“Dada“ of Chen, a researcher, a developer & a basketballer

About Me

Greetings!

I am currently a lecturer (assistant professor) in The University of Sydney.

“Study without desire spoils the memory, and it retains nothing that it takes in.” – Leonardo da Vinci

Publication

Publications

Huang, Y., Song, J., Wang, Z., Chen, H. and Ma, L., 2024. “Look before you leap: An exploratory study of uncertainty measurement for large language models”. arXiv preprint arXiv:2307.10236.
Li, Y., Chen, H., Bao, W., Xu, Z. and Yuan, D., 2024. “Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarios”. arXiv preprint arXiv:2311.05826.
Huang, L., Zhao, P., Chen, H. and Ma, L., 2024. “Large Language Models Based Fuzzing Techniques: A Survey”.
Li, H., Shi, J., Chen, H., Du, B., Maksour, S., Phillips, G., Dottori, M. and Shen, J., 2024. “FDNet: Frequency Domain Denoising Network For Cell Segmentation in Astrocytes Derived From Induced Pluripotent Stem Cells”. The IEEE International Symposium on Biomedical Imaging (ISBI) 2024
Ibrahim, J., Li, Y., Chen, H., Yuan, D., 2024, “Holistic Evaluation Metrics for Federated Learning”, The 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2024)
Wen, J., Yuan, D., Ma, L. and Chen, H., 2024. “Code Ownership in Open-Source AI Software Security”. RAIE @ 46th International Conference on Software Engineering (ICSE 2024)
Zhu, Z., Chen, H., Wang, X., Zhang, J., Jin, Z., Choo, K.K.R., Shen, J., Yuan, D., 2024. “GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model”. SIAM International Conference on Data Mining (SDM24)
Zhu, Z., Wang, X., Jin, Z., Zhang, J. and Chen, H., 2024. “Enhancing Transferable Adversarial Attacks on Vision Transformers through Gradient Normalization Scaling and High-Frequency Adaptation”. In The Twelfth International Conference on Learning Representations.
Zhu, Z., Jin, Z., Wang, X., Zhang, J., Chen, H. and Choo, K.K.R., 2024. “Rethinking Transferable Adversarial Attacks With Double Adversarial Neuron Attribution”. IEEE Transactions on Artificial Intelligence, 1(01), pp.1-11.
Liu, W., Zhou, G., Mao, X., Bao, S., Li, H., Shi, J., Chen, H., Shen, J. and Huang, Y., 2024. “Global disentangled graph convolutional neural network based on a graph topological metric”. Knowledge-Based Systems, 284, p.111283.
Jin, Z., Zhang, J., Zhu, Z. and Chen, H., 2024. “Benchmarking Transferable Adversarial Attacks”. AISCC@NDSS-symposium
Zhu, Z., Chen, H., Zhang, J., Wang, X., Jin, Z., Xue, J. and Salim, F.D., 2024. “AttEXplore: Attribution for Explanation with model parameters eXploration”. In The Twelfth International Conference on Learning Representations.
Liu, J., Chen, H., Shen, J. and Choo, K.K.R., 2024. “FairCompass: Operationalising Fairness in Machine Learning”. IEEE Transactions on Artificial Intelligence, 1(01), pp.1-10.
Chen, H. and Babar, M.A., 2024. “Security for Machine Learning-based Software Systems: A Survey of Threats, Practices, and Challenges”. ACM Computing Surveys, 56(6), pp.1-38.
Zhu, Z., Chen, H., Zhang, J., Wang, X., Jin, Z., Xue, M., Zhu, D. and Choo, K.K.R., 2023. MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks. The 38th Annual AAAI Conference on Artificial Intelligence
Zhang, Y., Chen, H., Lai, Z., Zhang, Z. and Yuan, D., 2023, November. “Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads”. In Australasian Joint Conference on Artificial Intelligence (pp. 79-90). Singapore: Springer Nature Singapore.
Zhu, Z., Chen, H., Zhang, J., Wang, X., Jin, Z., Lu, Q., Shen, J. and Choo, K.K.R., 2023, October. “Improving adversarial transferability via frequency-based stationary point search”. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 3626-3635).
Zhu, Z., Chen, H., Jin, Z., Wang, X., Zhang, J., Xue, M., Lu, Q., Shen, J. and Choo, K.K.R., 2023, October. “FVW: Finding Valuable Weight on Deep Neural Network for Model Pruning”. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 3657-3666).
