iGyro: Information Gyroscope

Journals/Research Articles


  1. Lim, A.C.M., Ng, L.H.X., & Taeihagh, A. (2025). Biometric Data Landscape in Southeast Asia: Challenges and Opportunities for Effective Regulation. Computer Law & Security Review, Volume 56.

  2. Taeihagh, A. (2025). Governance of Generative AI. Policy and Society.

  3. Hu J., Liang J.W., Qin Z., Liao X., Zhou W.B., & Lin X.D. (2024). ADA-FInfer: Inferring Face Representations from Adaptive Select Frames for High-Visual-Quality Deepfake Detection. IEEE Transactions on Dependable and Secure Computing (IEEE TDSC).

  4. Jaidka, K., Chen, T., Chesterman, S., Hsu, W., Kan, M.Y., Kankanhalli, M., Lee, M.L., Seres, G., Sim, T., Taeihagh, A., Tung, A., Xiao, X., & Yue, A. (2024). Misinformation, Disinformation, and Generative AI: Implications for Perception and Policy. Association for Computing Machinery. Digital Government: Research and Practice.

  5. Khanal, S., Zhang, H. & Taeihagh, A. (2024). Building an AI Ecosystem in a Small Nation: Lessons from Singapore's Journey to the Forefront of AI. Humanities and Social Sciences Communications, Volume 11, Article No. 866 (2024).

  6. Zhang, H., Khanal S., & Taeihagh, A. (2024). Public-Private Powerplays in Generative AI Era: Balancing Big Tech Regulation Amidst Global AI Race. Association for Computing Machinery. Digital Government: Research and Practice.

  7. Abbas, F. & Taeihagh, A. (2024). Unmasking Deepfakes: A Systematic Review of Deepfake Detection and Generation Techniques Using Artificial Intelligence. Expert Systems with Applications.

  8. Khanal, S., Zhang, H. & Taeihagh, A. (2024). Why and How is the Power of Big Tech Increasing in the Policy Process? The Case of Generative AI. Policy and Society.

  9. Chesterman, S. (2024). Good Models Borrow, Great Models Steal: Intellectual Property Rights and Generative AI. Policy and Society, 2024, 00(00), 1-15.


Conferences/Workshops


  1. Bai, Y., Cai, B.Y., Tan, Y.K., Chen, S., Shu.Y., & Chen, T. (2025). FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time Augmentation.. The 39th Annual AAAI Conference on Artificial Intelligence.

  2. Neo, D., Winkler, S., & Chen, T. (2024). MaxEnt Loss: Constrained Maximum Entropy for Calibration under Out-of-Distribution Shift.. The 38th AAAI Conference on Artificial Intelligence.

  3. Bai, Y., Cai, B.Y., Tan, Y.K., Zheng, Z., Chen, S., & Chen, T. (2024). FSL-QuickBoost: Minimal-Cost Ensemble for Few-Shot Learning. Proceedings of the 32nd ACM International Conference on Multimedia , P8326-8335.

  4. Huang, Y., Zhang, S., Lakshmanan, L.V.S., Lin, W., Xiao, K., & Tang, B. (2024). Efficient and Effective Algorithms for A Family of Influence Maximization Problems with A Matroid Constraint. Proceedings of the Very Large Databases (VLDB) Endowment, Vol. 18, Issue 2.

  5. Ang, Y., Huang, Q., Bao, Y., Tung, A.K.H., & Huang, Z. (2024). TSGBench: Time Series Generation Benchmark. Proceedings of the International Conference on Very Large Databases (VLDB), Vol. 17, Issue 3.

  6. Wu, B., Huang, Q., & Tung, A.K.H. (2024). From Zero to Hero: Detecting Leaked Data through Synthetic Data Injection and Model Querying. Proceedings of the International Conference on Very Large Databases (VLDB), Vol. 17, Issue 8.

  7. Ang, Y., Bao, Y., Huang, Q., Tung, A.K.H., & Huang, Z. (2024). TSGAssist: An Interactive Assistant Harnessing LLMs and RAG for Time Series Generation Recommendations and Benchmarking. Proceedings of the International Conference on Very Large Databases (VLDB), Vol. 17, Issue 12.

