Publications

Dong, X, Kedziora, DJ, Musial, K & Gabrys, B 2024, Automated Deep Learning: Neural Architecture Search Is Not the End, Now Publishers.
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Kedziora, DJ, Musial, K & Gabrys, B 2024, AutonoML: Towards an Integrated Framework for Autonomous Machine Learning, Now Publishers.
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Tran, HV, McGregor, C & Kennedy, PJ 2024, 'Detecting Stress from Multivariate Time Series Data Using Topological Data Analysis' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 341-353.
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Weng, H, Liu, Y & Chen, L 2024, 'Spatial Bottleneck Transformer for Cellular Traffic Prediction in the Urban City' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 265-276.
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Aboutorab, H, Hussain, OK, Saberi, M, Hussain, FK & Prior, D 2024, 'Adaptive identification of supply chain disruptions through reinforcement learning', Expert Systems with Applications, vol. 248, pp. 123477-123477.
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Adhiatma, A, Hakim, A, Fachrunnisa, O & Hussain, FK 2024, 'The role of social media business and organizational resources for successful digital transformation', Journal of Media Business Studies, vol. 21, no. 1, pp. 23-50.
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Alshuaibi, EA, Hamdi, AM & Hussain, FK 2024, 'Volunteer Computing for fog scalability: A systematic literature review', Internet of Things, vol. 25, pp. 101072-101072.
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Babakian, A, Huston, G, Braun, R & Lipman, J 2024, 'Internet Identifiers: A Survey of History, Challenges, and Future Perspectives', IEEE Access, vol. 12, pp. 51919-51941.
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Chen, Y, Li, G, An, P, Liu, Z, Huang, X & Wu, Q 2024, 'Light Field Salient Object Detection With Sparse Views via Complementary and Discriminative Interaction Network', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 2, pp. 1070-1085.
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Cui, Z, Sun, X, Chen, H, Pan, L, Cui, L, Liu, S & Xu, G 2024, 'Dynamic Recommendation Based on Graph Diffusion and Ebbinghaus Curve', IEEE Transactions on Computational Social Systems, vol. 11, no. 2, pp. 2755-2764.
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Dong, X, Kedziora, DJ, Musial, K & Gabrys, B 2024, 'Automated Deep Learning: Neural Architecture Search Is Not the End', Foundations and Trends® in Machine Learning, vol. 17, no. 5, pp. 767-920.
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Du, A, Zhou, T, Pang, S, Wu, Q & Zhang, J 2024, 'PCL: Point Contrast and Labeling for Weakly Supervised Point Cloud Semantic Segmentation', IEEE Transactions on Multimedia, pp. 1-12.
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Duong, TD, Li, Q & Xu, G 2024, 'Achieving counterfactual fairness with imperfect structural causal model', Expert Systems with Applications, vol. 240, pp. 122411-122411.
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He, H, Zhang, Q, Wang, S, Yi, K, Niu, Z & Cao, L 2024, 'Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective', IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-13.
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Huang, J, Gong, Y, Shi, Y, Zhang, X, Zhang, J & Yin, Y 2024, 'Focusing on Subtle Differences: A Feature Disentanglement Model for Series Photo Selection', IEEE Transactions on Multimedia, pp. 1-14.
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Huang, J, Gong, Y, Zhang, L, Zhang, J, Nie, L & Yin, Y 2024, 'Modeling Multiple Aesthetic Views for Series Photo Selection', IEEE Transactions on Multimedia, vol. 26, pp. 1983-1995.
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Huang, Y, Huang, Y, Zhang, Z, Wu, Q, Zhong, Y & Wang, L 2024, 'Enhancing Person Re-Identification Performance Through In Vivo Learning', IEEE Transactions on Image Processing, vol. 33, pp. 639-654.
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Huang, Y, Wu, Q, Zhang, Z, Shan, C, Huang, Y, Zhong, Y & Wang, L 2024, 'Meta Clothing Status Calibration for Long-Term Person Re-Identification', IEEE Transactions on Image Processing, vol. 33, pp. 2334-2346.
