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

  • PrVFL: Pruning-Aware Verifiable Federated Learning for Heterogeneous Edge Computing, DOI: 10.1109/TMC.2024.3450542. IEEE Trans. MC, CCF A (Accepted)

  • Z. Zhou, G. Lyu, Y. Huang, Z. Wang, Z. Jia, and Z. Yang, “SDformer: Transformer with Spectral Filter and Dynamic Attention for Multivariate Time Series Long-term Forecasting,” in Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI), Aug. 2024, pp. 5689–5697. doi: 10.24963/ijcai.2024/629.
  • G. Lyu, W. Kang, H. Wang, Z. Li, Z. Yang, and S. Feng, “Common-Individual Semantic Fusion for Multi-View Multi-Label Learning,” in Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI), Aug. 2024, pp. 4715–4723. doi: 10.24963/ijcai.2024/521.
  • B. Yao, Y. Deng, Y. Liu, H. Chen, Y. Li, and Z. Yang, “SAM-Event-Adapter: Adapting Segment Anything Model for Event-RGB Semantic Segmentation,” in Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan, 2024, pp. 9093–9100. doi: 10.1109/ICRA57147.2024.10611127.
  • H. Yu, H. Zhang, Z. Yang, and S. Yu, “Edasvic: Enabling Efficient and Dynamic Storage Verification for Clouds of Industrial Internet Platforms,” IEEE Transactions on Information Forensics and Security, vol. 19, pp. 6896–6909, 2024, doi: 10.1109/TIFS.2024.3422790. (Early Access)
  • H. Yu, Y. Chen, Z. Yang, Y. Chen, and S. Yu, “EDCOMA: Enabling Efficient Double Compressed Auditing for Blockchain-Based Decentralized Storage,” IEEE Transactions on Services Computing, pp. 1–14, 2024, doi: 10.1109/TSC.2024.3417337. (Early Access)
  • G. Lyu, Z. Yang, X. Deng, and S. Feng, “L-VSM: Label-Driven View-Specific Fusion for Multiview Multilabel Classification,” IEEE Transactions on Neural Networks and Learning Systems (Early Access)
  • Q. Zhong, G. Lyu, and Z. Yang, “Align While Fusion: A Generalized Nonaligned Multiview Multilabel Classification Method,” IEEE Transactions on Neural Networks and Learning Systems (Early Access)
  • Y. Liu, Y. Deng, H. Chen, and Z. Yang, “Video Frame Interpolation via Direct Synthesis with the Event-based Reference,” presented at the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024), 2024, pp. 8477–8487. Accessed: Jul. 24, 2024. [Online].
  • 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, vol. 32, no. 2, pp. 1723–1737, 2024, doi: 10.1109/TNET.2023.3328635.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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. ESI Highly Cited Paper
  • 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.
  • 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. 
  • N. Lu, Z. Yang, J. Huang, Y. Wu, and H. Wang, “Silence or Outbreak – a Real-Time Emergent Topic Identification System (RealTIS) for Social Media,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 11, Art. no. 11, Jun. 2022, doi: 10.1609/aaai.v36i11.21725. DEMO
  • Y. Zhang, R. Yin, and Z. Yang, “Data Poisoning Attacks to Session-Based Recommender Systems,” in Proceedings of the 2022 12th International Conference on Communication and Network Security, Beijing China, Dec. 2022, pp. 1–6. doi: 10.1145/3586102.3586103.
  • H. Yu, Z. Yang, S. Tu, M. Waqas, and H. Liu, “Blockchain-Based Offline Auditing for the Cloud in Vehicular Networks,” IEEE Trans. Netw. Serv. Manage., vol. 19, no. 3, pp. 2944–2956, Sep. 2022, doi: 10.1109/TNSM.2022.3164549.
  • Z. Yang, X. Liu, T. Li, D. Wu, J. Wang, Y. Zhao, H. Han, “A systematic literature review of methods and datasets for anomaly-based network intrusion detection,” Computers & Security, vol. 116, p. 102675, May 2022, doi: 10.1016/j.cose.2022.102675. ESI Highly Cited Paper
  • S. Yang, Y. Chen, S. Tu, and Z. Yang, “A Post-quantum Secure Aggregation for Federated Learning,” in Proceedings of the 2022 12th International Conference on Communication and Network Security, Beijing China, Dec. 2022, pp. 117–124. doi: 10.1145/3586102.3586120.
