Research

Founded Projects

  • The National Key Research and Development Program of China, 2022-2025
  • The Major Research Plan of National Natural Science Foundation of China, 2022-2025
  • The  Importation and Development of High-Caliber Talents Project of Beijing Municipal  Institutions, 2020-2022
  • The Ministry of Industry and Information Technology – Industrial Internet Innovation development PTechnology Project (Sub-project: Artificial Intelligence),2020-2022
  • The Ministry of Industry and Information Technology – Industrial Internet Innovation development Project (Sub-project: Reference Model), 2018-2020
  • The Ministry of Industry and Information Technology – Industrial Internet Innovation development Project (Sub-project: Emergency Response), 2018-2020
  • The National Key Research and Development Program of China (SubTask: Standardization), 2017-2020
  • The National Key Research and Development Program of China (SubTask: Temporal Summarization), 2017-2020
  • The National Natural Science Foundation of China, 2017-2021
  • The National Standardization Foundation, 2015-2016
  • The Beijing Excellent Talent Development Foundation, 2013-2015
  • The Importation and Development of High Caliber Talents Project of Beijing Municipal Institutions, 2013-2015
  • The National Information Security 242 Project of China, 2014-2015
  • The National Soft Science Program of China, 2011-2012
  • The National Natural Science Foundation of China, 2011-2014
  • The Scientific Research Common Program of Beijing Municipal Commission of Education, 2011-2014
  • The Beijing Natural Science Foundation, 2010-2013

Standards

Selected Papers

Data Mining / AI Theory and Application / Data Security / Cybersecurity

  • Lyu, Gengyu; Yang, Zhen; Deng, Xiang; Feng, Songhe “L-VSM: Label Driven View-Specific Fusion for Multi-View Multi-Label Classification”,IEEE Transactions on Neural Networks and Learning Systems (Accepted).
  • Zhong, Qiyu; Lyu, Gengyu; Yang, Zhen, Align while Fusion: A Generalized Non-Aligned Multi-View Multi-Label Classification Method” IEEE Transactions on Neural Networks and Learning Systems (Accepted)
  • Yuhan Liu, Yongjian Deng, Hao Chen, Zhen Yang. Video Frame Interpolation via Direct Synthesis with the Event-based Reference. 2024 International Conference on Computer Vision and Pattern Recognition (CVPR 2024), 2024 (Accepted) CCF A
  • Bowen Yao, Yongjian Deng, Yuhan Liu, Hao Chen, Youfu Li, Zhen Yang. SAM-Event-Adapter: Adapting Segment Anything Model for Event-RGB Semantic Segmentation. 2024 IEEE International Conference on Robotics and Automation (ICRA 2024), 2024 (Accepted) CCF B
  • M. Zhou, Z. Yang, H. Yu, and S. Yu, “VDFChain: Secure and verifiable decentralized federated learning via committee-based blockchain,” Journal of Network and Computer Applications, vol. 223, p. 103814, Mar. 2024, doi: 10.1016/j.jnca.2023.103814.
  • H. Yu, R. Xu, H. Zhang, Z. Yang, and H. Liu, “EV-FL: Efficient Verifiable Federated Learning With Weighted Aggregation for Industrial IoT Networks,” IEEE/ACM Transactions on Networking, pp. 1–15, 2024, doi: 10.1109/TNET.2023.3328635. CCF A 【 Early Access Article】
  • D. Wu, Z. Yang, T. Li, and J. Liu, “JOCP: A jointly optimized clustering protocol for industrial wireless sensor networks using double‐layer selection evolutionary algorithm,” Concurrency and Computation, vol. 36, no. 4, p. e7927, Feb. 2024, doi: 10.1002/cpe.7927.
