Publications

Ahmadi, S, Cheraghian, A, Saberi, M, Abir, MT, Dastmalchi, H, Hussain, F & Rahman, S 2025, 'Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-Free Adaptor' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 178-195.
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Alharbi, A, Sharma, N & Hussain, F 2025, 'Arabic Sentiment Analysis Leveraging Hybrid Word Embeddings with Deep Learning Techniques' in Lecture Notes on Data Engineering and Communications Technologies, Springer Nature Switzerland, pp. 129-140.
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Aljaafreh, R, Al-Doghman, F & Hussain, F 2025, 'Toward a Hybrid Cybersecurity Framework of Machine Learning and Business Intelligence into Supply Chain Risk Management' in Studies in Systems, Decision and Control, Springer Nature Switzerland, pp. 1021-1029.
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Alsuhaibani, A, Razzak, I, Jameel, S, Wang, X & Xu, G 2025, 'CLIMB: Imbalanced Data Modelling Using Contrastive Learning with Limited Labels' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 60-75.
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Cetindamar, D & Burdon, S 2025, 'Digital competitiveness and policy making: lessons from Indonesia, Malaysia, and the Republic of South Korea' in Digital Leadership, Edward Elgar Publishing, pp. 42-55.
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Cloutier, T, McGregor, C & Andersen, JP 2025, 'Challenges and Opportunities in a Big Data Pipeline for Resilience and Wellness Development: A Case Study for Police Training' in Communications in Computer and Information Science, Springer Nature Switzerland, pp. 340-350.
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Gill, AQ & Nikiforova, A 2025, 'Towards a Data Satellite Architecture for Federated Digital Ecosystems: Combating Data Pollution and Enhancing Trustworthiness' in Lecture Notes in Business Information Processing, Springer Nature Switzerland, pp. 95-102.
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Guo, T, Du, H, Huo, H, Liu, B & Yu, X 2025, 'Who is Being Impersonated? Deepfake Audio Detection and Impersonated Identification via Extraction of Id-Specific Features' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 301-320.
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Khuat, TT, Bassett, R, Otte, E & Gabrys, B 2025, 'Online Machine Learning for Real-Time Cell Culture Process Monitoring' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 363-375.
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Kim, J, Xuan, J, Liang, C & Hussain, F 2025, 'A Non-monolithic Policy Approach of Offline-to-Online Reinforcement Learning' in Communications in Computer and Information Science, Springer Nature Singapore, pp. 45-59.
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Kwon, I, Li, J, Shah, RR & Prasad, M 2025, 'Lightweight Motion-Aware Video Super-Resolution for Compressed Videos' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 254-267.
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Lin, Y, Guan, J, Fang, W, Ying, M & Su, Z 2025, 'A obustness fication Tool for  uantum Machine Learning Models' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 403-421.
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AbstractAdversarial noise attacks present a significant threat to quantum machine learning (QML) models, similar to their classical counterparts. This is especially true in the current Noisy Intermediate-Scale Quantum era, where noise is unavoidable. Therefore, it is essential to ensure the robustness of QML models before their deployment. To address this challenge, we introduce VeriQR, the first tool designed specifically for formally verifying and improving the robustness of QML models, to the best of our knowledge. This tool mimics real-world quantum hardware’s noisy impacts by incorporating random noise to formally validate a QML model’s robustness. VeriQR supports exact (sound and complete) algorithms for both local and global robustness verification. For enhanced efficiency, it implements an under-approximate (complete) algorithm and a tensor network-based algorithm to verify local and global robustness, respectively. As a formal verification tool, VeriQR can detect adversarial examples and utilize them for further analysis and to enhance the local robustness through adversarial training, as demonstrated by experiments on real-world quantum machine learning models. Moreover, it permits users to incorporate customized noise. Based on this feature, we assess VeriQR using various real-world examples, and experimental outcomes confirm that the addition of specific quantum noise can enhance the global robustness of QML models. These processes are made accessible through a user-friendly graphical interface provided by VeriQR, catering to general users without requiring a deep understanding of the counter-intuitive probabilistic nature of quantum computing.

Liu, B, Liu, B & Ding, M 2025, 'Outliers are Real: Detecting VLM-Generated Images via One-Class Classification' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 291-308.
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Liu, B, Zhu, T & Yu, PS 2025, 'Privacy in Location-Based Data Mining' in Encyclopedia of Cryptography, Security and Privacy, Springer Nature Switzerland, pp. 1887-1892.
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Loesch, N, Catchpoole, DR & Kennedy, PJ 2025, 'Three-Dimensional Latent Diffusion Model for Weakly-Supervised Brain Tumour Segmentation' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 242-251.
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Manthey, S & Konazoglu, DC 2025, 'From Technology Idea to Value Proposition' in Technology Entrepreneurship in Theory and Practice, Routledge, pp. 46-56.
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Nejad, MZ, Naderpour, M, Behbood, V, Ramezani, F & Lu, J 2025, 'Residential Real Estate Image Classification for Property Valuation' in Communications in Computer and Information Science, Springer Nature Switzerland, pp. 111-125.
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Priya, K, Adak, C, Chaudhuri, BB & Blumenstein, M 2025, 'Handwriting Intra-Variability Across Surface Transitions: Implications for Writer Identification' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 196-208.
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Xue, H, Yuan, X, Liu, B & Ding, M 2025, 'DBFIA: Diffusion-Based Face Image Anonymization' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 40-59.
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Zhang, C, Wang, W, Tian, Z, Yu, J, Kaafar, D, Liu, A & Yu, S 2025, 'Can Self Supervision Rejuvenate Similarity-Based Link Prediction?' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 271-283.
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Zhao, T, Zuo, H & Zhang, G 2025, 'Bidirectional Dependency Representation Disentanglement for Time Series Classification' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 98-110.
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Zi, X, Shi, Y, Zhu, T, Jin, K, Tao, X, Li, J, Thiyagarajan, K & Prasad, M 2025, 'BDC Dataset: A Comprehensive Dataset for Automated Build Damage Classification' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 91-104.
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Zuo, X, Wang, M, Ye, D & Yu, S 2025, 'A Scheme of Dynamic Location Privacy-Preserving with Blockchain in Intelligent Transportation System' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 21-39.
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Adak, C, Chattopadhyay, S & Saqib, M 2025, 'Deep Analysis of Visual Product Reviews', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 2, pp. 2033-2038.
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Ahmad, A, Xiao, X, Mo, H & Dong, D 2025, 'TFTformer: A novel transformer based model for short-term load forecasting', International Journal of Electrical Power & Energy Systems, vol. 166, pp. 110549-110549.
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Akbarzadeh, M, Oberst, S & Halkon, B 2025, 'Manipulation of an acoustically levitated object using externally excited standing waves', The Journal of the Acoustical Society of America, vol. 157, no. 3, pp. 1852-1861.
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Ultrasonic standing waves can be used to manipulate the position and control the movement of levitated objects through acoustic radiation forces. Within this context, the theory of the Gor'kov potential function and its acoustic contrast factor are revisited, considering the scenario of a harmonic disturbance to the standing wave and its influence on the levitated spherical object. This disturbance causes a levitated object—trapped within a standing, plane ultrasonic wave field in an ideal fluid—to undergo oscillations in sympathy with the resulting motion of the wave field. In this paper, we determine how the acoustic contrast factor depends on the properties of the object, the fluid and the external excitation, in combination. We show that positive, negative, and zero acoustic radiation forces can be achieved, causing the object to be pushed towards the nearest pressure or velocity node. We experimentally verify—through external excitation of an ultrasonic standing wave generator—that the disturbance vibration frequency and amplitude are transmitted to the object. The dependence on the external excitation amplitude and force reversal are novel features that can be employed in acoustic manipulation for non-contact dynamic characterization of small objects.

Akram, J, Hussain, W, Jhaveri, RH, Rathore, RS & Anaissi, A 2025, 'Dynamic GNN-based multimodal anomaly detection for spatial crowdsourcing drone services', Digital Communications and Networks.
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Alghanmi, NA, Alghanmi, NA, Alghanmi, SA, Zhao, M & Hussain, FK 2025, 'Data-driven approach for selection of on-chain vs off-chain carbon credits data storage methods', Knowledge-Based Systems, vol. 310, pp. 112871-112871.
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Alharbi, RS & Hussain, FK 2025, 'Smart-contracts-driven personal carbon credit management in smart cities: A review and future research directions', Internet of Things, vol. 32, pp. 101636-101636.
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Amirbagheri, K, Merigó, JM & Corrons, A 2025, '30 years of Energy for Sustainable Development: A bibliometric overview', Energy for Sustainable Development, vol. 87, pp. 101741-101741.
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Ansari, S, Finelli, M, Papaconstantinou, EA, McGregor, C & Nonoyama, ML 2025, 'Implementation Strategies Used to Reduce Unplanned Extubations in the Neonatal ICU', Respiratory Care, vol. 70, no. 2, pp. 143-152.
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Bai, J, Wu, D, Zeng, S, Zhao, Y, Qu, Y & Yu, S 2025, 'Non-IID Free Federated Learning with Fuzzy Optimization for Consumer Electronics Systems', IEEE Transactions on Consumer Electronics, pp. 1-1.
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Bakhshianlamouki, E, Augustijn, E-W, Brugnach, M, Voinov, A & Wijnberg, K 2025, 'Agent-based modelling of beach visitation patterns: insights for sustainable coastal design and management', Environmental Research Communications, vol. 7, no. 4, pp. 045004-045004.
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Abstract Coastal areas are complex socio-environmental systems, yet most models focus on biophysical processes, with few addressing social systems or their interaction with biophysical dynamics. This paper introduces an agent-based model (ABM) simulating beach visitor spatial behaviour, considering factors like landscape design and its impact on visitor distribution. We analyse how service and facility placement affects areas with high and low visitation intensity. Tailored for Dutch beaches created by mega-nourishments like the Sand Motor, the model’s modular design allows adaptation to other coastal areas. Qualitative calibration and validation against field data ensured realistic predictions of daily visitor numbers and representation of areas of high and low visitation intensity across the beach. We analysed model sensitivity by varying visitor activities and pathway preference probabilities, finding that pathway probabilities significantly influence visitor choices. The model was also applied to assess the impact of adding an entrance with access to a restaurant and car parking on visitor numbers, affected areas, and impact intensity. Scenario analysis, combined with its application to two case studies with different landscape layouts, highlights the model’s versatility as a valuable tool for designing beaches to effectively explore and manage visitation intensity and its effects on ecological systems, such as vegetation.