  • J. Liu, Z. Yang, T. Li, D. Wu, and R. Wang, “SPR: Similarity pairwise ranking for personalized recommendation,” Knowledge-Based Systems, vol. 239, p. 107828, Mar. 2022, doi: 10.1016/j.knosys.2021.107828.
  • Z. Li, T. Li, R. Zhang, D. Wu, and Z. Yang, “A Novel Network Alert Classification Model based on Behavior Semantic,” in Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering, Jul. 2022, pp. 553–558. doi: 10.18293/SEKE2022-116.
  • W. Feng, T. Li, and Z. Yang, “COAT: A Music Recommendation Model based on Chord Progression and Attention Mechanisms,” in Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering, Jul. 2022, pp. 616–621. doi: 10.18293/SEKE2022-115.
  • Y. Du, T. Li, M. S. Pathan, H. K. Teklehaimanot, and Z. Yang, “An Effective Sarcasm Detection Approach Based on Sentimental Context and Individual Expression Habits,” Cogn Comput, vol. 14, no. 1, pp. 78–90, Jan. 2022, doi: 10.1007/s12559-021-09832-x.
  • 卢瑞瑞, 于海阳, 杨震, 赖英旭, 杨石松, 周明, “基于能源分解的用户用电行为模式分析,” 北京航空航天大学学报, vol. 48, no. 2, pp. 311–323, 2022, doi: 10.13700/j.bh.1001-5965.2020.0557.
  • 关帅鹏, 于海阳, 杨震, 周明, 赖英旭, “基于关键字的海报自动合成系统,” 北京航空航天大学学报, vol. 48, no. 2, pp. 356–368, 2022, doi: 10.13700/j.bh.1001-5965.2020.0552.
  • M. Zhou, Z. Yang, H. Yu, Y. Lai, and Z. Ma, “Privacy-Preserving Verifiable Collaborative Learning with Chain Aggregation,” in 2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), Beijing, China, Nov. 2021, pp. 428–433. doi: 10.1109/IC-NIDC54101.2021.9660520. Best Paper Award
  • Y. Zhi, T. Li, and Z. Yang, “Extracting features from app descriptions based on POS and dependency,” in Proceedings of the 36th Annual ACM Symposium on Applied Computing, Virtual Event Republic of Korea, Mar. 2021, pp. 1354–1358. doi: 10.1145/3412841.3442120.
  • H. Zhang, Z. Yang, and H. Yu, “Lightweight and Privacy-preserving Search over Encryption Blockchain,” in 2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), Beijing, China, Nov. 2021, pp. 423–427. doi: 10.1109/IC-NIDC54101.2021.9660565.
  • H. Yu et al., “Efficient dynamic multi-replica auditing for the cloud with geographic location,” Future Generation Computer Systems, vol. 125, pp. 285–298, Dec. 2021, doi: 10.1016/j.future.2021.05.039.
  • H. Yu, S. Ma, Q. Hu, and Z. Yang, “Blockchain-Based Continuous Auditing for Dynamic Data Sharing in Autonomous Vehicular Networks,” Computer, vol. 54, no. 8, pp. 33–45, Aug. 2021, doi: 10.1109/MC.2021.3080332.
  • H. Yu, Q. Hu, Z. Yang, and H. Liu, “Efficient Continuous Big Data Integrity Checking for Decentralized Storage,” IEEE Trans. Netw. Sci. Eng., vol. 8, no. 2, pp. 1658–1673, Apr. 2021, doi: 10.1109/TNSE.2021.3068261.
  • Z. Yang, H. Zhang, H. Yu, Z. Li, B. Zhu, and R. O. Sinnott, “Attribute-Based Keyword Search over the Encrypted Blockchain,” Computer Modeling in Engineering & Sciences, vol. 128, no. 1, pp. 269–282, 2021, doi: 10.32604/cmes.2021.015210.
