Publications

• Integrating Sensor Data with Large Language Models for Enhanced Elderly Care: A Methodological Framework. — Z Momand, P Mongkolnam, JH Chan, N Charoenkitkarn (Cited by 2)
• Building Digital Twins for Elderly Care: An End-to-End Framework from Data Acquisition to Modeling — Z Momand, P Mongkolnam, JH Chan, N Charoenkitkarn, D Pal
• Mari: Automating Responses to Thai Legal Debt Queries Using Generative AI and Retrieval-Augmented Generation — T Nethassanai, N Seehabong, S Roteaim, P Mongkolnam, B Watanapa
• A Feature Engineering Approach to Improve Clustering-Based Persona Generation — T Wongabut, U Ninrutsirikun, C Nukoolkit, P Lavangnananda, K Warasup, et al.
• Enhancing AI-Driven Education: Integrating Cognitive Frameworks, Linguistic Feedback Analysis, and Ethical Considerations for Improved Content Generation — A Yaacoub, S Tarnpradab, P Khumprom, Z Assaghir, L Prevost, et al. (Cited by 1)
• Machine Learning-Driven Preventive Maintenance for Fibreboard Production in Industry 4.0 — S Suwatcharachaitiwong, N Sirivongpaisal, T Surasak, N Jiteurtragool, et al.
• Introducing CASUX: A standardized scale for measuring the user experience of artificial intelligence based conversational agents — LID Faruk, D Pal, S Funilkul, T Perumal, P Mongkolnam (Cited by 13)
• Switching to metaverse? Perspectives from push–pull–mooring model — D Pal, X Zhang, S Mukherjee, S Funilkul (Cited by 8)
• Developing a scale for measuring the information security awareness of stakeholders in higher education institutions — R Rohan, W Chutimaskul, R Roy, J Hautamäki, S Funilkul, D Pal (Cited by 1)
• Metaverse-based education for sustainable development and improving the performance: Discussing the future research agenda — R Roy, GA Rao, D Pal, S Anuradha, S Mukherjee
• Automating Persona Generation: Leveraging ChatGPT-4 in Educational Digital Services — T Wongabut, U Ninrutsirikun, C Arpnikanondt (Cited by 1)
• Automated ICD-9 and ICD-10 Coding with Machine Learning: A Real-World Study Using Electronic Medical Record Text from Udon Thani Cancer Hospital, Thailand — K Chuabsombat, P Padungweang
• A federated learning approach for classifying chest diseases from chest X-ray images — SK Satapathy, SB Cho, S Mishra, S Sah, SN Mohanty (Cited by 5)
• AEFA-FDB: A score-based artificial electric field algorithm for optimal reactive power dispatch problem with renewable and load demand uncertainties — D Chauhan, A Yadav, SB Cho (Cited by 2)
• Traffic prediction by graph transformer embedded with subgraphs — HJ Moon, SB Cho (Cited by 2)
• DT-GA-FLANN: Decision Tree based Genetically Optimized FLANN for Missing Value Imputation and Classification — M Jena, S Dehuri, SB Cho (Cited by 1)
• Improving myocardial infarction diagnosis with Siamese network-based ECG analysis — V Gadag, S Singh, AH Khatri, S Mishra, SK Satapathy, SB Cho, et al. (Cited by 1)
• Unsupervised video anomaly detection by memory network with autoencoders in euclidean and non-euclidean spaces — J Kim, SB Cho
• CyberEDU: An Interactive Educational Tool for DDoS Attack Simulation and Prevention — P Srivastava, V Shah, P Singh, SK Satapathy, SB Cho, S Mishra, et al.
• Mitigating DDoS Attacks: Empowering Network Infrastructure Resilience with AI and ML — T Pasonri, S Singh, V Shirapure, SK Satapathy, SB Cho, S Mishra, et al.
• KRAFT³-QA: Korean financial text-table benchmark for evaluating tool-augmented agents on QA tasks — S Park, SB Cho, HY Kim
• A federated learning approach for classifying chest diseases from chest X-ray images (vol 100, 107107, 2025) — SK Satapathy, SB Cho, S Mishra, S Sah, Kamaldeep, SN Mohanty, et al.
• Corrigendum to “A federated learning approach for classifying chest diseases from chest X-ray images”[Biomed. Signal Process. Control 100 (Part A)(2025) 107107](Biomedical … — SK Satapathy, SB Cho, S Mishra, S Sah, SN Mohanty, J Dinesh
• AI-Based Prioritization of Indicators of Intelligence in a Threat Intelligence Sharing Platform — KS Vijayadharshni, S Tiwari, S Mishra, SK Satapathy, SB Cho, et al.
• Scalable Cold-Start Optimization in Serverless Computing: Leveraging Function Fusion with PanOpticon Simulator — RK Behera, A Kumari, SB Cho
• Cryptographic Key Application for Biometric Implementation in Automobiles — P Chatap, K Paul, A Gupta, SK Satapathy, SB Cho, S Mishra, et al.
• Decoding Market Dynamics: Variational Quantum Circuit in Stock Prediction — BC Sahoo, SK Satapathy, SB Cho, S Mishra
• Continual Learning by Contrastive Learning of Regularized Classes in Multivariate Gaussian Distributions — HJ Moon, SB Cho
• A Transformer network calibrated with fuzzy logic for phishing URL detection — SJ Buu, SB Cho
• SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation — C Lee, J Choi, H Wi, SB Cho, N Park
• VR Cybersickness Classification using Machine Learning Models on Open-Access EEG Datasets from the Human Vestibular Network — G Li, HR Byeon, SB Cho, F Pollick
• Optimizing classifier performance using PCA-FLANN: a fast and reliable approach — M Jena, RK Behera, S Dehuri, SB Cho
• An Efficient Method of Lifelong Learning with Differentiable Memory for Edge Computing — HJ Moon, SB Cho
• A FUSION MODEL FOR STOCK MARKET PREDICTION USING PROPHET AND LONG SHORT-TERM MEMORY NEURAL NETWORKS — S Mishra, N Manjunathan, P Singh, SK Satapathy, SB Cho, SN Mohanty
• Mosaicfusion: Diffusion models as data augmenters for large vocabulary instance segmentation — J Xie, W Li, X Li, Z Liu, YS Ong, CC Loy (Cited by 45)
• Stabilizing modality gap & lowering gradient norms improve zero-shot adversarial robustness of vlms — J Dong, P Koniusz, X Qu, YS Ong (Cited by 5)
• LIST: learning to index spatio-textual data for embedding based spatial keyword queries — Z Yin, S Feng, S Liu, G Cong, YS Ong, B Cui (Cited by 5)
• MHR: A Multi-Modal Hyperbolic Representation Framework for Fake News Detection — S Feng, G Yu, D Liu, H Hu, Y Luo, H Lin, YS Ong (Cited by 5)
• Safe Delta: Consistently Preserving Safety when Fine-Tuning LLMs on Diverse Datasets — N Lu, S Liu, J Wu, W Chen, Z Zhang, YS Ong, Q Wang, K Tang (Cited by 4)
• Active large language model-based knowledge distillation for session-based recommendation — Y Du, Z Sun, Z Wang, H Chua, J Zhang, YS Ong (Cited by 4)
• Measuring undergraduate students' reliance on Generative AI during problem-solving: Scale development and validation — C Hou, G Zhu, V Sudarshan, FS Lim, YS Ong (Cited by 3)
• Physics-informed neuro-evolution (PINE): a survey and prospects — JC Wong, A Gupta, CC Ooi, PH Chiu, J Liu, YS Ong (Cited by 3)
• Model-agnostic social network refinement with diffusion models for robust social recommendation — Y Sun, Z Sun, Y Du, J Zhang, YS Ong (Cited by 2)
• Looks great, functions better: Physics compliance text-to-3D shape generation — Q Xu, J Liu, M Wong, C Chen, YS Ong (Cited by 2)
• Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation — KY Tan, Y Lyu, I Tsang, YS Ong (Cited by 2)
• Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis — Y Hua, W Liu, G Xu, Y Hou, YS Ong, Q Zhang (Cited by 2)
• Discovering Spatiotemporal–Individual Coupled Features From Nonstandard Tensors—A Novel Dynamic Graph Mixer Approach — F Bi, T He, YS Ong, X Luo (Cited by 1)
• RouteMark: A Fingerprint for Intellectual Property Attribution in Routing-based Model Merging — X He, J Shen, Z Tang, X Chu, B Li, IW Tsang, YS Ong (Cited by 1)
• On the Use of Matching Algorithms to Transfer Solutions for the Travelling Salesperson Problem — L Wigney, A Neumann, YS Ong, F Neumann (Cited by 1)
• Evolvable Conditional Diffusion — Z Wei, CC Ooi, A Gupta, JC Wong, PH Chiu, SXW Toh, YS Ong (Cited by 1)
• Scalable Structure Learning of Bayesian Networks by Learning Algorithm Ensembles — S Liu, H Ou-yang, Z Wang, C Chen, Q Cai, YS Ong, K Tang (Cited by 1)
• Llm-driven instance-specific heuristic generation and selection — S Zhang, S Liu, N Lu, J Wu, J Liu, YS Ong, K Tang (Cited by 1)
• MermaidFlow: Redefining Agentic Workflow Generation via Safety-Constrained Evolutionary Programming — C Zheng, J Chen, Y Lyu, WZT Ng, H Zhang, YS Ong, I Tsang, H Yin (Cited by 1)
• Improving Zero-Shot Adversarial Robustness in Vision-Language Models by Closed-form Alignment of Adversarial Path Simplices — J Dong, P Koniusz, Y Zhang, H Zhu, W Liu, X Qu, YS Ong (Cited by 1)
• Cooperative Multiagent Learning and Exploration With Min–Max Intrinsic Motivation — Y Hou, J Kang, H Piao, Y Zeng, YS Ong, Y Jin, Q Zhang (Cited by 1)
• Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs — FX Fan, C Tan, R Wattenhofer, YS Ong (Cited by 1)
• Towards Trustworthy Vital Sign Forecasting: Leveraging Uncertainty for Prediction Intervals — LR Wang, TC Henderson, YS Ong, YY Ng, X Fan
• Routing Distilled Knowledge via Mixture of LoRA Experts for Large Language Model based Bundle Generation — K Feng, Z Sun, H Fang, J Yang, W Liu, YS Ong
• (θₗ, θᵤ)-Parametric Multi-Task Optimization: Joint Search in Solution and Infinite Task Spaces — T Wei, J Liu, A Gupta, PS Tan, YS Ong
• Lifelong Learner: Discovering Versatile Neural Solvers for Vehicle Routing Problems — S Feng, Z Lin, J Zhou, C Zhang, J Li, KW Chen, S Jayavelu, YS Ong
• Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model — F Wang, Q Xu, YS Ong, M Pollefeys
• A Plug-and-Play Multi-Criteria Guidance for Diverse In-Betweening Human Motion Generation — H Yu, J Liu, X Gui, M Wong, Y Hou, YS Ong
• Hot off the Press: Bayesian Inverse Transfer in Evolutionary Multiobjective Optimization — J Liu, A Gupta, YS Ong
• Context-Adaptive Graph Neural Networks for Next POI Recommendation — Y Lei, L Shen, Z Sun, T He, YS Ong
• LLM-to-Phy3D: Physically Conform Online 3D Object Generation with LLMs — M Wong, Y Lyu, T Rios, S Menzel, YS Ong
• Convergence of Expensive Multi-Objective Optimizers: From ParEGO to ExTrEMO — H Wu, T Wei, L Jiao, M Xu, YS Ong, Y Jin
• BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs — J Yang, J Tu, H Liu, X Wang, C Zheng, Z Zhang, S Cui, C Chen, T He, et al.