  • Z. Tang, T. Li, D. Wu, J. Liu, and Z. Yang, “A Systematic Literature Review of Reinforcement Learning-based Knowledge Graph Research,” Expert Systems with Applications, vol. 238, p. 121880, Mar. 2024, doi: 10.1016/j.eswa.2023.121880.中科院1区
  • D. Wu, Z. Yang, T. Li, and J. Liu, “JOCP: A jointly optimized clustering protocol for industrial wireless sensor networks using double‐layer selection evolutionary algorithm,” Concurrency and Computation, vol. 36, no. 4, p. e7927, Feb. 2024, doi: 10.1002/cpe.7927.
  • Z. Tang, T. Li, D. Wu, J. Liu, and Z. Yang, “A Systematic Literature Review of Reinforcement Learning-based Knowledge Graph Research,” Expert Systems with Applications, vol. 238, p. 121880, Mar. 2024, doi: 10.1016/j.eswa.2023.121880.
  • J. Liu, T. Li, Z. Yang, D. Wu, and H. Liu, “Fusion learning of preference and bias from ratings and reviews for item recommendation,” Data & Knowledge Engineering, vol. 150, p. 102283, Mar. 2024, doi: 10.1016/j.datak.2024.102283.
  • L. Zhao, T. Li, Z. Yang, and J. Liu, “A novel subjective bias detection method based on multi-information fusion,” in Proceedings of the 35th International Conference on Software Engineering and Knowledge Engineering, Larkspur Landing South San Francisco Hotel,  USA and KSIR Virtual Conference Center, USA, Jul. 2023, pp. 449–455. doi: 10.18293/SEKE2023-103. CCF C
  • F. Sabah, Y. Chen, Z. Yang, M. Azam, N. Ahmad, and R. Sarwar, “Model optimization techniques in personalized federated learning: A survey,” Expert Systems with Applications, vol. 243, p. 122874, Jun. 2024, doi: 10.1016/j.eswa.2023.122874. 中科院1区
  • J. Liu, T. Li, Z. Yang, D. Wu, and H. Liu, “Fusion learning of preference and bias from ratings and reviews for item recommendation,” Data & Knowledge Engineering, vol. 150, p. 102283, Mar. 2024, doi: 10.1016/j.datak.2024.102283. CCF B
  • N. Li, M. Zhou, H. Yu, Y. Chen, and Z.Yang, “SVFLC: Secure and Verifiable Federated Learning With Chain Aggregation,” IEEE Internet Things J., vol. 11, no. 8, pp. 13125–13136, Apr. 2024, doi: 10.1109/JIOT.2023.3330813. 中科院1区
  • L. Zhao, T. Li, Z. Yang, and J. Liu, “A novel subjective bias detection method based on multi-information fusion,” in Proceedings of the 35th International Conference on Software Engineering and Knowledge Engineering, Larkspur Landing South San Francisco Hotel,  USA and KSIR Virtual Conference Center, USA, Jul. 2023, pp. 449–455.
  • M. Zhang, R. Yin, Z. Yang, Y. Wang, and K. Li, “Advances and Challenges of Multi-task Learning Method in Recommender System: A Survey.” arXiv, May 23, 2023. doi: 10.48550/arXiv.2305.13843.
  • Z. Yang, M. Zhou, H. Yu, R. O. Sinnott, and H. Liu, “Efficient and Secure Federated Learning With Verifiable Weighted Average Aggregation,” IEEE Trans. Netw. Sci. Eng., vol. 10, no. 1, pp. 205–222, Jan. 2023, doi: 10.1109/TNSE.2022.3206243.
  • Z. Yang, S. Yang, Y. Huang, J.-F. Martínez, L. López, and Y. Chen*, “AAIA: an efficient aggregation scheme against inverting attack for federated learning,” Int. J. Inf. Secur., vol. 22, pp. 919–930, Mar. 2023, doi: 10.1007/s10207-023-00670-6.
  • Z. Yang, J. Liu, T. Li, D. Wu, S. Yang, and H. Liu, “A Two-tier Shared Embedding Method for Review-based Recommender Systems,” in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Birmingham United Kingdom, Oct. 2023, pp. 2928–2938. doi: 10.1145/3583780.3614770. CCF B
  • S. Yang, Y. Chen, Z. Yang, B. Li, and H. Liu, “Fast Secure Aggregation With High Dropout Resilience for Federated Learning,” IEEE Trans. on Green Commun. Netw., vol. 7, no. 3, pp. 1501–1514, Sep. 2023, doi: 10.1109/TGCN.2023.3277251.
