Professor Xiaowei Huang

Computer Science

+44 (0)151 795 4282 Ext. 54282
Work email Xiaowei.Huang@liverpool.ac.uk
Personal Websitehttps://cgi.csc.liv.ac.uk/~xiaowei/

Publications

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2024

Scene Text Recognition via Dual-path Network with Shape-driven Attention Alignment (Journal article)

Hu, Y., Dong, B., Huang, K., Ding, L., Wang, W., Huang, X., & Wang, Q. -F. (2024). Scene Text Recognition via Dual-path Network with Shape-driven Attention Alignment. ACM Transactions on Multimedia Computing, Communications, and Applications, 20(4), 1-20. doi:10.1145/3633517

DOI: 10.1145/3633517

MathAttack: Attacking Large Language Models towards Math Solving Ability (Conference Paper)

Zhou, Z., Wang, Q., Jin, M., Yao, J., Ye, J., Liu, W., . . . Huang, K. (n.d.). MathAttack: Attacking Large Language Models towards Math Solving Ability. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 19750-19758). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v38i17.29949

DOI: 10.1609/aaai.v38i17.29949

Representation-Based Robustness in Goal-Conditioned Reinforcement Learning (Conference Paper)

Yin, X., Wu, S., Liu, J., Fang, M., Zhao, X., Huang, X., & Ruan, W. (n.d.). Representation-Based Robustness in Goal-Conditioned Reinforcement Learning. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 21761-21769). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v38i19.30176

DOI: 10.1609/aaai.v38i19.30176

Reward Certification for Policy Smoothed Reinforcement Learning (Conference Paper)

Mu, R., Soriano Marcolino, L., Zhang, Y., Zhang, T., Huang, X., & Ruan, W. (n.d.). Reward Certification for Policy Smoothed Reinforcement Learning. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 21429-21437). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v38i19.30139

DOI: 10.1609/aaai.v38i19.30139

Hierarchical Distribution-aware Testing of Deep Learning (Journal article)

Huang, W., Zhao, X., Banks, A., Cox, V., & Huang, X. (2024). Hierarchical Distribution-aware Testing of Deep Learning. ACM Transactions on Software Engineering and Methodology, 33(2), 1-35. doi:10.1145/3625290

DOI: 10.1145/3625290

ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion Models against Stochastic Perturbation (Preprint)

Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems (Journal article)

Dong, Y., Zhao, X., Wang, S., & Huang, X. (2024). Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems. IEEE Robotics and Automation Letters, 1-8. doi:10.1109/lra.2024.3364471

DOI: 10.1109/lra.2024.3364471

Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting (Journal article)

Dong, Y., Wang, Y., Gama, M., Mustafa, M. A., Deconinck, G., & Huang, X. (2024). Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting. IEEE Internet of Things Journal, 1. doi:10.1109/jiot.2024.3362587

DOI: 10.1109/jiot.2024.3362587

Building Guardrails for Large Language Models (Preprint)

Bridging formal methods and machine learning with model checking and global optimisation (Journal article)

Bensalem, S., Huang, X., Ruan, W., Tang, Q., Wu, C., & Zhao, X. (2024). Bridging formal methods and machine learning with model checking and global optimisation. Journal of Logical and Algebraic Methods in Programming, 137, 100941. doi:10.1016/j.jlamp.2023.100941

DOI: 10.1016/j.jlamp.2023.100941

Nrat: towards adversarial training with inherent label noise (Journal article)

Chen, Z., Wang, F., Mu, R., Xu, P., Huang, X., & Ruan, W. (2024). Nrat: towards adversarial training with inherent label noise. Machine Learning. doi:10.1007/s10994-023-06437-3

DOI: 10.1007/s10994-023-06437-3

Continuous Engineering for Trustworthy Learning-Enabled Autonomous Systems (Conference Paper)

Bensalem, S., Katsaros, P., Ničković, D., Liao, B. H. -C., Nolasco, R. R., Ahmed, M. A. E. S., . . . Wu, C. (2024). Continuous Engineering for Trustworthy Learning-Enabled Autonomous Systems. In Unknown Conference (pp. 256-278). Springer Nature Switzerland. doi:10.1007/978-3-031-46002-9_15

DOI: 10.1007/978-3-031-46002-9_15

Progressive Supervision for Tampering Localization in Document Images (Conference Paper)

Shao, H., Huang, K., Wang, W., Huang, X., & Wang, Q. (2024). Progressive Supervision for Tampering Localization in Document Images. In Unknown Conference (pp. 140-151). Springer Nature Singapore. doi:10.1007/978-981-99-8184-7_11

DOI: 10.1007/978-981-99-8184-7_11

Sim-to-Real Global Maximum Power Point Tracking with Domain Randomization and Adaptation for Photovoltaic Systems (Journal article)

Wang, K., Ma, J., Man, K. L., Huang, K., & Huang, X. (2024). Sim-to-Real Global Maximum Power Point Tracking with Domain Randomization and Adaptation for Photovoltaic Systems. IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 1-11. doi:10.1109/jestie.2023.3317803

DOI: 10.1109/jestie.2023.3317803

What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety-Critical Systems (Conference Paper)

Bensalem, S., Cheng, C. -H., Huang, W., Huang, X., Wu, C., & Zhao, X. (2024). What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety-Critical Systems. In Unknown Conference (pp. 55-76). Springer Nature Switzerland. doi:10.1007/978-3-031-46002-9_4

DOI: 10.1007/978-3-031-46002-9_4

2023

A Symbolic Characters Aware Model for Solving Geometry Problems (Conference Paper)

Ning, M., Wang, Q. -F., Huang, K., & Huang, X. (2023). A Symbolic Characters Aware Model for Solving Geometry Problems. In Proceedings of the 31st ACM International Conference on Multimedia. ACM. doi:10.1145/3581783.3612570

DOI: 10.1145/3581783.3612570

SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability (Conference Paper)

Huang, W., Zhao, X., Jin, G., & Huang, X. (2023). SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE. doi:10.1109/iccv51070.2023.00190

DOI: 10.1109/iccv51070.2023.00190

STPA for Learning-Enabled Systems: A Survey and A New Practice (Conference Paper)

Qi, Y., Dong, Y., Khastgir, S., Jennings, P., Zhao, X., & Huang, X. (2023). STPA for Learning-Enabled Systems: A Survey and A New Practice. In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). IEEE. doi:10.1109/itsc57777.2023.10422520

DOI: 10.1109/itsc57777.2023.10422520

Model Checking for Probabilistic Multiagent Systems (Journal article)

Fu, C., Turrini, A., Huang, X., Song, L., Feng, Y., & Zhang, L. -J. (2023). Model Checking for Probabilistic Multiagent Systems. Journal of Computer Science and Technology, 38(5), 1162-1186. doi:10.1007/s11390-022-1218-6

DOI: 10.1007/s11390-022-1218-6

An accident prediction architecture based on spatio-clock stochastic and hybrid model for autonomous driving safety (Conference Paper)

Wang, J., Huang, Z., Huang, X., Wang, T., Shen, G., & Xie, J. (2023). An accident prediction architecture based on spatio-clock stochastic and hybrid model for autonomous driving safety. In CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE Vol. 35. doi:10.1002/cpe.6550

DOI: 10.1002/cpe.6550

What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems (Preprint)

Risk Controlled Image Retrieval (Preprint)

Robust Bayesian Abstraction of Neural Networks (Conference Paper)

Alshareef, A., Berthier, N., Schewe, S., & Huang, X. (2023). Robust Bayesian Abstraction of Neural Networks. In 2023 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE. doi:10.1109/icmlc58545.2023.10327954

DOI: 10.1109/icmlc58545.2023.10327954

Towards Verifying the Geometric Robustness of Large-Scale Neural Networks (Conference Paper)

Wang, F., Xu, P., Ruan, W., & Huang, X. (2023). Towards Verifying the Geometric Robustness of Large-Scale Neural Networks. In Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 Vol. 37 (pp. 15197-15205).

