CybAI news

[Submitted on 12 Nov 2024 (v1), last revised 22 Nov 2024 (this version, v2)] View a PDF of the paper titled Enhancing Link Prediction with Fuzzy Graph Attention Networks and Dynamic Negative Sampling, by Jinming Xing and 1 other authors View PDF HTML (experimental) Abstract:Link prediction is crucial for understanding complex networks but traditional Graph Neural Networks (GNNs) often rely on random negative sampling, leading to suboptimal performance. This paper introduces Fuzzy Graph Attention Networks (FGAT), a novel approach integrating fuzzy rough sets for dynamic negative sampling and enhanced node feature aggregation. Fuzzy Negative Sampling (FNS) systematically selects high-quality negative…

arXiv:2411.14565v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) aims to automatically analyze spatiotemporal patterns in surveillance videos collected from open spaces to detect anomalous events that may cause harm without physical contact. However, vision-based surveillance systems such as closed-circuit television often capture personally identifiable information. The lack of transparency and interpretability in video transmission and usage raises public concerns about privacy and ethics, limiting the real-world application of VAD. Recently, researchers have focused on privacy concerns in VAD by conducting systematic studies from various perspectives including data, features, and systems, making Privacy-Preserving Video Anomaly Detection (P2VAD) a hotspot in…

[Submitted on 9 May 2024 (v1), last revised 21 Nov 2024 (this version, v2)] View a PDF of the paper titled Large Language Models Show Human-like Social Desirability Biases in Survey Responses, by Aadesh Salecha and 5 other authors View PDF HTML (experimental) Abstract:As Large Language Models (LLMs) become widely used to model and simulate human behavior, understanding their biases becomes critical. We developed an experimental framework using Big Five personality surveys and uncovered a previously undetected social desirability bias in a wide range of LLMs. By systematically varying the number of questions LLMs were exposed to, we demonstrate their…