Zhu, Z., Zhang, J., Jin, Z., Wang, X., Xue, M., Shen, J., Choo, K.K.R. and Chen, H., 2023, September. “Towards Minimising Perturbation Rate for Adversarial Machine Learning with Pruning”. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 147-163). Cham: Springer Nature Switzerland.
Jin, Z., Zhu, Z., Wang, X., Zhang, J., Shen, J. and Chen, H., 2023, August. “DANAA: Towards transferable attacks with double adversarial neuron attribution”. In International Conference on Advanced Data Mining and Applications (pp. 456-470). Cham: Springer Nature Switzerland.
Jin, Z., Zhu, Z., Hu, H., Xue, M. and Chen, H., 2023, July. “POSTER: ML-Compass: A Comprehensive Assessment Framework for Machine Learning Models”. In Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security (pp. 1031-1033).
Li, Y., Ye, C., Chen, H., Chen, S., Xue, M. and Shen, J., 2023, July. “Towards better ML-based software services: an investigation of source code engineering impact”. In 2023 IEEE International Conference on Software Services Engineering (SSE) (pp. 1-10). IEEE.
Lai, Z., Yuan, D., Chen, H., Zhang, Y. and Bao, W., 2023, May. “WirelessDT: A Digital Twin Platform for Real-Time Evaluation of Wireless Software Applications”. In 2023 IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (pp. 146-150). IEEE
Huang, F., Yang, N., Chen, H., Bao, W. and Yuan, D., 2023. “Distributed Online Multi-Label Learning with Privacy Protection in Internet of Things”. Applied Sciences, 13(4), p.2713.
Wang, Z., Byrnes, O., Wang, H., Sun, R., Ma, C., Chen, H., Wu, Q. and Xue, M., 2023. “Data hiding with deep learning: a survey unifying digital watermarking and steganography”. IEEE Transactions on Computational Social Systems, 10(6), pp.1-15.
Li, Y., Zhong, L., Yuan, D., Chen, H. and Bao, W., 2023. “ICB FL: Implicit Class Balancing Towards Fairness in Federated Learning”. In Proceedings of the 2023 Australasian Computer Science Week (pp. 135-142).
Hin, D., Kan, A., Chen, H. and Babar, M.A., 2022, May. “Linevd: Statement-level vulnerability detection using graph neural networks”. In Proceedings of the 19th international conference on mining software repositories (pp. 596-607).
Le, T.H., Chen, H. and Babar, M.A., 2022. “A survey on data-driven software vulnerability assessment and prioritization”. ACM Computing Surveys, 55(5), pp.1-39.
Croft, R., Babar, M.A. and Chen, H., 2022, May. “Noisy label learning for security defects”. In Proceedings of the 19th International Conference on Mining Software Repositories (pp. 435-447).
Deng, Y., Zhang, C., Yang, N. and Chen, H., 2022. “FocalMatch: Mitigating Class Imbalance of Pseudo Labels in Semi-Supervised Learning”. Applied Sciences, 12(20), p.10623.
Chen, H., Li, F., Wang, L., Jin, Y., Chi, H., Kurgan, L., Song, J. and Shen, J., 2021, “Systematic Evaluation of machine learning methods for identifying human-pathogen protein-protein Interactions”, Briefings in Bioinformatics, 22(3), p.bbaa068. doi: 10.1093/bib/bbaa068 (IF: 11.622, Q1)
Yong, B., Shen, J., Liu, X., Li, F., Chen, H. and Zhou, Q., 2020, “An intelligent blockchain-based system for safe vaccine supply and supervision”, International Journal of Information Management, 52, p.102024. (IF: 14.098, Q1)
Li, F., Yang, A., Chen, H., Sun, G., Wang, F., Xie, Y., Li, J., Shen, J., 2020, “Towards Industrial Internet of Things in Steel Manufacturing: A Multiple-Factor-based Detection System of Longitudinal Surface Cracks”, Proceedings of IEEE International Conference on Big Data, 2020, pp. 1–9 (H5-index: 41)
Chen, H., Li, F., Sun, G., Zhang, X., Dong, X., Wang, L., Liao, K., Shen, H. and Shen, J., 2020, “A Service Computing Framework for Proteomics Analysis and Collaboration of Pathogenic Mechanism Studies”, IEEE International Conference on Services Computing 2020, pp. 463-465.
Chen, H., Jin, Y., Wang, L., Chi, H. and Shen, J., 2020, “HIME: Mining and Ensembling Heterogeneous Inforamtion for Protein-Protein Interactions Prediction”, The 2020 International Joint Conference on Neural Networks, 2020, pp. 1–8
