AI for Health Care

We designed and implemented a system allowing Internet applications and individuals to search close contacts (e.g., same flight, same shuttle bus) with Covid-19 positive passengers during the pandemic; served approx. 1 billion queries in less than a month.

1. M. Hu, H. Lin, J. Wang, C. Xu, A. J. Tatem, B. Meng, X. Zhang, Y. Liu, P. Wang, G. Wu, H. Xie, and S. Lai, “Risk of Coronavirus Disease 2019 Transmission in Train Passengers: an Epidemiological and Modeling Study,” Clinical Infectious Diseases, vol. 72, no. 4, pp. 604–610, Feb. 2021, issn: 1058-4838.

We study how Artificial Intelligence can help solve challenges in health care, by collaborating with distinguished researchers from top tier hospitals (AnDing Hospital, TianTan Hospital, etc) and research institutions (for instance, CMU).

2. R Bai, Y Guo, X Tan, L Feng, H Xie. An EEG-based depression detection method using machine learning model. International Journal of Pharma Medicine and Biological Sciences 10 (1), 17-22

3. L. Chen, Y. Liu, W. Xiao, Y. Wang, and H. Xie, “SpeakerGAN: Speaker identification with conditional generative adversarial network,” Neurocomputing, vol. 418, pp. 211–220, 2020.

4. Y. Zhang, Y. Wang, X. Wang, B. Zou, and H. Xie, “Text-based decision fusion model for detecting depression,” in 2020 2nd Symposium on Signal Processing Systems, ser. SSPS 2020, Guangdong, China: Association for Computing Machinery, 2020, pp. 101–106

5. B. Zou, Z. Lin, H. Wang, Y. Wang, X. Lyu, and H. Xie, “Joint prediction of group-level emotion and cohesiveness with multi-task loss,” in Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence, ser. ICMAI 2020, Chengdu, China: Association for Computing Machinery, 2020, pp. 24–28.

6. L. Zhang, J. Li, S. Wang, X. Duan, W. Yan, H. Xie, and S. Huang, “Spatio-temporal fusion for macro- and micro-expression spotting in long video sequences,” in 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) (FG), Los Alamitos, CA, USA: IEEE Computer Society, May 2020, pp. 734–741.

7. J. Ding, Z. Tian, X. wen Lyu, Q. Wang, B. Zou, and H. Xie, “Real-time micro-expression detection in unlabeled long videos using optical flow and lstm neural network.,” in CAIP (1), ser. Lecture Notes in Computer Science, vol. 11678, Springer, 2019, pp. 622–634.

AI for Public Safety in Physical World

1. M Sun, P Zhou, H Tian, Y Liao, H Xie. Spatial-temporal attention network for crime prediction with adaptive graph learning. International Conference on Artificial Neural Networks, pp. 656-669

2. Y Lu, P Zhou, Y Liao, H Xie. Spatiotemporal and Semantic Zero-inflated Urban Anomaly Prediction. arXiv preprint arXiv:2304.01569

3. X. Tang, B. Gong, Y. Yu, H. Yao, Y. Li, H. Xie, and X. Wang, “Joint modeling of dense and incomplete trajectories for citywide traffic volume inference,” in The World Wide Web Conference, ser. WWW ’19, San Francisco, CA, USA: Association for Computing Machinery, 2019, pp. 1806–1817.

AI for Public  Safety in Cyberspace

1. Y Yang, R Yang, Y Li, K Cui, Z Yang, Y Wang, J Xu, H Xie. RoSGAS: Adaptive Social Bot Detection with Reinforced Self-supervised GNN Architecture Search. ACM Transactions on the Web 17 (3), 1-31

2. Q Guo, H Xie, Y Li, W Ma, C Zhang. Social bots detection via fusing BERT and graph convolutional networks. Symmetry 14 (1), 30

3. Y. Jin, T. Yang, Y. Li, and H. Xie, “Effective android malware detection based on deep learning,” in Artificial Intelligence and Security, ser. Communications in Computer and Information Science, Hohhot, China: Springer Singapore, 2020, pp. 206–218.

4. Y. Zhao, Y. Li, T. Yang, and H. Xie, “Suzzer: A vulnerability-guided fuzzer based on deep learning,” in Information Security and Cryptology, Cham: Springer International Publishing, 2020, pp. 134–153, isbn: 978-3-030-42921-8.

5. Y. Jin, T. Yang, Y. Li, and H. Xie, “Effective android malware detection based on deep learning,” in Artificial Intelligence and Security, ser. Communications in Computer and Information Science, Hohhot, China: Springer Singapore, 2020, pp. 206–218.

6. Y. Li, H. Jin, X. Yu, H. Xie, Y. Xu, H. Xu, and H. Zeng, “Intelligent prediction of private information diffusion in social networks,” Electronics, vol. 9, no. 5, 2020, issn: 2079-9292. doi: 10.3390/electronics9050719.

7. T. Yang, X. Shi, Y. Li, B. Huang, H. Xie, and Y. Shen, “Workload allocation based on user mobility in mobile edge computing,” Journal on Big Data, vol. 2, no. 3, pp. 105–115, 2020, issn: 2579-0056. doi: 10.32604/jbd.2020.010958.