Sora: Scalable Black-Box Reachability Analyser on Neural Networks (Conference Paper)

Xu, P., Wang, F., Ruan, W., Zhang, C., & Huang, X. (2023). Sora: Scalable Black-Box Reachability Analyser on Neural Networks. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. doi:10.1109/icassp49357.2023.10097180

DOI: 10.1109/icassp49357.2023.10097180

A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation (Preprint)

Generalizing universal adversarial perturbations for deep neural networks (Journal article)

Zhang, Y., Ruan, W., Wang, F., & Huang, X. (2023). Generalizing universal adversarial perturbations for deep neural networks. MACHINE LEARNING, 112(5), 1597-1626. doi:10.1007/s10994-023-06306-z

DOI: 10.1007/s10994-023-06306-z

Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings (Journal article)

Cai, K., Lu, C. X., & Huang, X. (2023). Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings. IEEE Robotics and Automation Letters, 8(5), 2558-2565. doi:10.1109/lra.2023.3256085

DOI: 10.1109/lra.2023.3256085

Short-term Load Forecasting with Distributed Long Short-Term Memory (Conference Paper)

Dong, Y., Chen, Y., Zhao, X., & Huang, X. (2023). Short-term Load Forecasting with Distributed Long Short-Term Memory. In 2023 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE, ISGT. doi:10.1109/ISGT51731.2023.10066368

DOI: 10.1109/ISGT51731.2023.10066368

Transportation Object Counting With Graph-Based Adaptive Auxiliary Learning (Journal article)

Meng, Y., Bridge, J., Zhao, Y., Joddrell, M., Qiao, Y., Yang, X., . . . Zheng, Y. (2023). Transportation Object Counting With Graph-Based Adaptive Auxiliary Learning. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 24(3), 3422-3437. doi:10.1109/TITS.2022.3226504

DOI: 10.1109/TITS.2022.3226504

The AAAI-2023 Workshop on Artificial Intelligence Safety (SafeAI-2023) (Conference Paper)

Pedroza, G., Huang, X., Chen, X. C., Theodorou, A., Hernández-Orallo, J., Castillo-Effen, M., . . . McDermid, J. (2023). The AAAI-2023 Workshop on Artificial Intelligence Safety (SafeAI-2023). In CEUR Workshop Proceedings Vol. 3381.

Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation (Conference Paper)

Dong, Y., Li, Z., Zhao, X., Ding, Z., & Huang, X. (2023). Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS Vol. 2023-May (pp. 1421-1429).

Learning by Analogy: Diverse Questions Generation in Math Word Problem (Conference Paper)

Zhou, Z., Ning, M., Wang, Q., Yao, J., Wang, W., Huang, X., & Huang, K. (2023). Learning by Analogy: Diverse Questions Generation in Math Word Problem. In Findings of the Association for Computational Linguistics: ACL 2023. Association for Computational Linguistics. doi:10.18653/v1/2023.findings-acl.705

DOI: 10.18653/v1/2023.findings-acl.705

Model-Agnostic Reachability Analysis on Deep Neural Networks (Conference Paper)

Zhang, C., Ruan, W., Wang, F., Xu, P., Min, G., & Huang, X. (2023). Model-Agnostic Reachability Analysis on Deep Neural Networks. In Unknown Conference (pp. 341-354). Springer Nature Switzerland. doi:10.1007/978-3-031-33374-3_27

DOI: 10.1007/978-3-031-33374-3_27

Negative Hesitation Fuzzy Sets and Their Application to Pattern Recognition (Journal article)

Yang, Y., Lee, S., Zhang, H., Huang, X., & Pedrycz, W. (2024). Negative Hesitation Fuzzy Sets and Their Application to Pattern Recognition. IEEE Transactions on Fuzzy Systems, 32(4), 1836-1847. doi:10.1109/tfuzz.2023.3336673

DOI: 10.1109/tfuzz.2023.3336673

Randomized Adversarial Training via Taylor Expansion (Conference Paper)

Jin, G., Yi, X., Wu, D., Mu, R., & Huang, X. (2023). Randomized Adversarial Training via Taylor Expansion. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. doi:10.1109/cvpr52729.2023.01578

DOI: 10.1109/cvpr52729.2023.01578

The IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL2023) (Conference Paper)

Pedroza, G., Chen, X. C., Hernández-Orallo, J., Huang, X., Theodorou, A., Matragkas, N., . . . Liu, A. (2023). The IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL2023). In CEUR Workshop Proceedings Vol. 3505.

Weight-based Semantic Testing Approach for Deep Neural Networks (Conference Paper)

Alshareef, A., Berthier, N., Schewe, S., & Huang, X. (2023). Weight-based Semantic Testing Approach for Deep Neural Networks. In CEUR Workshop Proceedings Vol. 3505.

2022

Multi-Scope Feature Extraction for Intracranial Aneurysm 3D Point Cloud Completion (Journal article)

Ma, W., Yang, X., Wang, Q., Huang, K., & Huang, X. (2022). Multi-Scope Feature Extraction for Intracranial Aneurysm 3D Point Cloud Completion. CELLS, 11(24). doi:10.3390/cells11244107

DOI: 10.3390/cells11244107

Formal Specification for Learning-Enabled Autonomous Systems (Conference Paper)

Bensalem, S., Cheng, C. -H., Huang, X., Katsaros, P., Molin, A., Nickovic, D., & Peled, D. (2022). Formal Specification for Learning-Enabled Autonomous Systems. In Unknown Conference (pp. 131-143). Springer International Publishing. doi:10.1007/978-3-031-21222-2_8

DOI: 10.1007/978-3-031-21222-2_8

Multi-Scope Feature Extraction for Point Cloud Completion (Conference Paper)

Ma, W., Wang, Q. -F., Huang, K., & Huang, X. (2022). Multi-Scope Feature Extraction for Point Cloud Completion. In 2022 International Conference on Cyber-Physical Social Intelligence (ICCSI). IEEE. doi:10.1109/iccsi55536.2022.9970616

