师资队伍

程红举

来源:     发布日期:2021-10-08    浏览次数:


基本信息


职称:教授

职务:博士生导师、硕士生导师

主讲课程:计算机网络、学科导论、计算机网络实践

研究方向:边缘计算、机器学习、多模态、数字孪生、区块链

办公室:2号楼305

电子邮件:cscheng@fzu.edu.cn

联系电话:13599099519


个人简介


程红举教授在计算机科学与技术一级学科(所有专业方向均可)招收博士、硕士和能力较强的本科生。2026年拟招收学术型博士生1名(2026年9月入学),请有意向的同学及时通过邮件或微信联系。

1997年和2000年毕业于武汉水利电力大学(现武汉大学),分别获工学学士和工学硕士学位。2004年9月至2007年6月就读于武汉大学计算机学院,2007年6月获得计算机软件与理论专业工学博士学位。

2006年在香港城市大学电脑系任研究助理,2007年6月毕业后至福州大学数学与计算机科学学院工作, 2014年4月至2015年2月赴美国阿拉巴马大学计算机科学系进行学术访问。


科研项目


1、国家自然基金面上项目, 复合场景下基于多模态融合的轻量化任务处理与模型泛化研究(62372111), 2024.1 – 2027.12,65万

2、福建省自然基金面上项目, 边端协同网络的数字孪生及优化研究(2023J01267), 2023.8 – 2026.8,10万。

3、某企业横向课题,2019 – 2024,120万。

4、福建省自然基金面上项目, 分布式异构物联网带认知的动态组网策略和智能大数据分析研究(2019J01245), 2019 – 2022,10万。

5、福建省教育厅高校新世纪优秀人才项目,新一代无线传感器网络中的智能协同机制研究, 2015 – 2017, 8万。

6、国家自然基金面上项目, 面向服务的无线传感器网络关联感知策略与容错协同机制研究(61370210), 2014.1 – 2017.12,71万。

7、横向课题,深圳东深电子股份有限公司,XXX供水工程水力计算自动化软件设计与开发,2016.8-2022.11,50万。

8、横向课题,泉州科力电气有限公司,XXX系统后台WEB软件开发,2012.12-2013.12,18万。

9、福建省自然基金面上项目, 三维无线传感器网络动态拓扑控制与实时数据汇集研究(2011J01345), 2011.4 – 2014.3,4万。

10、福建省教育厅项目,无线传感器网络中面向服务的多点协同机制研究(JA12027), 2013.1 – 2015.12,2万。

11、福州大学科技发展基金,面向服务的无线传感器网络关联感知策略研究(2013-XQ-35), 2013.6-2015.5,6万。



近年主要论文


(可以参考google scholar:https://scholar.google.com/citations?user=KxWm8VUAAAAJ)


  • [1] Zhan Z, Cao D, Chen Z, et al. Multimodal sentiment analysis based on slice aggregation and dynamic fusion. CCF Transactions on Pervasive Computing and Interaction, 2025: 1-20.

  • [2] Zhang J, Chen Z, Cheng H, et al. Improving Multi-Model Anomaly Traffic Detection in MEC Networks With Large-Model-Powered Continuous Learning. IEEE Network, 2025.

  • [3] Zhan Z, Cao D, Cheng H. When Sentiment Analysis Faces Missing Modalities: A Specific and Invariant Feature Learning Approach//2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE). IEEE, 2025: 533-537.

  • [4] Gao J, Hu Q, Cheng H. Data Synchronization Optimization Algorithm for The Digital Twin with Grouped Load Balance and Mask-assisted Power Control//2024 IEEE International Conference on High Performance Computing and Communications (HPCC). IEEE, 2024: 1132-1139.

  • [5] Chen Z, Huang Z, Zhang J, et al. Resource allocation and collaborative offloading in multi-UAV-assisted IoV with federated deep reinforcement learning. IEEE Internet of Things Journal, 2024.

  • [6] Hu Q, Cheng H, Yang Y, et al. CRNG-PBFT: An Efficient PBFT Algorithm Based on Comprehensive Reputation and Node Grouping for Data Sharing in Digital Twins//2024 20th International Conference on Mobility, Sensing and Networking (MSN). IEEE Computer Society, 2024: 1126-1131.