  • Z. Yang, D. Wu, T. Li, W. Feng, and S. Tu, “A Multi-objective Cluster Head Selection Optimization Algorithm for Industrial Wireless Sensor Networks,” in 2021 6th International Conference on Image, Vision and Computing (ICIVC), Qingdao, China, Jul. 2021, pp. 445–452. doi: 10.1109/ICIVC52351.2021.9526976. Wu Awarded Excellent Paper Presentation
  • Z. Yang, T. Li, K. Waedt, and Y. Lin, “The need of standardizing industrial Internet platform: challenges and threats,” SC 27 Journal, vol. 1, no. 2, pp. 15–30, 2021.
  • S. Yang, Y. Chen, and Z. Yang, “Research on Security and Privacy Problem in the Data Life Cycle for the IoT Scenario,” in 2021 2nd Asia Symposium on Signal Processing (ASSP), Nov. 2021, pp. 84–94. doi: 10.1109/ASSP54407.2021.00021.
  • J. Wu, H. Yu, Z. Yang, and R. Yin, “Disk Failure Prediction with Multiple Channel Convolutional Neural Network,” in 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China, Jul. 2021, pp. 1–8. doi: 10.1109/IJCNN52387.2021.9534457.
  • G. Wang, T. Li, H. Yue, Z. Yang, and R. Zhang, “Integrating Heterogeneous Security Knowledge Sources for Comprehensive Security Analysis,” in 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, Jul. 2021, pp. 714–724. doi: 10.1109/COMPSAC51774.2021.00103.
  • X. Liu, T. Li, R. Zhang, D. Wu, Y. Liu, and Z. Yang, “A GAN and Feature Selection-Based Oversampling Technique for Intrusion Detection,” Security and Communication Networks, vol. 2021, pp. 1–15, Jul. 2021, doi: 10.1155/2021/9947059.
  • H. Kidu, H. Misgna, T. Li, and Z. Yang, “User Response-Based Fake News Detection on Social Media,” in Applied Informatics, vol. 1455, H. Florez and M. F. Pollo-Cattaneo, Eds. Cham: Springer International Publishing, 2021, pp. 173–187. doi: 10.1007/978-3-030-89654-6_13.
  • W. Feng, T. Li, H. Yu, and Z. Yang, “A Hybrid Music Recommendation Algorithm Based on Attention Mechanism,” in MultiMedia Modeling, vol. 12572, J. Lokoč, T. Skopal, K. Schoeffmann, V. Mezaris, X. Li, S. Vrochidis, and I. Patras, Eds. Cham: Springer International Publishing, 2021, pp. 328–339. doi: 10.1007/978-3-030-67832-6_27.
  • Y. Chen, J.-F. Martinez-Ortega, L. Lopez, H. Yu, and Z. Yang, “A Dynamic Membership Group-Based Multiple-Data Aggregation Scheme for Smart Grid,” IEEE Internet Things J., vol. 8, no. 15, pp. 12360–12374, Aug. 2021, doi: 10.1109/JIOT.2021.3063412.
  • G. Zhao, T. Li, and Z. Yang, “An Extended Knowledge Representation Learning Approach for Context-Based Traceability Link Recovery: Extended Abstract,” in 2020 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engineering (AIRE), Zurich, Switzerland, Sep. 2020, pp. 22–22. doi: 10.1109/AIRE51212.2020.00010.
  • Z. Yang, Y. Yao, and S. Tu, “Exploiting Sparse Topics Mining for Temporal Event Summarization,” in 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC), Beijing, China, Jul. 2020, pp. 322–331. doi: 10.1109/ICIVC50857.2020.9177457.
  • H. Yu, Y. Cai, R. O. Sinnott, and Z. Yang, “ID‐based dynamic replicated data auditing for the cloud,” Concurrency and Computation, vol. 31, no. 11, p. e5051, Jun. 2019, doi: 10.1002/cpe.5051.
  • Z. Yang, H. Yu, J. Tang, and H. Liu, “Toward Keyword Extraction in Constrained Information Retrieval in Vehicle Social Network,” IEEE Trans. Veh. Technol., vol. 68, no. 5, pp. 4285–4294, May 2019, doi: 10.1109/TVT.2019.2906799.
  • S. Wang, T. Li, and Z. Yang, “Exploring Semantics of Software Artifacts to Improve Requirements Traceability Recovery: A Hybrid Approach,” in 2019 26th Asia-Pacific Software Engineering Conference (APSEC), Putrajaya, Malaysia, Dec. 2019, pp. 39–46. doi: 10.1109/APSEC48747.2019.00015.