  • D. Wu, W. Feng, T. Li, and Z. Yang, “Evaluating the intelligence capability of smart homes: A conceptual modeling approach,” Data & Knowledge Engineering, vol. 148, p. 102218, Nov. 2023, doi: 10.1016/j.datak.2023.102218. CCF B
  • X. Shan, H. Yu, Y. Chen, and Z. Yang, “Physical Unclonable Function-Based Lightweight and Verifiable Data Stream Transmission for Industrial IoT,” IEEE Trans. Ind. Inf., vol. 19, no. 12, pp. 11573–11583, Dec. 2023, doi: 10.1109/TII.2023.3248107.
  • F. Sabah, Y. Chen, Z. Yang, A. Raheem, M. Azam, and R. Sarwar, “Heart Disease Prediction with 100% Accuracy, Using Machine Learning: Performance Improvement with Features Selection and Sampling,” in 2023 8th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), Nov. 2023, pp. 41–45. doi: 10.1109/IC-NIDC59918.2023.10390693.
  • Z Ma, X. Lu, J. Xie, Z Yang, J. Xue, Z. Yan, B. Xiao, and J. Guo, “On the Comparisons of Decorrelation Approaches for Non-Gaussian Neutral Vector Variables,” IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 4, pp. 1823–1837, Apr. 2023, doi: 10.1109/TNNLS.2020.2978858. 中科院1区
  • G. Lyu, S. Feng, S. Wang, and Z. Yang, “Prior Knowledge Constrained Adaptive Graph Framework for Partial Label Learning,” ACM Trans. Intell. Syst. Technol., vol. 14, no. 2, pp. 1–16, Apr. 2023, doi: 10.1145/3569421.
  • Y. Liu, T. Li, Z. Huang, and Z. Yang, “BARA: A Dynamic State-based Serious Game for Teaching Requirements Elicitation,” in 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET), Melbourne, Australia, May 2023, pp. 141–152. doi: 10.1109/ICSE-SEET58685.2023.00020. CCF A
  • Y. Chen, S. Yang, J.-F. Martínez-Ortega, L. López, and Z. Yang, “A Resilient Group-Based Multisubset Data Aggregation Scheme for Smart Grid,” IEEE Internet of Things Journal, vol. 10, no. 15, pp. 13649–13661, Aug. 2023, doi: 10.1109/JIOT.2023.3262731中科院1区

  • Ning Lu, Zhen Yang*, Jian Huang, Yaxi Wu, and Hesong Wang. Silence or outbreak–a real-time emergent topic identification system (RealTIS) for social media. In Proceedings of the 2022 AAAI Conference on Artificial Intelligence (AAAI 2022), volume 36, pages 13194–13196, 2022. DEMO
  • Liu Y, Yang Z, Li T, Wu D. A novel POI recommendation model based on joint spatiotemporal effects and four-way interaction. Appl. Intell. 52(5): 5310-5324 (2022)
  • Du Y, Li T, Pathan M, Tekehaimanot H, Yang Z. An Effective Sarcasm Detection Approach Based on Sentimental Context and Individual Expression Habits. Cogn. Comput. 14(1): 78-90 (2022)
  • Liu Y, Yang Z, Li T, Wu D. A novel POI recommendation model based on joint spatiotemporal effects and four-way interaction. Appl. Intell. 52(5): 5310-5324 (2022)
  • Yang Z, Liu X, Li T, Wu D, Wang J, Zhao Y, Han H. A systematic literature review of methods and datasets for anomaly-based network intrusion detection. Comput. Secur. 116: 102675 (2022)
  • Liu J, Yang Z, Li T, Wu D, Wang R. SPR: Similarity pairwise ranking for personalized recommendation. Knowl. Based Syst. 239: 107828 (2022)
  • Li Z, Li T, Zhang R, Wu D, Yang Z. A Novel Network Alert Classification Model based on Behavior Semantic. SEKE 2022: 553-558
  • Feng W, Li T, Yang Z. COAT: A Music Recommendation Model based on Chord Progression and Attention Mechanisms. SEKE 2022: 616-621
  • Yang Z, Wu D, Li T*, Feng W, Tu S. A Mulit-objective Cluster Head SelectionOptimization Algorithm for Industrial Wireless Sensor Networks. 2021 IEEE 6th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2021. Ms. Wu Di Awarded Excellent Paper Presentation Award Winners
  • Feng, Weite, Tong Li*, Haiyang Yu, and Zhen Yang. A Hybrid Music Recommendation Algorithm Based on Attention Mechanism. In International Conference on Multimedia Modeling, pp. 328-339. Springer, Cham, 2021.