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Huang, Y, Zhang, Z, Huang, Y, Wu, Q, Huang, H, Zhong, Y & Wang, L 2024, 'Customized meta-dataset for automatic classifier accuracy evaluation', Pattern Recognition, vol. 146, pp. 110026-110026.
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Hussain, F, Khan, A & Mirdad, A 2024, 'Smart Contracts and Marketplace for Just-in-Time Management of Pharmaceutical Drugs', International Journal of Web and Grid Services, vol. 20, no. 1.
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Islam, MR, Akter, S, Islam, L, Razzak, I, Wang, X & Xu, G 2024, 'Strategies for evaluating visual analytics systems: A systematic review and new perspectives', Information Visualization, vol. 23, no. 1, pp. 84-101.
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In recent times, visual analytics systems (VAS) have been used to solve various complex issues in diverse application domains. Nonetheless, an inherent drawback arises from the insufficient evaluation of VAS, resulting in occasional inaccuracies when it comes to analytical reasoning, information synthesis, and deriving insights from vast, ever-changing, ambiguous, and frequently contradictory data. Hence, the significance of implementing an appropriate evaluation methodology cannot be overstated, as it plays a pivotal role in enhancing the design and development of visualization systems. This paper assesses visualization systems by providing a systematic exploration of various evaluation strategies (ES). While several existing studies have examined some ES, the extent of comprehensive and systematic review for visualization research remains limited. In this work, we introduce seven state-of-the-art and widely recognized ES namely (1) dashboard comparison; (2) insight-based evaluation; (3) log data analysis; (4) Likert scales; (5) qualitative and quantitative analysis; (6) Nielsen’s heuristics; and (7) eye trackers. Moreover, it delves into their historical context and explores numerous applications where these ES have been employed, shedding light on the associated evaluation practices. Through our comprehensive review, we overview and analyze the predominant evaluation goals within the visualization community, elucidating their evolution and the inherent contrasts. Additionally, we identify the open challenges that arise with the emergence of new ES, while also highlighting the key themes gleaned from the existing literature that hold potential for further exploration in future studies.

Kedziora, DJ, Musial, K & Gabrys, B 2024, 'AutonoML: Towards an Integrated Framework for Autonomous Machine Learning', Foundations and Trends® in Machine Learning, vol. 17, no. 4, pp. 590-766.
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Kedziora, DJ, Nguyen, T-D, Musial, K & Gabrys, B 2024, 'On taking advantage of opportunistic meta-knowledge to reduce configuration spaces for automated machine learning', Expert Systems with Applications, vol. 239, pp. 122359-122359.
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Khuat, TT, Bassett, R, Otte, E, Grevis-James, A & Gabrys, B 2024, 'Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities', Computers & Chemical Engineering, vol. 182, pp. 108585-108585.
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Liang, M, Huang, S & Liu, W 2024, 'Dynamic semantic structure distillation for low-resolution fine-grained recognition', Pattern Recognition, vol. 148, pp. 110216-110216.
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Mirdad, AR, Khan, AM & Hussain, FK 2024, 'Smart contracts and marketplace for just-in-time management of pharmaceutical drugs', International Journal of Web and Grid Services, vol. 20, no. 1, pp. 25-53.
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Musial, J, Stebel, K, Czeczot, J, Nowak, P & Gabrys, B 2024, 'Application of self-improving Q-learning controller for a class of dynamical processes: Implementation aspects', Applied Soft Computing, vol. 152, pp. 111250-111250.
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Nikkhah, N, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2024, 'Highly Sensitive Differential Microwave Sensor Using Enhanced Spiral Resonators for Precision Permittivity Measurement', IEEE Sensors Journal, pp. 1-1.
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Niu, C, Pang, G & Chen, L 2024, 'Affinity Uncertainty-Based Hard Negative Mining in Graph Contrastive Learning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-11.
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Piyathilaka, L, Kodagoda, S, Thiyagarajan, K, Piccardi, M, Preethichandra, DMG & Izhar, U 2024, 'Learning Spatial Affordances From 3D Point Clouds for Mapping Unseen Human Actions in Indoor Environments', IEEE Access, vol. 12, pp. 868-877.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2024, 'Multi-Agent Multi-Armed Bandit Learning for Grant-Free Access in Ultra-Dense IoT Networks', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Ren, Y, Zhang, H, Cao, X, Yang, C, Zhang, J & Li, H 2024, 'Promoting or Hindering: Stealthy Black-Box Attacks Against DRL-Based Traffic Signal Control', IEEE Internet of Things Journal, vol. 11, no. 4, pp. 5816-5825.