Bano, M, Chaudhri, ZH & Zowghi, D 2025, 'Mapping the Scholarly Landscape on AI and Diplomacy', The Hague Journal of Diplomacy, vol. 20, no. 2, pp. 171-206.
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Abstract This study examines how artificial intelligence (AI) has been discussed in the context of diplomacy in the 21st century, highlighting the emerging challenges and opportunities identified in academic literature. While recent developments such as OpenAI’s ChatGPT and other generative AI (GenAI) tools have revolutionised various sectors, their specific applications to diplomacy remain underexplored. This research maps the scholarly landscape by systematically reviewing 231 academic articles on AI and diplomacy, identifying key themes, topics and geographical focuses in the literature. The insights from this review are analysed to explore potential future directions for AI, including GenAI, in diplomacy, offering insights from the academic discourse and identifying gaps for future research.

Berezowski, V, Taoum, K, Wang, J, Birch, P, Roux, C & Huo, H 2025, 'Investigating identity crime and misuse in Australia: the role of prevention technologies and the likelihood of victimisation', Journal of Criminological Research, Policy and Practice, vol. 11, no. 1, pp. 50-63.
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PurposeThis study examines identity theft as a significant and growing issue in Australia, not only due to its financial impact but also because of the emotional, psychological, and physical harm it causes, making it a public health concern. This study aims to analyse the results of the 2019 Australian Institute of Criminology (AIC) survey to identify factors associated with an increased likelihood of identity theft victimisation.Design/methodology/approachThis study involved a detailed analysis of the 2019 AIC survey, which had 9,968 respondents from a sample of 10,000. The research focused on whether respondents had ever been victimised by identity theft and analysed various characteristics, including demographics (gender, age, Indigenous status, education), income, computer usage, and preventive technology use, as potential indicators of future victimisation. Univariate analyses (chi-squared test and two-sample t-test) were used to assess individual associations, whereas a multivariate analysis (logistic regression) identified significant predictors of victimisation.FindingsThe univariate analyses indicated that all sub-variables were individually associated with identity theft victimisation. However, the multivariate analysis revealed that only identifying as Aboriginal and Torres Strait Islander, having an income between $18,201 and $37,000, and using multiple preventive technologies were significant predictors of victimisation. The unexpected finding that increased preventive technology use correlates with a higher risk of victimisation contradicts the survey’s suggestion that victims adopt more careful behaviour post-victimisation....

Cai, Q, Cao, J, Xu, G & Zhu, N 2025, 'Distributed Recommendation Systems: Survey and Research Directions', ACM Transactions on Information Systems, vol. 43, no. 1, pp. 1-38.
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With the explosive growth of online information, recommendation systems have become essential tools for alleviating information overload. In recent years, researchers have increasingly focused on centralized recommendation systems, capitalizing on the powerful computing capabilities of cloud servers and the rich historical data they store. However, the rapid development of edge computing and mobile devices in recent years has provided new alternatives for building recommendation systems. These alternatives offer advantages such as privacy protection and low-latency recommendations. To leverage the advantages of different computing nodes, including cloud servers, edge servers, and terminal devices, researchers have proposed recommendation systems that involve the collaboration of these nodes, known as distributed recommendation systems. This survey provides a systematic review of distributed recommendation systems. Specifically, we design a taxonomy for these systems from four perspectives and comprehensively summarize each study by category. In particular, we conduct a detailed analysis of the collaboration mechanisms of distributed recommendation systems. Finally, we discuss potential future research directions in this field.

Cao, B, Li-Ting Tsai, C, Zhou, N, Do, T & Lin, C-T 2025, 'A Novel 3D Paradigm for Target Expansion of Augmented Reality SSVEP', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 33, pp. 1562-1573.
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Cao, Z, Zhao, D, Wang, Q, Yuan, H, Ma, H & Yu, S 2025, 'CrossTrace: Privacy-aware Cross-System Trajectory Recovery via Hybrid Split and Federated Learning', IEEE Transactions on Mobile Computing, pp. 1-18.
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Catchpoole, D, Hettiaratchi, A & Fonseca Tumilasci, V 2025, 'ISBER 2025 Annual Meeting and Exhibits: Celebrating the Impact of Biobanking Worldwide!', Biopreservation and Biobanking, vol. 23, no. 1, pp. 65-66.
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Chen, H, Zhu, T, Liu, B, Zhou, W & Yu, PS 2025, 'Fine-Tuning a Biased Model for Improving Fairness', IEEE Transactions on Big Data, vol. 11, no. 3, pp. 1397-1410.
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Chen, H, Zhu, T, Zhang, L, Liu, B, Wang, D, Zhou, W & Xue, M 2025, 'QUEEN: Query Unlearning Against Model Extraction', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 2143-2156.
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Chen, L, Wang, W, Tian, Z, Zhang, C & Yu, S 2025, 'Backdoored Sample Cleansing for Unlabeled Datasets Via Bootstrapped Dual Set Purification', IEEE Transactions on Dependable and Secure Computing, pp. 1-15.
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Chen, Z, Liang, J, Yu, Z, Cheng, H, Min, G & Li, J 2025, 'Resilient Collaborative Caching for Multi-Edge Systems With Robust Federated Deep Learning', IEEE Transactions on Networking, vol. 33, no. 2, pp. 654-669.
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Cui, L, Wu, F, Qu, Y, Gu, B, Gao, L & Yu, S 2025, 'RE4ETD: A Relative Entropy Optimization-Based Method for Efficient Electricity Theft Detection With Dual-Privacy-Preservation', IEEE Transactions on Smart Grid, pp. 1-1.
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Datta, B, Manasur, B, Sreelekha, G, Verma, P, Adak, C, Shukla, RP & Dutta, G 2025, 'Quantification of L-lactic acid in human plasma samples using Ni-based electrodes and machine learning approach', Talanta, vol. 286, pp. 127493-127493.
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Ding, W, Geng, Y, Huang, J, Ju, H, Wang, H & Lin, C-T 2025, 'MGRW-Transformer: Multigranularity Random Walk Transformer Model for Interpretable Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 1, pp. 1104-1118.
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Dong, D, Li, J-S & Jonckheere, E 2025, 'Technical Committee on Quantum Computing, Systems and Control [Technical Activities]', IEEE Control Systems, vol. 45, no. 3, pp. 22-23.
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Eklund, M, Voinov, A, Hossain, MJ & Khalilpour, K 2025, 'Evaluating the interplay of community behaviour and microgrid design through optimisation modelling in local energy markets', Renewable and Sustainable Energy Reviews, vol. 210, pp. 115271-115271.
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Ellis, JT & Kennedy, PJ 2025, 'Multi-criteria decision making and its application to in silico discovery of vaccine candidates for Toxoplasma gondii', Vaccine, vol. 58, pp. 127242-127242.
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Elsamadony, M, Pileggi, SF, Liu, J & Fujii, M 2025, 'Crafting a resilient and sustainable future amid major crises', Journal of Cleaner Production, vol. 511, pp. 145583-145583.
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Errey, N, Zowghi, D, Leong, TW, Wu, Y, Yuan, X, Huang, W & Liang, CJ 2025, 'Heuristics for evaluating narrative visualization: a validation study', Journal of Visualization, vol. 28, no. 3, pp. 645-659.
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Abstract Narrative visualization integrates data visualization and narrative techniques to convey a compelling story. Narrative visualization is notoriously difficult to evaluate. One solution is heuristic evaluation, using a domain-specific set of heuristics. This paper validates a set of heuristics proposed specifically for evaluating narrative visualization. We conducted studies with experienced narrative visualization practitioners in both summative and formative settings. We found that the set of heuristics showed promise in a summative setting, where similar responses evidenced that the set of heuristics could provide reliable evaluation metrics. Furthermore, in a formative setting, implementing the set of heuristics was reported to be useful in the design process; however, due to their limited focus, we recommend that it be implemented in conjunction with other evaluation guidelines. Graphical abstract

Fan, W, Xiao, F, Pan, Y, Chen, X, Han, L & Yu, S 2025, 'Latency-Aware Joint Task Offloading and Energy Control for Cooperative Mobile Edge Computing', IEEE Transactions on Services Computing, vol. 18, no. 3, pp. 1515-1528.
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Fang, Z, Lu, J & Zhang, G 2025, 'Out-of-distribution detection with non-semantic exploration', Information Sciences, vol. 705, pp. 121989-121989.
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Fei, Z, Ma, Y, Zhao, J, Wang, B & Yang, J 2025, 'KNEG-CL: Unveiling data patterns using a k-nearest neighbor evolutionary graph for efficient clustering', Information Sciences, vol. 690, pp. 121602-121602.
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Fei, Z, Zhai, H, Yang, J, Wang, B & Ma, Y 2025, 'Discovering generalized clusters with adaptive mixture density-based clustering', Knowledge-Based Systems, vol. 314, pp. 113250-113250.
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Flores-Sosa, M, Merigó, JM & Sanchez-Valenzuela, K 2025, '30 years of the Journal of Heuristics: a bibliometric analysis', Journal of Heuristics, vol. 31, no. 1.
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Gao, S, Wang, C, Gao, C, Luo, W, Han, P, Liao, Q & Xu, G 2025, 'Improving long‐tail classification via decoupling and regularisation', CAAI Transactions on Intelligence Technology, vol. 10, no. 1, pp. 62-71.
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AbstractReal‐world data always exhibit an imbalanced and long‐tailed distribution, which leads to poor performance for neural network‐based classification. Existing methods mainly tackle this problem by reweighting the loss function or rebalancing the classifier. However, one crucial aspect overlooked by previous research studies is the imbalanced feature space problem caused by the imbalanced angle distribution. In this paper, the authors shed light on the significance of the angle distribution in achieving a balanced feature space, which is essential for improving model performance under long‐tailed distributions. Nevertheless, it is challenging to effectively balance both the classifier norms and angle distribution due to problems such as the low feature norm. To tackle these challenges, the authors first thoroughly analyse the classifier and feature space by decoupling the classification logits into three key components: classifier norm (i.e. the magnitude of the classifier vector), feature norm (i.e. the magnitude of the feature vector), and cosine similarity between the classifier vector and feature vector. In this way, the authors analyse the change of each component in the training process and reveal three critical problems that should be solved, that is, the imbalanced angle distribution, the lack of feature discrimination, and the low feature norm. Drawing from this analysis, the authors propose a novel loss function that incorporates hyperspherical uniformity, additive angular margin, and feature norm regularisation. Each component of the loss function addresses a specific problem and synergistically contributes to achieving a balanced classifier and feature space. The authors conduct extensive experiments on three popular benchmark datasets including CIFAR‐10/100‐LT, ImageNet‐LT, and iNaturalist 2018. The experimental results demonstrate that the authors’ loss function outperforms several previous state‐of‐th...