Chen, H., Shen J., Wang L. and Jin Y., 2021, “Towards A More Effective Bidirectional LSTM-Based Learning Model for Human-Bacterium Protein-Protein Interactions” In: Panuccio G., Rocha M., Fdez-Riverola F., Mohamad M., Casado-Vara R. (eds) Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020). PACBB 2020. Advances in Intelligent Systems and Computing, vol 1240. Springer, Cham.
Chen, H., Shen, J., Wang, L. and Chi, H., 2020, “APEX2S: A Two-Layer Machine Learning Model for Discovery of Host-pathogen Protein-protein Interactions on Cloud-based Multi-Omics Data”, Concurrency and Computation: Practice and Experience, doi: 10.1002/cpe.5846 (IF: 1.536)
Sun, G., Lin, J., Shen, J., Cui, T., Xu, D. and Chen, H., 2020, “Evolutionary Learner Profile Optimization using Rare and Negative Association Rules for Micro Open Learning”, 16th International Conference on Intelligent Tutoring Systems 2020, pp. 1–8
Chen, H., Shen, J., Wang, L. and Song, J., 2020, “A Framework towards Data Analytics on Host-Pathogen Protein-Protein Interactions”, Journal of Ambient Intelligence and Humanized Computing, 11:4667–4679. (IF: 7.104, Q1)
Yong, B., Xu, Z., Shen, J., Chen, H., Wu, J., Li, F., and Zhou, Q., 2020. “A novel Monte Carlo-based neural network model for electricity load forecasting”. International Journal of Embedded Systems, 12(4), 522-533.
Chen, H., Wang, L., Chi, H. and Shen, J., 2019, “Leveraging SMOTE in A Two-Layer Model for Prediction of Protein-Protein Interactions”, The Seventh Intrnational Conference on Advanced Cloud and Big Data, 2019, pp. 133-138
Brown, P., RELISH Consortium and Zhou, Y., 2019, “Large expert-curated database for benchmarking document similarity detection in biomedical literature search”. Database, 2019. (IF: 2.593, Q1)
Chen, H., Wang, L., Jin, Y., Chi, C., Li, F., Chu, H. and Shen, J., 2019, “Hyperparameter Estimation in SVM with GPU Acceleration for Prediction of Protein-Protein Interactions”, Proceedings of IEEE International Conference on Big Data, 2019, pp. 1-8
Li, F., Wu, J., Dong, F., Lin, J., Sun, G., Chen, H. and Shen, J., 2018, “Ensemble Machine Learning Systems for the Estimation of Steel Quality Control”, Proceedings of IEEE International Conference on Big Data, 2018, pp.2245–2252
Chen, H., Shen, J., Wang, L. Song, J. and Chi, H., 2018, “Towards Biological Sequence Data Service with Insights”, Proceedings of IEEE International Conference on Big Data, 2018, pp. 2847–2854
Li, R., Rose, G., Chen, H. and Shen, J., 2018. “Effective long-term travel time prediction with fuzzy rules for tollway”. Neural Computing and Applications, 30(9), 2921-2933. (IF: 5.606, Q1)
Zhou, Q., Chen, C., Zhang, G., Chen, H., Chen, D., Yan, Y., Shen, J. and Zhou, R., 2018, “Real-time Management of groundwater resource based on wireless sensor networks”, Journal of Sensor and Actuator Networks, 4(1): 1-11
Yong, B., Shen, J., Shen, Z., Chen, H., Wang, X. and Zhou, Q., 2018. “GVM based intuitive simulation web application for collision detection”. Neurocomputing, 279, pp.63-73. (IF: 5.719, Q1)
Chen, H., Guo, W., Shen, J., Wang, L. and Song, J., 2018, “Structural Principles Analysis of Host-Pathogen Protein-Protein Interactions: A Structural Bioinformatics Survey”, IEEE Access, 6, pp.11760-11771. (IF: 3.367, Q1)
Chen, H., Song, J., Shen, J. and Wang, L., 2018. “Big data in genomics”. In Big Data Management and Processing (pp. 363-384). Chapman and Hall/CRC.
Yong, B., Shen, J., Sun, H., Chen, H. and Zhou, Q., 2017. “Parallel GPU-based collision detection of irregular vessel wall for massive particles”. Cluster Computing, 20(3), pp.2591-2603. (IF: 1.809)
Yong, B., Zhang, G., Chen, H. and Zhou, Q., 2017. “Intelligent monitor system based on cloud and convolutional neural networks”. The Journal of Supercomputing, 73(7), pp.3260-3276. (IF: 2.474)
Chen, H., Shen, J., Wang, L. and Song, J., 2017. “Leveraging Stacked Denoising Autoencoder for prediction of PHPPI”, IEEE International Congress on Big Data, pp. 368-375.
