2023
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). IEEE. doi:10.1109/isgt51731.2023.10066368DOI: 10.1109/isgt51731.2023.10066368
Dong, Y., Li, Z., Zhao, X., Ding, Z., & Huang, X. (n.d.). Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation. In The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
The Unnecessity of Assuming Statistically Independent Tests in Bayesian Software Reliability Assessments (Journal article)
Salako, K., & Zhao, X. (2023). The Unnecessity of Assuming Statistically Independent Tests in Bayesian Software Reliability Assessments. IEEE Transactions on Software Engineering, 1-9. doi:10.1109/tse.2022.3233802
2022
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/IROS47612.2022.9981794DOI: 10.1109/IROS47612.2022.9981794
Huang, X., Peng, B., & Zhao, X. (2022). Dependable learning-enabled multiagent systems. AI COMMUNICATIONS, 35(4), 407-420. doi:10.3233/AIC-220128DOI: 10.3233/AIC-220128
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
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_1DOI: 10.1007/978-3-031-17244-1_1
2021
Dong, Y., Huang, W., Bharti, V., Cox, V., Banks, A., Wang, S., . . . Huang, X. (2021). Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance. ACM Transactions on Embedded Computing Systems; 2022. Retrieved from http://dx.doi.org/10.1145/3570918
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
Salako, K., Strigini, L., & Zhao, X. (2021). Conservative Confidence Bounds in Safety, from Generalised Claims of Improvement & Statistical Evidence. In 51ST ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS (DSN 2021) (pp. 451-462). doi:10.1109/DSN48987.2021.00055DOI: 10.1109/DSN48987.2021.00055
Zhao, X., Huang, X., Robu, V., & Flynn, D. (n.d.). BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations. Retrieved from http://arxiv.org/abs/2012.03058v1
Huang, W., Sun, Y., Zhao, X., Sharp, J., Ruan, W., Meng, J., & Huang, X. (n.d.). Coverage Guided Testing for Recurrent Neural Networks. IEEE Transactions on Reliability.
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-S52858.2021.00013DOI: 10.1109/DSN-S52858.2021.00013
2020
Zhao, X., Calinescu, R., Gerasimou, S., Robu, V., & Flynn, D. (2020). Interval Change-Point Detection for Runtime Probabilistic Model Checking. In 2020 35TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2020) (pp. 163-174). doi:10.1145/3324884.3416565DOI: 10.1145/3324884.3416565
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
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
Assessing safety-critical systems from operational testing: A study on autonomous vehicles (Journal article)
Zhao, X., Salako, K., Strigini, L., Robu, V., & Flynn, D. (2020). Assessing safety-critical systems from operational testing: A study on autonomous vehicles. INFORMATION AND SOFTWARE TECHNOLOGY, 128. doi:10.1016/j.infsof.2020.106393DOI: 10.1016/j.infsof.2020.106393
On reliability assessment when a software-based system is replaced by a thought-to-be-better one (Journal article)
Littlewood, B., Salako, K., Strigini, L., & Zhao, X. (2020). On reliability assessment when a software-based system is replaced by a thought-to-be-better one. RELIABILITY ENGINEERING & SYSTEM SAFETY, 197. doi:10.1016/j.ress.2019.106752DOI: 10.1016/j.ress.2019.106752
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
2019
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.2021.3080664DOI: 10.1109/TR.2021.3080664
UAS Operators Safety and Reliability Survey: Emerging Technologies towards the Certification of Autonomous UAS (Conference Paper)
Osborne, M., Lantair, J., Shafiq, Z., Zhao, X., Robu, V., Flynn, D., & Perry, J. (2019). UAS Operators Safety and Reliability Survey: Emerging Technologies towards the Certification of Autonomous UAS. In 2019 4TH INTERNATIONAL CONFERENCE ON SYSTEM RELIABILITY AND SAFETY (ICSRS 2019) (pp. 203-212). Retrieved from https://www.webofscience.com/
The Application of Machine Learning and Low Frequency Sonar for Subsea Power Cable Integrity Evaluation (Conference Paper)