• A Mixed-Curvature based Pre-training Paradigm for Multi-Task Vehicle Routing Solver — S Liu, Z Cao, S Feng, YS Ong
• Diversifying Robot Locomotion Behaviors with Extrinsic Behavioral Curiosity — Z Wan, X Yu, DM Bossens, Y Lyu, Q Guo, FX Fan, YS Ong, I Tsang
• Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning — M Chen, X Wu, Q Liu, T He, YS Ong, Y Jin, Q Lao, H Yu
• Pareto Set Learning through Genetic Programming for Multi-Objective Dynamic Scheduling — M Xu, Y Mei, F Zhang, YS Ong, M Zhang
• Exploring Undergraduate Students' Computational Thinking Skills Across Engineering Design Processes — G Zhu, JF Kow, X Fan, IH Yeter, LS Chit, YS Ong
• Quality Diversity Genetic Programming for Learning Scheduling Heuristics — M Xu, F Neumann, A Neumann, YS Ong
• SiamNAS: Siamese Surrogate Model for Dominance Relation Prediction in Multi-objective Neural Architecture Search — Y Zhou, F Neri, YS Ong, R Bai
• Augmented Decision Spaces for Stackelberg Security Games: Sparse evolution begets scalability — A Żychowski, A Gupta, YS Ong, J Mańdziuk
• Cognitive Support in Aviation Operations Utilizing Multimodal Large Language Models — R Huang, S Feng, F Li, C Chen, YS Ong
• Mastering Continual Reinforcement Learning through Fine-Grained Sparse Network Allocation and Dormant Neuron Exploration — C Zheng, H Yin, J Chen, T Ng, YS Ong, I Tsang
• Sandbox: safeguarded multi-label learning through safe optimal transport — L Zhang, G Yu, J Yao, Y Ong, IW Tsang, JT Kwok
• Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization — H Wang, Z Sun, Y Du, L Zhang, T He, YS Ong
• Unified (Semi) Unbalanced and Classic Optimal Transport with Equivalent Transformation Mechanism and KKT-Multiplier Regularization — W Liu, X Liao, J Dan, F Wang, H Yu, J Dong, S Dong, L Qi, YS Ong
• Evolutionary Optimization of Physics-Informed Neural Networks: Survey and Prospects — JC Wong, A Gupta, CC Ooi, PH Chiu, J Liu, YS Ong
• Decentralized Next Point-of-Interest Recommendation Guided by Willingness to Share — Z Wu, Z Sun, D Wang, L Zhang, J Zhang, YS Ong
• AI-augmented Thinking: Exploring Human-AI Interaction in Generative AI-supported Programming Learning — T Zhong, G Zhu, KY Lim, YS Ong
• Context-aware masking and learnable diffusion-guided patch refinement in transformers via sparse supervision for hyperspectral image classification — A Chatterjee, S Ghosh, A Ghosh
• Neural Collapse Strikes Back: Lightweight Transfer of Large Vision Model for Cross-Scene Spectral Generalization — A Chatterjee, S Ghosh, A Ghosh
• Learning Hyperspectral Images with Curated Text Prompts for Efficient Multimodal Alignment — A Chatterjee, S Ghosh
• Band selection for hyperspectral image classification using DBSCAN-informed spectral attention network — A Chatterjee, S Ghosh, A Ghosh
• Geometric-Harmonic Mean based Late Fusion Ensemble for Improved Weather Image Classification — A Chatterjee, D Chattoraj, S Ghosh, A Ghosh
• Hyperspectral Image Classification using Patch-based Attention and Restricted Isometric Regularization — A Chatterjee, S Ghosh, A Ghosh
• Adaptive Attention-Guided Masking in Vision Transformers for Self-Supervised Hyperspectral Feature Learning — A Chatterjee, S Ghosh, A Ghosh
• Cross-instance Contrastive Masking in Vision Transformers for Self-supervised Hyperspectral Image Classification — A Chatterjee, S Ghosh, A Ghosh
• Optimizing Hyperspectral Image Classification: Biogeography-Based Band Selection and CNN Analysis — A Datta, G Niranjan, S Ghosh, A Ghosh
• A novel approach to navigate the taxonomic hierarchy to address the Open-World Scenarios in Medicinal Plant Classification — S Sinha, T Rana, S Ghosh, R Roy
• An active learning framework for adversarial training of deep neural networks — S Ghosh, A Chatterjee, L Fiondella
• Enhancing deep learning for demand forecasting to address large data gaps — C Riachy, M He, S Joneidy, S Qin, T Payne, G Boulton, A Occhipinti, et al. (Cited by 6)
• Molecular insights into α-synuclein fibrillation: A Raman spectroscopy and machine learning approach — NP Coles, S Elsheikh, A Quesnel, L Butler, C Jennings, C Tarzi, et al. (Cited by 6)
• Spatiotemporal diversity in molecular and functional abnormalities in the mdx dystrophic brain — J Pomeroy, M Borczyk, M Kawalec, J Hajto, E Carlson, S Svärd, S Verma, et al. (Cited by 4)
• Assessment of fluoride bio-accessibility in early childhood diets — J Kronic, RM Duckworth, C Angione, SM Levy, FV Zohoori (Cited by 3)
• A community-consensus reconstruction of Chinese Hamster metabolism enables structural systems biology analyses to decipher metabolic rewiring in lactate-free CHO cells — PD Giusto, DH Choi, A Antonakoudis, VG Duraikannan, P Craveur, et al. (Cited by 1)
• Alpha-synuclein aggregation induces prominent cellular lipid changes as revealed by Raman spectroscopy and machine learning analysis — NP Coles, S Elsheikh, A Quesnel, L Butler, O Achadu, M Islam, K Kalesh, et al. (Cited by 1)
• Mobilising UK Data and AI for All with a National Grid of Civic Learning Systems — A KAEHNE, I Buchan
• BRAIN COMMUNICATIONS — NP Coles, S Elsheikh, A Quesnel, L Butler, C Angione, J Marles-Wright, et al.
• HyperGenie: A method for predicting enzymatic gene essentiality using hypergraph neural networks and genome-scale metabolic models — P Ioannou, I Duta, S Verma, P Cicuta, P Lio, C Angione
• Serial lung ultrasound in predicting the need for surfactant and respiratory course in preterm infants—multicentre observational study (SLURP) — PK Loganathan, V Meau-Petit, B Bhojnagarwala, V Nair, J Holmes, et al. (Cited by 1)
• Use of real‐time respiratory function monitor improves neonatal face mask ventilation: Cross‐over simulation study — PK Loganathan, C Ashton, E Harrold, S Wigston, LMT Doan, A Occhipinti (Cited by 1)
• Computational Intelligence Methods for Bioinformatics and Biostatistics: 19th International Meeting, CIBB 2024, Benevento, Italy, September 4–6, 2024, Revised Selected Papers — L Cerulo, F Napolitano, F Bardozzo, L Cheng, A Occhipinti, SM Pagnotta
• Surrogate models for seismic and pushover response prediction of steel special moment resisting frames (vol 314, 118307, 2024) — D Samadian, IB Muhit, A Occhipinti, J Fayaz, N Dawood
• Corrigendum to''Surrogate models for seismic and pushover response prediction of steel special moment resisting frames''[Eng. Struct., 314 (2024) 118307] — D Samadian, IB Muhit, A Occhipinti, J Fayaz, N Dawood
• [Corrigendum to “Meta databases of steel frame buildings for surrogate modelling and machine learning-based feature importance analysis”Journal of Resilient Cities and … — D Samadian, J Fayaz, IB Muhit, A Occhipinti, N Dawood
• Fifty years of multi-objective optimization and decision-making: From mathematical programming to evolutionary computation — M Ehrgott, M Köksalan, M Kadziński, K Deb (Cited by 6)
• Effects of many conflicting objectives on decision-makers’ cognitive burden and decision consistency — JM Kivikangas, E Vilkkumaa, J Blank, V Harjunen, P Malo, K Deb, et al. (Cited by 5)
• Handling objectives with heterogeneous evaluation times in surrogate-assisted evolutionary multi-objective optimization — B Santoshkumar, K Deb (Cited by 4)
• Handling objective preference and variable uncertainty in evolutionary multi-objective optimization — D Yadav, P Ramu, K Deb (Cited by 4)
• Diagnostic benchmarking of many-objective evolutionary algorithms for real-world problems — J Zatarain Salazar, D Hadka, P Reed, H Seada, K Deb (Cited by 3)
• A directed batch growing self-organizing map based niching differential evolution for multimodal optimization problems — M Shankar, P Ramu, K Deb (Cited by 2)
• Reliability-Based MCDM Using Objective Preferences Under Variable Uncertainty — D Yadav, P Ramu, K Deb (Cited by 2)
• Towards an Efficient Innovation Path Seeking Algorithm Using Directed Domination — A Khan, K Deb (Cited by 2)
• Next-generation techniques for parameter reduction for BMP multiobjective optimization in watershed planning — HS Razavi, G Toscano, AP Nejadhashemi, K Deb, L Linker (Cited by 1)
• Numerical Global Optimization Competition on GNBG-II generated Test Suite — R Salgotra, AH Gandomi, K Deb (Cited by 1)
• Many-objective robust gait optimization for a 25-DOFs NAO robot using NSGA-III — P Gupta, DK Pratihar, K Deb (Cited by 1)
• Gradual Innovative Transitional Solutions Improving Current to a Desired Target: Innovation Path — A Khan, K Deb (Cited by 1)
• Identifier spaces for representing Pareto-optimal solutions in multi-objective optimization and decision-making — A Suresh, K Deb (Cited by 1)
• An adaptive weight optimization algorithm based on decision variable grouping for large-scale multi-objective optimization problems — H Wang, S Zhu, W Fang, K Deb
• Dynamic performance evaluation of evolutionary multi-objective optimization algorithms for gait cycle optimization of a 25-DOFs NAO humanoid robot — P Gupta, DK Pratihar, K Deb
• Regression and relation-assisted evolutionary algorithm for high-dimensional expensive multi-objective optimization — S Zhu, Y Zhang, W Fang, M Cui, K Deb
• Hierarchical Convergence to Multiple Alternate Solutions: Population versus Point-based Algorithms — A Khan, K Deb
• Machine Learning Assisted Evolutionary Multi-objective Optimization — K Deb, DK Saxena, S Mittal
• Machine Learning-Assisted Constraint Handling Under Variable Uncertainty for Preference-based Multi-Objective Optimization — D Yadav, P Ramu, K Deb
• Ranking-prediction based evolutionary algorithm for expensive many-objective optimization problems — Y Zhang, S Zhu, W Fang, K Deb, M Cui
• Addressing Heterogeneous Evaluation Times in Constrained Multi-Objective Optimization using a Mixed-Fidelity Evaluation Technique: Proof-of-Concept Results — B Santoshkumar, K Deb
• Beyond Performance: Designing a Super-Resolution Architecture Search Space and a Hybrid Multi-Objective Approach for Neural Architecture Optimization — JLL García, R Monroy, VAS Hernández, K Deb
• MaNSGA-II: Many-Objective NSGA-II — LM Pang, H Ishibuchi, K Deb, K Shang
• Progressive Surrogate Modeling for a Multi-objective Competitive Co-evolutionary (MoCCoEv) Wargame Strategy Optimization — R Guha, R Mckendrick, B Feest, K Deb