DOI: 10.1109/iccsi55536.2022.9970616

Adversarial Label Poisoning Attack on Graph Neural Networks via Label Propagation (Conference Paper)

Liu, G., Huang, X., & Yi, X. (2022). Adversarial Label Poisoning Attack on Graph Neural Networks via Label Propagation. In Unknown Conference (pp. 227-243). Springer Nature Switzerland. doi:10.1007/978-3-031-20065-6_14

DOI: 10.1007/978-3-031-20065-6_14

Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking (Conference Paper)

Dong, Y., Zhao, X., & Huang, X. (2022). Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking. In 2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 5171-5178). doi:10.1109/IROS47612.2022.9981794

DOI: 10.1109/IROS47612.2022.9981794

STUN: Self-Teaching Uncertainty Estimation for Place Recognition (Conference Paper)

Cai, K., Lu, C. X., & Huang, X. (2022). STUN: Self-Teaching Uncertainty Estimation for Place Recognition. In 2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 6614-6621). doi:10.1109/IROS47612.2022.9981546

DOI: 10.1109/IROS47612.2022.9981546

Dependable learning-enabled multiagent systems (Journal article)

Huang, X., Peng, B., & Zhao, X. (2022). Dependable learning-enabled multiagent systems. AI COMMUNICATIONS, 35(4), 407-420. doi:10.3233/AIC-220128

DOI: 10.3233/AIC-220128

Comparative study of modern heuristic algorithms for global maximum power point tracking in photovoltaic systems under partial shading conditions (Journal article)

Wang, K., Ma, J., Man, K. L., Huang, K., & Huang, X. (2022). Comparative study of modern heuristic algorithms for global maximum power point tracking in photovoltaic systems under partial shading conditions. FRONTIERS IN ENERGY RESEARCH, 10. doi:10.3389/fenrg.2022.946864

DOI: 10.3389/fenrg.2022.946864

Editorial to theme section on open environmental software systems modeling (Journal article)

Yue, T., Arcaini, P., Wu, J., & Huang, X. (2022). Editorial to theme section on open environmental software systems modeling. SOFTWARE AND SYSTEMS MODELING, 21(4), 1273-1275. doi:10.1007/s10270-022-01032-x

DOI: 10.1007/s10270-022-01032-x

Short-term Load Forecasting with Distributed Long Short-Term Memory (Conference Paper)

Dong, Y., Chen, Y., Zhao, X., & Huang, X. (2022). Short-term Load Forecasting with Distributed Long Short-Term Memory. Retrieved from http://arxiv.org/abs/2208.01147v2

Quantifying safety risks of deep neural networks (Journal article)

Xu, P., Ruan, W., & Huang, X. (2022). Quantifying safety risks of deep neural networks. Complex and Intelligent Systems. doi:10.1007/s40747-022-00790-x

DOI: 10.1007/s40747-022-00790-x

Soft pseudo-Label shrinkage for unsupervised domain adaptive person re-identification (Journal article)

Zheng, D., Xiao, J., Chen, K., Huang, X., Chen, L., & Zhao, Y. (2022). Soft pseudo-Label shrinkage for unsupervised domain adaptive person re-identification. PATTERN RECOGNITION, 127. doi:10.1016/j.patcog.2022.108615

DOI: 10.1016/j.patcog.2022.108615

Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking (Conference Paper)

Dong, Y., Zhao, X., & Huang, X. (2021). Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking. Retrieved from http://dx.doi.org/10.1109/IROS47612.2022.9981794

A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems (Conference Paper)

Qi, Y., Conmy, P. R., Huang, W., Zhao, X., & Huang, X. (2022). A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems. Retrieved from http://arxiv.org/abs/2206.10216v1

A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network From Convolutional Neural Network. (Journal article)

Wu, D., Yi, X., & Huang, X. (2022). A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network From Convolutional Neural Network.. Frontiers in neuroscience, 16, 759900. doi:10.3389/fnins.2022.759900

DOI: 10.3389/fnins.2022.759900

Graph-Based Region and Boundary Aggregation for Biomedical Image Segmentation (Journal article)

Meng, Y., Zhang, H., Zhao, Y., Yang, X., Qiao, Y., MacCormick, I. J. C., . . . Zheng, Y. (2022). Graph-Based Region and Boundary Aggregation for Biomedical Image Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING, 41(3), 690-701. doi:10.1109/TMI.2021.3123567

DOI: 10.1109/TMI.2021.3123567

A Graph Neural Network Reasoner for Game Description Language (Conference Paper)

Gunawan, A., Ruan, J., & Huang, X. (2022). A Graph Neural Network Reasoner for Game Description Language. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS Vol. 3 (pp. 1607-1609).

A Graph Neural Network Reasoner for Game Description Language (Conference Paper)

Gunawan, A., Ruan, J., & Huang, X. (2022). A Graph Neural Network Reasoner for Game Description Language. In Proceedings of the Nineteenth International Conference on Principles of Knowledge Representation and Reasoning. International Joint Conferences on Artificial Intelligence Organization. doi:10.24963/kr.2022/46

DOI: 10.24963/kr.2022/46

A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems (Conference Paper)

Qi, Y., Conmy, P. R., Huang, W., Zhao, X., & Huang, X. (2022). A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems. In CEUR Workshop Proceedings Vol. 3215.

Bridging Formal Methods and Machine Learning with Global Optimisation (Chapter)

Huang, X., Ruan, W., Tang, Q., & Zhao, X. (2022). Bridging Formal Methods and Machine Learning with Global Optimisation. In Formal Methods and Software Engineering (pp. 1-19). Springer International Publishing. doi:10.1007/978-3-031-17244-1_1

DOI: 10.1007/978-3-031-17244-1_1

Enhancing Adversarial Training with Second-Order Statistics of Weights (Conference Paper)

Jin, G., Yi, X., Huang, W., Schewe, S., & Huang, X. (2022). Enhancing Adversarial Training with Second-Order Statistics of Weights. In 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) (pp. 15252-15262). doi:10.1109/CVPR52688.2022.01484

DOI: 10.1109/CVPR52688.2022.01484

EnnCore: End-to-End Conceptual Guarding of Neural Architectures (Conference Paper)

Manino, E., Carvalho, D., Dong, Y., Rozanova, J., Song, X., Mustafa, M. A., . . . Cordeiro, L. (2022). EnnCore: End-to-End Conceptual Guarding of Neural Architectures. In CEUR Workshop Proceedings Vol. 3087.

Quantifying the Importance of Latent Features in Neural Networks (Conference Paper)

Alshareef, A., Berthier, N., Schewe, S., & Huang, X. (2022). Quantifying the Importance of Latent Features in Neural Networks. In CEUR Workshop Proceedings Vol. 3087.

The AAAI-22 Workshop on Artificial Intelligence Safety (SafeAI 2022) (Conference Paper)

Pedroza, G., Hernández-Orallo, J., Chen, X. C., Huang, X., Espinoza, H., Castillo-Effen, M., . . . ÓhÉigeartaigh, S. S. (2022). The AAAI-22 Workshop on Artificial Intelligence Safety (SafeAI 2022). In CEUR Workshop Proceedings Vol. 3087.