  • Kidu H, Misgna H, Li T, Yang Z. User Response-Based Fake News Detection on Social Media. ICAI 2021: 173-187
  • Feng W, Li T, Yu H, Yang Z. A Hybrid Music Recommendation Algorithm Based on Attention Mechanism. MMM (1) 2021: 328-339
  • Zhi Y, Li T, Yang Z. Extracting features from app descriptions based on POS and dependency. SAC 2021: 1354-1358
  • Zhao G, Li T, Yang Z. An Extended Knowledge Representation Learning Approach for Context-Based Traceability Link Recovery: Extended Abstract. AIRE@RE 2020: 22
  • Wang R, Li T, Yang Z, Yu H. Predicting Polypharmacy Side Effects Based on an Enhanced Domain Knowledge Graph. ICAI 2020: 89-103
  • Zhao G, Li T, Yang Z. An Extended Knowledge Representation Learning Approach for Context-based Traceability Link Recovery. SEKE 2020: 77-82
  • Ruiyi Wang, Tong Li* and Zhen Yang. Predicting Polypharmacy Side Effects Based on an Enhanced Domain Knowledge Graph. International Conference on Applied Informatics, 2020, pp. 89–103.
  • Yang Z*, Yao Y, Tu S. Exploiting Sparse Topics Mining for Temporal Event Summarization. 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2020: 322-331. Best Paper Award
  • Wang S, Li T, Yang Z. Exploring Semantics of Software Artifacts to Improve Requirements Traceability Recovery: A Hybrid Approach. APSEC 2019: 39-46
  • Wang S, Li T, Yang Z. Using Graph Embedding to Improve Requirements Traceability Recovery. ICAI 2019: 533-545
  • Yang Z*, Yu H, Tang J, Liu H. Toward Keyword Extraction in Constrained Information Retrieval in Vehicle Social Network. IEEE Transactions on Vehicular Technology, 2019, 68(5): 4285-4294. (5-year Impact factor: 5.119)
  • Ma, Z., Xue, J. H., Leijon, A., Tan, Z. H., Yang, Z., Guo, J. Decorrelation of neutral vector variables: Theory and applications. IEEE Transactions on Neural Network and Learning Systems (TNNLS), 2018, 29(1): 129-143. (5-year Impact factor: 8.823)
  • Yang Z, Chen W, Huang J. Enhancing recommendation on extremely sparse data with blocks-coupled non-negative matrix factorization. Neurocomputing, 2018, 278: 126-133.
  • Yang Z*, Tang J, Liu H. Cloud Information Retrieval: Model Description and Scheme Design. IEEE Access, 2018, 6: 15420-15430.
  • Yang Z, Gao K, Huang J. External Expansion Risk Management: Enhancing Microblogging Filtering Using Implicit Query. Wireless Personal Communications, 2018, 102(3): 2199-2209.
  • Yang Z, Yao F, Fan K, Huang J. Text Dimensionality Reduction with Mutual Information Preserving Mapping. Chinese Journal of Electronics 26.5 (2017): 919-925.