• Finding Multiple Alternate Solutions Using Evolutionary Multi-Objective Optimization — K Deb, R Guha
• EvoSort: A Genetic-Algorithm-Based Adaptive Parallel Sorting Framework for Large-Scale High Performance Computing — S Raj, K Deb
• A Comparative Study of Pareto Front of Optimal Solution Set for NAO Robot's — P Gupta, DK Pratihar, K Deb
• Hybrid machine learning model for code classification — C Wang, JM Wassick, V Rothhaar, K DEB, YD Dhebar, ED Goodman
• Multi-Objective Competitive Co-Evolutionary Optimization and Regularity-Based Decision-Making for two-Agent Wargame Strategy Optimization — R Guha, R Mckendrick, B Feest, K Deb
• A Mixed-Fidelity Evaluation Algorithm for Efficient Constrained Multi-and Many-Objective Optimization: First Results — B Santoshkumar, K Deb
• A Multi-objective Competitive Co-evolutionary Framework with Progressive Shrinking for Wargame Scenarios — R Guha, R McKendrick, B Feest, K Deb
• Comparative evaluation of deep learning techniques for multistage Alzheimer's prediction from magnetic resonance images — P Gupta, P Nahak, V Kumar, DK Pratihar, K Deb
• Hybmsearch: A Fast Multi-Level Search Algorithm Delivering Order-of-Magnitude Speedups on Multi-Billion Datasets — S Raj, K Deb
• What, how, where, and how well? a survey on test-time scaling in large language models — Q Zhang, F Lyu, Z Sun, L Wang, W Zhang, Z Guo, Y Wang, I King, X Liu, et al. (Cited by 42)
• A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? — Q Zhang, F Lyu, Z Sun, L Wang, W Zhang, W Hua, H Wu, Z Guo, Y Wang, et al. (Cited by 30)
• Deep graph anomaly detection: A survey and new perspectives — H Qiao, H Tong, B An, I King, C Aggarwal, G Pang (Cited by 26)
• Fast, sensitive detection of protein homologs using deep dense retrieval — L Hong, Z Hu, S Sun, X Tang, J Wang, Q Tan, L Zheng, S Wang, S Xu, et al. (Cited by 26)
• Cold-start recommendation towards the era of large language models (llms): A comprehensive survey and roadmap — W Zhang, Y Bei, L Yang, HP Zou, P Zhou, A Liu, Y Li, H Chen, J Wang, et al. (Cited by 25)
• G-refer: Graph retrieval-augmented large language model for explainable recommendation — Y Li, X Zhang, L Luo, H Chang, Y Ren, I King, J Li (Cited by 22)
• A survey of personalized large language models: Progress and future directions — J Liu, Z Qiu, Z Li, Q Dai, J Zhu, M Hu, M Yang, I King (Cited by 15)
• Position: Llms can be good tutors in foreign language education — J Ye, S Wang, D Zou, Y Yan, K Wang, HT Zheng, Z Xu, I King, PS Yu, et al. (Cited by 12)
• Voxeval: Benchmarking the knowledge understanding capabilities of end-to-end spoken language models — W Cui, X Jiao, Z Meng, I King (Cited by 10)
• Understanding and mitigating hyperbolic dimensional collapse in graph contrastive learning — Y Zhang, H Zhu, M Yang, J Liu, R Ying, I King, P Koniusz (Cited by 6)
• Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation? — L Pan, A Liu, S Huang, Y Lu, X Hu, L Wen, I King, PS Yu (Cited by 6)
• Context-aware inductive knowledge graph completion with latent type constraints and subgraph reasoning — M Li, C Yang, C Xu, Z Song, X Jiang, J Guo, H Leung, I King (Cited by 5)
• WebCoT: Enhancing Web Agent Reasoning by Reconstructing Chain-of-Thought in Reflection, Branching, and Rollback — M Hu, T Fang, J Zhang, J Ma, Z Zhang, J Zhou, H Zhang, H Mi, D Yu, et al. (Cited by 4)
• Parrot: Seamless spoken dialogue interaction with double-channel large language models — Q Wang, Z Meng, W Cui, Y Zhang, P Wu, B Wu, Z Zheng, I King, L Chen, et al. (Cited by 4)
• From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents — W Zhang, Y Li, Y Bei, J Luo, G Wan, L Yang, C Xie, Y Yang, WC Huang, et al. (Cited by 3)
• Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities — Y Bei, W Zhang, S Wang, W Chen, S Zhou, H Chen, Y Li, J Bu, S Pan, et al. (Cited by 3)
• 3d-moe: A mixture-of-experts multi-modal llm for 3d vision and pose diffusion via rectified flow — Y Ma, Y Zhuang, J Hao, I King (Cited by 3)
• From General to Targeted Rewards: Surpassing GPT-4 in Open-Ended Long-Context Generation — Z Guo, J Wu, W Cui, Y Zhang, M Hu, Y Wang, I King (Cited by 2)
• FedEDM: Federated Equivariant Diffusion Model for 3D Molecule Generation with Enhanced Communication Efficiency — Z Song, I King, JM Hernández-Lobato (Cited by 2)
• Position: Beyond Euclidean--Foundation Models Should Embrace Non-Euclidean Geometries — N He, J Liu, B Zhang, N Bui, A Maatouk, M Yang, I King, M Weber, R Ying (Cited by 2)
• Implicit Reasoning in Large Language Models: A Comprehensive Survey — J Li, Y Fu, L Fan, J Liu, Y Shu, C Qin, M Yang, I King, R Ying (Cited by 1)
• Efficient identity and position graph embedding via spectral-based random feature aggregation — M Qin, J Liu, I King (Cited by 1)
• Reinforcing Question Answering Agents with Minimalist Policy Gradient Optimization — Y Wu, L Ma, M Li, J Zhou, J Hao, H Leung, I King, Y Zhang, JY Nie (Cited by 1)
• cfDecon: Accurate and Interpretable methylation-based cell type deconvolution for cell-free DNA — Y Wang, J Li, J Li, S Yang, Y Huang, X Liu, Y Fan, I King, Y Li, Y Li (Cited by 1)
• CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey — J Li, Y Li, Y Fu, J Liu, Y Liu, M Yang, I King (Cited by 1)
• Federated Learning in the Age of Foundation Models-FL 2024 International Workshops: FL@ FM-WWW 2024, Singapore, May 14, 2024; FL@ FM-ICME 2024, Niagara Falls, ON, Canada, July … — H Yu, X Li, Z Xu, R Goebel, I King (Cited by 1)
• MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models — L Pan, S Guan, Z Fu, L Si, Z Wang, X Hu, I King, PS Yu, A Liu, L Wen
• A phenomenographic approach to students’ conceptions of learning artificial intelligence (AI) in secondary schools — T Dong, KW Yau, CS Chai, TKF Chiu, H Meng, I King, SWH Wong, Y Yam
• Deep Learning for Graph Anomaly Detection — H Qiao, H Tong, B An, I King, C Aggarwal, G Pang
• Think Before You Talk: Enhancing Meaningful Dialogue Generation in Full-Duplex Speech Language Models with Planning-Inspired Text Guidance — W Cui, L Zhu, X Li, Z Guo, H Bai, L Hou, I King
• Track and Tweak: Monitoring and Improving Group Fairness for Temporal Graph Neural Networks in Real Time — Z Song, M Li, Y Zhang, I King, JM Hernández-Lobato
• Soft Separation and Distillation: Toward Global Uniformity in Federated Unsupervised Learning — HC Fang, HT Lin, I King, Y Zhang
• Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey — J Li, Y Fu, J Liu, L Cao, W Ji, M Yang, I King, MH Yang
• Towards Geometry Problem Solving in the Large Model Era: A Survey — Y Zhao, X Wang, J Liu, I King, Z Huang
• Learning Binarized Representations with Pseudo-positive Sample Enhancement for Efficient Graph Collaborative Filtering — Y Chen, Y Que, X Zhang, C Ma, I King
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• FL@ FM-TheWebConf'25: International Workshop on Federated Foundation Models for the Web 2025 — I King, G Long, Z Xu, Y Zhang, H Yu

3D Point Cloud Generation via Autoregressive Up-sampling — Z Meng, Q Wang, Z Zhou, I King, P Zhao

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• A survey of trustworthy federated learning: Issues, solutions, and challenges — Y Zhang, D Zeng, J Luo, X Fu, G Chen, Z Xu, I King (Cited by 58)
• Markllm: An open-source toolkit for llm watermarking — L Pan, A Liu, Z He, Z Gao, X Zhao, Y Lu, B Zhou, S Liu, X Hu, L Wen, et al. (Cited by 51)
• An entropy-based text watermarking detection method — Y Lu, A Liu, D Yu, J Li, I King (Cited by 49)
• Recent advances in speech language models: A survey — W Cui, D Yu, X Jiao, Z Meng, G Zhang, Q Wang, Y Guo, I King (Cited by 43)
• MECCH: Metapath context convolution-based heterogeneous graph neural networks — X Fu, I King (Cited by 33)
• Mome: Mixture of multimodal experts for cancer survival prediction — C Xiong, H Chen, H Zheng, D Wei, Y Zheng, JJY Sung, I King (Cited by 32)
• Progress and opportunities of foundation models in bioinformatics — Q Li, Z Hu, Y Wang, L Li, Y Fan, I King, G Jia, S Wang, L Song, Y Li (Cited by 31)
• Deep generative design of RNA aptamers using structural predictions — F Wong, D He, A Krishnan, L Hong, AZ Wang, J Wang, Z Hu, S Omori, A Li, et al. (Cited by 29)
• Hypformer: Exploring efficient transformer fully in hyperbolic space — M Yang, H Verma, DC Zhang, J Liu, I King, R Ying (Cited by 26)
• Recent advances of multimodal continual learning: A comprehensive survey — D Yu, X Zhang, Y Chen, A Liu, Y Zhang, PS Yu, I King (Cited by 24)
• Influential exemplar replay for incremental learning in recommender systems — X Zhang, Y Chen, C Ma, Y Fang, I King (Cited by 22)
• Developing and validating measures for AI literacy tests: From self-reported to objective measures — TKF Chiu, Y Chen, KW Yau, C Chai, H Meng, I King, S Wong, Y Yam (Cited by 21)
• A diffusion-based pre-training framework for crystal property prediction — Z Song, Z Meng, I King (Cited by 20)
• Clongeval: A chinese benchmark for evaluating long-context large language models — Z Qiu, J Li, S Huang, X Jiao, W Zhong, I King (Cited by 20)
• Low-rank adaptation for foundation models: A comprehensive review — M Yang, J Chen, Y Zhang, J Liu, J Zhang, Q Ma, H Verma, Q Zhang, et al. (Cited by 19)
• Mitigating large language model hallucination with faithful finetuning — M Hu, B He, Y Wang, L Li, C Ma, I King (Cited by 19)
• Geometric view of soft decorrelation in self-supervised learning — Y Zhang, H Zhu, Z Song, Y Chen, X Fu, Z Meng, P Koniusz, I King (Cited by 18)
• Deep structural knowledge exploitation and synergy for estimating node importance value on heterogeneous information networks — Y Chen, Y Fang, Q Wang, X Cao, I King (Cited by 16)
• Shopping trajectory representation learning with pre-training for e-commerce customer understanding and recommendation — Y Chen, QT Truong, X Shen, J Li, I King (Cited by 14)
• Hyperbolic fine-tuning for large language models — M Yang, A Feng, B Xiong, J Liu, I King, R Ying (Cited by 13)
• Hihpq: Hierarchical hyperbolic product quantization for unsupervised image retrieval — Z Qiu, J Liu, Y Chen, I King (Cited by 13)
• The integration of semantic and structural knowledge in knowledge graph entity typing — M Li, M Hu, I King, H Leung (Cited by 11)
