https://cis.temple.edu/~jiewu/research/publications/Publication_files/jiang_www_2020.pdf
The goal is to generate a list of potential items,Ttar ⊂ T, each one of which is represented as a reCap, and provide expla-nations for , so as to enhance recommendation serendipity (a utar balance between diference and accuracy) and explainability.
https://cis.temple.edu/~wu/research/publications/Publication_files/J-CT-2024-Balancing%20Privacy%20and%20Accuracy%20using%20Siginificant%20Gradient%20Protection%20in%20Federated%20Learning.pdf
Abstract—Previous state-of-the-art studies have demonstrated that adversaries can access sensitive user data by membership inference attacks (MIAs) in Federated Learning (FL). Intro-ducing differential privacy (DP) into the FL framework is an effective way to enhance the privacy of FL. Nevertheless, in differentially private federated learning (DP-FL), local gradients become excessively ...
https://cis.temple.edu/~latecki/Papers/cvpr00.pdf
Abstract The Core Experiment CE-Shape-1 for shape descriptors performed for the MPEG-7 standard gave a unique oppor-tunity to compare various shape descriptors for non-rigid shapes with a single closed contour. There are two main dif-ferences with respect to other comparison results reported in the literature: (1) For each shape descriptor, the exper-iments were carried out by an institute ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/1-s2.0-S0957417424018475-main.pdf
However, due to the diversity and complexity, modeling human actions as general graphs and capturing discriminative spatial–temporal motion patterns is challenging. Besides, the inevitable interference, especially occlusion, impairs the robustness of existing methods that depend on complete skeletons. To solve these problems, we propose a Multi-Granular Spatial-Temporal Synchronous Graph ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/Distributed_Deep_Multi-Agent_Reinforcement_Learning_for_Cooperative_Edge_Caching_in_Internet-of-Vehicles.pdf
Abstract—Edge caching is a promising approach to reduce duplicate content transmission in Internet-of-Vehicles (IoVs). Sev-eral Reinforcement Learning (RL) based edge caching methods have been proposed to improve the resource utilization and reduce the backhaul trafic load. However, they only obtain the local sub-optimal solution, as they neglect the influence from environments by other ...