by identifying best-practice pathway fragments for breast cancer, lung cancer, and melanoma in public healthcare records. Additionally, we use synthetic data experiments to demonstrate the characteristics of the Defrag inference method, and to compare Defrag to several baselines, where it significantly outperforms non-NN-based methods. CONCLUSIONS: Defrag offers an innovative and effective approach for inferring treatment pathways from complex health data. Defrag significantly outperforms several existing pathway-inference methods, but ...
Wu, C, Chen, Y, Dong, Y, Zhou, F, Zhao, Y & Liang, CJ 2023, 'VizOPTICS: Getting insights into OPTICS via interactive visual analysis', Computers and Electrical Engineering, vol. 107, pp. 108624-108624. View/Download from: Publisher's site
Wu, G, Wang, H, Zhang, H, Zhao, Y, Yu, S & Shen, S 2023, 'Computation Offloading Method Using Stochastic Games for Software-Defined-Network-Based Multiagent Mobile Edge Computing', IEEE Internet of Things Journal, vol. 10, no. 20, pp. 17620-17634. View/Download from: Publisher's site
Wu, G, Xie, L, Zhang, H, Wang, J, Shen, S & Yu, S 2023, 'STSIR: An individual-group game-based model for disclosing virus spread in Social Internet of Things', Journal of Network and Computer Applications, vol. 214, pp. 103608-103608. View/Download from: Publisher's site
Wu, G, Xu, Z, Zhang, H, Shen, S & Yu, S 2023, 'Multi-agent DRL for joint completion delay and energy consumption with queuing theory in MEC-based IIoT', Journal of Parallel and Distributed Computing, vol. 176, pp. 80-94. View/Download from: Publisher's site
Wu, S, Li, W & Bai, Q 2023, 'GAC: A deep reinforcement learning model toward user incentivization in unknown social networks', Knowledge-Based Systems, vol. 259, pp. 110060-110060. View/Download from: Publisher's site
Wu, S, Li, W, Shen, H & Bai, Q 2023, 'Identifying influential users in unknown social networks for adaptive incentive allocation under budget restriction', Information Sciences, vol. 624, pp. 128-146. View/Download from: Publisher's site View description>>
In recent years, recommenze the social influence among users to enhance the effect of incentivization. Through incentivizing influential users directly, their followers in the social network are possibly incentivized indirectly. However, in many real-world applications, identifying influential users can be challenging because of the unknown network topology. In this paper, we propose a novel algorithm for exploring influential users in unknown networks, estimating the influential relationships among users based on their historical behaviors without knowing the network topology. In addition, we design an adaptive incentive allocation approach that determines incentive values based on each user's preferences and influence ability. We evaluate the performance of the proposed approaches by conducting experiments on synthetic and real-world datasets. The experimental results demonstrate the effectiveness of the proposed approaches.
Wu, Z, Liu, H, Xie, J, Xu, G, Li, G & Lu, C 2023, 'An effective method for the protection of user health topic privacy for health information services', World Wide Web, vol. 26, no. 6, pp. 3837-3859. View/Download from: Publisher's site
Wu, Z, Xie, J, Shen, S, Lin, C, Xu, G & Chen, E 2023, 'A Confusion Method for the Protection of User Topic Privacy in Chinese Keyword-based Book Retrieval', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 22, no. 5, pp. 1-19. View/Download from: Publisher's site View description>>
In this article, aiming at a Chinese keyword-based book search service, from a technological perspective, we propose to modify a user query sequence carefully to confuse the user query topics and thus protect the user topic privacy on the untrusted server, without compromising the accuracy of each book search service. First, we propose a client-based framework for the privacy protection of book search, and then a privacy model to formulate the constraints in terms of accuracy, efficiency, and security, which the cover queries generated based on a user query sequence should meet. Second, we present a modification algorithm for a user query sequence, based on some heuristic strategies, which can quickly generate a cover query sequence meeting the privacy model by replacing, deleting, and adding keywords for each user query. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed approach, i.e., which can improve the security of users’ topic privacy on the untrusted server without compromising the efficiency, accuracy, and usability of an existing Chinese keyword book search service, so it has a positive impact for the construction of a privacy-preserving text retrieval platform under an untrusted network environment.
Xiang, Y, Li, T, Ren, W, Zhu, T & Choo, K-KR 2023, 'A lightweight privacy-preserving scheme using pixel block mixing for facial image classification in deep learning', Engineering Applications of Artificial Intelligence, vol. 126, pp. 107180-107180. View/Download from: Publisher's site
Xiong, J, Guo, L, Shan, M, Liu, B, Yu, P & Guo, L 2023, 'Wireless Resources Cooperation of Assembled Small UAVs for Data Collections of IoT', IEEE Internet of Things Journal, vol. 10, no. 11, pp. 9411-9422. View/Download from: Publisher's site
Xu, H, Yan, Z, Xuan, J, Zhang, G & Lu, J 2023, 'Improving proximal policy optimization with alpha divergence', Neurocomputing, vol. 534, pp. 94-105. View/Download from: Publisher's site
Xu, Y, Fang, M, Chen, L, Du, Y, Xu, G & Zhang, C 2023, 'Shared dynamics learning for large-scale traveling salesman problem', Advanced Engineering Informatics, vol. 56, pp. 102005-102005. View/Download from: Publisher's site
Xue, H, Liu, B, Yuan, X, Ding, M & Zhu, T 2023, 'Face image de‐identification by feature space adversarial perturbation', Concurrency and Computation: Practice and Experience, vol. 35, no. 5. View/Download from: Publisher's site View description>>
SummaryPrivacy leakage in images attracts increasing concerns these days, as photos uploaded to large social platforms are usually not processed by proper privacy protection mechanisms. Moreover, with advanced artificial intelligence (AI) tools such as deep neural network (DNN), an adversary can detect people's identities and collect other sensitive personal information from images at an unprecedented scale. In this paper, we introduce a novel face image de‐identification framework using adversarial perturbations in the feature space. Manipulating the feature space vector ensures the good transferability of our framework. Moreover, the proposed feature space adversarial perturbation generation algorithm can successfully protect the identity‐related information while ensuring the other attributes remain similar. Finally, we conduct extensive experiments on two face image datasets to evaluate the performance of the proposed method. Our results show that the proposed method can generate real‐looking privacy‐preserving images efficiently. Although our framework has only been tested on two real‐life face image datasets, it can be easily extended to other types of images.
Yang, C, Wang, X, Yao, L, Long, G, Jiang, J & Xu, G 2023, 'Attentional Gated Res2Net for Multivariate Time Series Classification', Neural Processing Letters, vol. 55, no. 2, pp. 1371-1395. View/Download from: Publisher's site View description>>
AbstractMultivariate time series classification is a critical problem in data mining with broad applications. It requires harnessing the inter-relationship of multiple variables and various ranges of temporal dependencies to assign the correct classification label of the time series. Multivariate time series may come from a wide range of sources and be used in various scenarios, bringing the classifier challenge of temporal representation learning. We propose a novel convolutional neural network architecture called Attentional Gated Res2Net for multivariate time series classification. Our model uses hierarchical residual-like connections to achieve multi-scale receptive fields and capture multi-granular temporal information. The gating mechanism enables the model to consider the relations between the feature maps extracted by receptive fields of multiple sizes for information fusion. Further, we propose two types of attention modules, channel-wise attention and block-wise attention, to better leverage the multi-granular temporal patterns. Our experimental results on 14 benchmark multivariate time-series datasets show that our model outperforms several baselines and state-of-the-art methods by a large margin. Our model outperforms the SOTA by a large margin, the classification accuracy of our model is 10.16% better than the SOTA model. Besides, we demonstrate that our model improves the performance of existing models when used as a plugin. Further, based on our experiments and analysis, we provide practical advice on applying our model to a new problem.
Yang, J & Lin, C-T 2023, 'Multi-View Adjacency-Constrained Hierarchical Clustering', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 4, pp. 1126-1138. View/Download from: Publisher's site
Yang, M, Lam, K, Zhu, T & Tang, C 2023, 'SPoFC: A framework for stream data aggregation with local differential privacy', Concurrency and Computation: Practice and Experience, vol. 35, no. 5. View/Download from: Publisher's site View description>>
AbstractCollecting and analysing customers' data plays an essential role in the more intense market competition. It is critical to perform data analysis effectively while ensuring the user's privacy, especially after various privacy regulations are enacted. In this paper, we consider the problem of aggregating the stream data generated from wearable devices in a specific time period in a privacy‐preserving manner. Specifically, we adopt the local differential privacy mechanism to provide a strong privacy guarantee for users. One major challenge is that all values of the stream need to be perturbed. The additive noise makes it hard to release an accurate data stream. One way to reduce the noise scale is to select some data points to perturb instead of all. The intuition is that more privacy budgets are applied to a single data point, which ensures the statistical accuracy. The perturbed data points are used to predict the un‐selected data points without consuming an extra privacy budget. Based on this idea, we propose a novel stream data statistical framework, which includes four components, data fitting, skeleton point selection, noisy stream generation, and data aggregation. Extensive experiment results show that our proposed method achieves a much smaller mean square error given a fixed privacy budget compared with the state‐of‐the‐art.
Yang, M, Tjuawinata, I, Lam, K-Y, Zhu, T & Zhao, J 2023, 'Differentially Private Distributed Frequency Estimation', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 5, pp. 3910-3926. View/Download from: Publisher's site View description>>
In order to remain competitive, Internet companies collect and analyse user data for the purpose of the improvement of user experiences. Frequency estimation is a widely used statistical tool, which could potentially conflict with the relevant privacy regulations. Privacy preserving analytic methods based on differential privacy have been proposed, which require either a large user base or a trusted server. Although the requirements for such solutions may not be a problem for larger companies, they may be unattainable for smaller organizations. To address this issue, we propose a distributed privacy-preserving sampling-based frequency estimation method which has high accuracy even in the scenario with a small number of users while not requiring any trusted server. This is achieved by combining multi-party computation and sampling techniques. We also provide a relation between its privacy guarantee, output accuracy, and the number of participants. Distinct from most existing methods, our methods achieve centralized differential privacy guarantee without the need of any trusted server. We established that, even for a small number of participants, our mechanisms can produce estimates with high accuracy and hence they provide smaller companies with more opportunity for growth through privacy-preserving statistical analysis. We further propose an architectural model to support weighted aggregation in order to achieve a higher accuracy estimate to cater for users with varying privacy requirements. Compared to the unweighted aggregation, our method provides a more accurate estimate. Extensive experiments are conducted to show the effectiveness of the proposed methods.
Yang, S, Verma, S, Cai, B, Jiang, J, Yu, K, Chen, F & Yu, S 2023, 'Variational co-embedding learning for attributed network clustering', Knowledge-Based Systems, vol. 270, pp. 110530-110530. View/Download from: Publisher's site
Ye, D, Zhu, T, Zhu, C, Zhou, W & Yu, PS 2023, 'Model-Based Self-Advising for Multi-Agent Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 7934-7945. View/Download from: Publisher's site View description>>
In multiagent learning, one of the main ways to improve learning performance is to ask for advice from another agent. Contemporary advising methods share a common limitation that a teacher agent can only advise a student agent if the teacher has experience with an identical state. However, in highly complex learning scenarios, such as autonomous driving, it is rare for two agents to experience exactly the same state, which makes the advice less of a learning aid and more of a one-time instruction. In these scenarios, with contemporary methods, agents do not really help each other learn, and the main outcome of their back and forth requests for advice is an exorbitant communications' overhead. In human interactions, teachers are often asked for advice on what to do in situations that students are personally unfamiliar with. In these, we generally draw from similar experiences to formulate advice. This inspired us to provide agents with the same ability when asked for advice on an unfamiliar state. Hence, we propose a model-based self-advising method that allows agents to train a model based on states similar to the state in question to inform its response. As a result, the advice given can not only be used to resolve the current dilemma but also many other similar situations that the student may come across in the future via self-advising. Compared with contemporary methods, our method brings a significant improvement in learning performance with much lower communication overheads.
Ye, Z, Yan, D, Dong, L, Deng, J & Yu, S 2023, 'Stealthy Backdoor Attack Against Speaker Recognition Using Phase-Injection Hidden Trigger', IEEE Signal Processing Letters, vol. 30, pp. 1057-1061. View/Download from: Publisher's site
Yin, H, Sun, Y, Xu, G & Kanoulas, E 2023, 'Trustworthy Recommendation and Search: Introduction to the Special Issue - Part 1', ACM Transactions on Information Systems, vol. 41, no. 3, pp. 1-5. View/Download from: Publisher's site
Yu, D, Li, Q, Wang, X & Xu, G 2023, 'Deconfounded recommendation via causal intervention', Neurocomputing, vol. 529, pp. 128-139. View/Download from: Publisher's site
Yu, H, Li, J, Lu, J, Song, Y, Xie, S & Zhang, G 2023, 'Type-LDD: A Type-Driven Lite Concept Drift Detector for Data Streams', IEEE Transactions on Knowledge and Data Engineering, pp. 1-14. View/Download from: Publisher's site
Yu, H, Liu, W, Lu, J, Wen, Y, Luo, X & Zhang, G 2023, 'Detecting group concept drift from multiple data streams', Pattern Recognition, vol. 134, pp. 109113-109113. View/Download from: Publisher's site
Yu, S, Gu, B, Qu, Y & Wang, X 2023, 'Preface', Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 489 LNICST, pp. v-vii.