Chen, H., Song, J., Sun, G., Shen, J. and Wang, L., 2017. “Towards Elucidating the Structural Principles of Host-Pathogen Protein- Protein Interaction Networks: A bioinformatics survey”, IEEE International Congress on Big Data, pp. 177-184.
Sun, G., Cui, T., Xu, D., Chen, H., Chen, S. and Shen, J., 2017, “Assisting Open Education Resource Providers and Instructors to Deal With Cold Start Problem in Adaptive Micro Learning: a Service Oriented Solution”, 14th IEEE International Conference on Services Computing, pp. 196-203.
Granted Patent (China) : “Line-tracking navigation method and device for intelligent robot”. Application Number: 201310623873.3, issued in 2017.05.
Chen, H., Shen, J., Wang, L. and Song, J., 2017. “Collaborative data analytics towards prediction on pathogen-host protein-protein interactions”. In 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 269-274). IEEE.
Yong, B., Xu, Z., Shen, J., Chen, H., Tian, Y. and Zhou, Q., 2017. “Neural network model with Monte Carlo algorithm for electricity demand forecasting in Queensland”. In Proceedings of the Australasian Computer Science Week Multiconference, pp. 1-7.
Chen, H., Zhao, H., Wang, L., Song, J. and Shen, J., 2017. “A Comparison Study for Supervised Machine Learning Models in Cancer Classification”. 16th International Conference on Bioinformatics (InCoB 2017), pp. 1-2.
Wang, L., Shen, J., Zhou, Q., Shang, Z., Chen, H. and Zhao, H., 2016. “An evaluation of the dynamics of diluted neural network”. International Journal of Computational Intelligence Systems, 9(6), pp.1191-1199. (IF: 1.736)
Chen, H., Shen, J., Wang, L. and Song, J., 2016. “Towards Data Analytics of Pathogen-Host Protein-Protein Interaction: A survey”. IEEE International Congress on Big Data, pp. 377-388
Zhou, Q., Chen, H., Zhao, H., Zhang, G., Yong, J. and Shen, J., 2016. “A local field correlated and Monte Carlo based shallow neural network model for non-linear time series prediction”. EAI Endorsed Transactions on Scalable Information Systems, 16(8), pp. 1-7.
Chen, H., Zhao, H., Shen, J., Zhou, R. and Zhou, Q., 2015, “Supervised machine learning model for high dimensional gene data in colon cancer detection”. IEEE International Congress on Big Data, pp. 134-141.
Zhou, R., Chen, X., Chen, H., Yan, F., Chen, C., Yu, Q., Zhou, Q. and Li, K.C., 2015. “RCSoS: An IEC 61508 Compatible Server Model for Reliable Communication”. Journal of Signal Processing Systems, 80(3), pp. 323-337.
Zhou, R., Chen, H., Liu, Q., Sheng, Y., Zhou, Q., Wang, X. and Li, K.C., 2013. “A server model for reliable communication on cell/BE”. In 2013 42nd International Conference on Parallel Processing (pp. 1020-1027). IEEE.
Hung, J.C., Zhou, R., Hu, J., Chen, H., Zhou, Q., Qi, J. and Yang, L., 2013. “Cloud services aided e-tourism: In the case of low-cost airlines for backpacking”. In 2013 International Conference on Parallel and Distributed Systems (pp. 468-473). IEEE.
Chen, H., Sheng, Y., Zhou, R., Zhou, Q., Sun, S. and Hung, J.C., 2013. “The communication model between humanoid robot and mobile phone”. In 2013 International Joint Conference on Awareness Science and Technology & Ubi-Media Computing (iCAST 2013 & UMEDIA 2013) (pp. 697-701). IEEE.

A Little More About Me

My research interest is on trustworthy AI and software security, especially for AI-enabled systems. Particularly, a call on securing the AI-enabled systems in practice with dedicated software engineering techniques is a brief summary to my current work. Generally, I am also passionate in the research areas driven by artificial intelligence (AI for software engineering and science), including industrial 4.0, computational biology and so on.

I actively serve as the area chair, program committee, associate editor and reviewer at different conferences and journals, such as ACM CCS, ACM MM, IJCAI, KDD, TheWeb, ICSE, MSR, ISSTA, ISSRE, FORGE, IEEE CAI, IJCNN, CEC, SIAM ICDM, IEEE ICWS, ECML&PKDD, Computers&Security, Complex&Intelligent Systems, and so on.

Workshop for Trustworthy and Responsible AI: https://responsible-ai.wiki

Huaming Chen