  • Yang Z, Gao K, Fan K, Lai Y. “Sensational Headline Identification By Normalized Cross Entropy-Based Metric.” The Computer Journal, 2015, 58 (4): 644-655.
  • Yang Z*, Issac J, Hu X, Liu H. “Finding the Right Social Media Site for Questions.” 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’15), August 25-28, 2015, Paris, France, 639-644.
  • Harsh Dani, Fred Morstatter, Xia Hu, Zhen Yang, and Huan Liu. Social Answer: A System for Finding Appropriate Sites for Questions in Social Media. In Proceedings of IEEE International Conference on Data Mining (ICDM 2015), November 14 – 17, 2015. Atlantic City, NJ. DEMO, (This is a demo track paper that demonstrates a Q&A system ‘Social Answer’ proposed in our ASONAM’15 paper!)
  • Yang Z, Fan K, Lai Y, Gao K, Wang Y. “Short Texts Classification Through Reference Document Expansion.” Chinese Journal of Electronics, 2014, 23(2): 315-321.
  • Yang Z, Wang L, Lai Y. “ Online comment clustering based on an improved semantic distance.” Ruan Jian Xue Bao/Journal of Software, 2014, 25(12): 2777-2789. (in Chinese, 杨震, 王来涛, 赖英旭. 基于改进语义距离的网络评论聚类研究. 软件学报, 25(12): 2777-278, 2014)
  • Yang Z, Wang L, Fan K, Lai Y. “Exemplar-Based Clustering Analysis Optimized by Genetic Algorithm.” Chinese Journal of Electronics, 2013, 22(4): 735-740.
  • Yang Z*, Lei J, Fan K, Lai Y. “Keyword Extraction by Entropy Difference Between the Intrinsic and Extrinsic Mode.” Physica A: Statistical Mechanics and its Applications, 2013, 392(19): 4523-4531. [Paper, Code]
  • Yang Z, Lai Y, Duan L, Li Y, Xu X. “Spam Collaborative Filtering in Enron E-mail Network.” Acta Automatica Sinica, 2012, 38(3): 399-411. (in Chinese, 杨震, 赖英旭, 段立娟等. 邮件网络协同过滤机制研究. 自动化学报, 38(3): 399-411, 2012)
  • Yang Z, Lai Y, Duan L, Li Y. “Short Text Sentiment Classification Based on Context Reconstruction.” Acta Automatica Sinica, 2012, 38(1): 55-67. (in Chinese, 杨震, 赖英旭, 段立娟等. 基于上下文重构的短文本情感极性判别研究. 自动化学报, 38(1): 55-67, 2012)
  • Yang Z, Fan K, Lei J, Guo J. Text Manifold Based on Semantic Analysis. Acta Electronica Sinica, 37(3): 557-561, 2009. (in Chinese, 杨震, 范科峰, 雷建军, 郭军. 基于语义的文本流形研究. 电子学报, 37(3): 557-561, 2009)

  • Yu H, Yang Z*, Tu S, Waqas M, Liu H Blockchain-Based Offline Auditing for the Cloud in Vehicular Networks. IEEE Trans. Netw. Serv. Manag. 19(3): 2944-2956 (2022)
  • Yu H, Yang Z, Waqas M, Tu S, Han Z, Hailm Z, Sinnott R, Parampalli U. Efficient dynamic multi-replica auditing for the cloud with geographic location. Future Gener. Comput. Syst. 125: 285-298 (2021)
  • Zhang H, Yang Z, Yu H. Lightweight and Privacy-preserving Search over Encryption Blockchain. IC-NIDC 2021: 423-427
  • Zhou M, Yang Z*, Yu H, Lai Y, Ma Z. Privacy-Preserving Verifiable Collaborative Learning with Chain Aggregation[C], 7th IEEE International Conference on Network Intelligence and Digital Content, 2021, Nov. 17-19.   Best Paper Award
  • Wu J, Yu H, Yang Z, Yin R. Disk Failure Prediction with Multiple Channel Convolutional Neural Network. IJCNN 2021: 1-8
  • Yang Z, Li T, Waedt K. Li Y. The Need of Standardizing Industrial Internet Platform: Challenges and Threats. SC 27 Journal, 2021, 1(2), 15-30.