• SeRTS: Self-rewarding tree search for biomedical retrieval-augmented generation — M Hu, L Zong, H Wang, J Zhou, J Li, Y Gao, KF Wong, Y Li, I King (Cited by 10)
• Enhancing llm complex reasoning capability through hyperbolic geometry — M Yang, A Feng, B Xiong, J Liu, I King, R Ying (Cited by 10)
• Ease: Learning lightweight semantic feature adapters from large language models for ctr prediction — Z Qiu, J Zhu, Y Chen, G Cai, W Liu, Z Dong, I King (Cited by 9)
• Hypformer: Exploring efficient hyperbolic transformer fully in hyperbolic space — M Yang, H Verma, DC Zhang, J Liu, I King, R Ying (Cited by 9)
• Fedlfc: Towards efficient federated multilingual modeling with lora-based language family clustering — Z Guo, Y Zhang, Z Zhang, Z Xu, I King (Cited by 9)
• Actra: Optimized transformer architecture for vision-language-action models in robot learning — Y Ma, D Chi, S Wu, Y Liu, Y Zhuang, J Hao, I King (Cited by 7)
• Fedhlt: Efficient federated low-rank adaption with hierarchical language tree for multilingual modeling — Z Guo, Y Zhang, Z Zhang, Z Xu, I King (Cited by 7)
• Parrot: Autoregressive spoken dialogue language modeling with decoder-only transformers — Z Meng, Q Wang, W Cui, Y Zhang, B Wu, I King, L Chen, P Zhao (Cited by 7)
• Takt: Target-aware knowledge transfer for whole slide image classification — C Xiong, Y Lin, H Chen, H Zheng, D Wei, Y Zheng, JJY Sung, I King (Cited by 6)
• A systematic survey on federated semi-supervised learning — Z Song, X Yang, Y Zhang, X Fu, Z Xu, I King (Cited by 6)
• Fedcvd: The first real-world federated learning benchmark on cardiovascular disease data — Y Zhang, G Chen, Z Xu, J Wang, D Zeng, J Li, J Wang, Y Qi, I King (Cited by 5)
• Waterseeker: Pioneering efficient detection of watermarked segments in large documents — L Pan, A Liu, Y Lu, Z Gao, Y Di, S Huang, L Wen, I King, PS Yu (Cited by 5)
• Towards effective top-n hamming search via bipartite graph contrastive hashing — Y Chen, Y Fang, Y Zhang, C Ma, Y Hong, I King (Cited by 5)
• Federated learning for vehicle trajectory prediction: Methodology and benchmark study — H Wang, R Li, Z Xu, JL Li, I King, J Liu (Cited by 5)
• On the necessity of collaboration for online model selection with decentralized data — J Li, Z Wu, Z Xu, I King (Cited by 4)
• Less is more: Extreme gradient boost rank-1 adaption for efficient finetuning of llms — Y Zhang, H Zhu, A Liu, H Yu, P Koniusz, I King (Cited by 4)
• A pilot study on the development and validation of AI literacy test items for grade 7 to grade 9 students — Y Chen, KW Yau, CS Chai, TKF Chiu, H Meng, I King, SWH Wong, Y Yam (Cited by 4)
• Entropy-based decoding for retrieval-augmented large language models — Z Qiu, Z Ou, B Wu, J Li, A Liu, I King (Cited by 4)
• Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion — M Li, C Yang, C Xu, X Jiang, Y Qi, J Guo, H Leung, I King (Cited by 3)
• scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics — C Zheng, Y Wang, Y Cheng, X Wang, H Wei, I King, Y Li (Cited by 3)
• Towards geometric normalization techniques in se (3) equivariant graph neural networks for physical dynamics simulations — Z Meng, L Zeng, Z Song, T Xu, P Zhao, I King (Cited by 3)
• Client-specific hyperbolic federated learning — J Liu, X Fu, M Yang, W Zhang, R Ying, I King (Cited by 2)
• Nile: Internal consistency alignment in large language models — M Hu, Q Zhang, Y Wang, B He, H Wang, J Zhou, L Li, Y Wang, C Ma, et al. (Cited by 1)
• Data Augmentation Techniques for Chinese Disease Name Normalization — W Cui, X Fu, S Liu, M Gu, X Liu, J Wu, I King (Cited by 1)
• Waterseeker: Efficient detection of watermarked segments in large documents — L Pan, A Liu, Y Lu, Z Gao, Y Di, L Wen, I King, PS Yu (Cited by 1)
• Hc-glad: Dual hyperbolic contrastive learning for unsupervised graph-level anomaly detection — Y Fu, J Li, J Liu, Q Xing, Q Wang, I King (Cited by 1)
• Efficient Spatio-temporal Event Representation Based on Kalman Filtering and Linear Weighted Timestamps — J Zhong, W Zeng, Y Chen, J Xiao, I King (Cited by 1)
• FL@ FM-TheWebConf'24: International Workshop on Federated Foundation Models for the Web — I King, G Long, Z Xu, H Yu (Cited by 1)
• Uhcone: Universal hyperbolic cone for implicit hierarchical learning — M Yang, J Liu, I King, R Ying (Cited by 1)
• Video Piracy Websites Detection Using Continual Learning with Elastic Weight Consolidation — D Yu, W Zhang, Z Xu, I King
• Combining Explicit Priors and Set Attention Driven Implicit Priors for Demonstration-Guided Reinforcement Learning — S Tang, I King, Z Xu
• DuCoMo: Dual Counters in Model-Based Offline Reinforcement Learning — S Tang, I King, Z Xu
• Guest Editorial: Special Issue on Graph Learning — F Xia, R Lambiotte, N Shah, H Tong, I King
• Anomaly Detection on Attributed Network Based on Hyperbolic Radial Distance — T Liu, R Yan, I King
• Group IF Units with Membrane Potential Sharing for High-Accuracy Low-Latency Spiking Neural Networks — Z Ye, W Zeng, Y Chen, L Zhang, J Xiao, I King
• Enhancing Biomedical Knowledge Retrieval-Augmented Generation with Self-Rewarding Tree Search and Proximal Policy Optimization — M Hu, L Zong, H Wang, J Zhou, J Li, Y Gao, KF Wong, Y Li, I King

Black box medicine — I King, H Meng, TYT Lam

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• Enhancing cybersecurity situation awareness through visualization: A USB data exfiltration case study — MHM Chung, YA Yang, L Wang, G Cento, K Jerath, P Taank, A Raman, et al. (Cited by 8)
• Enhancing security in WhatsApp: a system for detecting malicious and inappropriate content — M Kazimierczak, T Thanapattheerakul, JH Chan (Cited by 4)
• Fall risk assessment dataset: older-adult participants undergoing the time up and go test — W Jutharee, C Paengkumhag, W Limpornchitwilai, WT Mo, JH Chan, et al. (Cited by 4)
• Freezing of gait prediction using deep learning — WT Mo, JH Chan (Cited by 3)
• A data mining model for predicting diarrhea in Afghan children — A Zahirzda, G Chanmas, JH Chan (Cited by 3)
• Predicting three types of freezing of gait events using deep learning models — WT Mo, JH Chan (Cited by 3)
• Immersive Technologies in Virtual Companions: A Systematic Literature Review — Z Momand, JH Chan, P Mongkolnam (Cited by 2)
• Automated Extraction and Visualization of Metabolic Networks from Biomedical Literature Using a Large Language Model — T Phongwattana, JH Chan (Cited by 2)
• A Machine Learning Approach to Detect Dehydration in Afghan Children — Z Momand, D Pal, P Mongkolnam, JH Chan (Cited by 2)
• Learning from Others: Daily COVID-19 Cases Prediction in India using Ensembles of LSTM-RNNs — D Chakraborty, D Goswami, S Ghosh, JH Chan, A Ghosh
• Gastro-Intestinal Tract Segmentation Using an Explainable 3D Unet — K Li, J Chan
• Personality perceptions of conversational agents: A task-based analysis using thai as the conversational language — N Soonpipatskul, D Pal, B Watanapa, N Charoenkitkarn (Cited by 6)
• Digital space economic transformation design: An innovation ecosystem approach — W Suksa-Ngiam, V Vanijja, B Watanapa, U Nithirochananont (Cited by 1)
• Comparing and Normalizing the Measurement of Step Counts and Heart Rates of Selected Wristbands and Smartwatches — S Yamsaengsung, T Khajontantichaikun, B Watanapa, S Jaiyen, V Vanijja, et al.
• Driving scenarios and environmental settings in simulator-based driving assessment systems for stroke: a systematic review — G Chanmas, P Taveekitworachai, P Paliyawan, R Thawonmas, et al. (Cited by 9)
• Recognizing fall risk factors with convolutional neural network — S Sukreep, P Dajpratham, C Nukoolkit, S Yamsaengsung, et al. (Cited by 1)
• A hybrid evolutionary cnn-lstm model for prognostics of c-mapss aircraft dataset — P Khumprom, A Davila-Frias, D Grewell, D Buakum (Cited by 11)
• Reliability estimation using long short-term memory networks — A Davila-Frias, P Khumprom, OP Yadav (Cited by 2)
• The Stock-flow Classification Model Using Ensemble Machine Learning Approach for Particle Board Inventory — P Khumprom, A Davila-Frias, N Sirivongpaisal, D Buakum, J Jindakul, et al.
• Development of a framework for metaverse in education: A systematic literature review approach — R Roy, MD Babakerkhell, S Mukherjee, D Pal, S Funilkul (Cited by 66)
• A systematic literature review of cybersecurity scales assessing information security awareness — R Rohan, D Pal, J Hautamäki, S Funilkul, W Chutimaskul, H Thapliyal (Cited by 61)
• University students’ acceptance and usage of generative AI (ChatGPT) from a psycho-technical perspective — LID Faruk, R Rohan, U Ninrutsirikun, D Pal (Cited by 53)
• What affects the usage of artificial conversational agents? An agent personality and love theory perspective — D Pal, V Vanijja, H Thapliyal, X Zhang (Cited by 33)
• Intelligent attributes of voice assistants and user’s love for AI: A SEM-based study — D Pal, MD Babakerkhell, B Papasratorn, S Funilkul (Cited by 19)
• Enhancing cybersecurity resilience: A comprehensive analysis of human factors and security practices aligned with the NIST cybersecurity framework — R Rohan, B Papasratorn, W Chutimaskul, J Hautamäki, S Funilkul, D Pal (Cited by 17)
• Unlocking the black box: Exploring the use of generative AI (ChatGPT) in information systems research — R Rohan, LID Faruk, K Puapholthep, D Pal (Cited by 12)
• Information security awareness in higher education institutes: A work in progress — R Rohan, S Funilkul, W Chutimaskul, P Kanthmanon, B Papasratorn, et al. (Cited by 12)
• What affects the adoption of metaverse in education? A SEM-based approach — R Rohan, P Roy, V Vanijja, S Funilkul, S Mukherjee, D Pal (Cited by 8)
• Hey Alexa… Examining factors influencing the educational use of ai-enabled voice assistants during the covid-19 pandemic — R Rohan, D Pal, S Funilkul (Cited by 6)
• Exploring user experience with voice assistants: Impact of prior experience on voice assistants — LID Faruk, S Funilkul, P Mongkolnam, P Puengwattanapong, D Pal (Cited by 5)
• A Data-Driven Approach to Detect Dehydration in Afghan Children Using Deep Learning — Z Momand, D Pal, P Mongkolnam (Cited by 1)
• Power and Delay Efficient Hardware Implementation with ATPG for Vedic Multiplier Using Urdhva Tiryagbhyam Sutra — A Arun, A Chakraborty, P Dutta, D Pal, T Nag, D De, S Ghosh, et al.