Yuan, B, Zhang, F, Wan, J, Zhao, H, Yu, S, Zou, D, Hua, Q & Jin, H 2023, 'Resource investment for DDoS attack resistant SDN: a practical assessment', Science China Information Sciences, vol. 66, no. 7. View/Download from: Publisher's site
Zhang, C, Zhang, S, Yu, JJQ & Yu, S 2023, 'SAM: Query-efficient Adversarial Attacks against Graph Neural Networks', ACM Transactions on Privacy and Security, vol. 26, no. 4, pp. 1-19. View/Download from: Publisher's site View description>>
Recent studies indicate that Graph Neural Networks (GNNs) are vulnerable to adversarial attacks. Particularly, adversarially perturbing the graph structure, e.g., flipping edges, can lead to salient degeneration of GNNs’ accuracy. In general, efficiency and stealthiness are two significant metrics to evaluate an attack method in practical use. However, most prevailing graph structure-based attack methods are query intensive, which impacts their practical use. Furthermore, while the stealthiness of perturbations has been discussed in previous studies, the majority of them focus on the attack scenario targeting a single node. To fill the research gap, we present a global attack method against GNNs, Saturation adversarial Attack with Meta-gradient, in this article. We first propose an enhanced meta-learning-based optimization method to obtain useful gradient information concerning graph structural perturbations. Then, leveraging the notion of saturation attack, we devise an effective algorithm to determine the perturbations based on the derived meta-gradients. Meanwhile, to ensure stealthiness, we introduce a similarity constraint to suppress the number of perturbed edges. Thorough experiments demonstrate that our method can effectively depreciate the accuracy of GNNs with a small number of queries. While achieving a higher misclassification rate, we also show that the perturbations developed by our method are not noticeable.
Zhang, C, Zhang, S, Zou, X, Yu, S & Yu, JJQ 2023, 'Toward Large-Scale Graph-Based Traffic Forecasting: A Data-Driven Network Partitioning Approach', IEEE Internet of Things Journal, vol. 10, no. 5, pp. 4506-4519. View/Download from: Publisher's site View description>>
Network partitioning is recognized as an effective auxiliary approach for solving transportation tasks on large-scale traffic networks in a domain-decomposition manner. Most of the existing related partitioning algorithms are explicitly designed to traffic management problems and merely focus on the implied topology of the networks. In this paper, towards the practical problems that happened to traffic forecasting tasks, we propose a network-partitioning-based domain-decomposition framework to improve GCN-based predictors’ performance on large-scale transportation networks. Particularly, we devise a data-driven network-partitioning approach, namely, Speed-Matching-Partitioning, which employs not only the topological features but also the traffic speed observations of traffic networks for partitioning. Additionally, we propose a data-parallel training strategy that feeds partitioned sub-networks into independent predictors for parallel training. The proposed approach is tested by comprehensive case studies on three real-world datasets to evaluate its effectiveness. The results indicate that the proposed approach can help improve GCN-based predictors’ accuracy and training efficiency on both small and relatively large traffic datasets. Furthermore, we investigate the model sensitivity to the selection of graph representations and framework parameters, and the learning efficiency of the data-parallel training strategy.
Zhang, G, Liu, B, Zhu, T, Ding, M & Zhou, W 2023, 'Label-Only Membership Inference Attacks and Defenses in Semantic Segmentation Models', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 2, pp. 1435-1449. View/Download from: Publisher's site
Zhang, J, Liu, Y, Wu, D, Lou, S, Chen, B & Yu, S 2023, 'VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems', Digital Communications and Networks, vol. 9, no. 4, pp. 981-989. View/Download from: Publisher's site
Zhang, K, Fan, Z, Song, X & Yu, S 2023, 'Enhancing Trajectory Recovery From Gradients via Mobility Prior Knowledge', IEEE Internet of Things Journal, vol. 10, no. 6, pp. 5583-5594. View/Download from: Publisher's site
Zhang, L, Shi, Y, Chang, Y-C & Lin, C-T 2023, 'Federated Fuzzy Neural Network With Evolutionary Rule Learning', IEEE Transactions on Fuzzy Systems, vol. 31, no. 5, pp. 1653-1664. View/Download from: Publisher's site View description>>
Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due to their learning abilities in handling data uncertainties in distributed scenarios. However, it is challenging for DFNNs to handle cases in which the local data are non-independent and identically distributed (non-IID). In this paper, we propose a federated fuzzy neural network (FedFNN) with evolutionary rule learning (ERL) to cope with non-IID issues as well as data uncertainties. The FedFNN maintains a global set of rules in a server and a personalized subset of these rules for each local client. ERL is inspired by the theory of biological evolution; it encourages rule variations while activating superior rules and deactivating inferior rules for local clients with non-IID data. Specifically, ERL consists of two stages in an iterative procedure: a rule cooperation stage that updates global rules by aggregating local rules based on their activation statuses and a rule evolution stage that evolves the global rules and updates the activation statuses of the local rules. This procedure improves both the generalization and personalization of the FedFNN for dealing with non-IID issues and data uncertainties. Extensive experiments conducted on a range of datasets demonstrate the superiority of the FedFNN over state-of-the-art methods. Our code is available online1
https://github.com/leijiezhang/FedFNN
Zhang, L, Xiao, F & Cao, Z 2023, 'Multi-channel EEG signals classification via CNN and multi-head self-attention on evidence theory', Information Sciences, vol. 642, pp. 119107-119107. View/Download from: Publisher's site
Zhang, L, Zhu, T, Hussain, FK, Ye, D & Zhou, W 2023, 'A Game-Theoretic Method for Defending Against Advanced Persistent Threats in Cyber Systems', IEEE Transactions on Information Forensics and Security, vol. 18, no. 99, pp. 1349-1364. View/Download from: Publisher's site View description>>
Advanced persistent threats (APTs) are one of today's major threats to cyber security. Highly determined attackers along with novel and evasive exfiltration techniques mean APT attacks elude most intrusion detection and prevention systems. The result has been significant losses for governments, organizations, and commercial entities. Intriguingly, despite greater efforts to defend against APTs in recent times, frequent upgrades in defense strategies are not leading to increased security and protection. In this paper, we demonstrate this phenomenon in an appropriately designed APT rivalry game that captures the interactions between attackers and defenders. What is shown is that the defender's strategy adjustments actually leave useful information for the attackers, and thus intelligent and rational attackers can improve themselves by analyzing this information. Hence, a critical part of one's defense strategy must be finding a suitable time to adjust one's strategy to ensure attackers learn the least possible information. Another challenge for defenders is determining how to make the best use of one's resources to achieve a satisfactory defense level. In support of these efforts, we figured out the optimal timings of a player's strategy adjustment in terms of information leakage, which form a family of Nash equilibria. Moreover, two learning mechanisms are proposed to help defenders find an appropriate defense level and allocate their resources reasonably. One is based on adversarial bandits, and the other is based on deep reinforcement learning. Experimental simulations show the rationales behind the game and the optimality of the equilibria. The results also demonstrate that players indeed have the ability to improve themselves by learning from past experiences, which shows the necessity of specifying optimal strategy adjustment timings when defending against APTs.
Zhang, L, Zhu, T, Xiong, P, Zhou, W & Yu, PS 2023, 'A Game-Theoretic Federated Learning Framework for Data Quality Improvement', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 10952-10966. View/Download from: Publisher's site
Zhang, L, Zhu, T, Xiong, P, Zhou, W & Yu, PS 2023, 'A Robust Game-Theoretical Federated Learning Framework With Joint Differential Privacy', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 3333-3346. View/Download from: Publisher's site View description>>
Federated learning is a promising distributed machine learning paradigm that has been playing a significant role in providing privacy-preserving learning solutions. However, alongside all its achievements, there are also limitations. First, traditional frameworks assume that all the clients are voluntary and so will want to participate in training only for improving the model's accuracy. However, in reality, clients usually want to be adequately compensated for the data and resources they will use before participating. Second, today's frameworks do not offer sufficient protection against malicious participants who try to skew a jointly trained model with poisoned updates. To address these concerns, we have developed a more robust federated learning scheme based on joint differential privacy. The framework provides two game-theoretic mechanisms to motivate clients to participate in training. These mechanisms are dominant-strategy truthful, individual rational, and budget-balanced. Further, the influence an adversarial client can have is quantified and restricted, and data privacy is similarly guaranteed in quantitative terms. Experiments with different training models on real-word datasets demonstrate the effectiveness of the proposed approach.
Zhang, L, Zhu, T, Zhang, H, Xiong, P & Zhou, W 2023, 'FedRecovery: Differentially Private Machine Unlearning for Federated Learning Frameworks', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 4732-4746. View/Download from: Publisher's site
Zhang, M, Zhou, J, Zhang, G, Cui, L, Gao, T & Yu, S 2023, 'APDP: Attribute-Based Personalized Differential Privacy Data Publishing Scheme for Social Networks', IEEE Transactions on Network Science and Engineering, vol. 10, no. 2, pp. 922-933. View/Download from: Publisher's site View description>>
In the Big Data era, the wide usage of mobile devices has led to large amounts of information release and sharing through social networks, where sensitive information of the data owners may be leaked. Traditional approaches that provide the identical privacy protection levels for all users result in poor quality of service, thus the concept of personalized privacy has been proposed in recent years. However, existing methods that add different noises to each user will require both high real-time performance and resource consumption. This paper presents a fine-grained personalized differential privacy data publishing scheme (APDP) for social networks. Specifically, we design a new mechanism that defines the privacy protection levels of different users based on their attribute values. In particular, we exploit the TOPSIS method to map the attribute values to the amount of noise required to add. Furthermore, to prevent illegal download of data, the access control is integrated with differential privacy. Compared with traditional attribute-based encryption data publishing schemes, our scheme can get rid of the expensive computation overhead. Theoretical analyses and simulations show that APDP can realize efficient personalized differential privacy data publishing with reasonable data utility.
Zhang, Q, Liao, W, Zhang, G, Yuan, B & Lu, J 2023, 'A Deep Dual Adversarial Network for Cross-Domain Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 3266-3278. View/Download from: Publisher's site View description>>
Data sparsity is a common issue for most recommender systems and can severely degrade the usefulness of a system. One of the most successful solutions has been cross-domain recommender systems, which supplement the sparse data of the target domain with knowledge transferred from a source domain rich with data that is in some way related. However, there are three challenges that, if overcome, could significantly improve the quality and accuracy of cross-domain recommendation: 1) ensuring latent feature spaces of the users and items are both maximally matched; 2) taking consideration of user-item relationship and their interaction in modelling user preference; 3) enabling a two-way cross-domain recommendation that both the source and the target domains benefit from a knowledge exchange. Hence, in this paper, we propose a novel deep neural network called Dual Adversarial network for Cross-Domain Recommendation. By training the shared encoders with a domain discriminator via dual adversarial learning, the latent feature spaces for both the users and items are maximally matched. Allowing the two domains to collaboratively benefit from each other results in better recommendations for both domains. Extensive experiments with real-world datasets on six tasks demonstrate that DA-CDR significantly outperforms seven state-of-the-art baselines.
Zhang, T, Ye, D, Zhu, T, Liao, T & Zhou, W 2023, 'Evolution of cooperation in malicious social networks with differential privacy mechanisms', Neural Computing and Applications, vol. 35, no. 18, pp. 12979-12994. View/Download from: Publisher's site View description>>
Zhang, T, Zhu, T, Gao, K, Zhou, W & Yu, PS 2023, 'Balancing Learning Model Privacy, Fairness, and Accuracy With Early Stopping Criteria', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 9, pp. 5557-5569. View/Download from: Publisher's site View description>>
As deep learning models mature, one of the most prescient questions we face is: what is the ideal tradeoff between accuracy, fairness, and privacy (AFP)? Unfortunately, both the privacy and the fairness of a model come at the cost of its accuracy. Hence, an efficient and effective means of fine-tuning the balance between this trinity of needs is critical. Motivated by some curious observations in privacy-accuracy tradeoffs with differentially private stochastic gradient descent (DP-SGD), where fair models sometimes result, we conjecture that fairness might be better managed as an indirect byproduct of this process. Hence, we conduct a series of analyses, both theoretical and empirical, on the impacts of implementing DP-SGD in deep neural network models through gradient clipping and noise addition. The results show that, in deep learning, the number of training epochs is central to striking a balance between AFP because DP-SGD makes the training less stable, providing the possibility of model updates at a low discrimination level without much loss in accuracy. Based on this observation, we designed two different early stopping criteria to help analysts choose the optimal epoch at which to stop training a model so as to achieve their ideal tradeoff. Extensive experiments show that our methods can achieve an ideal balance between AFP.
Zhang, T, Zhu, T, Han, M, Chen, F, Li, J, Zhou, W & Yu, PS 2023, 'Fairness in graph-based semi-supervised learning', Knowledge and Information Systems, vol. 65, no. 2, pp. 543-570. View/Download from: Publisher's site View description>>
AbstractMachine learning is widely deployed in society, unleashing its power in a wide range of applications owing to the advent of big data. One emerging problem faced by machine learning is the discrimination from data, and such discrimination is reflected in the eventual decisions made by the algorithms. Recent study has proved that increasing the size of training (labeled) data will promote the fairness criteria with model performance being maintained. In this work, we aim to explore a more general case where quantities of unlabeled data are provided, indeed leading to a new form of learning paradigm, namely fair semi-supervised learning. Taking the popularity of graph-based approaches in semi-supervised learning, we study this problem both on conventional label propagation method and graph neural networks, where various fairness criteria can be flexibly integrated. Our developed algorithms are proved to be non-trivial extensions to the existing supervised models with fairness constraints. Extensive experiments on real-world datasets exhibit that our methods achieve a better trade-off between classification accuracy and fairness than the compared baselines.