The IJCAI-ECAI-22 Workshop on Artificial Intelligence Safety (Conference Paper)

Pedroza, G., Chen, X. C., Hernández-Orallo, J., Huang, X., Espinoza, H., Mallah, R., . . . Castillo-Effen, M. (2022). The IJCAI-ECAI-22 Workshop on Artificial Intelligence Safety. In CEUR Workshop Proceedings Vol. 3215.

2021

Spatio-Clock Synchronous Constraint Guided Safe Reinforcement Learning for Autonomous Driving (Journal article)

Wang, J., Huang, Z., Yang, D., Huang, X., Zhu, Y., & Hua, G. (2021). Spatio-Clock Synchronous Constraint Guided Safe Reinforcement Learning for Autonomous Driving. Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 58(12), 2585-2603. doi:10.7544/issn1000-1239.2021.20211023

DOI: 10.7544/issn1000-1239.2021.20211023

Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance (Journal article)

Dong, Y., Huang, W., Bharti, V., Cox, V., Banks, A., Wang, S., . . . Huang, X. (2023). Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance. ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, 22(3). doi:10.1145/3570918

DOI: 10.1145/3570918

Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications (Conference Paper)

Ruan, W., Yi, X., & Huang, X. (2021). Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM. doi:10.1145/3459637.3482029

DOI: 10.1145/3459637.3482029

Sim-to-Real Deep Reinforcement Learning for Maximum Power Point Tracking of Photovoltaic Systems (Conference Paper)

Wang, K., Ma, J., Man, K. L., Huang, K., & Huang, X. (2021). Sim-to-Real Deep Reinforcement Learning for Maximum Power Point Tracking of Photovoltaic Systems. In 2021 21ST IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2021 5TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC/I&CPS EUROPE). doi:10.1109/EEEIC/ICPSEurope51590.2021.9584821

DOI: 10.1109/EEEIC/ICPSEurope51590.2021.9584821

Sim-to-Real Transfer with Domain Randomization for Maximum Power Point Estimation of Photovoltaic Systems (Conference Paper)

Wang, K., Ma, J., Man, K. L., Huang, K., & Huang, X. (2021). Sim-to-Real Transfer with Domain Randomization for Maximum Power Point Estimation of Photovoltaic Systems. In 2021 21ST IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2021 5TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC/I&CPS EUROPE). doi:10.1109/EEEIC/ICPSEurope51590.2021.9584526

DOI: 10.1109/EEEIC/ICPSEurope51590.2021.9584526

Tutorials on Testing Neural Networks (Preprint)

Spatial Uncertainty-Aware Semi-Supervised Crowd Counting (Conference Paper)

Meng, Y., Zhang, H., Zhao, Y., Yang, X., Qian, X., Huang, X., & Zheng, Y. (2021). Spatial Uncertainty-Aware Semi-Supervised Crowd Counting. In 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) (pp. 15529-15539). doi:10.1109/ICCV48922.2021.01526

DOI: 10.1109/ICCV48922.2021.01526

Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles (Conference Paper)

Zhao, X., Huang, W., Banks, A., Cox, V., Flynn, D., Schewe, S., & Huang, X. (2021). Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles. Retrieved from http://arxiv.org/abs/2106.01258v1

Enhancing Robustness Verification for Deep Neural Networks via Symbolic Propagation (Journal article)

Yang, P., Li, J., Liu, J., Huang, C. -C., Li, R., Chen, L., . . . Zhang, L. (2021). Enhancing Robustness Verification for Deep Neural Networks via Symbolic Propagation. FORMAL ASPECTS OF COMPUTING, 33(3), 407-435. doi:10.1007/s00165-021-00548-1

DOI: 10.1007/s00165-021-00548-1

Safety and reliability of deep learning (Conference Paper)

Huang, X. (2021). Safety and reliability of deep learning. In Proceedings of the 1st International Workshop on Verification of Autonomous & Robotic Systems. ACM. doi:10.1145/3459086.3459636

DOI: 10.1145/3459086.3459636

Detecting Operational Adversarial Examples for Reliable Deep Learning (Conference Paper)

Zhao, X., Huang, W., Schewe, S., Dong, Y., & Huang, X. (2021). Detecting Operational Adversarial Examples for Reliable Deep Learning. In 51ST ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS - SUPPLEMENTAL VOL (DSN 2021) (pp. 5-6). doi:10.1109/DSN-S52858.2021.00013

DOI: 10.1109/DSN-S52858.2021.00013

Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features (Preprint)

A Segment-Based Layout Aware Model for Information Extraction on Document Images (Conference Paper)

Ning, M., Wang, Q. -F., Huang, K., & Huang, X. (2021). A Segment-Based Layout Aware Model for Information Extraction on Document Images. In Unknown Conference (pp. 757-765). Springer International Publishing. doi:10.1007/978-3-030-92307-5_88

DOI: 10.1007/978-3-030-92307-5_88

An Overview of Verification and Validation Challenges for Inspection Robots (Journal article)

Fisher, M., Cardoso, R. C., Collins, E. C., Dadswell, C., Dennis, L. A., Dixon, C., . . . Webster, M. (2021). An Overview of Verification and Validation Challenges for Inspection Robots. ROBOTICS, 10(2). doi:10.3390/robotics10020067

DOI: 10.3390/robotics10020067

BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation (Conference Paper)

Meng, Y., Zhang, H., Gao, D., Zhao, Y., Yang, X., Qian, X., . . . Zheng, Y. (2021). BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation. In 32nd British Machine Vision Conference, BMVC 2021.

BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations (Conference Paper)

Zhao, X., Huang, W., Huang, X., Robu, V., & Flynn, D. (2021). BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations. In Proceedings of Machine Learning Research Vol. 161 (pp. 408-418).

Statistical Certification of Acceptable Robustness for Neural Networks (Conference Paper)

Huang, C., Hu, Z., Huang, X., & Pei, K. (2021). Statistical Certification of Acceptable Robustness for Neural Networks. In Unknown Conference (pp. 79-90). Springer International Publishing. doi:10.1007/978-3-030-86362-3_7

DOI: 10.1007/978-3-030-86362-3_7

The AAAI-21 workshop on artificial intelligence safety (safeai 2021) (Conference Paper)

Espinoza, H., Hernández-Orallo, J., Chen, X. C., ÓhÉigeartaigh, S. S., Huang, X., Castillo-Effen, M., . . . McDermid, J. (2021). The AAAI-21 workshop on artificial intelligence safety (safeai 2021). In CEUR Workshop Proceedings Vol. 2808.

The IJCAI-21 Workshop on Artificial Intelligence Safety (AISafety2021) (Conference Paper)

Espinoza, H., Pedroza, G., Hernández-Orallo, J., Chen, X. C., ÓhÉigeartaigh, S. S., Huang, X., . . . McDermid, J. (2021). The IJCAI-21 Workshop on Artificial Intelligence Safety (AISafety2021). In CEUR Workshop Proceedings Vol. 2916.