• Hardware Performance Analysis of N-bit CLA on FPGA and Programmable SoC — P Saha, S Ghosh, D Pal, H Rahaman
• Student Personality, Motivation and Sustainability of Technology Enhanced Learning: A SEM-Based Approach — R Rohan, S Mukherjee, S Patra, S Funilkul, D Pal
• Facial emotion detection for thai elderly people using yolov7 — T Khajontantichaikun, S Jaiyen, S Yamsaengsung, P Mongkolnam, et al. (Cited by 7)
• Partitioning graph clustering with user-specified density — R Tariq, K Lavangnananda, P Bouvry, P Mongkolnam (Cited by 3)
• An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density — R Tariq, K Lavangnananda, P Bouvry, P Mongkolnam (Cited by 2)
• Enhancing Diabetic Retinopathy Classification for Small Sample Training Data Through Supervised Feature Attention: Transfer and Multitask Learning Approach — R Sanguantrakool, P Padungweang (Cited by 2)
• Computational approaches to explainable artificial intelligence: advances in theory, applications and trends — JM Górriz, I Álvarez-Illán, A Álvarez-Marquina, JE Arco, M Atzmueller, et al. (Cited by 166)
• Gread: Graph neural reaction-diffusion networks — J Choi, S Hong, N Park, SB Cho (Cited by 58)
• Blurring-sharpening process models for collaborative filtering — J Choi, S Hong, N Park, SB Cho (Cited by 57)
• A residual graph convolutional network with spatio-temporal features for autism classification from fMRI brain images — KW Park, SB Cho (Cited by 39)
• Triplet-trained graph transformer with control flow graph for few-shot malware classification — SJ Bu, SB Cho (Cited by 11)
• An elitist artificial electric field algorithm based random vector functional link network for cryptocurrency prices forecasting — SC Nayak, S Das, S Dehuri, SB Cho (Cited by 11)
• A graph convolution network with subgraph embedding for mutagenic prediction in aromatic hydrocarbons — HJ Moon, SJ Bu, SB Cho (Cited by 11)
• Predicting residential energy consumption by explainable deep learning with long-term and short-term latent variables — JY Kim, SB Cho (Cited by 11)
• Malware classification with disentangled representation learning of evolutionary triplet network — SJ Bu, SB Cho (Cited by 10)
• A subgraph embedded GIN with attention for graph classification — HJ Moon, SB Cho (Cited by 6)
• RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation — J Choi, H Wi, C Lee, SB Cho, D Lee, N Park (Cited by 2)
• Phishing URL Detection with Prototypical Neural Network Disentangled by Triplet Sampling — SJ Bu, SB Cho (Cited by 2)
• Exploiting Local Information with Subgraph Embedding for Graph Neural Networks — HJ Moon, SB Cho (Cited by 1)
• Adversarial discriminator to mitigate gender bias in abusive language detection — J Park, SB Cho (Cited by 1)
• Neuro-Symbolic Integration for Detecting Phishing Attacks — S Cho
• A Causally Explainable Deep Learning Model with Modular Bayesian Network for Predicting Electric Energy Demand — SJ Bu, SB Cho
• Phishing URL Detection with Prototypical Neural Network Disentangled by Triplet — SJ Bu, SB Cho
• Preface for ACPR 2023 Proceedings — CL Liu, Y Yagi, T Kamiya, M Blumenstein, H Lu, W Yang, SB Cho
• Large language models can be guided to evade ai-generated text detection — N Lu, S Liu, R He, Q Wang, YS Ong, K Tang (Cited by 65)
• Survey on AI sustainability: emerging trends on learning algorithms and research challenges — Z Chen, M Wu, A Chan, X Li, YS Ong (Cited by 62)
• Prompt evolution for generative ai: A classifier-guided approach — M Wong, YS Ong, A Gupta, KK Bali, C Chen (Cited by 45)
• Bayesian federated learning: A survey — L Cao, H Chen, X Fan, J Gama, YS Ong, V Kumar (Cited by 43)
• A generic reinforced explainable framework with knowledge graph for session-based recommendation — H Wu, H Fang, Z Sun, C Geng, X Kong, YS Ong (Cited by 24)
• Understanding diversity in session-based recommendation — Q Yin, H Fang, Z Sun, YS Ong (Cited by 19)
• Lsa-pinn: Linear boundary connectivity loss for solving pdes on complex geometry — JC Wong, PH Chiu, C Ooi, MH Dao, YS Ong (Cited by 19)
• A multiagent cooperative learning system with evolution of social roles — Y Hou, M Sun, Y Zeng, YS Ong, Y Jin, H Ge, Q Zhang (Cited by 17)
• A multi-channel next POI recommendation framework with multi-granularity check-in signals — Z Sun, Y Lei, L Zhang, C Li, YS Ong, J Zhang (Cited by 16)
• Jack and masters of all trades: one-pass learning sets of model sets from large pre-trained models — HX Choong, YS Ong, A Gupta, C Chen, R Lim (Cited by 14)
• Learning multitask Gaussian process over heterogeneous input domains — H Liu, K Wu, YS Ong, C Bian, X Jiang, X Wang (Cited by 13)
• Pareto optimization with small data by learning across common objective spaces — CS Tan, A Gupta, YS Ong, M Pratama, PS Tan, SK Lam (Cited by 13)
• Evolutionary multi-task optimization — L Feng, A Gupta, KC Tan, YS Ong (Cited by 13)
• Neuroevolution of physics-informed neural nets: Benchmark problems and comparative results — N Sung, JC Wong, CC Ooi, A Gupta, PH Chiu, YS Ong (Cited by 11)
• Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary cities — J Wang, L Zhang, Z Sun, YS Ong (Cited by 10)
• Granger causality using Jacobian in neural networks — LY Chew, YS Ong (Cited by 10)
• Towards building voice-based conversational recommender systems: Datasets, potential solutions and prospects — X Qu, H Liu, Z Sun, X Yin, YS Ong, L Lu, Z Ma (Cited by 9)
• How to select physics-informed neural networks in the absence of ground truth: a pareto front-based strategy — Z Wei, JC Wong, NWY Sung, A Gupta, CC Ooi, PH Chiu, MH Dao, YS Ong (Cited by 8)
• Disentangling motives behind item consumption and social connection for mutually-enhanced joint prediction — Y Sun, Z Sun, X Sha, J Zhang, YS Ong (Cited by 7)
• A collaborative multimodal learning-based framework for COVID-19 diagnosis — Y Gao, M Gong, YS Ong, AK Qin, Y Wu, F Xie (Cited by 7)
• Neural influence estimator: Towards real-time solutions to influence blocking maximization — W Chen, S Liu, YS Ong, K Tang (Cited by 6)
• Compact network embedding for fast node classification — X Shen, YS Ong, Z Mao, S Pan, W Liu, Y Zheng (Cited by 6)
• Heterogeneous multi-party learning with data-driven network sampling — M Gong, Y Gao, Y Wu, Y Zhang, AK Qin, YS Ong (Cited by 5)
• The baldwin effect in advancing generalizability of physics-informed neural networks — JC Wong, CC Ooi, A Gupta, PH Chiu, JSZ Low, MH Dao, YS Ong (Cited by 4)
• Dynamic in-context learning from nearest neighbors for bundle generation — Z Sun, K Feng, J Yang, X Qu, H Fang, YS Ong, W Liu (Cited by 2)
• Unfolded self-reconstruction LSH: Towards machine unlearning in approximate nearest neighbour search — KY Tan, Y Lyu, YS Ong, IW Tsang (Cited by 2)
• Inverse transfer multiobjective optimization — J Liu, A Gupta, YS Ong (Cited by 2)
• Pedagogical Design Matters: Integrating Engineering Design and Knowledge Building for Undergraduates to Practice and Improve Computational Thinking Skills — G Zhu, JF Kow, X Fan, LS Chit, YS Ong (Cited by 1)
• Generalizable Neural Physics Solvers by Baldwinian Evolution — JC Wong, CC Ooi, A Gupta, PH Chiu, JSZ Low, MH Dao, YS Ong (Cited by 1)
• Data-Driven Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications — X Li, S Liu, J Wang, X Chen, YS Ong, K Tang (Cited by 1)
• Exploiting local inter-task relationships in adaptive multi-task learning — B Da, C Wang, YS Ong, A Gupta
• Policy Dispersion in Non-Markovian Environment — B Qu, X Cao, J Yang, H Chen, C Yi, IW Tsang, YS Ong
• CIS Publication Spotlight [Publication Spotlight] — Y Song, D Wu, CAC Coello, GN Yannakakis, H Tang, Y Cheung, et al.
• Classifying histopathological images of oral squamous cell carcinoma using deep transfer learning — S Panigrahi, BS Nanda, R Bhuyan, K Kumar, S Ghosh, T Swarnkar (Cited by 53)
• Transfer-Recursive-Ensemble Learning for Multi-day COVID-19 Prediction in India Using Recurrent Neural Networks — D Chakraborty, D Goswami, S Ghosh, A Ghosh, C Jonathan, W Lipo (Cited by 15)
• Automated COVID-19 CT image classification using multi-head channel attention in deep CNN — S Ghosh, A Chatterjee (Cited by 8)
• Introducing feature attention module on convolutional neural network for diabetic retinopathy detection — S Ghosh, A Chatterjee (Cited by 6)
• SkipFlowNet: Integrating Skip Connection and Spatial Dropout in Deep Convolutional Neural Network for Improved Pneumonia Detection — A Chatterjee, S Ghosh (Cited by 1)
• Standardized Analysis Ready (STAR) data cube for high-resolution Flood mapping using Sentinel-1 data — S Ghosh, A Dawn, S Kour, S Ghosh (Cited by 1)
• A Relative Density Based Biclustering Method for Identifying Non-Linear Feature Relations — N Jain, S Ghosh, CA Murthy, A Ghosh (Cited by 1)
• Ensemble-Based Classification of COVID-19 Radiographic Images using Spatial, Textural, and Intensity-Based Features — S Ghosh, A Chatterjee
• A Combination of Mutual and Neighborhood Information for Band Selection in Hyperspectral Images — A Dey, S Ghosh, EJ Ientilucci
• MICLUST: A Clustering Algorithm for High Dimensional Data — N Jain, S Ghosh, EJ Ientilucci
• Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities — G Zampieri, S Campanaro, C Angione, L Treu (Cited by 64)
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• Integration of epigenetic regulatory mechanisms in heart failure — M Sopic, EL Robinson, C Emanueli, P Srivastava, C Angione, C Gaetano, et al. (Cited by 21)
• Multi-dimensional experimental and computational exploration of metabolism pinpoints complex probiotic interactions — G Zampieri, G Efthimiou, C Angione (Cited by 21)
• Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer — LMT Doan, C Angione, A Occhipinti (Cited by 14)
• Recognition of behavioural intention in repeated games using machine learning — A Di Stefano, C Jayne, C Angione, TA Han (Cited by 4)
• Artificial intelligence for data discovery and reuse in endocrinology and metabolism — C Angione, H Wang, N Burtt (Cited by 1)
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• An interview with kalyanmoy deb 2022 ACM fellow — K Deb (Cited by 2)
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• IK-EMOViz: An Interactive Knowledge-Based Evolutionary Multi-objective Optimization Framework — A Ghosh, K Deb, R Averill, E Goodman (Cited by 1)
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• GNBG-Generated Test Suite for Box-Constrained Numerical Global Optimization — AH Gandomi, D Yazdani, MN Omidvar, K Deb
• This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. — K Deb, P Fleming, Y Jin, K Miettinen, PM Reed, K Deb, P Fleming, Y Jin, et al.