  8. Tang, Y. & Tung, A. (2024). Contextualized Speech Recognition: Rethinking Second-Pass Rescoring with Generative Large Language Models. Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI).

  9. Barik, A.M., Hsu, W., & Lee, M.L. (2024). Time Matters: An End-to-End Solution for Temporal Claim Verification. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP Industry).

  10. Fei, H., Luo, M., Xu, J., Wu, S., Ji, W., Lee, M.L., & Hsu, W. (2024). Fine-grained Structural Hallucination Detection for Unified Visual Comprehension and Generation in Multimodal LLM. Proceedings of ACM Multimedia (ACM MM) Workshop on Multi-modal Misinformation Governance in the Era of Foundation Models (MIS).

  11. Luo, M., Fei, H., Li, B., Wu, S., Liu, Q., Poria, S., Cambria, E., Lee, M.L., & Hsu, W. (2024). PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis. Proceedings of ACM Multimedia (ACM MM) Oral.

  12. Sun, Y., Huang, Q., Wang, Y., & Tung, A.K.H. (2024). DiversiNews: Enriching News Consumption with Relevant Yet Diverse News Articles Retrieval. Proceedings of Very Large Databases (VLDB) Endowment, Vol 17. Issue 12.

  13. Abbas, F., & Taeihagh, A. (2024). Semantic Facial Features and Expression Manipulation Using Multi-Level IC-DGAN Framework. International Joint Conference on Neural Networks (IJCNN).

  14. Furniturewala, S., Jaidka, K. & Sharma, Y. (2024). Impact of Decoding Methods on Human Alignment of Conversational LLMs. Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA). Association for Computational Linguistics (ACL).

  15. Yeo, G.C., Furniturewala, S., & Jaidka, K. (2024). Beyond Text: Leveraging Multi-Task Learning and Cognitive Appraisal Theory for Post-Purchase Intention Analysis. Findings of the Association for Computational Linguistics (ACL), pages 12353-12360.

  16. Furniturewala, S., Jandial, S., Java, A., Banerjee, P., Shahid, S. Bhatia, S., & Jaidka, K. (2024). “Thinking” Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 213–227, Association for Computational Linguistics (ACL).

  17. Xu, J., Fei, H., Pan, L., Liu, Q., Lee, M.L., & Hsu, W. (2024). Faithful Logical Reasoning via Symbolic Chain-of-Thought. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL).

  18. Fei, H., Wu, S., Ji, W., Zhang, H., Zhang, M., Lee, M.L., & Hsu, W. (2024). Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition. Proceedings of the 41st International Conference on Machine Learning (ICML, Oral).

  19. Qi, P., Yan, Z.H., Hsu, W., & Lee, M.L. (2024). SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection. Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR).

  20. Lee, S.J., Hsu, W., & Lee, M.L. (2024). An End-to-End Vision Transformer Approach for Image Copy Detection. CVPR Workshop on Multimodal Content Moderation (MMCM).

  21. Lee, S.J., Hsu, W., & Lee, M.L. (2024). Combatting Disinformation involving AI-Generated Images through Frequency-guided Channel and Spatial Attention. IJCAI Workshop on Trustworthy AI.

  22. Lu, X.Y., Pan, L.M., Liu, Q., Nakov, P., & Kan, M.Y. (2023). SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).

  23. Pan, L., Lu, X., Kan, M.Y., & Nakov, P. (2023). QACheck: A Demonstration System for Question-Guided Multi-hop Fact-Checking. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).

  24. Pan, Y.K., Pan, L.M., Chen, W.H., Nakov, P., Kan, M.Y., & Wang, W.Y. (2023). On the Risk of Misinformation Pollution with Large Language Models. Proceedings of the Conference on Findings of Empirical Methods in Natural Language Processing (Findings of EMNLP).

  25. Pan, L.M., Zhang, Y.X., & Kan, M.Y. (2023). Investigating Zero- and Few-shot Generalization in Fact Verification. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL).

  26. Xu, D., Fan, S.J., & Kankanhalli, M. (2023). Combating Misinformation in the Era of Generative AI Models. Proceedings of the 31st ACM International Conference on Multimedia (ACM MM).