2020

BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations (Conference Paper)

Zhao, X., Huang, W., Huang, X., Robu, V., & Flynn, D. (2020). BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations. Retrieved from http://arxiv.org/abs/2012.03058v5

Multiclock Constraint System Modelling and Verification for Ensuring Cooperative Autonomous Driving Safety (Journal article)

Wang, J., Huang, Z., Huang, X., Zhu, Y., & Wang, F. (2020). Multiclock Constraint System Modelling and Verification for Ensuring Cooperative Autonomous Driving Safety. JOURNAL OF ADVANCED TRANSPORTATION, 2020. doi:10.1155/2020/8830752

DOI: 10.1155/2020/8830752

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program (Journal article)

Bang, G., Barash, G., Bea, R., Cali, J., Castillo-Effen, M., Chen, X. C., . . . Zhang, J. (2020). The Association for the Advancement of Artificial Intelligence 2020 Workshop Program. AI MAGAZINE, 41(4), 100-114. Retrieved from https://www.webofscience.com/

PRODeep: a platform for robustness verification of deep neural networks (Conference Paper)

Li, R., Li, J., Huang, C. -C., Yang, P., Huang, X., Zhang, L., . . . Hermanns, H. (2020). PRODeep: a platform for robustness verification of deep neural networks. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM. doi:10.1145/3368089.3417918

DOI: 10.1145/3368089.3417918

Generalizing Universal Adversarial Attacks Beyond Additive Perturbations (Conference Paper)

Zhang, Y., Ruan, W., Wang, F., & Huang, X. (2020). Generalizing Universal Adversarial Attacks Beyond Additive Perturbations. In 20TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2020) (pp. 1412-1417). doi:10.1109/ICDM50108.2020.00186

DOI: 10.1109/ICDM50108.2020.00186

Practical Verification of Neural Network Enabled State Estimation System for Robotics (Conference Paper)

Huang, W., Zhou, Y., Sun, Y., Sharp, J., Maskell, S., & Huang, X. (2020). Practical Verification of Neural Network Enabled State Estimation System for Robotics. In 2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 7336-7343). doi:10.1109/IROS45743.2020.9340720

DOI: 10.1109/IROS45743.2020.9340720

Embedding and Extraction of Knowledge in Tree Ensemble Classifiers (Journal article)

Huang, W., Zhao, X., & Huang, X. (n.d.). Embedding and Extraction of Knowledge in Tree Ensemble Classifiers. Machine Learning. Retrieved from http://arxiv.org/abs/2010.08281v2

How does Weight Correlation Affect the Generalisation Ability of Deep Neural Networks (Conference Paper)

Jin, G., Yi, X., Zhang, L., Zhang, L., Schewe, S., & Huang, X. (2020). How does Weight Correlation Affect the Generalisation Ability of Deep Neural Networks. Retrieved from http://arxiv.org/abs/2010.05983v3

Adaptable and Verifiable BDI Reasoning* (Conference Paper)

Stringer, P., Cardoso, R. C., Huang, X., & Dennis, L. A. (2020). Adaptable and Verifiable BDI Reasoning*. In ELECTRONIC PROCEEDINGS IN THEORETICAL COMPUTER SCIENCE (pp. 117-125). doi:10.4204/EPTCS.319.9

DOI: 10.4204/EPTCS.319.9

Generating Adversarial Inputs Using A Black-box Differential Technique (Journal article)

Juúnior, J. B. P. M., Cordeiro, L. C., d'Amorim, M., & Huang, X. (2020). Generating Adversarial Inputs Using A Black-box Differential Technique. Retrieved from http://arxiv.org/abs/2007.05315v1

A Safety Framework for Critical Systems Utilising Deep Neural Networks (Conference Paper)

Zhao, X., Banks, A., Sharp, J., Robu, V., Flynn, D., Fisher, M., & Huang, X. (2020). A Safety Framework for Critical Systems Utilising Deep Neural Networks. Retrieved from http://dx.doi.org/10.1007/978-3-030-54549-9_16

A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees (Journal article)

Wu, M., Wicker, M., Ruan, W., Huang, X., & Kwiatkowska, M. (2020). A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees. Theoretical Computer Science, 807, 298-329. doi:10.1016/j.tcs.2019.05.046

DOI: 10.1016/j.tcs.2019.05.046

Reliability Validation of Learning Enabled Vehicle Tracking (Journal article)

Sun, Y., Zhou, Y., Maskell, S., Sharp, J., & Huang, X. (2020). Reliability Validation of Learning Enabled Vehicle Tracking. 2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 9390-9396. doi:10.1109/icra40945.2020.9196932

DOI: 10.1109/icra40945.2020.9196932

CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation (Conference Paper)

Meng, Y., Wei, M., Gao, D., Zhao, Y., Yang, X., Huang, X., & Zheng, Y. (2020). CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation. In Unknown Conference (pp. 352-362). Springer International Publishing. doi:10.1007/978-3-030-59719-1_35

DOI: 10.1007/978-3-030-59719-1_35

Regression of Instance Boundary by Aggregated CNN and GCN (Conference Paper)

Meng, Y., Meng, W., Gao, D., Zhao, Y., Yang, X., Huang, X., & Zheng, Y. (2020). Regression of Instance Boundary by Aggregated CNN and GCN. In Unknown Conference (pp. 190-207). Springer International Publishing. doi:10.1007/978-3-030-58598-3_12

DOI: 10.1007/978-3-030-58598-3_12

The AAAI-20 workshop on artificial intelligence safety (SafeAI 2020) (Conference Paper)

Espinoza, H., Hernández-Orallo, J., Chen, X. C., ÓhÉigeartaigh, S. S., Huang, X., Castillo-Effen, M., . . . McDermid, J. (2020). The AAAI-20 workshop on artificial intelligence safety (SafeAI 2020). In CEUR Workshop Proceedings Vol. 2560 (pp. I-IV).

The IJCAI-PRICAI-20 workshop on artificial intelligence safety (AISafety 2020) (Conference Paper)

Espinoza, H., McDermid, J., Huang, X., Castillo-Effen, M., Chen, X. C., Hernández-Orallo, J., . . . Mallah, R. (2020). The IJCAI-PRICAI-20 workshop on artificial intelligence safety (AISafety 2020). In CEUR Workshop Proceedings Vol. 2640.