Zhang, T, Zhu, T, Li, J, Zhou, W & Yu, PS 2023, 'Revisiting model fairness via adversarial examples', Knowledge-Based Systems, vol. 277, pp. 110777-110777. View/Download from: Publisher's site
Zhang, X, Li, Y, Wang, J, Xu, G & Gu, Y 2023, 'A Multi-perspective Model for Protein–Ligand-Binding Affinity Prediction', Interdisciplinary Sciences: Computational Life Sciences, vol. 15, no. 4, pp. 696-709. View/Download from: Publisher's site
Zhang, Y, Bai, G, Li, X, Nepal, S, Grobler, M, Chen, C & Ko, RKL 2023, 'Preserving Privacy for Distributed Genome-Wide Analysis Against Identity Tracing Attacks', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 4, pp. 3341-3357. View/Download from: Publisher's site
Zhang, Y, Wu, M, Zhang, G & Lu, J 2023, 'Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation', Journal of the Association for Information Science and Technology, vol. 74, no. 7, pp. 775-790. View/Download from: Publisher's site View description>>
AbstractPredicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter‐/cross‐/multi‐disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion‐based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co‐topic layer and a co‐authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments—one with a local dataset and the other with a global dataset—demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.
Zhang, Z, Wu, L, Ma, C, Li, J, Wang, J, Wang, Q & Yu, S 2023, 'LSFL: A Lightweight and Secure Federated Learning Scheme for Edge Computing', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 365-379. View/Download from: Publisher's site
Zhong, L, Fang, Z, Liu, F, Yuan, B, Zhang, G & Lu, J 2023, 'Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 8, pp. 3859-3873. View/Download from: Publisher's site
Zhou, J, Duan, Y, Zou, Y, Chang, Y-C, Wang, Y-K & Lin, C-T 2023, 'Speech2EEG: Leveraging Pretrained Speech Model for EEG Signal Recognition', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 2140-2153. View/Download from: Publisher's site
Zhou, L, Fu, A, Yang, G, Gao, Y, Yu, S & Deng, RH 2023, 'Fair Cloud Auditing Based on Blockchain for Resource-Constrained IoT Devices', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 5, pp. 4325-4342. View/Download from: Publisher's site View description>>
Internet of Things (IoT) devices upload their data into the cloud for storage because of their limited resources. However, cloud storage data has been subject to potential integrity threats, and consequently auditing techniques are demanded to ensure the integrity of stored data. Unfortunately, existing auditing approaches require owners to undertake expensive tag calculations, which is unsuitable for resource-constrained IoT devices. To resolve the issue, we present a Fair Cloud Auditing proposal by employing the Blockchain (FCAB). We combine certificateless signatures with the designed dynamic structure to constructively offload the cost of tag computation from the IoT device to the introduced fog node, significantly reducing the local burden. Considering that fog nodes may behave dishonestly during auditing, FCAB enables the IoT device to verify the audit result's authenticity by extracting reliable checking records from the blockchain, thereby achieving auditing fairness, which ensures that the honest cloud and fog node will gain the corresponding reward. Finally, FCAB is proved to satisfy tag unforgeability, proof unforgeability, privacy preserving, and auditing fairness. Experiment evaluations affirm that FCAB is computationally and communicationally efficient and retains a smaller and fixed computation locally at the data processing stage (mainly including tag computation) than existing auditing methods.
Zhou, M, Lu, J, Song, Y & Zhang, G 2023, 'Multi-Stream Concept Drift Self-Adaptation Using Graph Neural Network', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12828-12841. View/Download from: Publisher's site
Zhou, S, Liu, C, Ye, D, Zhu, T, Zhou, W & Yu, PS 2023, 'Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity', ACM Computing Surveys, vol. 55, no. 8, pp. 1-39. View/Download from: Publisher's site View description>>
The outstanding performance of deep neural networks has promoted deep learning applications in a broad set of domains. However, the potential risks caused by adversarial samples have hindered the large-scale deployment of deep learning. In these scenarios, adversarial perturbations, imperceptible to human eyes, significantly decrease the model’s final performance. Many papers have been published on adversarial attacks and their countermeasures in the realm of deep learning. Most focus on evasion attacks, where the adversarial examples are found at test time, as opposed to poisoning attacks where poisoned data is inserted into the training data. Further, it is difficult to evaluate the real threat of adversarial attacks or the robustness of a deep learning model, as there are no standard evaluation methods. Hence, with this article, we review the literature to date. Additionally, we attempt to offer the first analysis framework for a systematic understanding of adversarial attacks. The framework is built from the perspective of cybersecurity to provide a lifecycle for adversarial attacks and defenses.
Zhou, Y, Liu, X, Fu, Y, Wu, D, Wang, JH & Yu, S 2023, 'Optimizing the Numbers of Queries and Replies in Convex Federated Learning With Differential Privacy', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 6, pp. 4823-4837. View/Download from: Publisher's site
Zhou, Z, Xu, C, Wang, M, Kuang, X, Zhuang, Y & Yu, S 2023, 'A Multi-Shuffler Framework to Establish Mutual Confidence for Secure Federated Learning', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 5, pp. 4230-4244. View/Download from: Publisher's site View description>>
Albeit the popularity of federated learning (FL), recently emerging model-inversion and poisoning attacks arouse extensive concerns towards privacy or model integrity, which catalyzes the developments of secure federated learning (SFL) methods. Nonetheless, the collisions between its privacy and integrity, two equally crucial elements in collaborative learning scenarios, are relatively underexplored. Individuals' wish to “hide in the crowd” for privacy frequently clashes with aggregator' need to resist abnormal participants for integrity (i.e., the incompatibility between Byzantine robustness and differential privacy). The dilemma prompts researchers to reflect on how to build mutual confidence between individuals and aggregators. Against the backdrop, this paper proposes a multi-shuffler secure federated learning (MSFL) framework, based on which we further propound three modules (hierarchical shuffling mechanism, malice evaluation module, and composite defense strategy) to jointly guarantee strong privacy protection, efficient poisoning resistance, and agile adversary elimination. Extensive experiments on standard datasets exhibited the method's effectiveness in thwarting different FL poisoning attack paradigms with a minimal cost of privacy breaches.
Zhu, C, Cheng, Z, Ye, D, Hussain, FK, Zhu, T & Zhou, W 2023, 'Time-Driven and Privacy-Preserving Navigation Model for Vehicle-to-Vehicle Communication Systems', IEEE Transactions on Vehicular Technology, vol. 72, no. 7, pp. 8459-8470. View/Download from: Publisher's site
Zhu, HY, Chen, H-T & Lin, C-T 2023, 'The Effects of Virtual and Physical Elevation on Physiological Stress During Virtual Reality Height Exposure', IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 4, pp. 1937-1950. View/Download from: Publisher's site View description>>
Advances in virtual reality technology have greatly benefited the acrophobia research field. Virtual reality height exposure is a reliable method of inducing stress with low variance across ages and demographics. When creating a virtual height exposure environment, researchers have often used haptic feedback elements to improve the sense of realism of a virtual environment. While the quality of the rendered for the virtual environment increases over time, the physical environment is often simplified to a conservative passive haptic feedback platform. The impact of the increasing disparity between the virtual and physical environment on the induced stress levels is unclear. This paper presents an experiment that explored the effect of combining an elevated physical platform with different levels of virtual heights to induce stress. Eighteen participants experienced four different conditions of varying physical and virtual heights. The measurements included gait parameters, heart rate, heart rate variability, and electrodermal activity. The results show that the added physical elevation at a low virtual height shifts the participant's walking behaviour and increases the perception of danger. However, the virtual environment still plays an essential role in manipulating height exposure and inducing physiological stress. Another finding is that a person's behaviour always corresponds to the more significant perceived threat, whether from the physical or virtual environment.
Zhu, HY, Hossain, SN, Jin, C, Singh, AK, Nguyen, MTD, Deverell, L, Nguyen, V, Gates, FS, Fernandez, IG, Melencio, MV, Bell, J-AR & Lin, C-T 2023, 'An investigation into the effectiveness of using acoustic touch to assist people who are blind', PLOS ONE, vol. 18, no. 10, pp. e0290431-e0290431. View/Download from: Publisher's site View description>>
Wearable smart glasses are an emerging technology gaining popularity in the assistive technologies industry. Smart glasses aids typically leverage computer vision and other sensory information to translate the wearer’s surrounding into computer-synthesized speech. In this work, we explored the potential of a new technique known as “acoustic touch” to provide a wearable spatial audio solution for assisting people who are blind in finding objects. In contrast to traditional systems, this technique uses smart glasses to sonify objects into distinct sound auditory icons when the object enters the device’s field of view. We developed a wearable Foveated Audio Device to study the efficacy and usability of using acoustic touch to search, memorize, and reach items. Our evaluation study involved 14 participants, 7 blind or low-visioned and 7 blindfolded sighted (as a control group) participants. We compared the wearable device to two idealized conditions, a verbal clock face description and a sequential audio presentation through external speakers. We found that the wearable device can effectively aid the recognition and reaching of an object. We also observed that the device does not significantly increase the user’s cognitive workload. These promising results suggest that acoustic touch can provide a wearable and effective method of sensory augmentation.
Zhu, R, Wang, P, Geng, Z, Zhao, Y & Yu, S 2023, 'Double-Agent Reinforced vNFC Deployment in EONs for Cloud-Edge Computing', Journal of Lightwave Technology, vol. 41, no. 16, pp. 5193-5208. View/Download from: Publisher's site
Zhu, T, Ye, D, Cheng, Z, Zhou, W & Yu, PS 2023, 'Learning Games for Defending Advanced Persistent Threats in Cyber Systems', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 4, pp. 2410-2422. View/Download from: Publisher's site
Zhu, T, Ye, D, Zhou, S, Liu, B & Zhou, W 2023, 'Label-Only Model Inversion Attacks: Attack With the Least Information', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 991-1005. View/Download from: Publisher's site
Zhu, Y, Lin, Q, Lu, H, Shi, K, Liu, D, Chambua, J, Wan, S & Niu, Z 2023, 'Recommending Learning Objects Through Attentive Heterogeneous Graph Convolution and Operation-Aware Neural Network', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 4178-4189. View/Download from: Publisher's site
Zoccheddu, F, Gobbetti, E, Livesu, M, Pietroni, N & Cherchi, G 2023, 'HexBox: Interactive Box Modeling of Hexahedral Meshes', Computer Graphics Forum, vol. 42, no. 5. View/Download from: Publisher's site View description>>
AbstractWe introduce HexBox, an intuitive modeling method and interactive tool for creating and editing hexahedral meshes. Hexbox brings the major and widely validated surface modeling paradigm of surface box modeling into the world of hex meshing. The main idea is to allow the user to box‐model a volumetric mesh by primarily modifying its surface through a set of topological and geometric operations. We support, in particular, local and global subdivision, various instantiations of extrusion, removal, and cloning of elements, the creation of non‐conformal or conformal grids, as well as shape modifications through vertex positioning, including manual editing, automatic smoothing, or, eventually, projection on an externally‐provided target surface. At the core of the efficient implementation of the method is the coherent maintenance, at all steps, of two parallel data structures: a hexahedral mesh representing the topology and geometry of the currently modeled shape, and a directed acyclic graph that connects operation nodes to the affected mesh hexahedra. Operations are realized by exploiting recent advancements in grid‐based meshing, such as mixing of 3‐refinement, 2‐refinement, and face‐refinement, and using templated topological bridges to enforce on‐the‐fly mesh conformity across pairs of adjacent elements. A direct manipulation user interface lets users control all operations. The effectiveness of our tool, released as open source to the community, is demonstrated by modeling several complex shapes hard to realize with competing tools and techniques.
Zowghi, D & Bano, M 2023, 'What’s Missing in Requirements Engineering for Responsible AI?', IEEE Software, vol. 40, no. 6, pp. 11-15. View/Download from: Publisher's site
Abahussein, S, Zhu, T, Ye, D, Cheng, Z & Zhou, W 1970, 'Protect Trajectory Privacy in Food Delivery with Differential Privacy and Multi-agent Reinforcement Learning', Advanced Information Networking and Applications, Springer International Publishing, pp. 48-59. View/Download from: Publisher's site View description>>
Today, multiple food delivery companies work globally in different regions, and this expansion could expose users’ data to danger. This data could be stored by a third party and could be used in further analysis. The stored data needs to be stored in a proper way to prevent any other from identifying the real data if this data is disclosed. This work considers this issue to maintain the data privacy of stored customer data by leveraging differential privacy and multi-agent reinforcement learning. In the beginning, the agent delivers the food to the customer. Then the agent constructs N of obfuscated trajectories with different privacy budgets. The multi-agent reinforcement learning then chooses one trajectory from the constructed trajectories. The trajectory is then evaluated by considering three factors: the similarity between the selected trajectory and the original trajectory, the sensitivity of destination location and the frequency of the number of orders by the customer. We implemented our experiment on meal delivery data sets in Iowa City, USA.
