[1]Y. Zhang, F. Liang, G. Yuan, M. Yang, C. Li, X. Hu, "FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature Drift", International Conference on Computer Vision (ICCV), 2025. (CCF-A)
提出一个基于类别原型的对抗和协作机制应对特征漂移问题的联邦学习框架,利用基于原型的对抗学习来统一特征空间,并利用协作学习来强化特征中的类别信息。
[2]Z. Zhang, F. Liang, W. Wang, R. Zeng, V.C.M. Leung, and X. Hu, "Skeleton-Based Pre-Training with Discrete Labels for Emotion Recognition in IoT Environments", IEEE Internet of Things Journal (IoTJ), 2025. (Q1)
提出了一种基于离散标签的忽略肢体信息冗余的新型情绪识别框架,进行离散标签。
[3]H. Lu, J. Chen, F. Liang, M. Tan, R. Zeng, and X. Hu, "Understanding Emotional Body Expressions via Large Language Models", 39th Annual AAAI Conference on Artificial Intelligence (AAAI), 2025. (CCF-A)
提出了一种基于大型语言模型 (EAI-LLM) 的情绪-动作解释器,它不仅可以识别情绪,还可以通过将三维肢体动作数据视为大型语言模型 (LLM) 中唯一的输入标记来生成文本解释。即使在有限数量的带标签骨架数据上进行训练,模型也能够基于分类结果生成详细的情绪描述。
[4]T. Li*, F. Liang*, J. Quan, C. Huang, T. Wang, R. Huang, and X. Hu, "Taste: Towards Practical Deep Learning-based Approaches for Semantic Type Detection in the Cloud", 28th International Conference on Extending Database Technology (EDBT), 2025. (CCF-B会议,共同一作)
利用多任务倾斜模型及元数据甄别的两阶段方法,实现大规模云上关系型数据的高效、无侵入的语义类型识别。
[5]F. Liang, F.C.M. Lau, H. Cui, Y. Li, B. Lin, C. Li, and X. Hu, "RelJoin: Relative-cost-based Selection of Distributed Join Methods for Query Plan Optimization", Elsevier Information Sciences, 2024. (中科院1区)
通过一种新颖的基于数据集相对大小的成本计算方法,对分布式环境下的各种join操作选择最优的物理实现以实时优化查询计划。
[6]W. Guo, B. Lin, G. Chen, Y. Chen and F. Liang, "Cost-Driven Scheduling for Deadline-Based Workflow Across Multiple Clouds", in IEEE Transactions on Network and Service Management(TNSM), vol. 15, no. 4, pp. 1571-1585, 2018. (Q1)
在跨云环境中调度时间限制的科学工作流,使用分散粒子群优化技术降低计算和网络代价。
[7]F. Liang, F.C.M. Lau, H. Cui and C.-L. Wang, "Confluence: Speeding Up Iterative Distributed Operations by Key-dependency-aware Partitioning", IEEE Transactions on Parallel and Distributed Systems (TPDS), 2018. (CCF-A期刊)
在迭代式分布式计算中,定义和利用关键字的依赖关系,降低shuffle网络负荷。
[8]J. Jiang, S. Zhao, D. Alsayed, Y. Wang, H. Cui, F. Liang, and Z. Gu, "Kakute: A Precise, Unified Information Flow Analysis System for Big-data Security", Annual Computer Security Applications Conference (ACSAC), 2017. *Distinguished Paper Award (CCF-B类会议)
通过引用传播和标签共享技术实现高效的分布式框架下的信息流跟踪。(最佳论文奖)
[9]F. Liang and F.C.M. Lau, “BAShuffler: Maximizing Network Bandwidth Utilization in the Shuffle of YARN”, 25th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2016. (CCF-B会议)
根据TCP max-min fairness特性对分布式计算节点的网络负载进行监控,动态分配shuffle任务,通过最大化利用集群中网络资源实现shuffle计算性能的提升。
[10]F. Liang and F.C.M. Lau, “SMapReduce: Optimising Resource Allocation by Managing Working Slots at Runtime”, 29th IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2015. (CCF-B会议)
根据分布式计算中map和reduce计算不同特性,通过对两者不同的工作负荷进行实时动态的计算资源分配,最大化分布式计算资源利用和计算吞吐量。