arXiv subjects

Xueqing Zhang

Publications and source records attributed to Xueqing Zhang.

3 recordsLinked to original sources

arXiv2024

Shot Segmentation Based on Von Neumann Entropy for Key Frame Extraction

Video key frame extraction is important in various fields, such as video summary, retrieval, and compression. Therefore, we suggest a video key frame extraction algorithm based on shot segmentation using Von Neumann entropy. The segmentation of shots is achieved through the computation of Von Neumann entropy of the similarity matrix among frames within the video sequence. The initial frame of each shot is selected as key frames, which combines the temporal sequence information of frames. The experimental results show the extracted key frames can fully and accurately represent the original video content while minimizing the number of repeated frames.

cs.CV↗

arXiv2024

Visualizing the Shadows: Unveiling Data Poisoning Behaviors in Federated Learning

This demo paper examines the susceptibility of Federated Learning (FL) systems to targeted data poisoning attacks, presenting a novel system for visualizing and mitigating such threats. We simulate targeted data poisoning attacks via label flipping and analyze the impact on model performance, employing a five-component system that includes Simulation and Data Generation, Data Collection and Upload, User-friendly Interface, Analysis and Insight, and Advisory System. Observations from three demo modules: label manipulation, attack timing, and malicious attack availability, and two analysis components: utility and analytical behavior of local model updates highlight the risks to system integrity and offer insight into the resilience of FL systems. The demo is available at https://github.com/CathyXueqingZhang/DataPoisoningVis.

cs.CR↗

arXiv2022

The global linear convergence rate of the proximal version of the generalized alternating direction method of multipliers for separable convex programming

To solve the separable convex optimization problem with linear constraints, Eckstein and Bertsekas introduced the generalized alternating direction method of multipliers (in short, GADMM), which is an efficient and simple acceleration scheme of the aternating direction method of multipliers. Recently, \textbf{Fang et. al} proposed the linearized version of generalized alternating direction method of multipliers (in short, L-GADMM), where one of its subproblems is approximated by a linearization strategy, and proved its worst-case $\mathcal{O}(1/t)$ convergence rate measured by the iteration complexity in both ergodic and nonergodic senses. In this paper, we introduce the doubly linearized version of generalized alternating direction method of multipliers (in short, DL-GADMM), where both the $x$-subproblem and $y$-subproblem are approximated by linearization strategies. Based on the error bound approach, we establish the linear convergence rate of both L-GADMM and DL-GADMM for separable convex optimization problem that the subdifferentials of the underlying functions are piecewise linear multifunctions. The results in this paper extend, generalize and improve some known results in the literature.

math.OC↗