Aboutorab, H, Saberi, M, Hussain, OK & Hussain, FK 1970, 'POSSUM: PrOactive diSruption riSk identification for sUpply chain Management', 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), IEEE. View/Download from: Publisher's site
Aboutorab, H, Yu, R, Dsouza, A, Saberi, M & Hussain, OK 1970, 'A News Recommendation System for Environmental Risk Management', CEUR Workshop Proceedings. View description>>
Environmental risk events, such as flooding, can disrupt freight routes and cause business losses. It is therefore crucial to proactively identify and manage these risks. When identifying environmental risks, it is essential to examine the impact of these events on freight routes. In this paper, we extract knowledge about environmental risk events from the knowledge graph and build a machine-learning model to identify freight routes potentially affected by floods. We propose a news recommendation system, namely the News Recommender for Environmental Risk Identification & Analysis (NR-ERIA), to recommend news related to a location of interest that has the risk of being affected by environmental risk events to support the risk management. We conducted experiments on real-world datasets and achieved an accuracy of 0.908 in proactively detecting disruptions, which is 196% higher than the baseline approach, demonstrating the effectiveness of our proposed system.
Abualhamayl, AJ, Almalki, MA, Al-Doghman, F, Alyoubi, AA & Hussain, FK 1970, 'Towards Fractional NFTs for Joint Ownership and Provenance in Real Estate', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
Adak, C, Jaswanth, B, Akhtar, Z, Kåsen, A & Chanda, S 1970, 'Writer Identification from Nordic Historical Manuscripts using Transformer Networks', 2023 IEEE International Joint Conference on Biometrics (IJCB), 2023 IEEE International Joint Conference on Biometrics (IJCB), IEEE. View/Download from: Publisher's site
Adams, C & Oberst, S 1970, 'Modelling of noise due to impulsive excitation using nonlinear time series analysis', Noise and Vibrations Emerging Methods, Auckland, New Zealand.
Akbarzade, M, Oberst, S, Sepehrirahnama, S & Halkon, B 1970, 'Sensitivity and bifurcation analysis of an analytical model of a trapped object in an externally excited acoustic radiation force field', Proceedings of NOVEM23, Noise and Vibration Emerging Methods, Auckland, New Zealand, pp. 1-10. View description>>
Acoustic radiation force (ARF) is a nonlinear acoustic phenomenon for which the acoustic field properties and, to an even greater extent, the explicit dynamics of the object, have received limited attention in the published literature to date. Any oscillations due to the flow field or external perturbations are thereby negligible while the particle is trapped in a stable position. By changing the viewpoint from the acoustic field to the dynamics of a levitated particle, the amplitude and frequency of external oscillation is non-negligible, we ask the question of how external excitation changes the dynamics of the object. We explicitly derive an analytical formulation of a trapped object in the form of a Duffing-like equation with its constants being defined by the object itself, the fluid, the acoustic wave, and the external vibration properties. In this case, the bifurcation behaviour is studied, and we show this together with a sensitivity analysis to represent correct dynamic behaviour in certain regimes of the bifurcation diagram.
Alalyan, MS, Jaafari, NA & Hussain, FK 1970, 'Barriers to Blockchain Adoption by Saudi Higher Education Institutions: A Structural Equation Analysis', Lecture Notes on Data Engineering and Communications Technologies, Springer Nature Switzerland, pp. 52-61. View/Download from: Publisher's site View description>>
Empirical investigations of the factors influencing blockchain adoption by higher education institutions in Saudi Arabia are rare. This study aimed to partially fill this knowledge gap by focusing on barriers to blockchain adoption. The results from a survey of 289 academic and IT professionals in the Saudi higher education system confirmed the negative effects of privacy and security concerns, the association of blockchain with finance only, and language concerns on blockchain adoption. At the same time, the impact of the lack of knowledge was not observed. The theoretical and practical implications of the results are discussed.
Alfredo, RD, Nie, L, Kennedy, P, Power, T, Hayes, C, Chen, H, McGregor, C, Swiecki, Z, Gašević, D & Martinez-Maldonado, R 1970, ''That Student Should be a Lion Tamer!' StressViz: Designing a Stress Analytics Dashboard for Teachers', LAK23: 13th International Learning Analytics and Knowledge Conference, LAK 2023: 13th International Learning Analytics and Knowledge Conference, ACM, pp. 57-67. View/Download from: Publisher's site View description>>
In recent years, there has been a growing interest in creating multimodal learning analytics (LA) systems that automatically analyse students' states that are hard to see with the 'naked eye', such as cognitive load and stress levels, but that can considerably shape their learning experience. A rich body of research has focused on detecting such aspects by capturing bodily signals from students using wearables and computer vision. Yet, little work has aimed at designing end-user interfaces that visualise physiological data to support tasks deliberately designed for students to learn from stressful situations. This paper addresses this gap by designing a stress analytics dashboard that encodes students' physiological data into stress levels during different phases of an authentic team simulation in the context of nursing education. We conducted a qualitative study with teachers to understand (i) how they made sense of the stress analytics dashboard; (ii) the extent to which they trusted the dashboard in relation to students' cortisol data; and (iii) the potential adoption of this tool to communicate insights and aid teaching practices.
Alghanmi, NA, Alghanmi, N, Alhosaini, H & Hussain, FK 1970, 'Carbon Credits Storage: A Comparative Multifactor Analysis of On-chain vs Off-chain Approaches', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
Alharbi, S, Alorini, AFA, Alahmadi, KMM, Alhosaini, H, Zhu, Y & Wang, X 1970, 'Exploring Oversampling Techniques for Fraud Detection with Imbalanced Classes', 11th International Conference on Informatics, Electronics & Vision, 11th International Conference on Informatics, Electronics & Vision, London, UK.
Alkhalaf, A & Hussain, FK 1970, 'Optimisation of Volunteer Node Selection for Scalable and Trustworthy Fog Environments', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
Almadani, MS & Hussain, FK 1970, 'Implementing a Secure Blockchain-Based Wallet System with Multi-Factor Authentication', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
Alotaibi, S, Alsobhi, H, Zhao, M & Hussain, FK 1970, 'Blockchain for Identity Management: Ensuring Trust and Integrity in the Education Sector', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
Alsolmy, M & Hussain, FK 1970, 'Blockchain Adoption in Saudi Manufacturing: The Conceptual Model and Research Hypotheses.', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
Alsufyani, N & Gill, AQ 1970, 'A Knowledge-Graph Based Integrated Digital EA Maturity and Performance Framework', Lecture Notes in Business Information Processing, International Conference on Enterprise Design, Operations, and Computing (EDOC): EDOC Workshops, Springer International Publishing, Bozen-Bolzano, Italy, pp. 214-229. View/Download from: Publisher's site
Alsulaimani, S, Hussain, F & Hussain, O 1970, 'Digital Asset Ownership based on Blockchain: A Literature Review', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
Bandara, M, Jiang, Y, Gill, A, Rabhi, F & Beydoun, G 1970, 'An Application Ontology for Reproducibility of Machine Learning Solutions', Australasian Conference on Information Systems, Wellington, New Zealand.
Bayat, A, Oberst, S & Lai, JCS 1970, 'NUMERICAL SIMULATION OF HEAT TRANSFER IN TERMITE MOUNDS', Proceeding of International Heat Transfer Conference 17, International Heat Transfer Conference 17, Begellhouse. View/Download from: Publisher's site
Bin sawad, A, Alakhtar, R, Alturki, B, Narayan, B, Lin, S, Prasad, M & Kocaballi, B 1970, 'Towards a Design Framework for Conversational Agents for Diabetes Prevention', https://dl.acm.org/conference/ozchi/proceedings, Oz Computer Human Interaction, Wellington, NZ.
Blooma Mohan John, Ramanathan, S & Jayan Chirayath Kurian, J 1970, 'Design and Evaluation of a Virtual Reality Game to Improve Physical and Cognitive Acuity', Hyderabad, India.
Braytee, A, Anaissi, A & Naji, M 1970, 'A Comparative Analysis of Loss Functions for Handling Foreground-Background Imbalance in Image Segmentation', Springer International Publishing, pp. 3-13. View/Download from: Publisher's site
Cao, J, Liu, B, Wen, Y, Xie, R & Song, L 1970, 'Achieving Privacy-Preserving Multi-View Consistency with Advanced 3D-Aware Face De-identification', ACM Multimedia Asia 2023, MMAsia '23: ACM Multimedia Asia, ACM. View/Download from: Publisher's site
Cao, Z, Zhang, S & Lin, C-T 1970, 'Online Ensemble of Ensemble OVA Framework for Class Evolution with Dominant Emerging Classes', 2023 IEEE International Conference on Data Mining (ICDM), 2023 IEEE International Conference on Data Mining (ICDM), IEEE. View/Download from: Publisher's site
Chen, B, Wu, T, Zhang, Y, Chhetri, MB & Bai, G 1970, 'Investigating Users’ Understanding of Privacy Policies of Virtual Personal Assistant Applications', Proceedings of the ACM Asia Conference on Computer and Communications Security, ASIA CCS '23: ACM ASIA Conference on Computer and Communications Security, ACM. View/Download from: Publisher's site
Chen, C, Liu, Y, Chen, L & Zhang, C 1970, 'RiskContra: A Contrastive Approach to Forecast Traffic Risks with Multi-Kernel Networks', Advances in Knowledge Discovery and Data Mining, Springer Nature Switzerland, pp. 263-275. View/Download from: Publisher's site View description>>
Traffic accident forecasting is of vital importance to the intelligent transportation and public safety. Spatial-temporal learning is the mainstream approach to exploring complex evolving patterns. However, two intrinsic challenges lie in traffic accident forecasting, preventing the straightforward adoption of spatial-temporal learning. First, the temporal observations of traffic accidents exhibit ultra-rareness due to the inherent properties of accident occurrences (Fig. 1(a)), which leads to the severe scarcity of risk samples in learning accident patterns. Second, the spatial distribution of accidents is severely imbalanced from region to region (Fig. 1(b)), which poses a serious challenge to forecast the spatially diversified risks. To tackle the above challenges, we propose RiskContra, a Contra stive learning approach with multi-kernel networks, to forecast the Risk of traffic accidents. Specifically, to address the first challenge (i.e. temporal rareness), we design a novel contrastive learning approach, which leverages the periodic patterns to derive a tailored mixup strategy for risk sample augmentation. This way, the contrastively learned features can better represent the risk samples, thus capturing higher-quality accident patterns for forecasting. To address the second challenge (i.e. spatial imbalance), we design the multi-kernel networks to capture the hierarchical correlations from multiple spatial granularities. This way, disparate regions can utilize the multi-granularity correlations to enhance the forecasting performance across regions. Extensive experiments corroborate the effectiveness of each devised component in RiskContra.
Chen, Y, Li, Z, Yang, C, Wang, X, Long, G & Xu, G 1970, 'Adaptive Graph Recurrent Network for Multivariate Time Series Imputation', International Conference on Neural Information Processing, International Conference on Neural Information Processing, Springer Nature Singapore, New Delhi, India, pp. 64-73. View/Download from: Publisher's site
Chen, Y, Shi, K, Wang, X & Xu, G 1970, 'MTSTI: A Multi-task Learning Framework for Spatiotemporal Imputation', International Conference on Advanced Data Mining and Applications, International Conference on Advanced Data Mining and Applications, Springer Nature Switzerland, Shenyang, China, pp. 180-194. View/Download from: Publisher's site
Coluccia, A, Fascista, A, Sommer, L, Schumann, A, Dimou, A, Zarpalas, D & Sharma, N 1970, 'Drone-vs-Bird Detection Grand Challenge at ICASSP2023', ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. View/Download from: Publisher's site
Cortes, CAT, Lin, C-T, Do, T-TN & Chen, H-T 1970, 'An EEG-based Experiment on VR Sickness and Postural Instability While Walking in Virtual Environments', 2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR), 2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR), IEEE. View/Download from: Publisher's site
Diego Roman Arbelaez, J, Jayan Chirayath Kurian, J & Beydoun, G 1970, 'METAVERSE IN EDUCATION: A DYNAMIC CAPABILITY THEORY APPROACH', Proceedings of the AIS SIGED 2023 Conference, Hyderabad, India.
Doan, QM, Dinh, TH, Trung, NL, Nguyen, DN, Singh, AK & Lin, C-T 1970, 'Extended Upscale and Downscale Representation with Cascade Arrangement', 2023 IEEE Statistical Signal Processing Workshop (SSP), 2023 IEEE Statistical Signal Processing Workshop (SSP), IEEE. View/Download from: Publisher's site
Du, H, Yu, X, Hussain, F, Armin, MA, Petersson, L & Li, W 1970, 'Weakly-supervised Point Cloud Instance Segmentation with Geometric Priors', 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE. View/Download from: Publisher's site
Fan, Y, Pietroni, N & Ferguson, S 1970, 'A Neural Network-based Low-cost Soft Sensor for Touch Recognition and Deformation Capture', Proceedings of the 2023 ACM Designing Interactive Systems Conference, DIS '23: Designing Interactive Systems Conference, ACM. View/Download from: Publisher's site
Gao, L, Yin, M, Xiao, F & Cao, Z 1970, 'A Complex Belief Jensen-Shannon Divergence in Complex Evidence Theory for Decision-Making', 2023 IEEE International Conference on Unmanned Systems (ICUS), 2023 IEEE International Conference on Unmanned Systems (ICUS), IEEE. View/Download from: Publisher's site
Gavriel, J, Herr, D, Shaw, A, Bremner, MJ, Paler, A & Devitt, SJ 1970, 'Transversal Injection: Using the Surface Code to Prepare Non-Pauli Eigenstates', 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), IEEE. View/Download from: Publisher's site
Guan, L, Abbasi, A & Merigó, JM 1970, 'Applying social network analysis to risk management of complex projects', The 7th Annual Australian Social Network Analysis Conference, Sydney, Australia.