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• Innovations in Intelligent Computing and Communication: First International Conference, ICIICC 2022, Bhubaneswar, Odisha, India, December 16-17, 2022, Proceedings — M Panda, S Dehuri, MR Patra, PK Behera, GA Tsihrintzis, SB Cho, et al. (Cited by 7)
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• Large-scale multi-objective optimization for water quality in Chesapeake Bay watershed — G Toscano, JS Hernandez–Suarez, J Blank, P Nejadhashemi, K Deb, et al. (Cited by 7)
• Parameter tuning and control: A case study on differential evolution with polynomial mutation — J Blank, K Deb (Cited by 7)
• A survey on evaluation metrics for synthetic material micro-structure images from generative models — D Shah, A Suresh, A Admasu, D Upadhyay, K Deb (Cited by 6)
• Approximations for Pareto and proper Pareto solutions and their KKT conditions — P Kesarwani, PK Shukla, J Dutta, K Deb (Cited by 6)
• Visualization-aided multi-criterion decision-making using reference direction based Pareto race — D Yadav, P Ramu, K Deb (Cited by 5)
• A speciation-based bilevel niching method for multimodal truss design problems — MJ Islam, X Li, K Deb (Cited by 3)
• Decision tree analysis to evaluate risks associated with lameness on dairy farms with automated milking systems — L Davis, K Deb, J Siegford, ABA Ali (Cited by 2)
• Image-based benchmarking and visualization for large-scale global optimization — KR Harrison, AA Bidgoli, S Rahnamayan, K Deb (Cited by 2)
• A computationally fast but approximate MIP-DoM calculation for multi-objective optimization — CLV Lopes, FVC Martins, EF Wanner, K Deb
• On the Use of Grossone Methodology for Handling Priorities in Multi-objective Evolutionary Optimization — L Lai, L Fiaschi, M Cococcioni, K Deb
• Evolutionary Optimization — L Lai, L Fiaschi, M Cococcioni, K Deb
• A survey on deep semi-supervised learning — X Yang, Z Song, I King, Z Xu (Cited by 1025)
• Graph-based semi-supervised learning: A comprehensive review — Z Song, X Yang, Z Xu, I King (Cited by 349)
• Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions — J Chen, Z Hu, S Sun, Q Tan, Y Wang, Q Yu, L Zong, L Hong, J Xiao, et al. (Cited by 209)
• COSTA: covariance-preserving feature augmentation for graph contrastive learning — Y Zhang, H Zhu, Z Song, P Koniusz, I King (Cited by 153)
• HRCF: Enhancing collaborative filtering via hyperbolic geometric regularization — M Yang, M Zhou, J Liu, D Lian, I King (Cited by 112)
• Modeling scale-free graphs with hyperbolic geometry for knowledge-aware recommendation — Y Chen, M Yang, Y Zhang, M Zhao, Z Meng, J Hao, I King (Cited by 112)
• Attentive knowledge-aware graph convolutional networks with collaborative guidance for personalized recommendation — Y Chen, Y Yang, Y Wang, J Bai, X Song, I King (Cited by 101)
• Hyperbolic graph neural networks: A review of methods and applications — M Yang, M Zhou, Z Li, J Liu, L Pan, H Xiong, I King (Cited by 98)
• Hicf: Hyperbolic informative collaborative filtering — M Yang, Z Li, M Zhou, J Liu, I King (Cited by 96)
• Self-supervised contrastive learning for integrative single cell RNA-seq data analysis — W Han, Y Cheng, J Chen, H Zhong, Z Hu, S Chen, L Zong, L Hong, et al. (Cited by 81)
• E2Efold-3D: end-to-end deep learning method for accurate de novo RNA 3D structure prediction — T Shen, Z Hu, Z Peng, J Chen, P Xiong, L Hong, L Zheng, Y Wang, I King, et al. (Cited by 81)
• Multimodality in meta-learning: A comprehensive survey — Y Ma, S Zhao, W Wang, Y Li, I King (Cited by 80)
• Towards efficient post-training quantization of pre-trained language models — H Bai, L Hou, L Shang, X Jiang, I King, MR Lyu (Cited by 73)
• CenGCN: Centralized convolutional networks with vertex imbalance for scale-free graphs — F Xia, L Wang, T Tang, X Chen, X Kong, G Oatley, I King (Cited by 52)
• Hyperbolic temporal network embedding — M Yang, M Zhou, H Xiong, I King (Cited by 45)
• Learning binarized graph representations with multi-faceted quantization reinforcement for top-k recommendation — Y Chen, H Guo, Y Zhang, C Ma, R Tang, J Li, I King (Cited by 45)
• Towards an optimal asymmetric graph structure for robust semi-supervised node classification — Z Song, Y Zhang, I King (Cited by 42)
• Graph-adaptive rectified linear unit for graph neural networks — Y Zhang, H Zhu, Z Meng, P Koniusz, I King (Cited by 40)
• Hierarchical heterogeneous graph attention network for syntax-aware summarization — Z Song, I King (Cited by 31)
• Co-designing artificial intelligence curriculum for secondary schools: A grounded theory of teachers' experience — KW Yau, CS Chai, TKF Chiu, H Meng, I King, SWH Wong, Y Yam (Cited by 29)
• Text revision by on-the-fly representation optimization — J Li, Z Li, T Ge, I King, MR Lyu (Cited by 23)
• Understanding in-app advertising issues based on large scale app review analysis — C Gao, J Zeng, D Lo, X Xia, I King, MR Lyu (Cited by 23)
• Retrieval-augmented multilingual keyphrase generation with retriever-generator iterative training — Y Gao, Q Yin, Z Li, R Meng, T Zhao, B Yin, I King, MR Lyu (Cited by 21)
• Knowledge-aware neural networks with personalized feature referencing for cold-start recommendation — X Zhang, Y Chen, C Gao, Q Liao, S Zhao, I King (Cited by 19)
• Developing an AI literacy test for junior secondary students: The first stage — KW Yau, CS Chai, TKF Chiu, H Meng, I King, SWH Wong, C Saxena, et al. (Cited by 17)
• An effective post-training embedding binarization approach for fast online top-k passage matching — Y Chen, Y Zhang, H Guo, R Tang, I King (Cited by 17)
• Resnorm: Tackling long-tailed degree distribution issue in graph neural networks via normalization — L Liang, Z Xu, Z Song, I King, Y Qi, J Ye (Cited by 16)
• Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions. arXiv — J Chen, Z Hu, S Sun, Q Tan, Y Wang, Q Yu, L Zong, L Hong, J Xiao, et al. (Cited by 13)
• Interprofessional education—situations of a university in Hong Kong and major hurdles to teachers and students — JTS Li, JPC Chau, SYS Wong, ASN Lau, WCH Chan, PPS Yip, Y Yang, et al. (Cited by 11)
• Individual fairness in dynamic financial networks — Z Song, Y Ma, I King (Cited by 11)
• Exploiting inactive examples for natural language generation with data rejuvenation — W Jiao, X Wang, S He, Z Tu, I King, MR Lyu (Cited by 9)
• Momentum contrastive pre-training for question answering — M Hu, M Li, Y Wang, I King (Cited by 5)
• Hyperbolic graph representation learning: A tutorial — M Zhou, M Yang, L Pan, I King (Cited by 5)
• Global MOOC Landscape — I King, WI Lee (Cited by 5)
• Advanced technology empowering MOOCs — I King, WI Lee (Cited by 4)
• Hyperbolic curvature graph neural network — M Yang, M Zhou, L Pan, I King (Cited by 4)
• Single cell self-paced clustering with transcriptome sequencing data — P Zhao, Z Xu, J Chen, Y Ren, I King (Cited by 2)
• Facial expression synthesis using radial basis function networks — I King, XQ Li (Cited by 2)
• Roles of MOOCs in times of crises — I King, WI Lee (Cited by 1)
• Exploring Global MOOC Ecosystems — I King, WI Lee (Cited by 1)
• ACE: A coarse-to-fine learning framework for reliable representation learning against label noise — C Zhang, X Yang, J Liang, B Bai, K Bai, I King, Z Xu (Cited by 1)
• Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction — X Hu, S Meng, C Zhang, X Yang, L Wen, I King, PS Yu
• Innovative MOOC Pedagogy and Assessments — I King, WI Lee
• MOOC Policies Across the Globe — I King, WI Lee
• Embarking on a MOOC Journey — I King, WI Lee
• Outlook of MOOCs in Post-Pandemic Era — I King, WI Lee
• MOOC Development in Greater China — I King, WI Lee
• Encoded Gradients Aggregation against Gradient Leakage in Federated Learning — D Zeng, S Liu, S Liang, Z Li, H Wang, I King, Z Xu
• ConcreteGraph: A Data Augmentation Method Leveraging the Properties of Concept Relatedness Estimation. — Y Ma, Z Song, C Chang, Y Yu, I King

• Building energy consumption forecasting: A comparison of gradient boosting models — A Bassi, A Shenoy, A Sharma, H Sigurdson, C Glossop, JH Chan (Cited by 52)
• Machine Learning Applications to Sports Injury: A Review — H Sigurdson, JH Chan (Cited by 14)
• Hand Foot and Mouth Rash Detection Using Deep Convolution Neural Network — N Vakili, JH Chan, W Krathu, N Phattarakijtham, K Hirata (Cited by 7)
• Improving the robustness of a convolutional neural network with out-of-distribution data fine-tuning and image preprocessing — S Haque, AW Liu, S Liu, J H. Chan (Cited by 6)
• Learning from others: A data driven transfer learning based daily new COVID-19 case prediction in India using an ensemble of LSTM-RNNs — D Chakraborty, D Goswami, A Ghosh, J Chan, S Ghosh (Cited by 4)
• Resume Parser with Natural Language Processing — P Sroison, JH Chan (Cited by 3)
• Question Answering Model in Thai by using Squad Thai Wikipedia dataset — W Rueangkhajorn, JH Chan (Cited by 3)
• Testing the Effectiveness of CNN and GNN and Exploring the Influence of Different Channels on Decoding Covert Speech from EEG Signals: CNN and GNN on Decoding Covert Speech … — S Liu, J Chan (Cited by 2)
• An evaluation of transfer learning with CheXNet on lung opacity detection in COVID-19 and pneumonia chest radiographs — AW Liu, JH Chan (Cited by 2)
• Virtual Companion for the Elderly: Conceptual Framework — A Bassi, JH Chan, P Mongkolnam (Cited by 2)
• Handwritten Pashto Characters Dataset for Optical Character Recognition — W Mudaser, JH Chan (Cited by 2)
• HFMD skin rash detection using convolutional neural networks — N Vakili, N Phattarakijtham, JH Chan, W Krathu (Cited by 1)
• The Effect of PreTraining Thoracic Disease Detection Systems on Large-Scale Chest X-Ray Domain Datasets — S Haque, J H. Chan
• Combination of Transfer Learning, Recursive Learning and Ensemble Learning for Multi-Day Ahead COVID-19 Cases Prediction in India using Gated Recurrent Unit Networks — D Chakraborty, D Goswami, S Ghosh, A Ghosh, JH Chan
• Unified Model for Learning Style Recommendation — U Ninrutsirikun, D Pal, C Arpnikanondt, B Watanapa (Cited by 4)
• University students’ perception of video-based learning in times of COVID-19: A TAM/TTF perspective — D Pal, S Patra (Cited by 255)
• How do leaders influence innovation and creativity in employees? The mediating role of intrinsic motivation — S Siyal, C Xin, WA Umrani, S Fatima, D Pal (Cited by 173)
• Prohibitive factors to the acceptance of Internet of Things (IoT) technology in society: A smart-home context using a resistive modelling approach — D Pal, X Zhang, S Siyal (Cited by 107)
• Usability evaluation of artificial intelligence-based voice assistants: The case of Amazon Alexa — DS Zwakman, D Pal, C Arpnikanondt (Cited by 106)
• Human factors in cybersecurity: A scoping review — T Rahman, R Rohan, D Pal, P Kanthamanon (Cited by 99)
• Exploring the determinants of users’ continuance usage intention of smart voice assistants — D Pal, MD Babakerkhell, X Zhang (Cited by 68)
• How gamification leads to continued usage of MOOCs? A theoretical perspective — R Rohan, D Pal, S Funilkul, W Chutimaskul, W Eamsinvattana (Cited by 67)
• Analyzing the adoption and diffusion of voice-enabled smart-home systems: empirical evidence from Thailand — D Pal, C Arpnikanondt, S Funilkul, MA Razzaque (Cited by 50)
• Exploring the antecedents of consumer electronics IoT devices purchase decision: A mixed methods study — D Pal, V Vanijja, X Zhang, H Thapliyal (Cited by 48)
• Understanding of human factors in cybersecurity: a systematic literature review — R Rohan, S Funilkul, D Pal, W Chutimaskul (Cited by 43)
• An integrated TAM/ISS model based PLS-SEM approach for evaluating the continuous usage of voice enabled IoT systems — D Pal, C Arpnikanondt (Cited by 36)
• Consumer perceptions of factors affecting online shopping behavior: An empirical evidence from foreign students in China — M Siyal, S Siyal, J Wu, D Pal, MM Memon (Cited by 27)