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Shu, Y, Li, Q, Liu, L & Xu, G 2024, 'Semi-Supervised Adversarial Learning for Attribute-Aware Photo Aesthetic Assessment', IEEE Transactions on Multimedia, vol. 26, no. 99, pp. 4086-4096.
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Aesthetic attributes are crucial for aesthetics because they explicitly present some photo quality cues that a human expert might use to evaluate a photo's aesthetic quality. However, annotating aesthetic attributes is a time-consuming, costly, and error-prone task, which leads to the issue that photos available are partially annotated with attributes. To alleviate this issue, we propose a novel semi-supervised adversarial learning method for photo aesthetic assessment from partially attribute-annotated photos, which can greatly reduce the reliance on manual attribute annotation. Specifically, the proposed method consists of a score-attributes generator R, a photo generator G, and a discriminator D. The score-attributes generator learns the aesthetic score and attributes simultaneously to capture their dependencies and construct better feature representations. The photo generator reconstructs the photo by feeding aesthetic attributes, score, and informative feature representation. A discriminator is used to force the convergence of the features-attributes-score tuples generated from the score-attributes generator, the photo generator, and the ground-truth distribution in labeled data for training data. The proposed method significantly outperforms the state of the art, increasing the Spearman rank-order correlation coefficient (SRCC) from the existing best reported of 0.726 to 0.761 on Aesthetic and attributes database and 0.756 to 0.774 on Aesthetic visual analysis database, respectively.

Skarding, J, Gabrys, B & Musial, K 2024, 'On the Effectiveness of Heterogeneous Ensembles Combining Graph Neural Networks and Heuristics for Dynamic Link Prediction', IEEE Transactions on Network Science and Engineering, pp. 1-19.
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Skarding, J, Hellmich, M, Gabrys, B & Musial, K 2024, 'Corrections to “A Robust Comparative Analysis of Graph Neural Networks on Dynamic Link Prediction”', IEEE Access, vol. 12, pp. 6912-6913.
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Van Alboom, M, Baert, F, Bernardes, SF, Verhofstadt, L, Bracke, P, Jia, M, Musial, K, Gabrys, B & Goubert, L 2024, 'Examining the role of structural and functional social network characteristics in the context of chronic pain: An ego-centered network design', The Journal of Pain, pp. 104525-104525.
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Wan, S, Jin, Y, Xu, G & Nappi, M 2024, 'Editorial to Special Issue on Multimedia Cognitive Computing for Intelligent Transportation System', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 20, no. 2, pp. 1-2.
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Wang, W & Cao, L 2024, 'Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 5183-5197.
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Wang, X, Li, Q, Yu, D, Li, Q & Xu, G 2024, 'Counterfactual Explanation for Fairness in Recommendation', ACM Transactions on Information Systems, vol. 42, no. 4, pp. 1-30.
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Fairness-aware recommendation alleviates discrimination issues to build trustworthy recommendation systems. Explaining the causes of unfair recommendations is critical, as it promotes fairness diagnostics, and thus secures users’ trust in recommendation models. Existing fairness explanation methods suffer high computation burdens due to the large-scale search space and the greedy nature of the explanation search process. Besides, they perform feature-level optimizations with continuous values, which are not applicable to discrete attributes such as gender and age. In this work, we adopt counterfactual explanations from causal inference and propose to generate attribute-level counterfactual explanations, adapting to discrete attributes in recommendation models. We use real-world attributes from Heterogeneous Information Networks (HINs) to empower counterfactual reasoning on discrete attributes. We propose a Counterfactual Explanation for Fairness (CFairER) that generates attribute-level counterfactual explanations from HINs for item exposure fairness. Our CFairER conducts off-policy reinforcement learning to seek high-quality counterfactual explanations, with attentive action pruning reducing the search space of candidate counterfactuals. The counterfactual explanations help to provide rational and proximate explanations for model fairness, while the attentive action pruning narrows the search space of attributes. Extensive experiments demonstrate our proposed model can generate faithful explanations while maintaining favorable recommendation performance.