Guan, L, Chakrabortty, RK, Abbasi, A & Merigó, JM 1970, 'An intelligent decision support system for defence supply chain risk management', The 20th ANZAM Symposium 2023 Operations, Supply Chain and Services Management, The 20th ANZAM Operations, Supply Chain and Services Management Symposium, Sydney, Australia.
Guan, L, M. Merigó, J, K. Chakrabortty, R & Abbasi, A 1970, 'A Simulation-Optimisation-Based Decision Support System for Optimising Project Risk Treatment Decisions Considering Risk Interdependencies', Proceedings of the International Conference on Industrial Engineering and Operations Management, 2nd Australian International Conference on Industrial Engineering and Operations Management, IEOM Society International, Melbourne, Australia. View/Download from: Publisher's site
Guo, L, Xiong, J, Zhou, J & Liu, B 1970, 'Regional Scanning Strategy of UAV Cluster Platform for Mobile Emergency Broadcasting', 2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, pp. 1-6. View/Download from: Publisher's site
Guo, Y, Zhu, J, Lei, G, Lu, H & Jin, J 1970, 'Characterization of Electromagnetic Materials under 2D/3D Rotational Magnetization', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE. View/Download from: Publisher's site
Hason Rudd, D, Huo, H & Xu, G 1970, 'An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance', Advances in Knowledge Discovery and Data Mining, Springer Nature, pp. 219-231. View/Download from: Publisher's site View description>>
AbstractEmotion recognition (ER) from speech signals is a robust approach since it cannot be imitated like facial expression or text based sentiment analysis. Valuable information underlying the emotions are significant for human-computer interactions enabling intelligent machines to interact with sensitivity in the real world. Previous ER studies through speech signal processing have focused exclusively on associations between different signal mode decomposition methods and hidden informative features. However, improper decomposition parameter selections lead to informative signal component losses due to mode duplicating and mixing. In contrast, the current study proposes VGG-optiVMD, an empowered variational mode decomposition algorithm, to distinguish meaningful speech features and automatically select the number of decomposed modes and optimum balancing parameter for the data fidelity constraint by assessing their effects on the VGG16 flattening output layer. Various feature vectors were employed to train the VGG16 network on different databases and assess VGG-optiVMD reproducibility and reliability. One, two, and three-dimensional feature vectors were constructed by concatenating Mel-frequency cepstral coefficients, Chromagram, Mel spectrograms, Tonnetz diagrams, and spectral centroids. Results confirmed a synergistic relationship between the fine-tuning of the signal sample rate and decomposition parameters with classification accuracy, achieving state-of-the-art 96.09% accuracy in predicting seven emotions on the Berlin EMO-DB database.
He, L, Wang, X, Wang, D, Zou, H, Yin, H & Xu, G 1970, 'Simplifying Graph-based Collaborative Filtering for Recommendation', Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM '23: The Sixteenth ACM International Conference on Web Search and Data Mining, ACM, Singapore. View/Download from: Publisher's site
Hu, R, Wang, X, Chang, X, Hu, Y, Xin, X, Ding, X & Guo, B 1970, 'RASNet: A Reinforcement Assistant Network for Frame Selection in Video-based Posture Recognition', 2023 IEEE International Conference on Multimedia and Expo (ICME), 2023 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Brisbane. View/Download from: Publisher's site
Huang, C, Wang, S, Wang, X & Yao, L 1970, 'Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation', Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, Taipei, Taiwan. View/Download from: Publisher's site
Huang, C, Wang, S, Wang, X & Yao, L 1970, 'Modeling Temporal Positive and Negative Excitation for Sequential Recommendation', Proceedings of the ACM Web Conference 2023, WWW '23: The ACM Web Conference 2023, ACM, Austin, TX, USA. View/Download from: Publisher's site
Islam, MR, Kowsar Hossain Sakib, M, Prome, SA, Wang, X, Ulhaq, A, Sanin, C & Asirvatham, D 1970, 'Machine Learning with Explainability for Suicide Ideation Detection from Social Media Data', 2023 10th International Conference on Behavioural and Social Computing (BESC), 2023 10th International Conference on Behavioural and Social Computing (BESC), IEEE, Larnaca, Cyprus. View/Download from: Publisher's site
Ivanyos, G & Qiao, Y 1970, 'On the orbit closure intersection problems for matrix tuples under conjugation and left-right actions', Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 4115-4126. View description>>
Let G be a linear algebraic group acting on the vector space V. Given v, v′ ∈ V, the orbit closure intersection problem asks to decide if the orbit closures of v and v′ under G intersect. Due to connections with polynomial identity testing, the orbit closure intersection problems for the conjugation and left-right actions on matrix tuples received considerable attention in computational complexity and computational invariant theory, as seen in the works of Forbes-Shpilka (RANDOM 2013), Allen-Zhu-Garg-Li-Oliveira-Wigderson (STOC 2018), and Derksen-Makam (Algebra & Number Theory 2020). In this paper, we present new algorithms for the orbit closure problem for the conjugation and left-right actions on matrix tuples. The main novel feature is that in the case of intersecting orbit closures, our algorithm outputs cosets of one-parameter subgroups that drive the matrix tuples to a tuple in the intersection of the orbit closures.
Jayan Chirayath Kurian, J, Zeena, A, Simon, T, Luke, N-H & Rosetta, R 1970, 'INDUSTRY-BASED IT CERTIFICATIONS IN HIGHER EDUCATION INSTITUTIONS: A STAKEHOLDER PERSPECTIVE', Proceedings of the AIS SIGED 2023 Conference 2023, Hyderabad, India.
Jefry, W, Al-Doghman, F & Hussain, F 1970, 'Comparison of Artificial Intelligence Models in Cross-lingual Transfer Learning through Sentiment Analysis', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
John, BM, Jose, S, Thomas, J & Jayan Chirayath Kurian, J 1970, 'Designing an Artifact to Empower Chronic Patients for Monitoring Health During a Pandemic: A COVID-19 Screening App', Maui, US.
Karetla, GR, Catchpoole, D, Kennedy, P, Simoff, S & Nguyen, QV 1970, 'IR-ER- A Hybrid Pipeline for Classifying COVID-19 RNA Seq Data', 2023 Australasian Computer Science Week, ACSW 2023: 2023 Australasian Computer Science Week, ACM. View/Download from: Publisher's site
Kim, J, Xuan, J, Liang, C & Hussain, F 1970, 'An Autonomous Non-monolithic Agent with Multi-mode Exploration based on Options Framework', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE. View/Download from: Publisher's site
Lai, M, Cao, L, Lu, H, Ha, Q, Li, L, Hossain, J & Kennedy, P 1970, 'An Unsupervised Hierarchical Clustering Approach to Improve Hopfield Retrieval Accuracy', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE. View/Download from: Publisher's site View description>>
Despite its efficiency, the classical Hopfield network was a highly impractical data-searching solution due to its limited storage capacity. While the recently released modern Hopfield variant has increased its storage capacity, its searching ability is heavily affected by local minima and saddle points, which prevented it from becoming a worthy successor of the classical Hopfield network. We propose a novel unsupervised clustering approach to bypass local minima and saddle points to enhance the overall robustness of the Hopfield network. Our experimental results on benchmark MNIST indicate that our algorithm can increase the retrieval accuracy by over (20%) in general against the Hopfield Update Rule, proving that it is a far superior modelling solution.
Lestari, NI, Hussain, W, Merigo, JM & Bekhit, M 1970, 'A Survey of Trendy Financial Sector Applications of Machine and Deep Learning', Springer Nature Switzerland, pp. 619-633. View/Download from: Publisher's site
Li, J & Liu, W 1970, 'Summarization Attack via Paraphrasing', Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, pp. 16250-16251. View description>>
Many natural language processing models are perceived to be fragile on adversarial attacks. Recent work on adversarial attack has demonstrated a high success rate on sentiment analysis as well as classification models. However, attacks to summarization models have not been well studied. Summarization tasks are rarely influenced by word substitution, since advanced abstractive summary models utilize sentence level information. In this paper, we propose a paraphrasing-based attack method to attack summarization models. We first rank the sentences in the document according to their impacts to summarization. Then, we apply paraphrasing procedure to generate adversarial samples. Finally, we test our algorithm on benchmarks datasets against others methods. Our approach achieved the highest success rate and the lowest sentence substitution rate. In addition, the adversarial samples have high semantic similarity with the original sentences.
Li, J, Pang, G, Chen, L & Namazi-Rad, M-R 1970, 'HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks', 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), IEEE. View/Download from: Publisher's site
Li, K, Lu, J, Zuo, H & Zhang, G 1970, 'Attention-Bridging TS Fuzzy Rules for Universal Multi-Domain Adaptation Without Source Data', 2023 IEEE International Conference on Fuzzy Systems (FUZZ), 2023 IEEE International Conference on Fuzzy Systems (FUZZ), IEEE. View/Download from: Publisher's site
Li, Z, Wang, X, Yang, C, Yao, L, McAuley, J & Xu, G 1970, 'Exploiting Explicit and Implicit Item relationships for Session-based Recommendation', Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM '23: The Sixteenth ACM International Conference on Web Search and Data Mining, ACM, Singapore. View/Download from: Publisher's site
Liu, B, Liu, B, Ding, M, Zhu, T & Yu, X 1970, 'TI2Net: Temporal Identity Inconsistency Network for Deepfake Detection', 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 4680-4689. View/Download from: Publisher's site
Liu, C, Zhu, T, Shen, S & Zhou, W 1970, 'Towards Robust Gan-Generated Image Detection: A Multi-View Completion Representation', Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}, International Joint Conferences on Artificial Intelligence Organization. View/Download from: Publisher's site View description>>
GAN-generated image detection now becomes the first line of defense against the malicious uses of machine-synthesized image manipulations such as deepfakes. Although some existing detectors work well in detecting clean, known GAN samples, their success is largely attributable to overfitting unstable features such as frequency artifacts, which will cause failures when facing unknown GANs or perturbation attacks. To overcome the issue, we propose a robust detection framework based on a novel multi-view image completion representation. The framework first learns various view-to-image tasks to model the diverse distributions of genuine images. Frequency-irrelevant features can be represented from the distributional discrepancies characterized by the completion models, which are stable, generalized, and robust for detecting unknown fake patterns. Then, a multi-view classification is devised with elaborated intra- and inter-view learning strategies to enhance view-specific feature representation and cross-view feature aggregation, respectively. We evaluated the generalization ability of our framework across six popular GANs at different resolutions and its robustness against a broad range of perturbation attacks. The results confirm our method's improved effectiveness, generalization, and robustness over various baselines.
Liu, K, Zhao, F, Xu, G & Wu, S 1970, 'IE-Evo: Internal and External Evolution-Enhanced Temporal Knowledge Graph Forecasting', 2023 IEEE International Conference on Data Mining (ICDM), 2023 IEEE International Conference on Data Mining (ICDM), IEEE, Shanghai, China. View/Download from: Publisher's site
Liu, K, Zhao, F, Xu, G, Wang, X & Jin, H 1970, 'RETIA: Relation-Entity Twin-Interact Aggregation for Temporal Knowledge Graph Extrapolation', 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023 IEEE 39th International Conference on Data Engineering (ICDE), IEEE, Anaheim, CA, USA. View/Download from: Publisher's site
Liu, S, Benchasattabuse, N, Morgan, DQC, Hajdušek, M, Devitt, SJ & Van Meter, R 1970, 'A Substrate Scheduler for Compiling Arbitrary Fault-Tolerant Graph States', 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), IEEE. View/Download from: Publisher's site
Liu, X, Cheng, X, Yang, Y, Huo, H, Liu, Y & Nielsen, PS 1970, 'Understanding crowd energy consumption behaviors', Advances in Database Technology - EDBT, pp. 799-802. View/Download from: Publisher's site View description>>
Understanding crowd behavior is crucial for energy demand-side management. In this paper, we employ the fluid dynamics concept potential flow to model the energy demand shift patterns of the crowd in both temporal and spatial dimensions. To facilitate the use of the proposed method, we implement a visual analysis platform that allows users to interactively explore and interpret the shift patterns. The effectiveness of the proposed method will be evaluated through a hands-on experience with a real case study during the conference demonstration.
Lu, K, Zhang, Q, Zhang, G & Lu, J 1970, 'BERT-RS: A neural personalized recommender system with BERT', Machine Learning, Multi Agent and Cyber Physical Systems, Conference on Machine learning, Multi Agent and Cyber Physical Systems (FLINS 2022), WORLD SCIENTIFIC. View/Download from: Publisher's site
Luo, Y, Lu, L, Cui, X, Du, Y, Bi, Y, Zhu, L & Liang, CJ 1970, 'Novel few-shot learning based fuzzy feature detection algorithms', 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), IEEE. View/Download from: Publisher's site
Ma, G, Lu, J & Zhang, G 1970, 'Interval-Valued Observations-Based Multi-Source Domain Adaptation Using Fuzzy Neural Networks', 2023 IEEE International Conference on Fuzzy Systems (FUZZ), 2023 IEEE International Conference on Fuzzy Systems (FUZZ), IEEE. View/Download from: Publisher's site
Ma, M, Zhang, Y, Arachchige, PCM, Zhang, LY, Chhetri, MB & Bai, G 1970, 'LoDen: Making Every Client in Federated Learning a Defender Against the Poisoning Membership Inference Attacks', Proceedings of the ACM Asia Conference on Computer and Communications Security, ASIA CCS '23: ACM ASIA Conference on Computer and Communications Security, ACM. View/Download from: Publisher's site
Manchanda, C, Hussain, W, Rabhi, L & Rabhi, F 1970, 'Towards an API Marketplace for an e-Invoicing Ecosystem', Springer International Publishing, pp. 82-96. View/Download from: Publisher's site
Matin, A, Islam, MR, Zhu, Y, Wang, X, Huo, H & Xu, G 1970, 'Hybrid Deep Learning for Assembly Action Recognition in Smart Manufacturing', 11th International Conference on Informatics, Electronics & Vision, 11th International Conference on Informatics, Electronics & Vision, London, UK.