• Humans in the loop: Cybersecurity aspects in the consumer IoT context — R Rohan, S Funilkul, D Pal, H Thapliyal (Cited by 21)
• CyberIoT: An initial conceptualization of a web-based cyber range for IoT — AZ Sharifi, V Vanijja, D Pal, W Anantasabkit (Cited by 9)
• Diabetic retinopathy classification with pre-trained image enhancement model — W Mudaser, P Padungweang, P Mongkolnam, P Lavangnananda (Cited by 14)
• Virtual companion for the elderly: conceptual framework — A Bassi, JH Chan, P Mongkolnam (Cited by 2)
• Optimizing CNN-LSTM neural networks with PSO for anomalous query access control — TY Kim, SB Cho (Cited by 60)
• Deep character-level anomaly detection based on a convolutional autoencoder for zero-day phishing URL detection — SJ Bu, SB Cho (Cited by 60)
• Explainable prediction of electric energy demand using a deep autoencoder with interpretable latent space — JY Kim, SB Cho (Cited by 48)
• Integrating deep learning with first-order logic programmed constraints for zero-day phishing attack detection — SJ Bu, SB Cho (Cited by 37)
• Feature space transformation for fault diagnosis of rotating machinery under different working conditions — GB Jang, SB Cho (Cited by 27)
• Biogeography based optimization for mining rules to assess credit risk — PK Giri, SS De, S Dehuri, SB Cho (Cited by 26)
• Deep CNN transferred from VAE and GAN for classifying irritating noise in automobile — JY Kim, SB Cho (Cited by 19)
• A systematic analysis and guidelines of graph neural networks for practical applications — JY Kim, SB Cho (Cited by 17)
• Evolutionary optimization of neuro-symbolic integration for phishing URL detection — KW Park, SJ Bu, SB Cho (Cited by 13)
• Adversarial signal augmentation for CNN-LSTM to classify impact noise in automobiles — SJ Bu, HJ Moon, SB Cho (Cited by 10)
• Clustered embedding using deep learning to analyze urban mobility based on complex transportation data — SB Cho, JY Kim (Cited by 8)
• Directional graph transformer-based control flow embedding for malware classification — HJ Moon, SJ Bu, SB Cho (Cited by 7)
• Learning dynamic connectivity with residual-attention network for autism classification in 4D fMRI brain images — KW Park, SJ Bu, SB Cho (Cited by 5)
• Cross-domain fault diagnosis of rotating machinery using discriminative feature attention network — GB Jang, JY Kim, SB Cho (Cited by 5)
• Anomaly detection for health monitoring of heavy equipment using hierarchical prediction with correlative feature learning — G Jang, SB Cho (Cited by 2)
• Extracting salient information from discarded features via attribute selection and pruning — AJ Tallón-Ballesteros, SB Cho (Cited by 2)
• Mutagenic prediction for chemical compound discovery with partitioned graph convolution network — HJ Moon, SJ Bu, SB Cho (Cited by 1)
• Inference of Other’s Minds with Limited Information in Evolutionary Robotics — KJ Kim, SB Cho
• Indicator-based evolutionary algorithm for solving constrained multiobjective optimization problems — J Yuan, HL Liu, YS Ong, Z He (Cited by 149)
• Learning conjoint attentions for graph neural nets — T He, YS Ong, L Bai (Cited by 104)
• Unsupervised object-level representation learning from scene images — J Xie, X Zhan, Z Liu, YS Ong, CC Loy (Cited by 93)
• Learnable evolutionary search across heterogeneous problems via kernelized autoencoding — L Zhou, L Feng, A Gupta, YS Ong (Cited by 70)
• Choose appropriate subproblems for collaborative modeling in expensive multiobjective optimization — Z Wang, Q Zhang, YS Ong, S Yao, H Liu, J Luo (Cited by 68)
• Two-type weight adjustments in MOEA/D for highly constrained many-objective optimization — R Jiao, S Zeng, C Li, YS Ong (Cited by 62)
• Non-linear domain adaptation in transfer evolutionary optimization — R Lim, A Gupta, YS Ong, L Feng, AN Zhang (Cited by 54)
• Evolutionary machine learning with minions: A case study in feature selection — N Zhang, A Gupta, Z Chen, YS Ong (Cited by 51)
• From multitask gradient descent to gradient-free evolutionary multitasking: A proof of faster convergence — L Bai, W Lin, A Gupta, YS Ong (Cited by 46)
• Grpavoid: Multigroup collision-avoidance control and optimization for UAV swarm — L Luo, X Wang, J Ma, YS Ong (Cited by 38)
• Can transfer neuroevolution tractably solve your differential equations? — JC Wong, A Gupta, YS Ong (Cited by 29)
• Solution representation learning in multi-objective transfer evolutionary optimization — R Lim, L Zhou, A Gupta, YS Ong, AN Zhang (Cited by 28)
• Vicinal vertex allocation for matrix factorization in networks — T He, L Bai, YS Ong (Cited by 24)
• Frame-correlation transfers trigger economical attacks on deep reinforcement learning policies — X Qu, YS Ong, A Gupta (Cited by 20)
• Breaking neural reasoning architectures with metamorphic relation-based adversarial examples — A Chan, L Ma, F Juefei-Xu, YS Ong, X Xie, M Xue, Y Liu (Cited by 19)
• Scalable Gaussian process classification with additive noise for non-Gaussian likelihoods — H Liu, YS Ong, Z Yu, J Cai, X Shen (Cited by 18)
• Calibrated and recalibrated expected improvements for Bayesian optimization — Z Guo, YS Ong, H Liu (Cited by 15)
• Deep latent-variable kernel learning — H Liu, YS Ong, X Jiang, X Wang (Cited by 14)
• Affect recognition for multimodal natural language processing — S Poria, OY Soon, B Liu, L Bing (Cited by 12)
• Adversary agnostic robust deep reinforcement learning — X Qu, A Gupta, YS Ong, Z Sun (Cited by 11)
• Efficient implementation of activation functions for LSTM accelerators — YS Chong, WL Goh, YS Ong, VP Nambiar, AT Do (Cited by 11)
• An energy-efficient convolution unit for depthwise separable convolutional neural networks — YS Chong, WL Goh, YS Ong, VP Nambiar, AT Do (Cited by 11)
• Modulating scalable Gaussian processes for expressive statistical learning — H Liu, YS Ong, X Jiang, X Wang (Cited by 5)
• Multi-space evolutionary search for large-scale optimization — L Feng, Q Shang, Y Hou, KC Tan, YS Ong (Cited by 4)
• Graph joint attention networks — T He, L Bai, YS Ong (Cited by 4)
• Multi-source propagation aware network clustering — T He, YS Ong, P Hu (Cited by 3)
• cognitive multitasking–towards augmented intelligence — L Feng, YS Ong, H Tang, WN Browne (Cited by 2)
• Cognitive Multitasking–Towards Augmented Intelligence — YS Ong, L Feng, H Tang, WN Browne (Cited by 1)
• CIS Publication Spotlight [Publication Spotlight] — H He, J Garibaldi, CAC Coello, J Togelius, Y Jin, YS Ong, H Abbass
• Multi-Party Dual Learning — M Gong, Y Gao, Y Xie, AK Qin, K Pan, YS Ong
• CIS Publication Spotlight — H He, J Garibaldi, CAC Coello, J Togelius, Y Jin, YS Ong, H Abbass
• Learning from Others: A Data Driven Transfer Learning based Daily New COVID-19 Case Prediction in India using an Ensemble of LSTM-RNNs — D Chakraborty, D Goswami, A Ghosh, J Chan, S Ghosh (Cited by 4)
• Machine learning method to discover the novel association of vaccine and vitamin a supplement on covid 19 infection: a biclustering approach — N Jain, S Ghosh (Cited by 1)
• Learning from Others: Daily COVID-19 Cases Prediction in India using Ensembles of LSTM-RNNs — D Chakraborty, D Goswami, S Ghosh, JH Chan, A Ghosh
• Integrated multi-omics analysis of ovarian cancer using variational autoencoders — MT Hira, MA Razzaque, C Angione, J Scrivens, S Sawan, M Sarker (Cited by 111)
• Situating agent-based modelling in population health research — E Silverman, U Gostoli, S Picascia, J Almagor, M McCann, R Shaw, et al. (Cited by 102)
• Multimodal regularized linear models with flux balance analysis for mechanistic integration of omics data — G Magazzù, G Zampieri, C Angione (Cited by 31)
• An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications — A Adegbenro, M Short, C Angione (Cited by 26)
• Genome‐scale metabolic modelling of SARS‐CoV‐2 in cancer cells reveals an increased shift to glycolytic energy production — E Yaneske, G Zampieri, L Bertoldi, G Benvenuto, C Angione (Cited by 23)
• Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002 — S Vijayakumar, C Angione (Cited by 13)
• Modeling Customer Experience in a Contact Center through Process Log Mining — T Fu, G Zampieri, D Hodgson, C Angione, Y Zeng (Cited by 5)
• A Computational Model of Cancer Metabolism for Personalised Medicine — A Occhipinti, C Angione (Cited by 2)
• 2021 Reviewers List à — W Abbasi, O Abbaszadeh, M Abdelsamea, S Abeln, A Abou Tabl, et al.
• Author Correction: Integrated multi‑omics analysis of ovarian cancer using variational autoencoders — MT Hira, MA Razzaque, C Angione, J Scrivens, S Sawan, M Sarker
• Cosmonet: An r package for survival analysis using screening-network methods — A Iuliano, A Occhipinti, C Angelini, I De Feis, P Liò (Cited by 7)
• Neural architecture transfer — Z Lu, G Sreekumar, E Goodman, W Banzhaf, K Deb, VN Boddeti (Cited by 226)
• Multiobjective optimization and analysis of petroleum refinery catalytic processes: A review — HA Al-Jamimi, GM BinMakhashen, K Deb, TA Saleh (Cited by 52)
• Constrained bi-objective surrogate-assisted optimization of problems with heterogeneous evaluation times: Expensive objectives and inexpensive constraints — J Blank, K Deb (Cited by 41)
• Enhanced Innovized Progress Operator for Evolutionary Multi- and Many-Objective Optimization — S Mittal, DK Saxena, K Deb, ED Goodman (Cited by 34)
• A Learning-based Innovized Progress Operator for Faster Convergence in Evolutionary Multi-objective Optimization — S Mittal, DK Saxena, K Deb, ED Goodman (Cited by 32)
• Solving mixed pareto-lexicographic multiobjective optimization problems: the case of priority levels — L Lai, L Fiaschi, M Cococcioni, K Deb (Cited by 28)
• Effect of objective normalization and penalty parameter on penalty boundary intersection decomposition-based evolutionary many-objective optimization algorithms — L Chen, K Deb, HL Liu, Q Zhang (Cited by 28)
• A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem — JBC Chagas, J Blank, M Wagner, MJF Souza, K Deb (Cited by 27)
• Reference point based evolutionary multi-objective optimization algorithms with convergence properties using KKTPM and ASF metrics — M Abouhawwash, K Deb (Cited by 25)
• A new gradient free local search mechanism for constrained multi-objective optimization problems — L Uribe, A Lara, K Deb, O Schütze (Cited by 18)
• A novel multi-objective model calibration method for ecohydrological applications — JS Hernandez-Suarez, AP Nejadhashemi, K Deb (Cited by 18)
• Evolutionary multi-objective optimization algorithm for community detection in complex social networks — T Shaik, V Ravi, K Deb (Cited by 17)
• Solving the maximum edge disjoint path problem using a modified Lagrangian particle swarm optimisation hybrid — J Weiner, AT Ernst, X Li, Y Sun, K Deb (Cited by 16)
• A proximity-based surrogate-assisted method for simulation-based design optimization of a cylinder head water jacket — A Ahrari, J Blank, K Deb, X Li (Cited by 16)
• Online clustering reduction based on parametric and non-parametric correlation for a many-objective vehicle routing problem with demand responsive transport — RS Mendes, V Lush, EF Wanner, FVC Martins, JFM Sarubbi, K Deb (Cited by 16)
• Analyzing dominance move (mip-dom) indicator for multiobjective and many-objective optimization — CL do Val Lopes, FVC Martins, EF Wanner, K Deb (Cited by 15)
• Interpretable self-organizing maps (isom) for visualization of pareto front in multiple objective optimization — D Nagar, P Ramu, K Deb (Cited by 15)
• Multidimensional aspects of sustainable biofuel feedstock production — A Raschke, JS Hernandez-Suarez, AP Nejadhashemi, K Deb (Cited by 12)
• Combining User Knowledge and Online Innovization for Faster Solution to Multi-objective Design Optimization Problems — A Ghosh, K Deb, R Averill, E Goodman (Cited by 11)