  • Yu H, Ma S, Hu Q, Yang Z*. Blockchain-Based Continuous Auditing for Dynamic Data Sharing in Autonomous Vehicular Networks. Computer, 54 (8), 33-45 (Impact factor: 4.419) August 2021 Features Article
  • Yu H, Hu Q, Yang Z*, Liu H. Efficient Continuous Big Data Integrity Checking for Decentralized Storage. IEEE Transactions on Network Science and Engineering, 8(2), 1658-1673, 2021 (DOI: 10.1109/TNSE.2021.3068261, 5-year Impact factor: 5.213)
  • Liu X, Li T, Zhang R, Wu D, Liu Y, Yang Z. A GAN and Feature Selection-Based Oversampling Technique for Intrusion Detection. Secur. Commun. Networks 2021: 9947059:1-9947059:15 (2021)
  • Wang G, Li T, Yue H, Yang Z, Zhang R. Integrating Heterogeneous Security Knowledge Sources for Comprehensive Security Analysis. COMPSAC 2021: 714-724
  • Chen Y, Martínez J, López L, Yu H, Yang Z*. A dynamic membership group-based multiple-data aggregation scheme for smart grid. IEEE Internet of Things Journal, 8(15), 12360-12374, 2021 (5-year Impact factor: 11.705)
  • Yang Z, Zhang H, Yu H*, Li Z, Zhu B, Sinnott R O. Attribute-Based Keyword Search over the Encrypted Blockchain, Computer Modeling in Engineering and Sciences (CMES), 2021. (Accepted)
  • Wu J, Yu H, Yang Z, Yin R. Disk Failure Prediction with Multiple Channel Convolutional Neural Network, International Joint Conference on Neural Networks (IJCNN 2021), 2021. (Accepted)
  • Yu H, Yang Z, Sinnott R O. Decentralized Big Data Auditing for Smart City Environments Leveraging Blockchain Technology. IEEE Access, 2019, 7: 6288-6296.
  • Yu H, Cai Y, Sinnott R O*, Yang Z. ID-Based Dynamic Replicated Data Auditing for the Cloud, Concurrency and Computation: Practice and Experience (CCPE), 31(11): e5051, 2019.
  • Yang Z, Yang T, Fan K, Wang Y. Cloud Services Composition Based on Trust Combination. Acta Electronica Sinica, 2018,46(3):614-620. (in Chinese, 杨震, 杨甜甜, 范科峰,等. 基于信任合成的云服务动态组合机制研究. 电子学报, 2018,46(3):614-620).
  • 杨震, 刘贤刚, 范科峰. 工业互联网平台安全参考模型国际标准提案研究. 信息技术与标准化, 2018, 401(05):34-37.
  • 李怡德, 杨震*, 龚洁中, 何通海. 物联网安全参考架构研究. 信息安全研究, 2016, 2(5):417-423.
  • Guoshuai Zhao, Tong Li* and Zhen Yang. “An Extended Knowledge Representation Learning Approach for Context-Based Traceability Link Recovery (S).” International Conference on Software Engineering and Knowledge Engineering (SEKE), 2020, pp. 77–82.
  • Wang, Shiheng, Tong Li*, and Zhen Yang. “Exploring Semantics of Software Artifacts to Improve Requirements Traceability Recovery: A Hybrid Approach.” In 2019 26th Asia-Pacific Software Engineering Conference (APSEC), pp. 39-46. IEEE, 2019.
  • Wang, Shiheng, Tong Li*, and Zhen Yang. “Using Graph Embedding to Improve Requirements Traceability Recovery.” In International Conference on Applied Informatics, pp. 533-545. Springer, Cham, 2019.
  • Yung M, Zhang J, Yang Z (Eds.). Trusted Systems-7th International Conference, INTRUST 2015, Beijing, China, December 7-8, 2015. INTRUST 2015, LNCS 9565, 2016

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