2019

Coverage-Guided Testing for Recurrent Neural Networks (Journal article)

Huang, W., Sun, Y., Zhao, X., Sharp, J., Ruan, W., Meng, J., & Huang, X. (2022). Coverage-Guided Testing for Recurrent Neural Networks. IEEE TRANSACTIONS ON RELIABILITY, 71(3), 1191-1206. doi:10.1109/TR.2021.3080664

DOI: 10.1109/TR.2021.3080664

Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles (Conference Paper)

Wu, M., Louw, T., Lahijanian, M., Ruan, W., Huang, X., Merat, N., & Kwiatkowska, M. (2019). Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles. In 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 6210-6216). doi:10.1109/iros40897.2019.8967779

DOI: 10.1109/iros40897.2019.8967779

Reasoning about Cognitive Trust in Stochastic Multiagent Systems (Journal article)

Huang, X. H., Kwiatkowska, M., & Olejnik, M. (2019). Reasoning about Cognitive Trust in Stochastic Multiagent Systems. ACM Transactions on Computational Logic, 20(04), 64 pages. doi:10.1145/3329123

DOI: 10.1145/3329123

Structural Test Coverage Criteria for Deep Neural Networks (Journal article)

Sun, Y., Huang, X., Kroening, D., Sharp, J., Hill, M., & Ashmore, R. (2019). Structural Test Coverage Criteria for Deep Neural Networks. ACM Transactions on Embedded Computing Systems, 18(5S). doi:10.1145/3358233

DOI: 10.1145/3358233

Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management (Conference Paper)

Zhao, X., Osborne, M., Lantair, J., Robu, V., Flynn, D., Huang, X., . . . Ferrando, A. (2019). Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management. In SOFTWARE ENGINEERING AND FORMAL METHODS (SEFM 2019) Vol. 11724 (pp. 105-124). doi:10.1007/978-3-030-30446-1_6

DOI: 10.1007/978-3-030-30446-1_6

Explaining Image Classifiers using Statistical Fault Localization (Journal article)

Sun, Y., Chockler, H., Huang, X., & Kroening, D. (2019). Explaining Image Classifiers using Statistical Fault Localization. Retrieved from http://arxiv.org/abs/1908.02374v2

Reasoning about Cognitive Trust in Stochastic Multiagent Systems (Conference Paper)

Huang, X., & Kwiatkowska, M. (2017). Reasoning about Cognitive Trust in Stochastic Multiagent Systems. In THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 3768-3774). Retrieved from https://www.webofscience.com/

DeepConcolic: Testing and Debugging Deep Neural Networks (Conference Paper)

Sun, Y., Huang, X., Kroening, D., Sharp, J., Hill, M., & Ashmore, R. (2019). DeepConcolic: Testing and Debugging Deep Neural Networks. In 2019 IEEE/ACM 41ST INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: COMPANION PROCEEDINGS (ICSE-COMPANION 2019) (pp. 111-114). doi:10.1109/ICSE-Companion.2019.00051

DOI: 10.1109/ICSE-Companion.2019.00051

Structural Test Coverage Criteria for Deep Neural Networks (Journal article)

Sun, Y., Huang, X., Kroening, D., Sharp, J., Hill, M., & Ashmore, R. (2019). Structural Test Coverage Criteria for Deep Neural Networks. 2019 IEEE/ACM 41ST INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: COMPANION PROCEEDINGS (ICSE-COMPANION 2019), 320-321. doi:10.1109/ICSE-Companion.2019.00134

DOI: 10.1109/ICSE-Companion.2019.00134

Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification (Conference Paper)

Li, J., Liu, J., Yang, P., Chen, L., Huang, X., & Zhang, L. (2019). Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification. In Unknown Conference (pp. 296-319). Springer International Publishing. doi:10.1007/978-3-030-32304-2_15

DOI: 10.1007/978-3-030-32304-2_15

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence (Conference Paper)

Barash, G., Castillo-Effen, M., Chhaya, N., Clark, P., Espinoza, H., Farchi, E., . . . Zitouni, I. (2019). Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence. In AI MAGAZINE Vol. 40 (pp. 67-78). doi:10.1609/aimag.v40i3.4981

DOI: 10.1609/aimag.v40i3.4981

The AAAI-19 workshop on artificial intelligence safety (SafeAI 2019) (Conference Paper)

Espinoza, H., hÉigeartaigh, S., Huang, X., Hernández-Orallo, J., & Castillo-Effen, M. (2019). The AAAI-19 workshop on artificial intelligence safety (SafeAI 2019). In CEUR Workshop Proceedings Vol. 2301.

The IJCAI-19 workshop on artificial intelligence safety (AI Safety 2019) (Conference Paper)

Espinoza, H., Yu, H., Huang, X., Lecue, F., Chen, C., Hernández-Orallo, J., . . . Mallah, R. (2019). The IJCAI-19 workshop on artificial intelligence safety (AI Safety 2019). In CEUR Workshop Proceedings Vol. 2419.

2018

A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability? (Journal article)

Huang, X., Kroening, D., Ruan, W., Sharp, J., Sun, Y., Thamo, E., . . . Yi, X. (2020). A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability?. COMPUTER SCIENCE REVIEW, 37. doi:10.1016/j.cosrev.2020.100270

DOI: 10.1016/j.cosrev.2020.100270

An Epistemic Strategy Logic (Journal article)

Huang, X., & Meyden, R. V. D. (2018). An Epistemic Strategy Logic. ACM Transactions on Computational Logic, 19(4). doi:10.1145/3233769

DOI: 10.1145/3233769

Reachability Analysis of Deep Neural Networks with Provable Guarantees (Conference Paper)

Ruan, W., Huang, X., & Kwiatkowska, M. (2018). Reachability Analysis of Deep Neural Networks with Provable Guarantees. Retrieved from http://arxiv.org/abs/1805.02242v1

Concolic Testing for Deep Neural Networks (Conference Paper)

Sun, Y., Wu, M., Ruan, W., Huang, X., Kwiatkowska, M., & Kroening, D. (2018, September 3). Concolic Testing for Deep Neural Networks. In 33rd IEEE/ACM International Conference on Automated Software Engineering. Montpellier, France. Retrieved from http://arxiv.org/abs/1805.00089v1

Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm (Journal article)

Ruan, W., Wu, M., Sun, Y., Huang, X., Kroening, D., & Kwiatkowska, M. (2018). Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm. Retrieved from http://arxiv.org/abs/1804.05805v2

Testing Deep Neural Networks (Journal article)

Sun, Y., Huang, X., Kroening, D., Sharp, J., Hill, M., & Ashmore, R. (2018). Testing Deep Neural Networks. Retrieved from http://arxiv.org/abs/1803.04792v4

Feature-Guided Black-Box Safety Testing of Deep Neural Networks (Conference Paper)

Wicker, M., Huang, X., & Kwiatkowska, M. (2018). Feature-Guided Black-Box Safety Testing of Deep Neural Networks. In Unknown Conference (pp. 408-426). Springer International Publishing. doi:10.1007/978-3-319-89960-2_22

DOI: 10.1007/978-3-319-89960-2_22

Model Checking Probabilistic Epistemic Logic for Probabilistic Multiagent Systems (Conference Paper)

Fu, C., Turrini, A., Huang, X., Song, L., Feng, Y., & Zhang, L. (2018). Model Checking Probabilistic Epistemic Logic for Probabilistic Multiagent Systems. In PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 4757-4763). Retrieved from https://www.webofscience.com/

Reachability Analysis of Deep Neural Networks with Provable Guarantees (Conference Paper)

