Qin, Z., & Liu, W. (2022, September). Automate Page Layout Optimization: An Offline Deep Q-Learning Approach. In Proceedings of the 16th ACM Conference on Recommender Systems (pp. 522-524). (Link, PDF)
Xie, X., Hong, Z., Qin, Z., Fang, Z., Tian, Y., & Zhang, D. (2022, May). TransRisk: Mobility Privacy Risk Prediction based on Transferred Knowledge. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Link, PDF).
Wang, G., Qin, Z., Wang, S., Sun, H., Dong, Z., & Zhang, D. (2021, August). Record: Joint Real-Time Repositioning and Charging for Electric Carsharing with Dynamic Deadlines. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3660-3669). (Link, PDF)
Song, Y., Jiang, D., Liu, Y., Qin, Z., Tan, C., & Zhang, D. HERMAS: A Human Mobility Embedding Framework with Large-scale Cellular Signaling Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(3), 1-21. (Link, PDF)
Qin, Z., Xian, Y., Zhang, F., & Zhang, D. (2020). MIMU: Mobile WiFi Usage Inference by Mining Diverse User Behaviors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(4), 1-22. (Link, PDF)
Song, Y., Liu, Y., Qiu, W., Qin, Z., Tan, C., Yang, C., and Zhang, D. (2020). MIFF: Human Mobility Extractions with Cellular Signaling Data under Spatio-temporal Uncertainty. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(4), 1-19. (Link, PDF)
Xian, Y., Fu, Z., Zhao, H., Ge, Y., Chen, X., Huang, Q., Geng, S., Qin, Z., G de Melo, S. Muthukrishnan and Zhang, Y. (2020, October). CAFE: Coarse-to-fine neural symbolic reasoning for explainable recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 1645-1654). (Link, PDF)
Qin, Z., Cao, F., Yang, Y., Liu, Y., Wang, S., & Zhang, D. (2020). CellPred: A behavior-aware scheme for cellular data usage prediction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(1), 1-24. (Link, PDF)
Fang, Z., Fu, B., Qin, Z., Zhang, F., & Zhang, D. (2020). PrivateBus: Privacy Identification and Protection in Large-Scale Bus WiFi Systems. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(1), 1-23. (Link, PDF)
Qin, Z., Xian, Y., & Zhang, D. (2019, November). A neural networks based caching scheme for mobile edge networks. In Proceedings of the 17th Conference on Embedded Networked Sensor Systems (pp. 408-409). (Link, PDF)
Qin, Z., Fang, Z., Liu, Y., Tan, C., Chang, W., & Zhang, D. (2018, November). EXIMIUS: A measurement framework for explicit and implicit urban traffic sensing. In Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems (pp. 1-14). (Acceptance Rate=15.6%, Link, PDF)
Wang, G., Qin, Z., Wang, S., Sun, H., Dong, Z., & Zhang, D.. Joint Repositioning and Charging for Efficient Fleet Management of Electric Carsharing with Dynamic Deadlines. (TKDE, submitted)
Qin, Z., Fang, Z., Liu, Y., Tan, C., & Zhang, D. (2021). A Measurement Framework for Explicit and Implicit Urban Traffic Sensing. ACM Transactions on Sensor Networks (TOSN), 17(4), 1-27. (Link ,PDF)
Habibzadeh, H., Boggio-Dandry, A., Qin, Z., Soyata, T., Kantarci, B., & Mouftah, H. T. (2018). Soft sensing in smart cities: Handling 3vs using recommender systems, machine intelligence, and data analytics. IEEE Communications Magazine, 56(2), 78-86. (Link, PDF)
Habibzadeh, H., Qin, Z., Soyata, T., & Kantarci, B. (2017). Large-scale distributed dedicated-and non-dedicated smart city sensing systems. IEEE Sensors Journal, 17(23), 7649-7658. (Link, PDF)
Qin, Z. & Zhang, D. Mobile WiFi Deployment in New Cities: A Cross-City Domain Adaptation Scheme.
Qin, Z. & Zhang, D. CUP: Context-aware User Behavior Prediction in Bus WiFi Scenarios.
Qin, Z. & Zhang, D. BISD: Behavior Inference from Large-scale Cellular Signaling Data.
Qin, Z. & Zhang, D. CellPat: Understanding Cellular Networks and User Behaviors from Cellular Signaling Data.
Qin, Z. & Zhang, D. A Measurement Study for a Nationwide Bus WiFi System.