Wang, Y, Zhao, Y, Wang, Z, Zhang, C & Wang, X 2024, 'Robust Multi-Graph Multi-Label Learning With Dual-Granularity Labeling', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-16.
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Wu, Y, Cao, J & Xu, G 2024, 'FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems', ACM Transactions on Information Systems, vol. 42, no. 1, pp. 1-26.
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When only users’ preferences and interests are considered by a recommendation algorithm, it will lead to the severe long-tail problem over items. Therefore, the unfair exposure phenomenon of recommended items caused by this problem has attracted widespread attention in recent years. For the first time, we reveal the fact that there is a more serious unfair exposure problem in session-based recommender systems (SRSs), which learn the short-term and dynamic preferences of users from anonymous sessions. Considering the fact that in SRSs, recommendations are provided multiple times and item exposures are accumulated over interactions in a session, we define new metrics both for the fairness of item exposure and recommendation quality among sessions. Moreover, we design a dynamic F airness- A ssurance ST rategy for s E ssion-based R ecommender systems ( FASTER ). FASTER is a post-processing strategy that tries to keep a balance between item exposure fairness and recommendation quality. It can also maintain the fairness of recommendation quality among sessions. The effectiveness of FASTER is verified on three real-world datasets and five original algorithms. The experiment results show that FASTER can generally reduce the unfair exposure of different session-based recommendation algorithms while still ensuring a high level of recommendation quality.

Xu, J & Cao, L 2024, 'Copula Variational LSTM for High-Dimensional Cross-Market Multivariate Dependence Modeling', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Yang, C, Wang, X, Yao, L, Long, G & Xu, G 2024, 'Dyformer: A dynamic transformer-based architecture for multivariate time series classification', Information Sciences, vol. 656, pp. 119881-119881.
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Zhang, C, Sheng, Z, Zhang, C & Wen, S 2024, 'Multi-lead-time short-term runoff forecasting based on Ensemble Attention Temporal Convolutional Network', Expert Systems with Applications, vol. 243, pp. 122935-122935.
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Zhang, Y, Liu, Y, Hu, R, Wu, Q & Zhang, J 2024, 'Mutual Dual-Task Generator With Adaptive Attention Fusion for Image Inpainting', IEEE Transactions on Multimedia, vol. 26, pp. 1539-1550.
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Zhao, Z, Cao, L & Lin, K-Y 2024, 'Out-of-Distribution Detection by Cross-Class Vicinity Distribution of In-Distribution Data', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Zhao, Z, Cao, L & Wang, C-D 2024, 'Gray Learning From Non-IID Data With Out-of-Distribution Samples', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Zheng, Y, Zhao, X, Yao, L & Cao, L 2024, 'Deep Multidilation Temporal and Spatial Dependence Modeling in Stereoscopic 3-D EEG for Visual Discomfort Assessment', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 4, pp. 2125-2136.
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Zhou, I, Tofigh, F, Piccardi, M, Abolhasan, M, Franklin, D & Lipman, J 2024, 'Secure Multi-Party Computation for Machine Learning: A Survey', IEEE Access, pp. 1-1.
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Zogan, H, Razzak, I, Jameel, S & Xu, G 2024, 'Hierarchical Convolutional Attention Network for Depression Detection on Social Media and Its Impact During Pandemic', IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 4, pp. 1815-1823.
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Parnell, J, Unanue, IJ & Piccardi, M 2024, 'SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual Summarization'.

Ping, P, Su, S, Cai, X, Lan, T, Zhang, X, Peng, H, Pan, Y, Liu, W & Li, J 2024, 'Turn ‘noise’ to signal: accurately rectify millions of erroneous short reads through graph learning on edit distances', Cold Spring Harbor Laboratory.
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Roth, T, Unanue, IJ, Abuadbba, A & Piccardi, M 2024, 'A Generative Adversarial Attack for Multilingual Text Classifiers'.