Ruan, W., Huang, X., & Kwiatkowska, M. (2018). Reachability Analysis of Deep Neural Networks with Provable Guarantees. In PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 2651-2659). Retrieved from https://www.webofscience.com/

2017

Feature-Guided Black-Box Safety Testing of Deep Neural Networks (Journal article)

Wicker, M., Huang, X., & Kwiatkowska, M. (2017). Feature-Guided Black-Box Safety Testing of Deep Neural Networks. Retrieved from http://arxiv.org/abs/1710.07859v2

ATL Strategic Reasoning Meets Correlated Equilibrium (Conference Paper)

Huang, X., & Ruan, J. (2017). ATL Strategic Reasoning Meets Correlated Equilibrium. In PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 1102-1108). Retrieved from https://www.webofscience.com/

Quantified Coalition Logic of Knowledge, Belief and Certainty (Conference Paper)

Chen, Q., Huang, X., Su, K., & Sattar, A. (2017). Quantified Coalition Logic of Knowledge, Belief and Certainty. In ADVANCES IN ARTIFICIAL INTELLIGENCE, CANADIAN AI 2017 Vol. 10233 (pp. 351-360). doi:10.1007/978-3-319-57351-9_40

DOI: 10.1007/978-3-319-57351-9_40

Reasoning about cognitive trust in stochastic multiagent systems (Conference Paper)

Huang, X., & Kwiatkowska, M. (2017). Reasoning about cognitive trust in stochastic multiagent systems. In 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 3768-3774).

2016

Safety Verification of Deep Neural Networks (Conference Paper)

Huang, X., Kwiatkowska, M., Wang, S., & Wu, M. (2017). Safety Verification of Deep Neural Networks. In Computer Aided Verification Vol. 10426 (pp. 3-29). Heidelberg, Germany. doi:10.1007/978-3-319-63387-9_1

DOI: 10.1007/978-3-319-63387-9_1

Normative Multiagent Systems: A Dynamic Generalization (Conference Paper)

Huang, X., Ruan, J., Chen, Q., & Su, K. (2016). Normative Multiagent Systems: A Dynamic Generalization. Retrieved from http://arxiv.org/abs/1604.05086v1

The complexity of approximations for epistemic synthesis (extended abstract) (Conference Paper)

Huang, X., & van der Meyden, R. (n.d.). The complexity of approximations for epistemic synthesis (extended abstract). In Electronic Proceedings in Theoretical Computer Science Vol. 202 (pp. 120-137). Open Publishing Association. doi:10.4204/eptcs.202.9

DOI: 10.4204/eptcs.202.9

Model checking probabilistic knowledge: A PSPACE case (Conference Paper)

Huang, X., & Kwiatkowska, M. (2016). Model checking probabilistic knowledge: A PSPACE case. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 2516-2522).

Normative multiagent systems: A dynamic generalization (Journal article)

Huang, X., Ruan, J., Chen, Q., & Su, K. (2016). Normative multiagent systems: A dynamic generalization. IJCAI International Joint Conference on Artificial Intelligence, 2016-January, 1123-1129.

Reconfigurability in reactive multiagent systems (Conference Paper)

Huang, X., Chen, Q., Meng, J., & Su, K. (2016). Reconfigurability in reactive multiagent systems. In IJCAI International Joint Conference on Artificial Intelligence Vol. 2016-January (pp. 315-321).

Safety Verification of Deep Neural Networks (Journal article)

Huang, X., Kwiatkowska, M., Wang, S., & Wu, M. (2016). Safety Verification of Deep Neural Networks. CoRR, abs/1610.06940. Retrieved from http://arxiv.org/abs/1610.06940

Strengthening agents strategic ability with communication (Conference Paper)

Huang, X., Chen, Q., & Su, K. (2016). Strengthening agents strategic ability with communication. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 2509-2515).

2015

Bounded model checking of strategy ability with perfect recall (Journal article)

Huang, X. (2015). Bounded model checking of strategy ability with perfect recall. Artificial Intelligence, 222, 182-200. doi:10.1016/j.artint.2015.01.005

DOI: 10.1016/j.artint.2015.01.005

The Complexity of Model Checking Succinct Multiagent Systems (Conference Paper)

Huang, X., Chen, Q., & Su, K. (2015). The Complexity of Model Checking Succinct Multiagent Systems. In PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI) (pp. 1076-1082). Retrieved from https://www.webofscience.com/

2014

An Epistemic Strategy Logic (Journal article)

Huang, X., & Meyden, R. V. D. (2014). An Epistemic Strategy Logic. Retrieved from http://arxiv.org/abs/1409.2193v3

An Epistemic Strategy Logic (Extended Abstract) (Conference Paper)

Huang, X., & van der Meyden, R. (n.d.). An Epistemic Strategy Logic (Extended Abstract). In Electronic Proceedings in Theoretical Computer Science Vol. 146 (pp. 35-41). Open Publishing Association. doi:10.4204/eptcs.146.5

DOI: 10.4204/eptcs.146.5

A Temporal Logic of Strategic Knowledge (Conference Paper)

Huang, X., & van der Meyden, R. (2014). A Temporal Logic of Strategic Knowledge. In FOURTEENTH INTERNATIONAL CONFERENCE ON THE PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING (pp. 418-427). Retrieved from https://www.webofscience.com/

Symbolic Model Checking Epistemic Strategy Logic (Conference Paper)

Huang, X., & Van der Meyden, R. (2014). Symbolic Model Checking Epistemic Strategy Logic. In PROCEEDINGS OF THE TWENTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 1426-1432). Retrieved from https://www.webofscience.com/

Symbolic Synthesis for Epistemic Specifications with Observational Semantics (Conference Paper)

Huang, X., & van der Meyden, R. (2014). Symbolic Synthesis for Epistemic Specifications with Observational Semantics. In Unknown Conference (pp. 455-469). Springer Berlin Heidelberg. doi:10.1007/978-3-642-54862-8_39

DOI: 10.1007/978-3-642-54862-8_39

2013

Model Checking for Reasoning about Incomplete Information Games (Conference Paper)

Huang, X., Ruan, J., & Thielscher, M. (2013). Model Checking for Reasoning about Incomplete Information Games. In Unknown Conference (pp. 246-258). Springer International Publishing. doi:10.1007/978-3-319-03680-9_27

DOI: 10.1007/978-3-319-03680-9_27

Symbolic Synthesis of Knowledge-based Program Implementations with Synchronous Semantics (Conference Paper)

Huang, X., & Meyden, R. V. D. (2013). Symbolic Synthesis of Knowledge-based Program Implementations with Synchronous Semantics. Retrieved from http://arxiv.org/abs/1310.6423v1

A logic of probabilistic knowledge and strategy (Conference Paper)

Huang, X., & Luo, C. (2013). A logic of probabilistic knowledge and strategy. In 12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 Vol. 2 (pp. 845-852).

Bounded planning for strategic goals with incomplete information and perfect recall (Conference Paper)

Huang, X. (2013). Bounded planning for strategic goals with incomplete information and perfect recall. In 12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 Vol. 2 (pp. 885-892).