Meng, MH, Zhang, Q, Xia, G, Zheng, Y, Zhang, Y, Bai, G, Liu, Z, Teo, SG & Dong, JS 1970, 'Post-GDPR Threat Hunting on Android Phones: Dissecting OS-level Safeguards of User-unresettable Identifiers', Proceedings 2023 Network and Distributed System Security Symposium, Network and Distributed System Security Symposium, Internet Society. View/Download from: Publisher's site
Mian, A, Gill, AQ & Sharma, N 1970, 'Towards the Development of a Security Threat Identification Framework for UAV Information Infrastructure.', SmartIoT, IEEE, pp. 305-309.
Murad, MAU, Ahmad, F & Cetindamar, D 1970, 'Critical Success Factors of Technology Transfer: An Investigation into the Health Sector of Bangladesh Using ISM-DEMATEL Approach', 2023 Portland International Conference on Management of Engineering and Technology (PICMET), 2023 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE. View/Download from: Publisher's site
Murad, MAU, Cetindamar, D & Chakraborty, S 1970, 'Big Data Analytics Capability and Sustainability: A Systematic Literature Review', 2023 Portland International Conference on Management of Engineering and Technology (PICMET), 2023 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE. View/Download from: Publisher's site
Nerse, C, Oberst, S, Navarro-Payá, D, Etxeberria, J, Matus, JT, Bianco, L, Casacci, LP & Barbero, F 1970, 'PROPENSITY TO EFFICIENTLY TRANSMIT VIBRATIONS IN SNAPDRAGONS IN RESPONSE TO VIBROACOUSTIC SIGNALLING', Proceedings of the International Congress on Sound and Vibration, Proceedings of the 29th International Congress on Sound and Vibration, Prague, Czech Republic. View description>>
The coevolution of angiosperms and pollinating insects has drawn a diverse repertoire of plant-pollinator interactions leading to different mechanisms of pollen transfer. Buzz pollination is a dynamic pollen removal process between plants and insects that is thought to have emerged due to ecological and evolutionary factors. The floral morphology in buzz-pollinated flowers restricts the pollen access to non-pollinating insects to increase the conditions that favour fertilisation. Efficient pollinators such as bumblebees use sonication to vibrate the anthers of the flower, thereby causing pollen grains to be expelled through the apical pores of the anthers. Recent studies have shown that the conditions in which buzz pollination occurs vary with respect to the floral morphology and vibration signals produced by the bees mainly determined by the duration, amplitude, and frequency of the vibration. The structural topology and material properties of the flower also induce resonance and damping behaviour, thus mediating the transmission of substrate-borne vibrations. Along with the best-studied mechanism of buzz pollination, it is increasingly clear that vibroacoustic (VA) signals may have played a role in co-evolutionary responses that significantly impact several aspects of plant and insect ecology. However, studies on the material properties in terms of VA transmission are still in their infancy. Therefore, in this study, we numerically investigate the sensitivity of the floral topology on transmission of vibrations from a source signal. For this purpose, stamen structures at different scales and loading conditions are modelled and analysed in a finite element software package. A representative VA signal is implemented as the excitation at locations impacting the flower during feeding events. The results demonstrate that natural frequencies and mode shapes of the stamen may influence the conditions in which the vibrational energy is scattered across the fl...
Nguyen, M, Zhu, H, Sun, H, Nguyen, V, Deverell, L, Singh, A, Jin, C & Lin, C-T 1970, 'An Evaluation of the Presentation of Acoustic Cues for Shorelining Techniques', 2023 Immersive and 3D Audio: from Architecture to Automotive (I3DA), 2023 Immersive and 3D Audio: from Architecture to Automotive (I3DA), IEEE. View/Download from: Publisher's site
Nii, Y, Raj, C, Tiwana, MS, Samarawickrama, M, Simoff, S, Jan, T & Prasad, M 1970, 'Understanding Social Media Engagement in Response to Disaster Fundraising Attempts During Australian Bushfires', Proceedings of International Conference on Intelligent Vision and Computing (ICIVC 2022), Springer Nature Switzerland, pp. 277-289. View/Download from: Publisher's site
Niu, C, Pang, G & Chen, L 1970, 'Graph-Level Anomaly Detection via Hierarchical Memory Networks', Springer Nature Switzerland, pp. 201-218. View/Download from: Publisher's site
Novakovic, A, Marshall, AH, McGregor, C, Bressan, N, McAllister, K & Courcier, E 1970, 'Using machine learning to improve bovine tuberculosis control in herd level outbreaks', 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE. View/Download from: Publisher's site
Oberst, S, Sepehrirahnama, S, Halkon, B, Lai, JCS, Atkinson, T & Evans, TAE 1970, 'A microactuator device for the detection of termite damage in timber poles', International Union of Forest Research Organisations, Cairns.
Oberst, S, Sepehrirahnama, S, Nerse, C, Brodzeli, Z, Lai, JCS, Mankowski, M, Atkinson, T, Arango, R, Kirker, G & Evans, T 1970, 'Towards a microactuator-sensing network for structural health monitoring of timber poles', Proceedings IRG Annual Meeting, International Research Group on Wood Protection, The International Research Group on Wood Protection, Cairns, Queensland, Australia, pp. 1-9.
Ordibazar, AH, Hussain, O, Chakrabortty, RK, Saberi, M & Irannezhad, E 1970, 'Developing Supply Chain Risk Management Strategies by Using Counterfactual Explanation', Springer Nature Switzerland, pp. 53-65. View/Download from: Publisher's site
Ou, L, Chang, Y-C, Wang, Y-K & Lin, C-T 1970, 'Explain Reinforcement Learning Agents Through Fuzzy Rule Reconstruction', 2023 IEEE International Conference on Fuzzy Systems (FUZZ), 2023 IEEE International Conference on Fuzzy Systems (FUZZ), IEEE. View/Download from: Publisher's site
Overdevest, N, Patibanda, R, Saini, A, Van Den Hoven, E & Mueller, FF 1970, 'Towards Designing for Everyday Embodied Remembering: Findings from a Diary Study', Proceedings of the 2023 ACM Designing Interactive Systems Conference, DIS '23: Designing Interactive Systems Conference, ACM. View/Download from: Publisher's site
Pang, X, Xie, K, Zhang, Y, Fleming, M, Xu, DC & Liu, W 1970, 'Adversarial Active Learning with Guided BERT Feature Encoding', Springer Nature Switzerland, pp. 508-520. View/Download from: Publisher's site
Raza, MR & Hussain, W 1970, 'Preserving Academic Integrity in Teaching with ChatGPT: Practical Strategies', 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), IEEE. View/Download from: Publisher's site
Rudd, DH, Huo, H, Islam, MR & Xu, G 1970, 'Churn Prediction via Multimodal Fusion Learning: Integrating Customer Financial Literacy, Voice, and Behavioral Data', 2023 10th International Conference on Behavioural and Social Computing (BESC), 2023 10th International Conference on Behavioural and Social Computing (BESC), IEEE. View/Download from: Publisher's site
Sadaf, A, Mathieson, L, Bródka, P & Musial, K 1970, 'Maximising Influence Spread in Complex Networks by Utilising Community-Based Driver Nodes as Seeds', Springer Nature Switzerland, pp. 126-141. View/Download from: Publisher's site
Saini, A, Sridhar, S, Raheja, A, Patibanda, R, Overdevest, N, Wang, P-YC, Van Den Hoven, E & Mueller, FF 1970, 'Pneunocchio: A playful nose augmentation for facilitating embodied representation', Adjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, UIST '23: The 36th Annual ACM Symposium on User Interface Software and Technology, ACM. View/Download from: Publisher's site
Sepehrirahnama, S, Sansom, T, Lai, JCS & Oberst, S 1970, 'DESIGN OF A MINIATURISED MICRO-FORCE PLATE TO STUDY THE LOCOMOTION OF SMALL ARTHROPODS', Proceedings of the International Congress on Sound and Vibration, 29, International Institute of Acoustics and Vibrations (IIAV) - IIAV CZECH s.r.o., Prague, Czech Republic. View description>>
Walking insects exhibit various types of locomotory gaits to adjust to their environment, for foraging activities, defence, building, courtship and communication. Among hexapods, the walking gaits of ants had been studied by measuring the ground reaction force from their steps with a Micro-Force Plate, which is a bespoke and sensitive thin plate mounted on a structure similar to the suspension system of a vehicle. Compared to ants, termites have quieter footsteps on the same substrate (peak velocity of 0.004 ms-1 compared to 0.4 ms-1 for ants). To study common gaits of small arthropods, we designed a miniaturised micro-force plate capable of measuring forces in the order of 1 μN in the z-direction (out-of-plane); an order of magnitude smaller than the one used for study of ants walking gaits. This is achieved by more compliant beams (halved width, at least 50% longer in the x and y-directions) from 3D-printed resin with minimum required curing; compared to the one for ants from the completed stereolithography. The improved design is assessed numerically by characterising its vibration response in terms of settling time due to an axial force excitation. Our micro-force plate can be used to increase the signal-to-noise ratio, as measured for a 3D-printed prototype, and better resolved force (approximately 1 μN without using any MEMS component). Results presented here indicate our micro-force plate is suitable for studying quiet tiny hexapods to provide insights for bio-inspired engineering of robotic locomotion systems.
Shahsavari, M, Hussain, OK, Saberi, M & Sharma, P 1970, 'A lightweight and unsupervised approach for identifying risk events in news articles', 2023 IEEE International Conference on Data Mining Workshops (ICDMW), 2023 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE. View/Download from: Publisher's site
Shams, RA, Bano, M, Zowghi, D, Lu, Q & Whittle, J 1970, 'Human Value Requirements in AI Systems: Empirical Analysis of Amazon Alexa.', REW, 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW), IEEE, pp. 138-145. View/Download from: Publisher's site
Sheng, J, Xiong, J & Liu, B 1970, 'Federated Learning Technology in Serial Topology for IoT Networks', 2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE. View/Download from: Publisher's site
Shi, K, Sun, X, He, L, Wang, D, Li, Q & Xu, G 1970, 'AMR-TST: Abstract Meaning Representation-based Text Style Transfer', Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 4231-4243. View description>>
Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input. In this paper, we propose the AMR-TST, an AMR-based text style transfer (TST) technique. The AMR-TST converts the source text to an AMR graph and generates the transferred text based on the AMR graph modified by a TST policy named style rewriting. Our method combines both the ex-plainability and diversity of explicit and implicit TST methods. The experiments show that the proposed method achieves state-of-the-art results compared with other baseline models in automatic and human evaluations. The generated transferred text in qualitative evaluation proves the AMR-TST have significant advantages in keeping semantic features and reducing hallucinations. To the best of our knowledge, this work is the first to apply the AMR method focusing on node-level features to the TST task.
Shi, Z, Xu, Y, Fang, M & Chen, L 1970, 'Self-imitation Learning for Action Generation in Text-based Games', EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, pp. 703-726. View description>>
In this work, we study reinforcement learning (RL) in solving text-based games. We address the challenge of combinatorial action space, by proposing a confidence-based self-imitation model to generate action candidates for the RL agent. Firstly, we leverage the self-imitation learning to rank and exploit past valuable trajectories to adapt a pre-trained language model (LM) towards a target game. Then, we devise a confidence-based strategy to measure the LM's confidence with respect to a state, thus adaptively pruning the generated actions to yield a more compact set of action candidates. In multiple challenging games, our model demonstrates promising performance in comparison to the baselines.