Gao, Y, Chen, L, Lai, J, Wang, T, Wu, X & Yu, S 2025, 'IoT-Dedup: Device Relationship-Based IoT Data Deduplication Scheme', IEEE Transactions on Parallel and Distributed Systems, vol. 36, no. 5, pp. 847-860.
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Geethukrishnan, Bagde, PP, KH, S, Adak, C, Shukla, RP & Tadi, KK 2025, 'Smart sensing of creatinine in urine samples: Leveraging Cu-nanowires/MoS2 quantum dots and machine learning', Sensing and Bio-Sensing Research, vol. 47, pp. 100727-100727.
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Gill, AQ 2025, 'Trimodal Thinking for Architecting Human-Centric AI Systems: Fast, Slow, and Control', IEEE Transactions on Technology and Society, pp. 1-11.
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Guan, L, Abbasi, A, Ryan, MJ & Merigó, JM 2025, 'A dynamic risk interdependency network-based model for project risk assessment and treatment throughout a project life cycle', Computers & Industrial Engineering, vol. 201, pp. 110921-110921.
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Guan, L, Laporte, G, Merigó, JM, Nickel, S, Rahimi, I & Saldanha-da-Gama, F 2025, '50 years of Computers & Operations Research: A bibliometric analysis', Computers & Operations Research, vol. 175, pp. 106910-106910.
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Guan, L, Merigó, JM & Beydoun, G 2025, '40 years of Decision Support Systems: A bibliometric analysis', Decision Support Systems, vol. 194, pp. 114469-114469.
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Guillen-Pujadas, M, Alaminos, D, Vizuete-Luciano, E & Merigo, JM 2025, 'Half a Century of The Journal of Portfolio Management: A Bibliometric Overview', JOURNAL OF PORTFOLIO MANAGEMENT, vol. 51, no. 6, pp. 175-215.

Gul, OM, Hamidoglu, A, Khan, MK & Yu, S 2025, 'Novel Game Theoretical Approach in Blockchain Aided Communication at Internet of Drones', IEEE Consumer Electronics Magazine, pp. 1-11.
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Han, M, Zhu, T, Zhang, L, Huo, H & Zhou, W 2025, 'Vertical Federated Unlearning via Backdoor Certification', IEEE Transactions on Services Computing, vol. 18, no. 2, pp. 1110-1123.
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Hedman, J, Gleasure, R & Bandara, M 2025, 'Preface', Lecture Notes in Business Information Processing, vol. 541 LNBIP, pp. v-viii.

Hiep Dinh, T, Kumar Singh, A, Manh Doan, Q, Linh Trung, N, Nguyen, DN & Lin, C-T 2025, 'An EEG signal smoothing algorithm using upscale and downscale representation^*', Journal of Neural Engineering, vol. 22, no. 3, pp. 036012-036012.
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Abstract Objective. Effective smoothing of electroencephalogram (EEG) signals while maintaining the original signal’s features is important in EEG signal analysis and brain–computer interface. This paper proposes a novel EEG signal-smoothing algorithm and its potential application in cognitive conflict (CC) processing. Approach. Instead of being processed in the time domain, the input signal is visualized in increasing line width, the representation frame of which is converted into a binary image. An effective thinning algorithm is employed to obtain a unit-width skeleton as the smoothed signal. Main results. Experimental results on data fitting have verified the effectiveness of the proposed approach on different levels of signal-to-noise (SNR) ratio, especially on high noise levels (SNR ⩽ 5 dB), where our fitting error is only 86.4%–90.4% compared to that of its best counterpart. The potential application of the proposed algorithm in EEG-based CC processing is comprehensively evaluated in a classification and a visual inspection task. The employment of the proposed approach in pre-processing the input data has significantly boosted the F 1 score of state-of-the-art models by more than 1%. The robustness of our algorithm is also evaluated via a visual inspection task, where specific CC peaks, i.e. the prediction error negativity and error-related positive potential (Pe), can be easily observed at ...

Huang, L, Ge, C, Mao, X & Yu, S 2025, 'DARB: Decentralized, Accountable and Redactable Blockchain for Data Management', IEEE Transactions on Network and Service Management, vol. 22, no. 2, pp. 1608-1617.
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Huang, Q, Zhou, L, Fang, W, Zhao, M & Ying, M 2025, 'Efficient Formal Verification of Quantum Error Correcting Programs', Proceedings of the ACM on Programming Languages, vol. 9, no. PLDI, pp. 1068-1093.
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Quantum error correction (QEC) is fundamental for suppressing noise in quantum hardware and enabling fault-tolerant quantum computation. In this paper, we propose an efficient verification framework for QEC programs. We define an assertion logic and a program logic specifically crafted for QEC programs and establish a sound proof system. We then develop an efficient method for handling verification conditions (VCs) of QEC programs: for Pauli errors, the VCs are reduced to classical assertions that can be solved by SMT solvers, and for non-Pauli errors, we provide a heuristic algorithm. We formalize the proposed program logic in Coq proof assistant, making it a verified QEC verifier. Additionally, we implement an automated QEC verifier, Veri-QEC, for verifying various fault-tolerant scenarios. We demonstrate the efficiency and broad functionality of the framework by performing different verification tasks across various scenarios. Finally, we present a benchmark of 14 verified stabilizer codes.

Huang, X, Li, J, Yang, H, Zhou, T, Luo, Y, Yu, S, Liu, J, Shen, PK, Chen, L & Tian, ZQ 2025, 'Rich {1 0 0} faceted PtIrW nanocubes with high-filling bonding orbitals of NH2 dimerization for enhancing electrochemical ammonia oxidation', Journal of Energy Chemistry, vol. 103, pp. 361-370.
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Huang, Y, Yang, G, Yuan, D & Yu, S 2025, 'DBSSL: A Scheme to Detect Backdoor Attacks in Self-Supervised Learning Models', IEEE Transactions on Dependable and Secure Computing, pp. 1-12.
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Hussain, W, Merigó, JM, Rahimi, I & Lev, B 2025, 'Half a century of Omega – The International Journal of Management Science: A bibliometric analysis', Omega, vol. 133, pp. 103226-103226.
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Iqra, NA, Li, J, Wang, XH & Yang, G 2025, 'Wave prediction using Graph Neural Network at Darwin Harbour, Australia', Regional Studies in Marine Science, vol. 84, pp. 104088-104088.
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Jacob Kedziora, D, Musiał, A, Rudno-Rudziński, W & Gabrys, B 2025, 'Transfer learning and the early estimation of single-photon source quality using machine learning methods', Machine Learning: Science and Technology, vol. 6, no. 2, pp. 025014-025014.
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Abstract The use of single-photon sources (SPSs) is central to numerous systems and devices proposed amidst a modern surge in quantum technology. However, manufacturing schemes remain imperfect, and single-photon emission purity must often be experimentally verified via interferometry. Such a process is typically slow and costly, which has motivated growing research into whether SPS quality can be more rapidly inferred from incomplete emission statistics. Hence, this study is a sequel to previous work that demonstrated significant uncertainty in the standard method of quality estimation, i.e. the least-squares fitting of a physically motivated function, and asks: can machine learning (ML) do better? The study leverages eight datasets obtained from measurements involving an exemplary quantum emitter, i.e. a single InGaAs/GaAs epitaxial quantum dot; these eight contexts predominantly vary in the intensity of the exciting laser. Specifically, via a form of ‘transfer learning’, five ML models, three linear and two ensemble-based, are trained on data from seven of the contexts and tested on the eighth. Validation metrics quickly reveal that even a linear regressor can outperform standard fitting when it is tested on the same contexts it was trained on, but the success of transfer learning is less assured, even though statistical analysis, made possible by data augmentation, suggests its superiority as an early estimator. Accordingly, the study concludes by discussing future strategies for grappling with the problem of SPS context dissimilarity, e.g. feature engineering and model adaptation.

Jayan Chirayath Kurian, J & Mohan John, B 2025, 'Designing a Digital Health Intervention Using Protection Motivation Theory: A Design Science Research', Journal of Technology in Behavioral Science.

Jiang, B, Liu, J, Wang, Z, Zhang, C, Yang, J, Wang, Y, Sheng, W & Ding, W 2025, 'Semi-supervised multi-view feature selection with adaptive similarity fusion and learning', Pattern Recognition, vol. 159, pp. 111159-111159.
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Jiang, W, Li, H, Xu, G, Ren, H, Yang, H, Zhang, T & Yu, S 2025, 'Rethinking the Design of Backdoor Triggers and Adversarial Perturbations: A Color Space Perspective', IEEE Transactions on Dependable and Secure Computing, vol. 22, no. 3, pp. 2823-2840.
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Jiang, X, Ou, L, Chen, Y, Ao, N, Chang, Y-C, Do, T & Lin, C-T 2025, 'A Fuzzy Logic-Based Approach to Predict Human Interaction by Functional Near-Infrared Spectroscopy', IEEE Transactions on Fuzzy Systems, vol. 33, no. 5, pp. 1664-1677.
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Khorshidi, MS, Merigó, JM & Beydoun, G 2025, 'Half a Century of Information Processing & Management: A bibliometric retrospective', Information Processing & Management, vol. 62, no. 6, pp. 104238-104238.
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Kim, J, Xuan, J, Liang, C & Hussain, FK 2025, 'Hierarchical Reinforcement Learning with Optimal Level Synchronization Based on Flow-Based Deep Generative Model'.
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Kirsch, C, Baki Kocaballi, A, Johnston, A, Naweed, A & Stevenson, I 2025, 'Co‐Designing the Sound of Safety: Embracing Complexity in the Acoustic Vehicle Alerting System Sound for Zero Emission Buses', Human Factors and Ergonomics in Manufacturing & Service Industries, vol. 35, no. 3.
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ABSTRACTAround the world, transport organizations are transitioning their bus fleets from internal combustion engines to electrified zero emission buses (ZEBs). The quiet nature of these buses raises safety concerns for vulnerable road users. To address these concerns, new standards mandate that electric vehicles, including ZEBs, be equipped with an acoustic vehicle alerting system (AVAS) emitting sound at low speeds, to make vehicles more detectable. However, developing an effective AVAS sound requires balancing safety and technical constraints with diverse stakeholder needs. Using codesign, this study conducted a series of risk‐focused subject matter expertize workshops (n = 15), and a large user‐experience focused participatory design workshop (n = 41) to inform the AVAS sound design. The latter included empathy and user journey mapping techniques, facilitating the collection of insights from various perspectives. Results revealed stakeholder preferences for an AVAS sound that was both alerting and positive, and embodied qualities like calmness, politeness, and vibrancy. The workshops allowed for refinement of sound design requirements, although challenges emerged in balancing conflicting preferences and managing technical limitations to create a sound that could be both perceptible and nonintrusive. This study provides a framework for the development of an AVAS sound that could capture a range of stakeholder needs and preferences and lays a foundation for AVAS sounds that enhance safety while being positively received. It highlights the importance of inclusive, iterative design in advancing public transport safety and sustainability, with outcomes supporting the future sound design, testing, and implementation.