• Interpretable AI agent through nonlinear decision trees for lane change problem — A Ghosh, Y Dhebar, R Guha, K Deb, S Nageshrao, L Zhu, E Tseng, et al. (Cited by 10)
• PSAF: A probabilistic surrogate-assisted framework for single-objective optimization — J Blank, K Deb (Cited by 10)
• Handling priority levels in mixed pareto-lexicographic many-objective optimization problems — L Lai, L Fiaschi, M Cococcioni, K Deb (Cited by 10)
• Ensembled crossover based evolutionary algorithm for single and multi-objective optimization — S Sharma, J Blank, K Deb, BK Panigrahi (Cited by 5)
• Optimized electric machine design solutions with efficient handling of constraints — B Khoshoo, J Blank, T Pham, K Deb, S Foster (Cited by 4)
• Embedding a repair operator in evolutionary single and multi-objective algorithms-an exploitation-exploration perspective — K Deb, S Mittal, DK Saxena, ED Goodman (Cited by 4)
• Visualization-based multi-criterion decision making with NIMBUS method using Palette Viz — K Deb, AKMKA Talukder (Cited by 3)
• Evolutionary multi-and many-objective optimization: methodologies, applications and demonstration — K Deb, J Blank (Cited by 3)
• The (M-1)+ 1 framework of relaxed pareto dominance for evolutionary many-objective optimization — S Zhu, L Xu, E Goodman, K Deb, Z Lu (Cited by 3)
• SOLVeR: A Blueprint for Collaborative Optimization in Practice — J Blank, K Deb (Cited by 3)
• Multimodal optimization by evolution strategies with repelling subpopulations — A Ahrari, K Deb (Cited by 2)
• Evolutionary computation: An emerging framework for practical single and multicriterion optimization and decision making — K Deb (Cited by 2)
• An improved visual analytics framework for high-dimensional pareto-optimal front: a case for multi-objective portfolio optimization — AKMK Talukder, K Deb (Cited by 2)
• Aggregation or selection? clustering many objectives for vehicle routing problem with demand responsive transport — RS Mendes, EF Wanner, FVC Martins, K Deb (Cited by 2)
• Towards multi-objective co-evolutionary problem solving — A Suresh, K Deb, VN Boddeti (Cited by 2)
• Development of an efficient optimization framework for improving water quality in the chesapeake bay watershed — J Hernandez Suarez, G Toscano-Pulido, AP Nejadhashemi, K Deb (Cited by 1)
• Multi-objective Coevolution and Decision-making for Cooperative and Competitive Environments — A Suresh, J Kongmanee, K Deb, VN Boddeti (Cited by 1)
• Systematic development of single-objective optimization approaches for Chesapeake Bay watershed management — G Toscano, JS Hernandez-Suarez, J Blank, P Nejadhashemi, K Deb (Cited by 1)
• An approximate MIP-DoM calculation for multi-objective optimization using affinity propagation clustering algorithm — CLV Lopes, FVC Martins, EF Wanner, K Deb
• Dynamic multi-objective optimization — M Helbig, K Deb, A Engelbrecht
• A Learning-based Innovized Progress Operator for Faster Convergence in Evolutionary Multi-objective Optimization COIN Report Number 2021003 — S MITTAL, DK SAXENA, K DEB, ED GOODMAN
• Creation and evaluation of a pretertiary artificial intelligence (AI) curriculum — TKF Chiu, H Meng, CS Chai, I King, S Wong, Y Yam (Cited by 424)
• Discrete-time temporal network embedding via implicit hierarchical learning in hyperbolic space — M Yang, M Zhou, M Kalander, Z Huang, I King (Cited by 164)
• Improving the transferability of adversarial samples with adversarial transformations — W Wu, Y Su, MR Lyu, I King (Cited by 131)
• Self-training sampling with monolingual data uncertainty for neural machine translation — W Jiao, X Wang, Z Tu, S Shi, MR Lyu, I King (Cited by 46)
• Efficient community search over large directed graphs: An augmented index-based approach — Y Chen, J Zhang, Y Fang, X Cao, I King (Cited by 40)
• Temporal context-aware task recommendation in crowdsourcing systems — MC Yuen, I King, KS Leung (Cited by 39)
• Semi-supervised multi-label learning for graph-structured data — Z Song, Z Meng, Y Zhang, I King (Cited by 35)
• Emerging app issue identification via online joint sentiment-topic tracing — C Gao, J Zeng, Z Wen, D Lo, X Xia, I King, MR Lyu (Cited by 33)
• Controllable summarization with constrained Markov decision process — HP Chan, L Wang, I King (Cited by 32)
• A training-free and reference-free summarization evaluation metric via centrality-weighted relevance and self-referenced redundancy — W Chen, P Li, I King (Cited by 27)
• Characterising frailty, metrics of continuous glucose monitoring, and mortality hazards in older adults with type 2 diabetes on insulin therapy (HARE): a prospective … — E Fung, LT Lui, L Huang, KF Cheng, GHW Lau, YT Chung, et al. (Cited by 26)
• Rethinking engineering education: Policy, pedagogy, and assessment during crises — I King, C Saxena, C Pak, C Lam, H Cai (Cited by 24)
• Open-retrieval conversational machine reading — Y Gao, J Li, CS Wu, MR Lyu, I King (Cited by 20)
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• A condense-then-select strategy for text summarization — HP Chan, I King (Cited by 14)
• Dialogue summarization with supporting utterance flow modelling and fact regularization — W Chen, P Li, HP Chan, I King (Cited by 12)
• Learning by distillation: a self-supervised learning framework for optical flow estimation — P Liu, MR Lyu, I King, J Xu (Cited by 12)
• Towards low-loss 1-bit quantization of user-item representations for top-k recommendation — Y Chen, Y Zhang, Y Zhang, H Guo, J Li, R Tang, X He, I King (Cited by 5)
• Discrete auto-regressive variational attention models for text modeling — X Fang, H Bai, J Li, Z Xu, M Lyu, I King (Cited by 4)
• DeepCURATER: Deep Learning for CoURse And Teaching Evaluation and Review — Z Hu, B Thumu, Y Qin, ZS Tao, Y Lu, O Kan, Y Li, I King (Cited by 2)
• Dap-bert: Differentiable architecture pruning of bert — CY Yau, H Bai, I King, MR Lyu (Cited by 1)
• Motion Interpretation of Trochoidal Paths — IK King, MA Arbib
• Introduction to Game Theory — I King
• Making Predictions for Sequentially Available Choices: A Semiparametric Gaussian Copula Approach (SGC) — M Hu, X Zhou, I King, J Cao
• Controllable Summarization with Constrained Markov Decision Process — H Pong Chan, L Wang, I King
• ml4bio/CLEAR: CLEAR: Self-supervised contrastive learning for integrative single-cell RNA-seq data analysis — W Han, Y Cheng, J Chen, H Zhong, Z Hu, S Chen, L Zong, L Hong, et al.
• A Condense-then-Select Strategy for Text Summarization — H Pong Chan, I King

A Literature Review of Recent Graph Embedding Techniques for Biomedical Data — Y Chen, Y Wu, S Ma, I King

• Neural information processing — Yang, JH Chan, I King, JT Kwok, ACS Leung, K Pasupa, H Yang (Cited by 284)
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• Data Mining Based Prediction of Malnutrition in Afghan Children — Z Momand, P Mongkolnam, P Kositpanthavong, JH Chan (Cited by 42)
• Detecting Covid-19 in Chest X-Rays using Transfer Learning with VGG16 — A Chen, J Jaegerman, D Matic, H Inayatali, N Charoenkitkarn, J Chan (Cited by 23)
• Predicting the effect of variants on splicing using Convolutional Neural Networks — T Thanapattheerakul, W Engchuan, JH Chan (Cited by 6)
• DLAI3 Hackathon Phase3 COVID-19 CXR Challenge — JH Chan (Cited by 6)
• Augmented Reality Project Poster: Using Mobile Augmented Reality Application to Enhance Project Poster — AYC Yew, HMDH Morsidi, JH Chan (Cited by 6)
• COVID19 Chest X-Ray Classification with Simple Convolutional Neural Network — C Li, M Wang, G Wu, K Rana, N Charoenkitkarn, J Chan (Cited by 4)
• Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18–22, 2020, Proceedings, Part V — H Yang, K Pasupa, ACS Leung, JT Kwok, JH Chan, I King (Cited by 1)
• Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18–22, 2020, Proceedings, Part IV — H Yang, K Pasupa, ACS Leung, JT Kwok, JH Chan, I King (Cited by 1)
• Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18–22, 2020, Proceedings, Part III — H Yang, K Pasupa, ACS Leung, JT Kwok, JH Chan, I King (Cited by 1)
• Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18–22, 2020, Proceedings, Part II — H Yang, K Pasupa, ACS Leung, JT Kwok, JH Chan, I King (Cited by 1)
• Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18–22, 2020, Proceedings, Part I — H Yang, K Pasupa, ACS Leung, JT Kwok, JH Chan, I King (Cited by 1)
• Predicting Dihydroartemisinin Resistance in Plasmodium falciparum using Pathway Activity Inference — N Lawford, JH Chan
• A Survey for User's Awareness and Practices Regarding Smartphone Security and Privacy in Afghanistan — J Hamdard, A Khamosh, JH Chan
• Identification of Gene Subnetwork Biomarkers of Lung Cancer from RNA-seq Data — K Sreebunpeng, JH Chan, A Meechai
• Biomarker Identification in Colorectal Cancer Using Subnetwork Analysis with Feature Selection — JH Chan
• Principal component clustered factors for determining study performance in computer programming class — U Ninrutsirikun, H Imai, B Watanapa, C Arpnikanondt (Cited by 18)
• A 3D-CNN siamese network for motion gesture Sign Language alphabets recognition — H Jirathampradub, C Nukoolkit, K Suriyathumrongkul, B Watanapa (Cited by 4)
• Indoor Position Detection Using Smartwatch and Beacons. — S Sukreep, C Nukoolkit, P Mongkolnam (Cited by 9)
• Deep neural network feature selection approaches for data-driven prognostic model of aircraft engines — P Khumprom, D Grewell, N Yodo (Cited by 38)
• Neural networks based feature selection approaches for prognostics of aircraft engines — P Khumprom, N Yodo, D Grewell (Cited by 2)
• Perceived usability evaluation of Microsoft Teams as an online learning platform during COVID-19 using system usability scale and technology acceptance model in India — D Pal, V Vanijja (Cited by 630)
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• Voice usability scale: measuring the user experience with voice assistants — DS Zwakman, D Pal, T Triyason, C Arpnikanondt (Cited by 20)
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• 2020 Index IEEE Computational Intelligence Magazine Vol. 15 — M Akhtar, C Alippi, J An, R Arcucci, J Bacardit, J Bai, S Banshal, A Basu, et al.
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• Dynamic vessel-to-vessel routing using level-wise evolutionary optimization — Y Vesikar, J Blank, K Deb, M Kalio, A Maskooki
• Evolutionary computation's niche for solving multi-criterion optimization problems — K Deb
• Many-Objective Software Remodularization using NSGA-III — M Wiem Mkaouer, M Kessentini, A Shaout, P Koligheu, S Bechikh, K Deb, et al.
• Pareto enveloped selection ant system (PESAS) 62–64 pheromone updating model 21 population-based ACO (P-ACO) 48–50 — A Emrouznejad, MM Jamshidi, K Deb, I Rahimi
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• Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding — X Fu, J Zhang, Z Meng, I King (Cited by 1244)
• Binarybert: Pushing the limit of bert quantization — H Bai, W Zhang, L Hou, L Shang, J Jin, X Jiang, Q Liu, M Lyu, I King (Cited by 289)
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• Proceedings of The Web Conference 2020 — Y Huang, I King, TY Liu, M Steen (Cited by 3)
• Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23–27, 2020, Proceedings, Part II — H Yang, K Pasupa, ACS Leung, JT Kwok, JH Chan, I King (Cited by 1)
• An Empirical Study of In-App Advertising Issues Based on Large Scale App Review Analysis — C Gao, J Zeng, D Lo, X Xia, I King, MR Lyu (Cited by 1)
• A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss — H Pong Chan, W Chen, I King
• Discrete Variational Attention Models for Language Generation — X Fang, H Bai, Z Xu, M Lyu, I King
• Conference Committee Co-Chair — Q He, I King, R Cajote, L Lee, F Huang, ROCJ Han, J XIAO, J Yang, et al.

The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. This book totally includes 83 papers and covers 5 chapters: 1 … — I King