Tang, W., Flynn, D., Brown, K., Valentin, R., & Zhao, X. (2019). The Application of Machine Learning and Low Frequency Sonar for Subsea Power Cable Integrity Evaluation. In OCEANS 2019 MTS/IEEE SEATTLE. Retrieved from https://www.webofscience.com/
The Design of a Fusion Prognostic Model and Health Management System for Subsea Power Cables (Conference Paper)
Tang, W., Flynn, D., Brown, K., Valentin, R., & Zhao, X. (2019). The Design of a Fusion Prognostic Model and Health Management System for Subsea Power Cables. In OCEANS 2019 MTS/IEEE SEATTLE. Retrieved from https://www.webofscience.com/
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_6DOI: 10.1007/978-3-030-30446-1_6
Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing (Conference Paper)
Zhao, X., Robu, V., Flynn, D., Salako, K., & Strigini, L. (2019). Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing. In 2019 IEEE 30TH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING (ISSRE) (pp. 13-23). doi:10.1109/ISSRE.2019.00012DOI: 10.1109/ISSRE.2019.00012
Machine learning methods for wind turbine condition monitoring: A review (Journal article)
Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., . . . Nenadic, G. (2019). Machine learning methods for wind turbine condition monitoring: A review. RENEWABLE ENERGY, 133, 620-635. doi:10.1016/j.renene.2018.10.047DOI: 10.1016/j.renene.2018.10.047
2018
Zhao, X., Robu, V., Flynn, D., Dinmohammadi, F., Fisher, M., & Webster, M. (2019). Probabilistic Model Checking of Robots Deployed in Extreme Environments. In THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE (pp. 8066-8074). Retrieved from https://www.webofscience.com/
Fisher, M., Collins, E., Dennis, L., Luckcuck, M., Webster, M., Jump, M., . . . Zhao, X. (2018). Verifiable Self-Certifying Autonomous Systems. In 2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). IEEE. doi:10.1109/issrew.2018.00028DOI: 10.1109/issrew.2018.00028
Conservative claims for the probability of perfection of a software-based system using operational experience of previous similar systems (Journal article)
Zhao, X., Littlewood, B., Povyakalo, A., Strigini, L., & Wright, D. (2018). Conservative claims for the probability of perfection of a software-based system using operational experience of previous similar systems. RELIABILITY ENGINEERING & SYSTEM SAFETY, 175, 265-282. doi:10.1016/j.ress.2018.03.032DOI: 10.1016/j.ress.2018.03.032
2017
Modeling the probability of failure on demand (pfd) of a 1-out-of-2 system in which one channel is "quasi-perfect" (Journal article)
Zhao, X., Littlewood, B., Povyakalo, A., Strigini, L., & Wright, D. (2017). Modeling the probability of failure on demand (pfd) of a 1-out-of-2 system in which one channel is "quasi-perfect". RELIABILITY ENGINEERING & SYSTEM SAFETY, 158, 230-245. doi:10.1016/j.ress.2016.09.002DOI: 10.1016/j.ress.2016.09.002
2016
Conservative Claims about the Probability of Perfection of Software-based Systems (Conference Paper)
Zhao, X., Littlewood, B., Povyakalo, A., & Wright, D. (2015). Conservative Claims about the Probability of Perfection of Software-based Systems. In 2015 IEEE 26TH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING (ISSRE) (pp. 130-140). Retrieved from https://www.webofscience.com/
2014
A Dependability Case Construction Approach Based on Dependability Deviation Analysis (Journal article)
Zhang, D. J., Lu, M. Y., & Zhao, X. Y. (n.d.). A Dependability Case Construction Approach Based on Dependability Deviation Analysis. Applied Mechanics and Materials, 543-547, 3682-3687. doi:10.4028/www.scientific.net/amm.543-547.3682DOI: 10.4028/www.scientific.net/amm.543-547.3682
2012
A New Approach to Assessment of Confidence in Assurance Cases (Conference Paper)
Zhao, X., Zhang, D., Lu, M., & Zeng, F. (2012). A New Approach to Assessment of Confidence in Assurance Cases. In Unknown Conference (pp. 79-91). Springer Berlin Heidelberg. doi:10.1007/978-3-642-33675-1_7DOI: 10.1007/978-3-642-33675-1_7
2011
Variance Analysis Based Software Fault Localization (Conference Paper)
Variance Analysis Based Software Fault Localization (2011). In Unknown Conference (pp. 333-339). ASME Press. doi:10.1115/1.859858.paper46DOI: 10.1115/1.859858.paper46