Klettner, A, Cetindamar, D & Sainty, R 2025, 'Stakeholder Governance and Corporate Purpose in Certified B Corps: Minimizing Conflict and Fostering Collaboration', Business & Society.
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Although a stakeholder-inclusive approach to corporate governance is becoming accepted more widely, we have little idea on how it can be achieved in practice or understood theoretically. B Lab Australia and Aotearoa New Zealand requires certified B Corporations (B Corps) to include in their corporate constitution formal commitments to pursuing a pro-social purpose and taking their stakeholders’ interests into account. Through interviews with 20 B Corp leaders, we explore how this model of stakeholder governance is implemented. We find that an organization-specific corporate purpose helps leaders to attract, select, and retain supportive stakeholders and minimize stakeholder conflicts. We identify three decision-making processes (Establishing, Applying, Developing) and three signaling processes (Announcing, Demonstrating, Sharing) through which stakeholder governance is achieved. Our findings extend emerging theories of stakeholder governance by moving away from a focus on conflict resolution to demonstrate its potential to foster collaborative systemic change. B Corps display a helpful model for purpose-led stakeholder governance with the potential to be applied more widely.

Le, TP 2025, 'The limits of quantum correlations', Nature Physics, vol. 21, no. 4, pp. 501-502.
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Leong, D, Do, T & Lin, C-T 2025, 'The Distinction Between Object Recognition and Object Identification in Brain Connectivity for Brain–Computer Interface Applications', IEEE Transactions on Cognitive and Developmental Systems, vol. 17, no. 1, pp. 89-101.
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Li, A, Yang, B, Huo, H, Hussain, FK & Xu, G 2025, 'Self-supervised dual graph learning for recommendation', Knowledge-Based Systems, vol. 310, pp. 112967-112967.
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Li, C, Lai, JCS & Oberst, S 2025, 'Optimizing self-organized topology of recurrence-based complex networks', Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 35, no. 3.
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Networks and graphs have emerged as powerful tools to model and analyze nonlinear dynamical systems. By constructing an adjacency matrix from recurrence networks, it is possible to capture critical structural and geometric information about the underlying dynamics of a time series. However, randomization of data often raises concerns about the potential loss of deterministic relationships. Here, in using the spring-electrical-force model, we demonstrate that by optimizing the distances between randomized points through minimizing an entropy-related energy measure, the deterministic structure of the original system is not destroyed. This process allows us to approximate the time series shape and correct the phase, effectively reconstructing the initial invariant set and attracting dynamics of the system. Our approach highlights the importance of adjacency matrices derived from recurrence plots, which preserve crucial information about the nonlinear dynamics. By using recurrence plots and the entropy of diagonal line lengths and leveraging the Kullback–Leibler divergence as a relative entropic measure, we fine-tune the parameters and initial conditions for recurrence plots, ensuring an optimal representation of the system’s dynamics. Through the integration of network geometry and energy minimization, we show that data-driven graphs can self-organize to retain and regenerate the fundamental features of the time series, including its phase space structures. This study underscores the robustness of recurrence networks as a tool for analyzing nonlinear systems and demonstrates that randomization, when guided by informed optimization, does not erase deterministic relationships, opening new avenues for reconstructing dynamical systems from observational data.

Li, G, Luan, TH, Zheng, J, Lai, C, Zhang, K & Yu, S 2025, 'SECR: A Secure and Efficient Charging Reservation Scheme Based on Digital Twin in Vehicular Network', IEEE Internet of Things Journal, vol. 12, no. 8, pp. 10434-10452.
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Li, K, Lu, J, Zuo, H & Zhang, G 2025, 'Fuzzy Domain Adaptation From Heterogeneous Source Teacher Models', IEEE Transactions on Fuzzy Systems, vol. 33, no. 6, pp. 1840-1852.
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Li, Q, Wang, Z, Xia, H, Li, G, Cao, Y, Yao, L & Xu, G 2025, 'HOT-GAN: Hilbert Optimal Transport for Generative Adversarial Network', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 3, pp. 4371-4384.
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Li, W, Zhao, B, Zhu, L, Wang, Y, Zhong, Q & Yu, S 2025, 'TCEC: Integrity Protection for Containers by Trusted Chip on IoT Edge Computing Nodes', IEEE Sensors Journal, vol. 25, no. 9, pp. 16269-16280.
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Li, Y, Wang, H, Duan, Y, Zhang, J & Li, X 2025, 'A closer look at the explainability of Contrastive language-image pre-training', Pattern Recognition, vol. 162, pp. 111409-111409.
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Li, Y, Wang, H, Xu, W, Xiao, T, Liu, H, Tu, M, Wang, Y, Yang, X, Zhang, R, Yu, S, Guo, S & Li, R 2025, 'Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey', IEEE Communications Surveys & Tutorials, pp. 1-1.
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Li, Z, Yang, C, Chen, Y, Wang, X, Chen, H, Xu, G, Yao, L & Sheng, M 2025, 'Graph and Sequential Neural Networks in Session-based Recommendation: A Survey', ACM Computing Surveys, vol. 57, no. 2, pp. 1-37.
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Recent years have witnessed the remarkable success of recommendation systems (RSs) in alleviating the information overload problem. As a new paradigm of RSs, session-based recommendation (SR) specializes in users’ short-term preferences and aims at providing a more dynamic and timely recommendation based on ongoing interactions. This survey presents a comprehensive overview of the recent works on SR. First, we clarify the key definitions within SR and compare the characteristics of SR against other recommendation tasks. Then, we summarize the existing methods in two categories: sequential neural network based methods and graph neural network (GNN) based methods. The relevant frameworks and technical details are further introduced. Finally, we discuss the challenges of SR and new research directions in this area.

Lin, A, Xiang, Y, Li, J & Prasad, M 2025, 'Dynamic Appearance Particle Neural Radiance Field', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Liu, B, Liu, B, Zhu, T & Ding, M 2025, 'A Review of Deepfake and Its Detection: From Generative Adversarial Networks to Diffusion Models', International Journal of Intelligent Systems, vol. 2025, no. 1.
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Deepfake technology, leveraging advanced artificial intelligence (AI) algorithms, has emerged as a powerful tool for generating hyper‐realistic synthetic human faces, presenting both innovative opportunities and significant challenges. Meanwhile, the development of Deepfake detectors represents another branch of models striving to recognize AI‐generated fake faces and protect people from the misinformation of Deepfake. This ongoing cat‐and‐mouse game between generation and detection has spurred a dynamic evolution in the landscape of Deepfake. This survey comprehensively studies recent advancements in Deepfake generation and detection techniques, focusing particularly on the utilization of generative adversarial networks (GANs) and diffusion models (DMs). For both GAN‐based and DM‐based Deepfake generators, we categorize them based on whether they synthesize new content or manipulate existing content. Correspondingly, we examine various strategies employed to identify synthetic and manipulated Deepfake, respectively. Finally, we summarize our findings by discussing the unique capabilities and limitations of GANs and DM in the context of Deepfake. We also identify promising future directions for research, including the development of hybrid approaches that leverage the strengths of both GANs and DM, the exploration of novel detection strategies utilizing advanced AI techniques, and the ethical considerations surrounding the development of Deepfake. This survey paper serves as a valuable resource for researchers, practitioners, and policymakers seeking to understand the state‐of‐the‐art in Deepfake technology, its implications, and potential avenues for future research and development.

Liu, G, Zhang, W, Wang, X, King, S & Yu, S 2025, 'A Membership Inference and Adversarial Attack Defense Framework for Network Traffic Classifiers', IEEE Transactions on Artificial Intelligence, vol. 6, no. 2, pp. 317-332.
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Liu, J, Huang, X-L, Hu, F & Yu, S 2025, 'Higher-Order Cumulant-Assisted Constant Wideband Compressive Spectrum Sensing Using Semantic Correlation Mining', IEEE Transactions on Wireless Communications, vol. 24, no. 2, pp. 893-908.
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Liu, Z, Lu, J, Xuan, J & Zhang, G 2025, 'Learning Latent and Changing Dynamics in Real Non-Stationary Environments', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 4, pp. 1930-1942.
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Luo, C, Wang, Y, Zhang, Y & Zhang, LY 2025, 'Distributed Differentially Private Matrix Factorization for Implicit Data via Secure Aggregation', IEEE Transactions on Computers, vol. 74, no. 2, pp. 705-716.
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Ma, G, Lu, J, Fang, Z, Liu, F & Zhang, G 2025, 'Multiview Classification Through Learning From Interval-Valued Data', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 5, pp. 9606-9620.
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Ma, H, Sun, Z, Dong, D & Gong, D 2025, 'Learning Informative Latent Representation for Quantum State Tomography', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-11.
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Ma, H, Sun, Z, Dong, D, Chen, C & Rabitz, H 2025, 'Tomography of Quantum States From Structured Measurements via Quantum-Aware Transformer', IEEE Transactions on Cybernetics, vol. 55, no. 6, pp. 2571-2582.
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Ma, N, Xuan, J, Zhang, G & Lu, J 2025, 'Global–Local Decomposition of Contextual Representations in Meta-Reinforcement Learning', IEEE Transactions on Cybernetics, vol. 55, no. 3, pp. 1277-1287.
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Mann, RL, Elman, SJ, Wood, DR & Chapman, A 2025, 'A graph-theoretic framework for free-parafermion solvability', Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 481, no. 2312.
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We present a graph-theoretic characterization of when a quantum spin model admits an exact solution via a mapping to free parafermions. Our characterization is based on the concept of a frustration graph, which represents the commutation relations between Weyl operators of a Hamiltonian. We show that a quantum spin system has an exact free-parafermion solution if its frustration graph is an oriented indifference graph. Furthermore, we show that if the frustration graph of a model can be dipath oriented via switching operations, then the model is integrable in the sense that there is a family of commuting independent set charges. Additionally, we establish an efficient algorithm for deciding whether this is possible. Our characterization extends that given for free-fermion solvability. Finally, we apply our results to solve three qudit spin models.