  • [7] Chen Z, Liang J, Yu Z, et al. Resilient collaborative caching for multi-edge systems with robust federated deep learning. IEEE/ACM Transactions on Networking, 2024.

  • [8] Zhan Y, Yang Y, Cheng H, et al. PIAS: Privacy-preserving incentive announcement system based on blockchain for Internet of Vehicles. IEEE Transactions on Services Computing, 2024, 17(5): 2762-2775.

  • [9] Zhang X, Lin T, Lin C K, et al. Computational task offloading algorithm based on deep reinforcement learning and multi-task dependency. Theoretical Computer Science, 2024, 993: 114462.

  • [10] Zhong L, Chen Z, Cheng H, et al. Lightweight federated graph learning for accelerating classification inference in UAV-assisted MEC systems. IEEE Internet of Things Journal, 2024, 11(12): 21180-21190.

  • [11] Lin C, Cheng H, Rao Q, et al. M $^{3} $ SA: Multimodal Sentiment Analysis Based on Multi-Scale Feature Extraction and Multi-Task Learning. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2024, 32: 1416-1429.

  • [12] Zhang J, Chen Z, Cheng H, et al. Resource-Efficient Offloading and Network Access Optimization Using Deep Reinforcement Learning in Mobile Edge Networks. Available at SSRN 5031320, 2024.

  • [13] Ding J, Hu Q, Lin C, et al. BPPV-Chain: A Sharding Blockchain System with Output Shard Batch Processing and Parallel Transaction Verification//Proceedings of the 2023 5th International Conference on Blockchain Technology. 2023: 8-14.

  • [14] Zhang X, Cheng H. Joint task assignment and migration in cloud-edge-end collaborative computing based on DRL//2023 International Conference on Intelligent Communication and Networking (ICN). IEEE, 2023: 265-270.

  • [15] Shi Y, Zhang X, Hu Q, et al. Data recovery algorithm based on generative adversarial networks in crowd sensing Internet of Things. Personal and Ubiquitous Computing, 2023, 27(3): 537-550.

  • [16] Shi M, Zhang X, Chen J, et al. UAV cluster-assisted task offloading for emergent disaster scenarios. Applied Sciences, 2023, 13(8): 4724.

  • [17] Cheng H, Yang Z, Zhang X, et al. Multimodal sentiment analysis based on attentional temporal convolutional network and multi-layer feature fusion. IEEE transactions on affective computing, 2023, 14(4): 3149-3163.

  • [18] Li X Y, Zhang Y, Liu X, et al. Reliability evaluation of clustered faults for regular networks under the probabilistic diagnosis model. The Computer Journal, 2023, 66(2): 441-462.

  • [19] Lin T, Lin C K, Chen Z, et al. Computation offloading algorithm based on deep reinforcement learning and multi-task dependency for edge computing//International Computer Symposium. Singapore: Springer Nature Singapore, 2022: 111-122.

  • [20] Yang Y, Rong C, Zheng X, et al. Time controlled expressive predicate query with accountable anonymity. IEEE Transactions on Services Computing, 2022, 16(2): 1444-1457.

  • [21] Hu Q, Cheng H, Zhang X, et al. Trusted resource allocation based on proof-of-reputation consensus mechanism for edge computing. Peer-to-Peer Networking and Applications, 2022, 15(1): 444-460.

  • [22] Zhang X, Cheng H, Yu Z, et al. Design and analysis of an efficient multiresource allocation system for cooperative computing in Internet of Things. IEEE Internet of Things Journal, 2021, 9(16): 14463-14477.

  • [23] Yang Y, Deng R H, Guo W, et al. Dual traceable distributed attribute-based searchable encryption and ownership transfer. IEEE Transactions on Cloud Computing, 2021, 11(1): 247-262.

  • [24] Cheng H, Shi Y, Wu L, et al. An intelligent scheme for big data recovery in Internet of Things based on multi-attribute assistance and extremely randomized trees. Information Sciences, 2021, 557: 66-83.

  • [25] Cheng H, Wu L, Li R, et al. Data recovery in wireless sensor networks based on attribute correlation and extremely randomized trees. Journal of Ambient Intelligence and Humanized Computing, 2021, 12(1): 245-259.


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