  • H. Yu and Z. Yang, “Decentralized and Smart Public Auditing for Cloud Storage,” in 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, Nov. 2018, pp. 491–494. doi: 10.1109/ICSESS.2018.8663780.
  • Z. Yang, K. Gao, and J. Huang, “External Expansion Risk Management: Enhancing Microblogging Filtering Using Implicit Query,” Wireless Pers Commun, vol. 102, no. 3, pp. 2199–2209, Oct. 2018, doi: 10.1007/s11277-017-5075-5.
  • Z. Yang, W. Chen, and J. Huang, “Enhancing recommendation on extremely sparse data with blocks-coupled non-negative matrix factorization,” Neurocomputing, vol. 278, pp. 126–133, Feb. 2018, doi: 10.1016/j.neucom.2017.04.080.
  • Z. Ma, J.-H. Xue, A. Leijon, Z.-H. Tan, Z. Yang, and J. Guo, “Decorrelation of Neutral Vector Variables: Theory and Applications,” IEEE Trans. Neural Netw. Learning Syst., vol. 29, no. 1, pp. 129–143, Jan. 2018, doi: 10.1109/TNNLS.2016.2616445.
  • 杨震, 杨甜甜, 范科峰, 王勇, “基于信任合成的云服务动态组合机制研究,” 电子学报, vol. 46, no. 3, pp. 614–620, 2018.
  • Z. Yang, F. Yao, K. Fan, and J. Huang, “Text Dimensionality Reduction with Mutual Information Preserving Mapping,” Chinese Journal of Electronics, vol. 26, no. 5, pp. 919–925, 2017, doi: 10.1049/cje.2017.08.020.
  • Z. Yang, I. Jones, X. Hu, and H. Liu, “Finding the Right Social Media Site for Questions,” in Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, Paris France, Aug. 2015, pp. 639–644. doi: 10.1145/2808797.2809391.
  • Z. Yang, K. Gao, K. Fan, and Y. Lai, “Sensational Headline Identification By Normalized Cross Entropy-Based Metric,” The Computer Journal, vol. 58, no. 4, pp. 644–655, Apr. 2015, doi: 10.1093/comjnl/bxu107.
  • H. Dani, F. Morstatter, X. Hu, Z. Yang, and H. Liu, “Social Answer: A System for Finding Appropriate Sites for Questions in Social Media,” in 2015 IEEE International Conference on Data Mining Workshop (ICDMW), Atlantic City, NJ, USA, Nov. 2015, pp. 1632–1635. doi: 10.1109/ICDMW.2015.253. DEMO (a Q&A “Social Answer” demo system of ASONAM’15 Paper)
  • Y. Zhen, F. Kefeng, L. Yingxu, G. Kaiming, and W. Yong, “Short Texts Classification Through Reference Document Expansion,” Chinese Journal of Electronics, 2014.
  • 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, vol. 25, no. 12, pp. 2777–2789, 2014, doi: 10.13328/j.cnki.jos.004729.
  • Z. Yang, J. Lei, K. Fan, and Y. Lai, “Keyword extraction by entropy difference between the intrinsic and extrinsic mode,” Physica A: Statistical Mechanics and its Applications, vol. 392, no. 19, pp. 4523–4531, Oct. 2013, doi: 10.1016/j.physa.2013.05.052. [Paper, Code]
  • Z. Yang, L. Wang, K. Fan, and Y. Lai, “Exemplar-Based Clustering Analysis Optimized by Genetic Algorithm,” Chinese Journal of Electronics, vol. 22, no. 4, pp. 735–740, Oct. 2013, doi: 10.23919/CJE.2013.10285816.
  • 杨震, 赖英旭, 段立娟, 李玉鑑, 许昕, “邮件网络协同过滤机制研究,” 自动化学报, vol. 38, no. 3, pp. 399–411, 2012.
  • 杨震, 赖英旭, 段立娟, 李玉鑑, “基于上下文重构的短文本情感极性判别研究,” 自动化学报, vol. 38, no. 1, pp. 55–67, 2012.
  • 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, vol. 37, no. 3, pp. 557–561, 2009.

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