Nik-Khorasani, A, Khuat, TT & Gabrys, B 2025, 'Hyperbox Mixture Regression for process performance prediction in antibody production', Digital Chemical Engineering, vol. 14, pp. 100221-100221.
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Pan, Y, Fan, W, Lv, M, Sun, X & Yu, S 2025, 'An Incremental Scalable Network Architecture With Fault-Tolerant Communication', IEEE Transactions on Reliability, pp. 1-15.
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Pennington, W, Bandara, M, Peloquin, A & McMillen, C 2025, 'Where are the crystals? X-DES: Deep Eutectic Solvents based on Halogen Bonding', Structural Dynamics, vol. 12, no. 2_Supplement, pp. A134-A134.
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As a crystallographer, nothing is sadder than failed crystal growth. As an “experienced” crystallographer (a basketball ref couldn't hand signal my number of years), I’ve thrown away more than my share of oily smudges (always according to established hazardous waste procedures, of course). Fortunately, my students are more curious than I. Recently we reported the first halogen-bonding-based deep eutectic solvent, which consisted of a mixture of 1,3-dithiane and o- diiodotetrafluorobenzene – a system that simply refused to cooperate during crystal growth (Peloquin et al. Angew Chem, Int. Ed.2021, 60, 22983–22989). Based on this result and memories of many other liquid samples, we have been exploring a number of different systems, most consisting of tetraalkylammonium triiodides with a variety of organoiodines. As it turns out, many of these systems that resist crystal growth are doing exactly what they are “supposed” to do. Most of these are pseudo binary systems with two or more eutectic points and at least one cocrystalline composition (see phase diagram below). The preparation and characterization of these systems by thermal analysis and, in the case of cocrystals, structural characterization will be discussed. Efforts to correlate halogen bonding in triiodide-based cocrystals with halogen bonding in triiodide-based deep eutectic solvents are now in progress.

Qin, JC, Jiang, R, Mo, H & Dong, D 2025, 'A data-driven mixed integer programming approach for joint chance-constrained optimal power flow under uncertainty', International Journal of Machine Learning and Cybernetics, vol. 16, no. 2, pp. 1111-1127.
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Abstract This paper introduces a novel mixed integer programming (MIP) reformulation for the joint chance-constrained optimal power flow problem under uncertain load and renewable energy generation. Unlike traditional models, our approach incorporates a comprehensive evaluation of system-wide risk without decomposing joint chance constraints into individual constraints, thus preventing overly conservative solutions and ensuring robust system security. A significant innovation in our method is the use of historical data to form a sample average approximation that directly informs the MIP model, bypassing the need for distributional assumptions to enhance solution robustness. Additionally, we implement a model improvement strategy to reduce the computational burden, making our method more scalable for large-scale power systems. Our approach is validated against benchmark systems, i.e., IEEE 14-, 57- and 118-bus systems, demonstrating superior performance in terms of cost-efficiency and robustness, with lower computational demand compared to existing methods.

Qin, Y, Zhang, X, Yu, S & Feng, G 2025, 'A survey on representation learning for multi-view data', Neural Networks, vol. 181, pp. 106842-106842.
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Rathod, S, Sahni, M & Merigo, JM 2025, 'Development and Applications of Penalty‐Based Aggregation Operators in Multicriteria Decision Making', International Journal of Intelligent Systems, vol. 2025, no. 1.
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This article develops a new penalty‐based aggregation operator known as the penalty‐based induced ordered weighted averaging (P‐IOWA) operator which is an extension of penalty‐based ordered weighted averaging (P‐OWA) operator. Our goal is to figure out how the induced variable realigns penalties when gathering information. We extend the P‐OWA and P‐IOWA operators with the different means such as generalized mean and quasi‐arithmetic mean. This article also includes different families of P‐OWA and P‐IOWA operators. The value of these new operators is demonstrated through a case study centered on investment matters. This study evaluates the economic and governance performance of seven South Asian nations utilizing nine indicators from 2021 data. The research examines 5 economic indicators including GDP growth, exports and imports (% of GDP), inflation, and labor force metrics, alongside 4 governance indicators focusing on corruption control, government effectiveness, and political stability. We use min–max normalization to standardize the varied values, which originally ranged from 0.5% to 77.7% across various metrics. Following this, the normalized inverse penalty method is used to derive optimal weights for these indicators, tackling the task of combining multidimensional data. Subsequently, we implement and evaluate various penalty‐based aggregation methodologies on the normalized data, each offering a distinct approach to penalizing outliers and balancing indicator weights. The study compares the results obtained from these operators to assess their impact on country rankings and overall performance evaluation. This approach allows for a comprehensive comparison of countries’ performances, integrating both economic and governance dimensions into a single, quantifiable framework.

Sezgin, E, Jackson, DI, Kocaballi, AB, Bibart, M, Zupanec, S, Landier, W, Audino, A, Ranalli, M & Skeens, M 2025, 'Can Large Language Models Aid Caregivers of Pediatric Cancer Patients in Information Seeking? A Cross‐Sectional Investigation', Cancer Medicine, vol. 14, no. 1.
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ABSTRACTPurposeCaregivers in pediatric oncology need accurate and understandable information about their child's condition, treatment, and side effects. This study assesses the performance of publicly accessible large language model (LLM)‐supported tools in providing valuable and reliable information to caregivers of children with cancer.MethodsIn this cross‐sectional study, we evaluated the performance of the four LLM‐supported tools—ChatGPT (GPT‐4), Google Bard (Gemini Pro), Microsoft Bing Chat, and Google SGE—against a set of frequently asked questions (FAQs) derived from the Children's Oncology Group Family Handbook and expert input (In total, 26 FAQs and 104 generated responses). Five pediatric oncology experts assessed the generated LLM responses using measures including accuracy, clarity, inclusivity, completeness, clinical utility, and overall rating. Additionally, the content quality was evaluated including readability, AI disclosure, source credibility, resource matching, and content originality. We used descriptive analysis and statistical tests including Shapiro–Wilk, Levene's, Kruskal–Wallis H‐tests, and Dunn's post hoc tests for pairwise comparisons.ResultsChatGPT shows high overall performance when evaluated by the experts. Bard also performed well, especially in accuracy and clarity of the responses, whereas Bing Chat and Google SGE had lower overall scores. Regarding the disclosure of responses being generated by AI, it was observed less frequently in ChatGPT responses, which may have affected the clarity of responses, whereas Bard maintained a balance between AI disclosure and response clarity. Google SGE generated the most readable responses whereas ChatGPT answered with the most complexity. LLM tools varied significa...

Shahsavari, M, Hussain, OK, Sharma, P & Saberi, M 2025, 'Modelling supply chain risk events by considering their contributing events: a systematic literature review', Enterprise Information Systems, vol. 19, no. 5-6.
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Proactive Supply Chain Risk Management (SCRM) helps organisations anticipate and mitigate risks, ensuring business continuity and resilience in a violet market. Existing research proposes various techniques to quantify risk occurrence, but none account for the causal relationships between contributing events and risk events. This paper addresses this gap through a systematic literature review of SCRM techniques and outlines future research directions to enhance proactive SCRM by incorporating causal dependencies in risk quantification.

Shams, RA, Zowghi, D & Bano, M 2025, 'AI and the quest for diversity and inclusion: a systematic literature review', AI and Ethics, vol. 5, no. 1, pp. 411-438.
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Abstract The pervasive presence and wide-ranging variety of artificial intelligence (AI) systems underscore the necessity for inclusivity and diversity in their design and implementation, to effectively address critical issues of fairness, trust, bias, and transparency. However, diversity and inclusion (D&I) considerations are significantly neglected in AI systems design, development, and deployment. Ignoring D&I in AI systems can cause digital redlining, discrimination, and algorithmic oppression, leading to AI systems being perceived as untrustworthy and unfair. Therefore, we conducted a systematic literature review (SLR) to identify the challenges and their corresponding solutions (guidelines/ strategies/ approaches/ practices) about D&I in AI and about the applications of AI for D&I practices. Through a rigorous search and selection, 48 relevant academic papers published from 2017 to 2022 were identified. By applying open coding on the extracted data from the selected papers, we identified 55 unique challenges and 33 unique solutions in addressing D&I in AI. We also identified 24 unique challenges and 23 unique solutions for enhancing D&I practices by AI. The result of our analysis and synthesis of the selected studies contributes to a deeper understanding of diversity and inclusion issues and considerations in the design, development and deployment of the AI ecosystem. The findings would play an important role in enhancing awareness and attracting the attention of researchers and practitioners in their quest to embed D&I principles and practices in future AI systems. This study also identifies important gaps in the research literature that will inspire future direction for researchers.

Shen, S, Cai, C, Shen, Y, Wu, X, Ke, W & Yu, S 2025, 'Joint Mean-Field Game and Multiagent Asynchronous Advantage Actor-Critic for Edge Intelligence-Based IoT Malware Propagation Defense', IEEE Transactions on Dependable and Secure Computing, pp. 1-15.
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Shen, Y, Shepherd, C, Ahmed, CM, Shen, S & Yu, S 2025, 'Integrating Deep Spiking Q-Network Into Hypergame-Theoretic Deceptive Defense for Mitigating Malware Propagation in Edge Intelligence-Enabled IoT Systems', IEEE Transactions on Services Computing, vol. 18, no. 3, pp. 1487-1499.
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Shen, Y, Shepherd, C, Ahmed, CM, Shen, S & Yu, S 2025, 'Privacy Preservation Strategies for Malware-Infected Edge Intelligence Systems: A Bayesian Stochastic Game-Based Approach', IEEE Transactions on Mobile Computing, pp. 1-15.
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Singh, VK, Prakash, S, Dixit, P & Prasad, M 2025, 'EEG Signal Based Human Emotion Recognition Brain-computer Interface using Deep Learning and High-Performance Computing', Wireless Personal Communications, vol. 140, no. 1-2, pp. 165-192.
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Sohaib, O, Alshemeili, A & Bhatti, T 2025, 'Exploring AI-enabled green marketing and green intention: An integrated PLS-SEM and NCA approach', Cleaner and Responsible Consumption, vol. 17, pp. 100269-100269.
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Sood, K, Liu, S, Nguyen, DDN, Kumar, N, Feng, B & Yu, S 2025, 'Alleviating Data Sparsity to Enhance AI Models Robustness in IoT Network Security Context', IEEE Transactions on Mobile Computing, vol. 24, no. 5, pp. 3764-3778.
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Su, X, Alatas, B & Sohaib, O 2025, 'An Express Management System With Graph Recurrent Neural Network for Estimated Time of Arrival', Journal of Organizational and End User Computing, vol. 37, no. 1, pp. 1-26.
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Estimated Time of Arrival (ETA) is a crucial task in the logistics and transportation industry, aiding businesses and individuals in optimizing time management and improving operational efficiency. This study proposes a novel Graph Recurrent Neural Network (GRNN) model that integrates external factor data. The model first employs a Multilayer Perceptron (MLP)-based external factor data embedding layer to categorize and combine influencing factors into a vector representation. A Graph Recurrent Neural Network, combining Long Short-Term Memory (LSTM) and GNN models, is then used to predict ETA based on historical data. The model undergoes both offline and online evaluation experiments. Specifically, the offline experiments demonstrate a 5.3% reduction in RMSE on the BikeNYC dataset and a 6.1% reduction on the DidiShenzhen dataset, compared to baseline models. Online evaluation using Baidu Maps data further validates the model's effectiveness in real-time scenarios. These results underscore the model's potential in improving ETA predictions for urban traffic systems.