Shubho, FH, Chowdhury, TF, Cheraghian, A, Saberi, M, Mohammed, N & Rahman, S 1970, 'ChatGPT-guided Semantics for Zero-shot Learning', 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE. View/Download from: Publisher's site
Sia, J, Chang, Y-C, Lin, C-T & Wang, Y-K 1970, 'EEG-BASED TNN for Driver Vigilance Monitoring', 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 2023 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE. View/Download from: Publisher's site
Tang, H, Wu, S, Xu, G & Li, Q 1970, 'Dynamic Graph Evolution Learning for Recommendation', Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM. View/Download from: Publisher's site
Tian, Y, Zhang, L, Do, TT-T, Liu, J, Wang, Y-K & Lin, C-T 1970, 'Classification of inattentional blindness using brain dynamics of ERPs', 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE. View/Download from: Publisher's site
Tian, Z, Zhang, C, Sood, K & Yu, S 1970, 'Inferring Private Data from AI Models in Metaverse through Black-box Model Inversion Attacks', 2023 IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom), 2023 IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom), IEEE. View/Download from: Publisher's site
Van Nguyen, K, Islam, MR, Huo, H, Tilocca, P & Xu, G 1970, 'Explainable exclusion in the life insurance using multi-label classifier', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE. View/Download from: Publisher's site
Van Nguyen, K, Tilocca, P, Huo, H & Xu, G 1970, 'Underwriting Knowledge Graph Construction, Maintenance and Its Application on Explainable Exclusion', 2023 10th International Conference on Behavioural and Social Computing (BESC), 2023 10th International Conference on Behavioural and Social Computing (BESC), IEEE. View/Download from: Publisher's site
Wang, K, Ling, Y, Zhang, Y, Yu, Z, Wang, H, Bai, G, Ooi, BC & Dong, JS 1970, 'Characterizing Cryptocurrency-themed Malicious Browser Extensions', Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS '23: ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, ACM. View/Download from: Publisher's site
Wang, Q, Liu, D, Carmichael, MG & Lin, C-T 1970, 'Robot Trust and Self-Confidence Based Role Arbitration Method for Physical Human-Robot Collaboration', 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE, ENGLAND, London, pp. 9896-9902. View/Download from: Publisher's site View description>>
Role arbitration in human-robot collaboration (HRC) is a dynamically changing process that is affected by many factors such as physical workload, environmental changes and trust. In order to address this dynamic process, a trust-based role arbitration method is studied in this research. A computational model of robot trust and self-confidence (TSC) in physical human-robot collaboration (pHRC) is proposed. The TSC model is defined as a function of objective robot and human co-worker performance. A role arbitration method is then proposed based on the TSC model presented. The human-in-the-loop experiments with a collaborative robot are conducted to verify the TSC-based role arbitration method. The results show that the proposed method could achieve superior human-robot combined performance, reduce human co-workers' workload, and improve subjective preference.
Wang, R, Liu, Y, Gong, Y, Liu, W, Chen, M, Yin, Y & Zheng, Y 1970, 'Fine-grained Urban Flow Inference with Unobservable Data via Space-Time Attraction Learning', 2023 IEEE International Conference on Data Mining (ICDM), 2023 IEEE International Conference on Data Mining (ICDM), IEEE. View/Download from: Publisher's site
Wang, W, Tian, Z, Zhang, C, Liu, A & Yu, S 1970, 'BFU: Bayesian Federated Unlearning with Parameter Self-Sharing', Proceedings of the ACM Asia Conference on Computer and Communications Security, ASIA CCS '23: ACM ASIA Conference on Computer and Communications Security, ACM. View/Download from: Publisher's site
Wang, W, Zhang, C, Liu, S, Tang, M, Liu, A & Yu, S 1970, 'FedMC: Federated Learning with Mode Connectivity Against Distributed Backdoor Attacks', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE. View/Download from: Publisher's site
Wen, Y, Liu, B, Cao, J, Xie, R & Song, L 1970, 'Divide and Conquer: a Two-Step Method for High Quality Face De-identification with Model Explainability', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE. View/Download from: Publisher's site
Wu, H, Zhao, G, Wang, S, Xu, C & Yu, S 1970, 'A Location Privacy and Query Privacy Joint Protection Scheme for POI Query in Vehicular Networks', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE. View/Download from: Publisher's site
Wu, M, Zhang, Y, Lin, H, Grosser, M, Zhang, G & Lu, J 1970, 'BiblioEngine: An AI-Empowered Platform for Disease Genetic Knowledge Mining', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Singapore, pp. 187-198. View/Download from: Publisher's site View description>>
Recent decades have seen significant advancements in contemporary genetic research with the aid of artificial intelligence (AI) techniques. However, researchers lack a comprehensive platform for fully exploiting these AI tools and conducting customized analyses. This paper introduces BiblioEngine, a literature analysis platform that helps researchers profile the research landscape and gain genetic insights into diseases. BiblioEngine integrates multiple AI-empowered data sources and employs heterogeneous network analysis to identify and emphasize genes and other biomedical entities for further investigation. Its effectiveness is demonstrated through a case study on stroke-related genetic research. Analysis with BiblioEngine uncovers valuable research intelligence and genetic insights. It provides a profile of leading research institutions and the knowledge landscape in the field. The gene co-occurrence map reveals frequent research of NOTCH3, prothrombotic factors, inflammatory cytokines, and other potential risk factors. The heterogeneous biomedical entity network analysis highlights infrequently studied genes and biomedical entities with potential significance for future stroke studies. In conclusion, BiblioEngine is a valuable tool enabling efficient navigation and comprehension of expanding biomedical knowledge from scientific literature, empowering researchers in their pursuit of disease-specific genetic knowledge.
Wu, S, Xu, G & Wang, X 1970, 'SOAC: Supervised Off-Policy Actor-Critic for Recommender Systems', 2023 IEEE International Conference on Data Mining (ICDM), 2023 IEEE International Conference on Data Mining (ICDM), IEEE. View/Download from: Publisher's site
Wu, W, Li, B, Chen, L, Gao, J & Zhang, C 1970, 'A Review for Weighted MinHash Algorithms (Extended abstract)', 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023 IEEE 39th International Conference on Data Engineering (ICDE), IEEE, Anaheim, CA, USA, pp. 2553-2573. View/Download from: Publisher's site View description>>
Data similarity computation is a fundamental research topic which underpins many high level applications based on similarity measures However the exact similarity computation has become daunting in large scale real world scenarios Currently MinHash is a popular technique for efficiently estimating the Jaccard similarity of binary sets and furthermore weighted MinHash is utilized to estimate the generalized Jaccard similarity of weighted sets This review focuses on categorizing and discussing the existing works of weighted MinHash algorithms Also we have developed a Python toolbox for the algorithms and released it in our github
Wu, X, Lu, J, Fang, Z & Zhang, G 1970, 'Meta OOD Learning For Continuously Adaptive OOD Detection', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Paris. View/Download from: Publisher's site
Xiang, S, Zhu, M, Cheng, D, Li, E, Zhao, R, Ouyang, Y, Chen, L & Zheng, Y 1970, 'Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation', Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, pp. 14557-14565. View description>>
Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. Specifically, we leverage transaction records to construct a temporal transaction graph, which is composed of temporal transactions (nodes) and interactions (edges) among them. Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation. We further model the fraud patterns through risk propagation among transactions. The extensive experiments are conducted on a real-world transaction dataset and two publicly available fraud detection datasets. The result shows that our proposed method, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets. Semi-supervised experiments demonstrate the excellent fraud detection performance of our model with only a tiny proportion of labeled data.
Yang, C, Wang, X, Yao, L, Long, G & Xu, G 1970, 'From Time Series to Multi-modality: Classifying Multivariate Time Series via Both 1D and 2D Representations', International Conference on Advanced Data Mining and Applications, International Conference on Advanced Data Mining and Applications, Springer Nature Switzerland, Shenyang, China, pp. 19-33. View/Download from: Publisher's site
Yang, H, Chen, H, Zhang, S, Sun, X, Li, Q, Zhao, X & Xu, G 1970, 'Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning', Proceedings of the ACM Web Conference 2023, WWW '23: The ACM Web Conference 2023, ACM. View/Download from: Publisher's site
Yang, X, Liu, W & Liu, W 1970, 'Tensor Canonical Correlation Analysis Networks for Multi-view Remote Sensing Scene Recognition (Extended Abstract)', 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023 IEEE 39th International Conference on Data Engineering (ICDE), IEEE. View/Download from: Publisher's site
Yue, Z, Zhang, Y & Liang, J 1970, 'Learning Conflict-Noticed Architecture for Multi-Task Learning', Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, pp. 11078-11086. View description>>
Multi-task learning has been widely used in many applications to enable more efficient learning by sharing part of the architecture across multiple tasks. However, a major challenge is the gradient conflict when optimizing the shared parameters, where the gradients of different tasks could have opposite directions. Directly averaging those gradients will impair the performance of some tasks and cause negative transfer. Different from most existing works that manipulate gradients to mitigate the gradient conflict, in this paper, we address this problem from the perspective of architecture learning and propose a Conflict-Noticed Architecture Learning (CoNAL) method to alleviate the gradient conflict by learning architectures. By introducing purely-specific modules specific to each task in the search space, the CoNAL method can automatically learn when to switch to purely-specific modules in the tree-structured network architectures when the gradient conflict occurs. To handle multi-task problems with a large number of tasks, we propose a progressive extension of the CoNAL method. Extensive experiments on computer vision, natural language processing, and reinforcement learning benchmarks demonstrate the effectiveness of the proposed methods. The code of CoNAL is publicly available.1
Zeng, G, Fang, Z, Zhang, G & Lu, J 1970, 'One-step Domain Adaptation Approach with Partial Label', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE. View/Download from: Publisher's site
Zhang, C, Tian, Z, Yu, JJQ & Yu, S 1970, 'Construct New Graphs Using Information Bottleneck Against Property Inference Attacks', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE. View/Download from: Publisher's site
Zhang, C, Wang, W, Yu, JJQ & Yu, S 1970, 'Extracting Privacy-Preserving Subgraphs in Federated Graph Learning using Information Bottleneck', Proceedings of the ACM Asia Conference on Computer and Communications Security, ASIA CCS '23: ACM ASIA Conference on Computer and Communications Security, ACM. View/Download from: Publisher's site
Zhang, S, Liu, J, Bao, Z, Yu, S & Lin, Y 1970, 'Adversarial Domain Generalization Defense for Automatic Modulation Classification', 2023 IEEE/CIC International Conference on Communications in China (ICCC), 2023 IEEE/CIC International Conference on Communications in China (ICCC), IEEE. View/Download from: Publisher's site
Zhang, X, Zhang, Z, Zhong, Q, Zheng, X, Zhang, Y, Hu, S & Zhang, LY 1970, 'Masked Language Model Based Textual Adversarial Example Detection', Proceedings of the ACM Asia Conference on Computer and Communications Security, ASIA CCS '23: ACM ASIA Conference on Computer and Communications Security, ACM. View/Download from: Publisher's site
Zhang, Y, Bai, G, Chamikara, MAP, Ma, M, Shen, L, Wang, J, Nepal, S, Xue, M, Wang, L & Liu, J 1970, 'AgrEvader: Poisoning Membership Inference against Byzantine-robust Federated Learning', Proceedings of the ACM Web Conference 2023, WWW '23: The ACM Web Conference 2023, ACM. View/Download from: Publisher's site
Zhang, Z, Fang, M, Chen, L & Namazi-Rad, M-R 1970, 'CITB: A Benchmark for Continual Instruction Tuning', Findings of the Association for Computational Linguistics: EMNLP 2023, Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics. View/Download from: Publisher's site
Zhang, Z, Fang, M, Chen, L, Namazi-Rad, MR & Wang, J 1970, 'How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances', EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings, pp. 8289-8311. View description>>
Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment. Maintaining their up-to-date status is a pressing concern in the current era. This paper provides a comprehensive review of recent advances in aligning LLMs with the ever-changing world knowledge without re-training from scratch. We categorize research works systemically and provide in-depth comparisons and discussion. We also discuss existing challenges and highlight future directions to facilitate research in this field.
Zhang, Z, Fang, M, Ye, F, Chen, L & Namazi-Rad, M-R 1970, 'Turn-Level Active Learning for Dialogue State Tracking', Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics. View/Download from: Publisher's site
Zhao, J, Fang, M, Shi, Z, Li, Y, Chen, L & Pechenizkiy, M 1970, 'CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models', Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics. View/Download from: Publisher's site
Zhao, Y, Liu, B, Ding, M, Liu, B, Zhu, T & Yu, X 1970, 'Proactive Deepfake Defence via Identity Watermarking', 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 4591-4600. View/Download from: Publisher's site
Devitt, SJ & Langford, NK Standards Australia 2023, At the Intersection between Quantum Communication Networks and Standardisation, pp. 1-47, Sydney, Australia.
Knight, S, Heggart, K, Dickson-Deane, C, Ford, H, Hunter, J, Johns, A, Kitto, K, Cetindamar Kozanoglu, D, Maher, D & Narayan, B House Standing Committee on Employment, Education and Training 2023, UTS:CREDS member submission in response to the House Standing Committee on Employment, Education and Training’s inquiry into the use of generative artificial intelligence in the Australian education system, House Standing Committee on Employment, Education and Training’s inquiry into the use of generative artificial intelligence in the Australian education system, no. sub019, pp. 1-20, Australia.
Langford, NK & Devitt, SJ Standards Australia 2023, At the Intersection Between Scalable Quantum Computing and Standardisation, pp. 1-42, Sydney, Australia.
Langford, NK & Devitt, SJ Standards Australia 2023, Quantum Education, Training and Literacy: Laying Good Foundations for the Future Quantum Industry, Sydney, Australia.
Langford, NK & Devitt, SJ Standards Australia 2023, Quantum Technologies and Standardisation Globally and in Australia, Sydney, Australia.
This report, supported by Investment NSW, shows the state of NSW’s ecosystem of coworking spaces, accelerators, incubators, and startup hubs and outlines how policymakers and partners in the innovation ecosystem can more robustly measure the impact of these entities on the wider economy.