Diagnosability in concurrent probabilistic systems (Conference Paper)

Huang, X. (2013). Diagnosability in concurrent probabilistic systems. In 12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 Vol. 2 (pp. 853-860).

Symbolic synthesis of knowledge-based program implementations with synchronous semantics (Journal article)

Huang, X., & Van Der Meyden, R. (2013). Symbolic synthesis of knowledge-based program implementations with synchronous semantics. Proceedings of the 14th Conference on Theoretical Aspects of Rationality and Knowledge, TARK 2013, 121-130.

2012

Probabilistic alternating-time temporal logic of incomplete information and synchronous perfect recall (Conference Paper)

Huang, X., Su, K., & Zhang, C. (2012). Probabilistic alternating-time temporal logic of incomplete information and synchronous perfect recall. In Proceedings of the National Conference on Artificial Intelligence Vol. 1 (pp. 765-771).

Synthesizing strategies for epistemic goals by epistemic model checking: An application to pursuit evasion games (Conference Paper)

Huang, X., & Van Der Meyden, R. (2012). Synthesizing strategies for epistemic goals by epistemic model checking: An application to pursuit evasion games. In Proceedings of the National Conference on Artificial Intelligence Vol. 1 (pp. 772-778).

Probabilistic Alternating-Time Temporal Logic of Incomplete Information and Synchronous Perfect Recall (Conference Paper)

Huang, X., Su, K., & Zhang, C. (2012). Probabilistic Alternating-Time Temporal Logic of Incomplete Information and Synchronous Perfect Recall. In Proceedings of the 26th AAAI Conference on Artificial Intelligence, AAAI 2012 (pp. 765-771).

Synthesizing Strategies for Epistemic Goals by Epistemic Model Checking: An Application to Pursuit Evasion Games (Conference Paper)

Huang, X., & van der Meyden, R. (2012). Synthesizing Strategies for Epistemic Goals by Epistemic Model Checking: An Application to Pursuit Evasion Games. In Proceedings of the 26th AAAI Conference on Artificial Intelligence, AAAI 2012 (pp. 772-778).

2011

Model checking knowledge in pursuit evasion games (Conference Paper)

Huang, X., Maupin, P., & Van Der Meyden, R. (2011). Model checking knowledge in pursuit evasion games. In IJCAI International Joint Conference on Artificial Intelligence (pp. 240-245). doi:10.5591/978-1-57735-516-8/IJCAI11-051

DOI: 10.5591/978-1-57735-516-8/IJCAI11-051

Symbolic model checking of probabilistic knowledge (Conference Paper)

Huang, X., Luo, C., & van der Meyden, R. (2011). Symbolic model checking of probabilistic knowledge. In Proceedings of the 13th Conference on Theoretical Aspects of Rationality and Knowledge. ACM. doi:10.1145/2000378.2000399

DOI: 10.1145/2000378.2000399

Improved Bounded Model Checking for a Fair Branching-Time Temporal Epistemic Logic (Conference Paper)

Huang, X., Luo, C., & van der Meyden, R. (2011). Improved Bounded Model Checking for a Fair Branching-Time Temporal Epistemic Logic. In Unknown Conference (pp. 95-111). Springer Berlin Heidelberg. doi:10.1007/978-3-642-20674-0_7

DOI: 10.1007/978-3-642-20674-0_7

2010

A precongruence format for should testing preorder (Journal article)

Huang, X., Jiao, L., & Lu, W. (2010). A precongruence format for should testing preorder. JOURNAL OF LOGIC AND ALGEBRAIC PROGRAMMING, 79(3-5), 245-263. doi:10.1016/j.jlap.2010.03.001

DOI: 10.1016/j.jlap.2010.03.001

Congruence Formats for Weak Readiness Equivalence and Weak Possible Future Equivalence (Journal article)

Huang, X., Jiao, L., & Lu, W. (2010). Congruence Formats for Weak Readiness Equivalence and Weak Possible Future Equivalence. COMPUTER JOURNAL, 53(1), 21-36. doi:10.1093/comjnl/bxn009

DOI: 10.1093/comjnl/bxn009

Improved bounded model checking for a fair branching-time temporal epistemic logic (Conference Paper)

Huang, X., Luo, C., & Meyden, R. V. D. (2010). Improved bounded model checking for a fair branching-time temporal epistemic logic. In 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada, May 10-14, 2010, Volume 1-3 (pp. 1403-1404). doi:10.1145/1838206.1838403

DOI: 10.1145/1838206.1838403

The complexity of epistemic model checking: Clock semantics and branching time (Conference Paper)

Huang, X., & Van Der Meyden, R. (2010). The complexity of epistemic model checking: Clock semantics and branching time. In Frontiers in Artificial Intelligence and Applications Vol. 215 (pp. 549-554). doi:10.3233/978-1-60750-606-5-549

DOI: 10.3233/978-1-60750-606-5-549

2009

Model Checking Games for a Fair Branching-Time Temporal Epistemic Logic (Conference Paper)

Huang, X., & van der Meyden, R. (2009). Model Checking Games for a Fair Branching-Time Temporal Epistemic Logic. In Unknown Conference (pp. 11-20). Springer Berlin Heidelberg. doi:10.1007/978-3-642-10439-8_2

DOI: 10.1007/978-3-642-10439-8_2

2008

Weak Parametric Failure Equivalences and Their Congruence Formats (Conference Paper)

Huang, X., Jiao, L., & Lu, W. (2008). Weak Parametric Failure Equivalences and Their Congruence Formats. In Theory of Computing 2008. Proc. Fourteenth Computing: The Australasian Theory Symposium (CATS 2008), Wollongong, NSW, Australia, January 22-25, 2008. Proceedings (pp. 15-26). Retrieved from http://crpit.com/abstracts/CRPITV77Huang.html

2007

A Modular Petri Net Used in Synchronous Communication of Sequential Processes (Conference Paper)

Huang, X., & Meng, J. (2007). A Modular Petri Net Used in Synchronous Communication of Sequential Processes. In Proceedings of the 2007 International Conference on Modeling, Simulation & Visualization Methods, MSV 2007, Las Vegas, Nevada, USA, June 25-28, 2007 (pp. 194-200).

A Semantic Preorder Combining ST Notion and Fair Testing Semantic (Conference Paper)

Huang, X., & Meng, J. (2007). A Semantic Preorder Combining ST Notion and Fair Testing Semantic. In Proceedings of the 2007 International Conference on Foundations of Computer Science, FCS 2007, June 25-28, 2007, Las Vegas, Nevada, USA (pp. 82-88).

What Semantic Equivalences Are Suitable for Non-interference Properties in Computer Security (Conference Paper)

Huang, X., Jiao, L., & Lu, W. (2007). What Semantic Equivalences Are Suitable for Non-interference Properties in Computer Security. In Unknown Conference (pp. 334-349). Springer Berlin Heidelberg. doi:10.1007/978-3-540-77048-0_26

DOI: 10.1007/978-3-540-77048-0_26