Tanveer, M, Tiwari, A, Akhtar, M & Lin, C-T 2025, 'Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-10.
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Tian, H, Liu, B, Zhu, T, Zhou, W & Yu, PS 2025, 'Distilling Fair Representations From Fair Teachers', IEEE Transactions on Big Data, vol. 11, no. 3, pp. 1419-1433.
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Tian, H, Liu, B, Zhu, T, Zhou, W & Yu, PS 2025, 'MultiFair: Model Fairness With Multiple Sensitive Attributes', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 3, pp. 5654-5667.
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Tian, H, Zhang, G, Liu, B, Zhu, T, Ding, M & Zhou, W 2025, 'Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers', IEEE Transactions on Dependable and Secure Computing, pp. 1-16.
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Tian, Z, Wang, W, Zhang, C & Yu, S 2025, 'Model-Enabled Task-Oriented Semantic Communications Through Knowledge Synchronization', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Tian, Z, Zhang, C, Wang, W, Bogucka, H & Yu, S 2025, 'ROSE: A Receiver-Oriented Semantic Communication Framework', IEEE Network, vol. 39, no. 2, pp. 216-223.
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Wang, C, Chaturvedi, K, Nham, B, Reid, N, Bradshaw, A, Rosengren, S, Black, D, Bein, K, Halmagyi, M, Braytee, A, Prasad, M, Bharathy, G & Welgampola, M 2025, 'Separating Stroke and Vestibular Neuritis Using History, Examination and Vestibular Tests: A Machine Learning Approach (S25.009)', Neurology, vol. 104, no. 7_Supplement_1.
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Wang, D, Yao, H, Yu, D, Song, S, Weng, H, Xu, G & Deng, S 2025, 'Graph Intention Embedding Neural Network for tag-aware recommendation', Neural Networks, vol. 184, pp. 107062-107062.
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Wang, K, Qu, C, Wang, J, Li, Z & Lu, H 2025, 'Ensemble Multitask Prediction of Air Pollutants Time Series: Based on Variational Inference, Data Projection, and Generative Adversarial Network', Journal of Forecasting, vol. 44, no. 2, pp. 646-675.
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ABSTRACTIn light of the mounting environmental pressures, especially the significant threat urban air pollution poses to public health, there arises an imperative need to develop a data‐driven model for air pollution prediction. However, contemporary deep learning techniques, such as recurrent neural networks, often struggle to effectively capture the underlying data patterns and distributions, resulting in reduced model stability. To address this gap, this study introduces an ensemble Wasserstein generative adversarial network framework (EWGF) to enhance the stability and accuracy of PM2.5 predictions by facilitating the acquisition of more informative data representations through Wasserstein generative adversarial network. The framework contains an intricate feature extraction pipeline that automatically learns features containing residual information as representations of potential features, effectively ameliorating the underutilization of feature information. We address a nonconvex multi‐objective optimization problem associated with amalgamating diverse Wasserstein generative adversarial network architectures, which enhance the inherent instability of the predictions. Furthermore, an adaptive search strategy is introduced to ascertain the optimal distribution of prediction residuals, thereby expanding the prediction interval estimation method based on residual distribution. We rigorously evaluate the proposed framework using datasets from three major Indian cities, and our experiments unequivocally show that the EWGF outperforms existing solutions in both PM2.5 point prediction and interval prediction, evidenced by an approximate 8.07% reduction in mean absolute percentage error and an approximate 19.41% improvement in prediction interval score compared to the baseline model.

Wang, R, Xi, L, Zhang, F, Fan, H, Yu, X, Liu, L, Yu, S & Leung, VCM 2025, 'Context Correlation Discrepancy Analysis for Graph Anomaly Detection', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 1, pp. 174-187.
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Wang, R, Zuo, H, Fang, Z & Lu, J 2025, 'Integrated Image-Text Augmentation for Few-Shot Learning in Vision-Language Models', ACM Transactions on Intelligent Systems and Technology, vol. 16, no. 2, pp. 1-19.
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Vision-language models, such as the Contrastive Language-Image Pre-Training (CLIP) model, have achieved significant success in image classification tasks. CLIP demonstrates high expressive power in few-shot learning scenarios due to its pairing of text and image encoders. However, CLIP still faces over-fitting when trained with a limited number of samples. To mitigate this, image augmentation techniques have been proposed in few-shot learning tasks to prevent over-fitting by enriching the dataset. Existing image augmentation methods, primarily designed for single-modal image models, focus solely on transformations within the image itself. However, for CLIP, merely increasing visual variety without considering textual content can reduce generalization ability and may even mislead the model. To address this issue, we introduce a novel image augmentation approach—Integrated Image-Text Augmentation (ITA)— for CLIP model in few-shot learning tasks. This method generates new and diverse augmented images to increase the diversity of the training data and reduce over-fitting. Additionally, ITA establishes an alignment between the augmented images and their textual descriptions. Through this alignment, the model not only learns to recognize visual elements in the images but also understands the semantic connections between these elements and the text descriptions. This dual-modal approach enhances the model’s flexibility and accuracy in processing few-shot learning tasks. Extensive experiments in few-shot image classification scenarios have demonstrated that ITA shows significant improvements compared to various image augmentation techniques.

Wang, S, Wang, Q, Lu, H, Zhang, D, Xing, Q & Wang, J 2025, 'Learning about tail risk: Machine learning and combination with regularization in market risk management', Omega, vol. 133, pp. 103249-103249.
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Wang, S, Xu, C, Zhao, G, Han, Z, Hu, R & Yu, S 2025, 'APQA: An anonymous post quantum access authentication scheme based on lattice for space ground integrated network', Computer Networks, vol. 257, pp. 110979-110979.
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Wang, S, Zhao, G, Xu, C, Han, Z & Yu, S 2025, 'LPQAA: a lightweight post-quantum access authentication scheme for satellite network', The Journal of Supercomputing, vol. 81, no. 1.
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Wang, W, Tian, Z, Zhang, C & Yu, S 2025, 'SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 547-558.
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Wang, W, Zhang, C, Tian, Z & Yu, S 2025, 'FedU: Federated Unlearning via User-Side Influence Approximation Forgetting', IEEE Transactions on Dependable and Secure Computing, vol. 22, no. 3, pp. 2550-2562.
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Wang, W, Zhang, C, Tian, Z, Liu, S & Yu, S 2025, 'CRFU: Compressive Representation Forgetting Against Privacy Leakage on Machine Unlearning', IEEE Transactions on Dependable and Secure Computing, pp. 1-14.
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Wang, W, Zhang, C, Tian, Z, Yu, S & Su, Z 2025, 'Evaluation of Machine Unlearning Through Model Difference', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 5211-5223.
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Wang, Y, Qi, B, Wang, X, Liu, T & Dong, D 2025, 'Power Characterization of Noisy Quantum Kernels', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Wei, J, Zhang, Y, Yu Zhang, L, Chen, C, Pan, S, Ong, K-L, Zhang, J & Xiang, Y 2025, 'Extracting Private Training Data in Federated Learning From Clients', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 4525-4540.
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Wu, S, Li, Y, Wang, W, Guo, J, Fan, W, Liu, Q, Jia, W, Yu, S, Cao, J & Wang, T 2025, 'Enhancing Collaborative Inference on Heterogeneous Edge Devices via Adaptive Ensemble Knowledge Distillation', IEEE Journal on Selected Areas in Communications, pp. 1-1.
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Wu, T, Cui, C, Xian, X, Qiao, S, Wang, C, Yuan, L & Yu, S 2025, 'Understanding the robustness of graph neural networks against adversarial attacks', Knowledge-Based Systems, vol. 323, pp. 113714-113714.
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Wu, X, Li, P, Gu, Y, Tao, J, Shen, S & Yu, S 2025, 'Improved Gale–Shapley-Based Hierarchical Federated Learning for IoT Scenarios', IEEE Internet of Things Journal, vol. 12, no. 7, pp. 9195-9205.
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Wu, X, Lin, Y, Zhong, H, Tao, J, Gu, Y, Shen, S & Yu, S 2025, 'A diversity-aware incentive mechanism for cross-silo federated learning with budget constraint', Knowledge-Based Systems, vol. 315, pp. 113212-113212.
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Wu, Z, Liu, Y, Cen, J, Zheng, Z & Xu, G 2025, 'A cross-domain knowledge tracing model based on graph optimal transport', World Wide Web, vol. 28, no. 1.
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Xiao, S, Wang, Y, Zhang, J, Dong, D, Mooney, GJ, Petersen, IR & Yonezawa, H 2025, 'A Two-Stage Solution to Quantum Process Tomography: Error Analysis and Optimal Design', IEEE Transactions on Information Theory, vol. 71, no. 3, pp. 1803-1823.
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Xu, G, Fan, X, Xu, S, Cao, Y, Chen, X-B, Shang, T & Yu, S 2025, 'Anonymity-Enhanced Sequential Multi-Signer Ring Signature for Secure Medical Data Sharing in IoMT', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 5647-5662.
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Xu, G, Xu, S, Cao, Y, Xiao, K, Mao, Y, Chen, X-B, Dong, M & Yu, S 2025, 'AAQ-PEKS: An Attribute-based Anti-Quantum Public Key Encryption Scheme with Keyword Search for E-healthcare Scenarios', Peer-to-Peer Networking and Applications, vol. 18, no. 2.
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Xu, H, Fang, L, Lv, H, Xu, S, Shen, S & Yu, S 2025, 'Maximizing VANETs Secrecy Data Rate Using Dueling Double Deep Q-Networks', IEEE Transactions on Vehicular Technology, vol. 74, no. 1, pp. 1267-1279.
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Xu, H, Xuan, J, Zhang, G & Lu, J 2025, 'Twin Trust Region Policy Optimization', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-15.
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Xu, Z, Mittal, PS, Ahmed, MM, Adak, C & Cai, ZG 2025, 'Assessing penmanship of Chinese handwriting: a deep learning-based approach', Reading and Writing, vol. 38, no. 3, pp. 723-743.
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Abstract The rise of the digital era has led to a decline in handwriting as the primary mode of communication, resulting in negative effects on handwriting literacy, particularly in complex writing systems such as Chinese. The marginalization of handwriting has contributed to the deterioration of penmanship, defined as the ability to write aesthetically and legibly. Despite penmanship being widely acknowledged as a crucial factor in predicting language literacy, research on its evaluation remains limited, with existing assessments primarily dependent on expert subjective ratings. Recent initiatives have started to explore the application of convolutional neural networks (CNN) for automated penmanship assessment. In this study, we adopted a similar approach, developing a CNN-based automatic assessment system for penmanship in traditional Chinese handwriting. Utilizing an existing database of 39,207 accurately handwritten characters (penscripts) from 40 handwriters, we had three human raters evaluate each penscript’s penmanship on a 10-point scale and calculated an average penmanship score. We trained a CNN on 90% of the penscripts and their corresponding penmanship scores. Upon testing the CNN model on the remaining 10% of penscripts, it achieved a remarkable performance (overall 9.82% normalized Mean Absolute Percentage Error) in predicting human penmanship scores, illustrating its potential for assessing handwriters’ penmanship. To enhance accessibility, we developed a mobile application based on the CNN model, allowing users to conveniently evaluate their penmanship.