Tovey, A, Heydon, G, Phansalkar, A, Ruoso, L-E, Alexandra, B, Qureshi Atif, M, Gill, A, Siddiqui, S, Perry, C, Liu, B, Runcie, P, McIntyre, E, Goodman, N, Vardoulakis, S, Hu, T, Izzo, K, Surawski, N, Kulkarni, Y, Liyanage, L & Barns, S Operational Network of Air Quality Impact Resources (OPENAIR) 2023, The OPENAIR Best Practice Guide for smart air quality monitoring, pp. 1-9, Australia. View description>>
The OPENAIR Best Practice Guide for smart air quality monitoring has been developed to help local governments implement smart air quality monitoring projects. It has been developed through collaboration between five NSW universities, the NSW Government, and the NSW Smart Sensing Network (NSSN). It contains world-leading best practice guidance for smart air quality monitoring and we believe it to be the most comprehensively broad and simultaneously in-depth practical resource on this topic in the world.The Guide is divided into sections that reflect the six stages of the OPENAIR Impact Planning Cycle. Each section is organised into topic areas, with a suite of associated resources.Each factsheet, Best Practice Guide chapter, and supplementary resource is available as an individual download. You can also download the entire Best Practice Guide as a single PDF document, with all chapters combined.
Abboodi, B, Pileggi, SF & Bharathy, G 2023, 'Social Networks in Crisis Management: a Concise Literature Review', MDPI AG. View/Download from: Publisher's site
Aboutorab, H, Hussain, OK, Saberi, M, Hussain, FK & Prior, D 2023, 'Adaptive Identification of Supply Chain Disruptions Through Reinforcement Learning', Elsevier BV. View/Download from: Publisher's site
Adak, C, Karkera, T, Chattopadhyay, S & Saqib, M 2023, 'Detecting Severity of Diabetic Retinopathy from Fundus Images using Ensembled Transformers'.
Andersen, JP, Di Nota, PM, Alavi, N, Anderson, G, Bennell, C, McGregor, C, Ricciardelli, R, Scott, SC, Shipley, P & Vincent, ML 2023, 'A Biological Approach to Building Resilience and Wellness Capacity Among Police Exposed to Posttraumatic Stress Injuries: Protocol for a Randomized Controlled Trial (Preprint)', JMIR Publications Inc.. View/Download from: Publisher's site
Bérubé, C, Maritsch, M, Lehmann, VF, Kraus, M, Feuerriegel, S, Züger, T, Wortmann, F, Stettler, C, Fleisch, E, Kocaballi, AB & Kowatsch, T 2023, 'Multimodal In-Vehicle Hypoglycemia Warning for Drivers With Type 1 Diabetes: Design and Evaluation in Simulated and Real-World Driving (Preprint)', JMIR Publications Inc.. View/Download from: Publisher's site
Braytee, A, He, S, Tang, S, Sun, Y, Jiang, X, Yu, X, Khatri, I, Prasad, M & Anaissi, A 2023, 'Identification of Cancer Risk Groups through Multi-Omics Integration using Autoencoder and Tensor Analysis', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
Bremner, MJ, Cheng, B & Ji, Z 2023, 'IQP Sampling and Verifiable Quantum Advantage: Stabilizer Scheme and Classical Security'.
Chapman, A, Elman, SJ & Mann, RL 2023, 'A Unified Graph-Theoretic Framework for Free-Fermion Solvability'.
Chaturvedi, K, Braytee, A, Li, J & Prasad, M 2023, 'SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation'.
Cortes, CAT, Lin, C-T, Do, T-TN & Chen, H-T 2023, 'An EEG-based Experiment on VR Sickness and Postural Instability While Walking in Virtual Environments'.
Elgharabawy, A, Prasad, M, Lin, C-T & Elgharabawy, A 2023, 'Preference Neural Network', MDPI AG. View/Download from: Publisher's site
Feng, C, Cao, L, Wu, D, Zhang, E, Wang, T, Jiang, X, Zhou, C, Chen, J, Wu, H, Lin, S, Hou, Q, Lin, C-T, Zhu, J, Yang, J, Sawan, M & Zhang, Y 2023, 'Acoustic inspired brain-to-sentence decoder for logosyllabic language', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
Gill, A 2023, 'Adaptive Architecture for Data, Analytics, and AI: Government's Navigation of Digital Disruption', 9th Annual FST Government Australia Summit 2023, Canberra, Australia.
Gill, A 2023, 'Adaptive Data Architecture For Responsible & Safe AI Adoption in Government: Reflections & Learnings from 2023', Public Sector Network.
Gill, A 2023, 'Adaptive Data Architecture For Responsible & Safe AI Adoption in Government: Reflections & Learnings from 2023', Public Sector Network.
Gill, A 2023, 'ADAPTIVE ENTERPRISE ARCHITECTURE WORKSHOP FOR DECISION MAKERS'.
Gill, A 2023, 'Data Sharing: Architecture, Patterns and Technology Solutions'.
Gill, A 2023, 'The ArcOps: Connected Enterprise Architecture-Operations Pipeline', Enterprise Architecture Professional Journal.
Gill, A & Fitzgibbon, T 2023, 'Why trusted digital identity verification platform is critical for supporting student lifecycle journey?', Australasian Higher Education Cyber Security Service, Canberra, Australia.
In the educational ecosystem, problem-solving, critical thinking, and teamwork skills play acritical role and remain as main concerns in graduate employability (Bhatti et al., 2023). Thepurpose of this study is to examine the employability skills perceived by students across UTSand increase the employability skills of students from low-socioeconomic (LSE) backgroundstudying at UTS. To accomplish this objective, we will use the UTS Social Impact Dashboard(2023) which represents the opinion of students from LSE background on course learningoutcomes with respect to problem-solving, critical thinking, and teamwork skills. According tothe Dashboard, over 73% of students from LSE background in a graduate school at UTS perceivethat they acquired a more developed and advanced set of skills (Problem solving – 73%, Criticalthinking – 73% and Teamwork skills – 80%) useful in future employability opportunities.Furthermore, comparing all faculties, minimum scores recorded for problem solving (44%),critical thinking (54%), and teamwork skills (59%) require further investigation. The UTS SocialImpact Dashboard indicates that a 3% increase from the previous year in attrition rates and1.7% decrease in student success, for students from an LSE background. With respect tograduate outcomes, the gap between UTS domestic students from high-socioeconomic (HSE)background and those from low-socioeconomic (LSE) background is 6.7%. This clear gaprequires an urgent examination of factors that could influence the perception of graduateemployability skills of UTS students compared to those of an LSE background. This will likelybenefit UTS students from an LSE background to gain employment. Furthermore, we canreduce the gap between Australians from the lowest socio-economic backgrounds and highestsocio-economic backgrounds who are not in employment (Strawa, 2022), which is the targetedsocial change that this study intends to address.
Jia, M, Gabrys, B & Musial, K 2023, 'A Network Science perspective of Graph Convolutional Networks: A survey', arXiv.
Kocaballi, AB 2023, 'Conversational AI-Powered Design: ChatGPT as Designer, User, and Product'.
Leon-Castro, E, Sahni, M, Blanco-Mesa, F, Alfaro-Garcia, V & Merigo, J 2023, 'Innovation and sustainability in governments and companies: A perspective to the new realities', pp. 1-331. View description>>
Innovation and sustainability are issues that have become very relevant in recent years. This book presents a compilation of investigations on these topics, divided into those applied in government or enterprises. The objective is to demonstrate to the audience how these issues have been worked around the world and in different scenarios. Among the papers, there are works related to economic variables, imports, exports, and analysis in different sectors such as tourism, agriculture, education, and even in countries in general.
Li, Z, Chen, Y, Wang, X, Yao, L & Xu, G 2023, 'Multi-view GCN for Loan Default Risk Prediction', Research Square Platform LLC. View/Download from: Publisher's site
Mandal, S, Oberst, S & Lai, JCS 2023, 'Modelling termites’ tunnelling and decision-making behaviors', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
Nareti, UK, Adak, C & Chattopadhyay, S 2023, 'Demystifying Visual Features of Movie Posters for Multi-Label Genre Identification'.
Qu, Z, Nguyen, QV, Lau, CW, Johnston, A, Kennedy, PJ, Simoff, S & Catchpoole, D 2023, 'Understanding Cancer Patient Cohorts in Virtual Reality Environment for Better Clinical Decisions: A Usability Study', Research Square Platform LLC. View/Download from: Publisher's site
Rudd, DH, Huo, H & Xu, G 2023, 'An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance'.
Rudd, DH, Huo, H & Xu, G 2023, 'Causal Analysis of Customer Churn Using Deep Learning'.
Rudd, DH, Huo, H & Xu, G 2023, 'Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions'.
Rudd, DH, Huo, H & Xu, G 2023, 'Leveraged Mel spectrograms using Harmonic and Percussive Components in Speech Emotion Recognition'.
Rudd, DH, Huo, H & Xu, G 2023, 'Predicting Financial Literacy via Semi-supervised Learning'.
Rudd, DH, Huo, H, Islam, MR & Xu, G 2023, 'Churn Prediction via Multimodal Fusion Learning:Integrating Customer Financial Literacy, Voice, and Behavioral Data'.
Acoustic Black Hole (ABH), as a non-reflecting wave effect, has been realised in beams and plates by locally changing their thickness. Geometrical ABH designs stem from their transverse-load bearing properties, assuming isotropic material properties associated with engineering materials (e.g., steels). The underlying physics is to manipulate stiffness locally for a transversely-loaded structure, leading to a change in the elastic wave speed. However, the same ABH would have limitations for axial loading, i.e., slender beams for which longitudinal waves dominate. Here, we present a wave propagation approach using wavefront tracking to identify a potential ABH for axially-loaded circular beams. We study wave propagation in three exponential designs of finite length, monitoring the wave-travel time. Indicative of an effective ABH in finite length sections, the wave-travel time increases compared to the case of a beam without ABH. By employing the wave front tracking method for the design of an ABH with axial loading, it is possible to verify the effectiveness of ABHs. Also, various material models, e.g., orthotropic materials such as wood, and different loading conditions can be considered, which opens a new avenue in applications of ABH phenomena beyond conventional vibro-acoustic control problems.
Sezgin, E, Kocaballi, AB, Dolce, M, Skeens, M, Militello, L, Huang, Y, Stevens, J & Kemper, AR 2023, 'Chatbot for social needs screening and resource sharing with vulnerable families: Iterative design and evaluation study', Research Square Platform LLC. View/Download from: Publisher's site
Singh, A, Liu, D & Lin, C-T 2023, 'Neuroadaptation in Physical Human-Robot Collaboration'.
Sulimani, H, Sulimani, R, Ramezani, F, Naderpour, M, Huo, H, Jan, T & Prasad, M 2023, 'HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks', Research Square Platform LLC. View/Download from: Publisher's site
Sun, H, Nguyen, M, Zhu, H, Nguyen, V, Lin, C-T & Jin, C 2023, 'A binaural room impulse response dataset and Shorelining psychophysical task for the evaluation of auditory sensory augmentation', Acoustical Society of America (ASA), pp. A195-A195. View/Download from: Publisher's site View description>>
Sensory augmentation using spatial sound presented in augmented-reality (AR) can assist people with low vision or blindness in navigating their environment (Katz et al., 2012). Nonetheless, in many situations, the poor quality of the binaural sound rendered using current tool sets limits the potential capability of the assistive technology. In particular, acoustic environments with near-field sources and reflections pose significant challenges. In this work, we provide a reference binaural room impulse response (BRIR) dataset with near-field sources and reflections and an associated shorelining psychophysical task that is useful for the evaluation of AR spatial audio. The dataset consists of 12∼small loudspeakers arranged on a 3-by-4 grid in a complex acoustic environment. BRIR measurements are recorded using the Head and Torso Simulator (HATS) for 17∼different receiver positions with a 5o angular resolution. Room impulse response measurements are also recorded using the Eigenmike for each of the 17 receiver positions. Using the Razer Anzu smart glasses to render the binaural AR spatial audio, we compare psychophysical performance on the shorelining navigation task using the recorded dataset and various existing binaural AR tool sets.
Sun, Z, Tavakoli, S, Khalilpour, K, Voinov, A & Marshall, J 2023, 'Barriers of Peer-to-Peer Energy Trading Networks: A Multi-dimensional PESTLE Analysis', MDPI AG. View/Download from: Publisher's site
Wang, K, Ling, Y, Zhang, Y, Yu, Z, Wang, H, Bai, G, Ooi, BC & Dong, JS 2023, 'Characterizing Cryptocurrency-themed Malicious Browser Extensions', Association for Computing Machinery (ACM), pp. 91-92. View/Download from: Publisher's site View description>>
Due to the surging popularity of various cryptocurrencies in recent years, a large number of browser extensions have been developed as portals to access relevant services, such as cryptocurrency exchanges and wallets. This has stimulated a wild growth of cryptocurrency-themed malicious extensions that cause heavy financial losses to the users and legitimate service providers. They have shown their capability of evading the stringent vetting processes of the extension stores, highlighting a lack of understanding of this emerging type of malware in our community. In this work, we conduct the first systematic study to identify and characterize cryptocurrency-themed malicious extensions. We monitor seven official and third-party extension distribution venues for 18 months (December 2020 to June 2022) and have collected around 3600 unique cryptocurrency-themed extensions. Leveraging a hybrid analysis, we have identified 186 malicious extensions that belong to five categories. We then characterize those extensions from various perspectives including their distribution channels, life cycles, developers, illicit behaviors, and illegal gains. Our work unveils the status quo of the cryptocurrency-themed malicious extensions and reveals their disguises and programmatic features on which detection techniques can be based. Our work serves as a warning to extension users, and an appeal to extension store operators to enact dedicated countermeasures. To facilitate future research in this area, we release our dataset of the identified malicious extensions and open-source our analyzer.
Zhu, HY, Hieu, NQ, Hoang, DT, Nguyen, DN & Lin, C-T 2023, 'A Human-Centric Metaverse Enabled by Brain-Computer Interface: A Survey'.