Yang, C, Chen, Y, Li, Z, Wang, X, Shi, K, Yao, L, Xu, G & Guo, Z 2025, 'Deep multimodal learning for time series analysis in social computing: a survey', International Journal of Multimedia Information Retrieval, vol. 14, no. 2.
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Yang, J & Lin, C-T 2025, 'Autonomous Clustering by Fast Find of Mass and Distance Peaks', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 7, pp. 5336-5349.
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Yang, J & Lin, C-T 2025, 'Toward Autonomous Distributed Clustering', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 2, pp. 2065-2072.
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Yang, S, Yu, S, Li, Q, Xia, K & Zhu, H 2025, 'Few-shot specific emitter identification via asymmetric dual-path masked autoencoder', Digital Signal Processing, vol. 163, pp. 105201-105201.
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Yao, L, McAuley, J, Wang, X & Jannach, D 2025, 'Special Issue on Responsible Recommender Systems Part 2', ACM Transactions on Intelligent Systems and Technology, vol. 16, no. 1, pp. 1-3.
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Yin, Y, Liu, X, Cheng, X, Liu, Y, Zhu, T & Huo, H 2025, 'SpatialIE: Towards adaptive floating waste detection in unpredictable weather', Knowledge-Based Systems, vol. 323, pp. 113621-113621.
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Yosun, T & Cetindamar, D 2025, 'A Typology of Competitive Strategies for Social Enterprises', Journal of Social Entrepreneurship, vol. 16, no. 1, pp. 183-209.
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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.

Yu, Y, Wang, J, Jiang, H & Lu, H 2025, 'How to manage a multifactor-driven crude oil market more effectively? A revisit based on the multiple criteria perspective', Resources Policy, vol. 100, pp. 105446-105446.
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Zeng, A, Tang, Q, O’Hagan, E, McCaffery, K, Ijaz, K, Quiroz, JC, Kocaballi, AB, Rezazadegan, D, Trivedi, R, Siette, J, Shaw, T, Makeham, M, Thiagalingam, A, Chow, CK & Laranjo, L 2025, 'Use of digital patient decision-support tools for atrial fibrillation treatments: a systematic review and meta-analysis', BMJ Evidence-Based Medicine, vol. 30, no. 1, pp. 10-21.
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ObjectivesTo assess the effects of digital patient decision-support tools for atrial fibrillation (AF) treatment decisions in adults with AF.Study designSystematic review and meta-analysis.Eligibility criteriaEligible randomised controlled trials (RCTs) evaluated digital patient decision-support tools for AF treatment decisions in adults with AF.Information sourcesWe searched MEDLINE, EMBASE and Scopus from 2005 to 2023.Risk-of-bias (RoB) assessment: We assessed RoB using the Cochrane Risk of Bias Tool 2 for RCTs and cluster RCT and the ROBINS-I tool for quasi-experimental studies.Synthesis of resultsWe used random effects meta-analysis to synthesise decisional conflict and patient knowledge outcomes reported in RCTs. We performed narrative synthesis for all outcomes. The main outcomes of interest were decisional conflict and patient knowledge.Results13 articles, reporting on 11 studies (4 RCTs, 1 cluster RCT and 6 quasi-experimental) met the inclusion criteria. There were 2714 participants across all studies (2372 in RCTs), of which 26% were women and the mean age was 71 years. Socioeconomically disadvantaged groups were poorly represented in the included studies. Seven studies (n=2508) focused on non-valvular AF and the mean CHAD2DS2-VASc across studies was 3.2 and for HAS-BLED 1.9. All tools focused on decisions regarding thromboembolic stroke prevention and most enabled calculation of individualised stroke risk. Tools were heterogeneous in features and functions; four tools were patient decision aids. The readability of content was reported in one study. Meta-analyses showed a redu...

Zeng, G, Zhang, Q, Zhang, G & Lu, J 2025, 'Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 6, pp. 4244-4257.
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Zhang, K, Li, R & Ying, M 2025, 'A Divide-And-Conquer Pebbling Strategy for Oracle Synthesis in Quantum Computing', IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pp. 1-1.
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Zhang, Y, Gu, C, Shi, P, Jing, Z, Li, B & Liu, B 2025, 'Bring Your Device Group (BYDG): Efficient and Privacy-Preserving User-Device Authentication Protocol in Multi-Access Edge Computing', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 3346-3361.
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Zhang, Z & Ying, M 2025, 'Quantum Register Machine: Efficient Implementation of Quantum Recursive Programs', Proceedings of the ACM on Programming Languages, vol. 9, no. PLDI, pp. 822-847.
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Quantum recursive programming has been recently introduced for describing sophisticated and complicated quantum algorithms in a compact and elegant way. However, implementation of quantum recursion involves intricate interplay between quantum control flow and recursive procedure calls. In this paper, we aim at resolving this fundamental challenge and develop a series of techniques to efficiently implement quantum recursive programs. Our main contributions include: 1. We propose a notion of quantum register machine, the first quantum architecture (including an instruction set) that provides instruction-level support for quantum control flow and recursive procedure calls at the same time. 2. Based on quantum register machine, we describe the first comprehensive implementation process of quantum recursive programs, including the compilation, the partial evaluation of quantum control flow, and the execution on the quantum register machine. 3. As a bonus, our efficient implementation of quantum recursive programs also offers automatic parallelisation of quantum algorithms. For implementing certain quantum algorithmic subroutine, like the widely used quantum multiplexor, we can even obtain exponential parallel speed-up (over the straightforward implementation) from this automatic parallelisation. This demonstrates that quantum recursive programming can be win-win for both modularity of programs and efficiency of their implementation.

Zhang, Z, Jiang, Y, Wei, X, Chen, M, Dong, H & Yu, S 2025, 'Generative-AI for XR Content Transmission in the Metaverse: Potential Approaches, Challenges, and a Generation-Driven Transmission Framework', IEEE Network, pp. 1-1.
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Zhang, Z, Zhou, Z, Tian, Z & Yu, S 2025, 'CRCGAN: Toward robust feature extraction in finger vein recognition', Pattern Recognition, vol. 158, pp. 111064-111064.
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Zhao, Y, Liu, B, Zhu, T, Ding, M & Zhou, W 2025, 'ROSIN: Robust Semantic Image Hiding Network', Knowledge-Based Systems, vol. 309, pp. 112885-112885.
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Zheng, J, Gao, Q, Dong, D, Lü, J & Deng, Y 2025, 'A Quantum Multimodal Neural Network Model for Sentiment Analysis on Quantum Circuits', IEEE Transactions on Artificial Intelligence, vol. 6, no. 5, pp. 1128-1142.
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Zhou, K, Jiang, M, Gabrys, B & Xu, Y 2025, 'Learning Causal Representations Based on a GAE Embedded Autoencoder', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 6, pp. 3472-3484.
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Zhou, S, Ma, H, Kuang, S & Dong, D 2025, 'Auxiliary Task-Based Deep Reinforcement Learning for Quantum Control', IEEE Transactions on Cybernetics, vol. 55, no. 2, pp. 712-725.
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Zhou, T, Yu, S, Tang, Y, Huang, X, Fu, JJ, Shen, PK & Tian, ZQ 2025, 'Built-in electric field engineering of MoO2-Ni heterostructure by P3p-Mo4d-Tm4f orbitals hybridization for efficient industrial-level hydrogen production', Chemical Engineering Journal, vol. 508, pp. 160921-160921.
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Zuo, X, Wang, M, Zhu, T, Zhang, L, Yu, S & Zhou, W 2025, 'Federated Learning With Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach', IEEE Transactions on Services Computing, vol. 18, no. 3, pp. 1428-1444.
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Alabdali, SA & Pileggi, SF 1970, 'A Research Framework to Identify Determinants for Smart Technology Adoption in Rural Regions', Springer Nature Singapore, Jeddah, Saudi Arabia, pp. 3-15.
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Abstract Despite rural regions cover a significant portion of the world's land, they remain overlooked entities within scholarly discourse as well as in a broader socio-economic context. However, their integration into sustainable development efforts is gaining relevance and should be considered as a strategic priority. This paper presents a research framework to identify determinants for Smart Technology adoption in rural regions. The main goal is to support organizations in rural areas to better understand and analyse the challenges with a scientific focus. It is expected to contribute to unlock new opportunities for innovative solutions, as well as to improve performance and enhance business sustainability in their unique context. The framework results from the combination and in-context interpretation of three different theories to define integrated strategic solutions: Diffusion of Innovation theory (DOI), Technology Organizations-Environment (TOE) framework, and Technology Acceptance Model (TAM). This approach ideally provides support to mixed methods to enable research in fact within real organizational contexts. Such a conceptual asset is expected to contribute in practice by facilitating (i) the formulation of theories as a response to open research issues, (ii) the development of appropriate integrated strategies, and (iii) the identification of major determinants to establish a consistent road map. The combination of chosen theories contributes to better investigating the causes of the research problem through a set of factors targeting the technology adoption in the rural context, providing a clear insight into solutions for decision-makers.

Alshatri, N, Ghannam, S & Hussain, FK 1970, 'Multi-factor Predictive Models of Carbon Credit Prices', Springer Nature Switzerland, pp. 200-212.
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Gill, A 1970, 'Adaptive Information Architecture: Information Element Identification and Organisation Patterns for Digital Ecosystem', The 20th International Conference on Information and Knowledge Engineering, Springer, Las Vegas.

Haddas, M & Hussain, FK 1970, 'Human Factors Influencing Australian Manufacturing SMEs’ Adoption of Collaborative Robots: A Qualitative Study', Springer Nature Switzerland, pp. 154-164.
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Jin, K, Zi, X, Thiyagarajan, K, Braytee, A & Prasad, M 1970, 'IP-VQA Dataset: Empowering Precision Agriculture with Autonomous Insect Pest Management through Visual Question Answering', Companion Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 2000-2007.
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Li, J, Wang, S, Zhang, Q, Yu, S & Chen, F 1970, 'Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 2787-2798.
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Li, Z, Chen, Y, Zhang, T & Wang, X 1970, 'Reembedding and Reweighting are Needed for Tail Item Sequential Recommendation', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, Sydney, Australia, pp. 4925-4936.
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Lin, Y, Guan, J, Fang, W, Ying, M & Su, Z 1970, 'VeriQR: A Robustness Verification Tool for Quantum Machine Learning Models', Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 403-421.
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Adversarial noise attacks present a significant threat to quantum machine learning (QML) models, similar to their classical counterparts. This is especially true in the current Noisy Intermediate-Scale Quantum era, where noise is unavoidable. Therefore, it is essential to ensure the robustness of QML models before their deployment. To address this challenge, we introduce VeriQR, the first tool designed specifically for formally verifying and improving the robustness of QML models, to the best of our knowledge. This tool mimics real-world quantum hardware’s noisy impacts by incorporating random noise to formally validate a QML model’s robustness. VeriQR supports exact (sound and complete) algorithms for both local and global robustness verification. For enhanced efficiency, it implements an under-approximate (complete) algorithm and a tensor network-based algorithm to verify local and global robustness, respectively. As a formal verification tool, VeriQR can detect adversarial examples and utilize them for further analysis and to enhance the local robustness through adversarial training, as demonstrated by experiments on real-world quantum machine learning models. Moreover, it permits users to incorporate customized noise. Based on this feature, we assess VeriQR using various real-world examples, and experimental outcomes confirm that the addition of specific quantum noise can enhance the global robustness of QML models. These processes are made accessible through a user-friendly graphical interface provided by VeriQR, catering to general users without requiring a deep understanding of the counter-intuitive probabilistic nature of quantum computing.

Lu, J, Dawson, A, Guo, K, Fox, D, Ramezani, F, Morton, S, Baird, K, Soo, Z, Lu, K, Long, G & Anderson, D 1970, 'AI for Women's Health: Framework and Focus Areas', Companion Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 845-848.
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Minton, AG, Zhu, HY, Chen, H-T, Wang, Y-K, Zhuang, Z, Notaro, G, Galvan, R, Allen, J, Ziegler, MD & Lin, C-T 1970, 'A Longitudinal Study on the Effects of Circadian Fatigue on Sound Source Identification and Localization using a Heads-Up Display', Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI 2025: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-12.
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Oraki, S, Zhuang, H & Liang, J 1970, 'LORTSAR: Low-Rank Transformer for Skeleton-Based Action Recognition', Springer Nature Switzerland, pp. 196-207.
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Patibanda, R, Saini, A, Li, X, Chen, Y, van den Hoven, E & Mueller, FF 1970, 'Playing with Yourself: How Electrical Muscle Stimulation Can Turn Your Body Into A Playful Interface', Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, CHI EA '25: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-3.
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Peng, B, Lu, J, Zhang, Y, Zhang, G & Fang, Z 1970, 'Distributional Prototype Learning for Out-of-distribution Detection', Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 1104-1114.
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Saini, A, Vs, S, Montoya, MF, Overdevest, N, Patibanda, R, Elvitigala, DS, van den Hoven, E & Mueller, FF 1970, 'Inflated Exertion: Designing a Bodily Extension that Embodies Physical Activity', Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction, TEI '25: Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction, ACM, pp. 1-7.
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Shen, S, Tan, CT, Chen, H-T, Raffe, WL & Leong, TW 1970, 'Educator Perceptions of XRAuthor: An Accessible Tool for Authoring Learning Content with Different Immersion Levels', Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI 2025: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-11.
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Singh, A, Liu, J, Voinov, A & Lin, C-T 1970, 'Identifying Neural Biomarkers of Confirmation Bias', 2025 13th International Conference on Brain-Computer Interface (BCI), 2025 13th International Conference on Brain-Computer Interface (BCI), IEEE, pp. 1-6.
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Sood, R, Anaissi, A, Huang, W & Braytee, A 1970, 'MemHateCaptioning: Enhancing Hate Speech Detection in Memes with Context-Aware Captioning and Chain-of-Thought', Companion Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 2034-2041.
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Tang, M, Wang, W & Yu, S 1970, 'OPMUS: A Win-Win Pricing Strategy for Machine Unlearning Service', Springer Nature Singapore, pp. 386-400.
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Vo, H, Tian, Z, Zhang, C, Zheng, XJ & Yu, S 1970, 'Inversion Triplet - A Contrastive Backdoor Mitigation Method for Self-Supervised Vision Encoders', Springer Nature Singapore, pp. 277-289.
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Wang, W, Tian, Z, Liu, A & Yu, S 1970, 'TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 3061-3072.
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Ye, S, Lu, J & Zhang, G 1970, 'Towards Safe Machine Unlearning: A Paradigm that Mitigates Performance Degradation', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 4635-4652.
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Yu, X, Hoggenmüller, M, Tran, TTM, Wang, Y, Zhang, Q & Tomitsch, M 1970, 'Peek into the `White-Box': A Field Study on Bystander Engagement with Urban Robot Uncertainty', Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI 2025: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-15.
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Zhang, T, Wu, F, Tian, Z, Zhang, C & Yu, S 1970, 'MOUSSE: A Multimodality-Oriented Unified Semantic Communication System by Contrastive Learning', 2025 IEEE Wireless Communications and Networking Conference (WCNC), 2025 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1-6.
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Zhengyang, F, Jayan Chirayath Kurian, Mukesh, P, Nimish, B & Priya, S 1970, 'A Systematic Review of IS Literature in Dementia Care: An Application of the NASSS Framework', ANNUAL CONFERENCE OF THE UK ACADEMY FOR INFORMATION SYSTEMS 2025, Newcastle, UK.

Zhuang, Z, Lu, C-Y, Chang, Y-CF, Wang, Y-K, Do, T & Lin, C-T 1970, 'AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systems', Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI 2025: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-19.
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Anaissi, A, Akram, J, Chaturvedi, K & Braytee, A 2025, 'Detecting and Understanding Hateful Contents in Memes Through Captioning and Visual Question-Answering'.

Gill, AQ 2025, 'Agile System Development Lifecycle for AI Systems: Decision Architecture'.

Hussein, A, Prasad, M, Anaissi, A & Braytee, A 2025, 'Vision Transformers with Autoencoders and Explainable AI for Cancer Patient Risk Stratification Using Whole Slide Imaging'.

Jiang, X, Ma, W, Duan, Y, Do, T & Lin, C-T 2025, 'Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification'.

Khan, F, Wu, Y, Dray, J, Hemsley, B & Kocaballi, AB 2025, 'Conversational Agents to Support People with Communication Disability: A Co-design Study with Speech Pathologists', ACM, pp. 1-9.
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Lewis, CC, Taba, M, Allen, TB, Caldwell, PHY, Skinner, SR, Kang, M, Henderson, H, Bray, L, Borthwick, M, Collin, P, McCaffery, K & Scott, KM 2025, 'Authors’ Reply: “Adolescent Cocreation in Digital Health: From Passive Subjects to Active Stakeholders” (Preprint)', JMIR Publications Inc..
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Miao, Y, Yang, X, Fan, H, Li, Y, Hong, Y, Guo, X, Braytee, A, Huang, W & Anaissi, A 2025, 'FedSAF: A Federated Learning Framework for Enhanced Gastric Cancer Detection and Privacy Preservation'.

Nareti, UK, Chattopadhyay, S, Mallick, P, Kumar, S, Daga, AV, Adak, C, Wase, A & Roy, A 2025, 'An Adaptive Data-Resilient Multi-Modal Framework for Hierarchical Multi-Label Book Genre Identification'.

Sezgin, E, Jackson, DI, Hussain, S-A, Baki Kocaballi, A, Richardson, C, Skeens, M, Fosler-Lussier, E, Ewing, A, Donneyong, M & Pai, A 2025, 'Evaluating User Experiences with an AI Chatbot for Health-Related Social Needs: A Cross-Sectional Mixed Methods Study', Cold Spring Harbor Laboratory.
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Thu, S & Kocaballi, AB 2025, 'From Prompting to Partnering: Personalization Features for Human-LLM Interactions'.

Ubaid, A, Lie, A & Lin, X 2025, 'SMART Restaurant Recommender: A ContextAware Restaurant Recommendation Engine', MDPI AG.
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Wu, Y, Ji, Y, Lee, S, Akram, J, Braytee, A & Anaissi, A 2025, 'Simplified Swarm Learning Framework for Robust and Scalable Diagnostic Services in Cancer Histopathology'.

Zhang, CJ, Gill, AQ, Liu, B & Anwar, MJ 2025, 'AI-based Identity Fraud Detection: A Systematic Review'.

Zhao, T, Li, M, Chen, K, Wang, L, Zhou, X, Chaturvedi, K, Prasad, M, Anaissi, A & Braytee, A 2025, 'Enhancing Sentiment Analysis through Multimodal Fusion: A BERT-DINOv2 Approach'.