Zobrazeno 1 - 10
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pro vyhledávání: '"Hooi"'
Autor:
Wang, Yuan1 (AUTHOR) wyuan@hainanu.edu.cn, Wang, Xianpeng1 (AUTHOR) wxpeng2016@hainanu.edu.cn, Su, Ting1 (AUTHOR), Guo, Yuehao1 (AUTHOR), Lan, Xiang1 (AUTHOR)
Publikováno v:
Sensors (14248220). Dec2023, Vol. 23 Issue 24, p9682. 17p.
Akademický článek
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Publikováno v:
Sensors, Vol 23, Iss 24, p 9682 (2023)
In this paper, we introduce a Reduced-Dimension Multiple-Signal Classification (RD-MUSIC) technique via Higher-Order Orthogonal Iteration (HOOI), which facilitates the estimation of the target range and angle for Frequency-Diverse Array Multiple-Inpu
Externí odkaz:
https://doaj.org/article/38f93afd23da425cae5528047ff247a1
Autor:
Li, Yuexin, Tan, Hiok Kuek, Meng, Qiaoran, Lock, Mei Lin, Cao, Tri, Deng, Shumin, Oo, Nay, Lim, Hoon Wei, Hooi, Bryan
Phishing is a critical cyber threat, exploiting deceptive tactics to compromise victims and cause significant financial losses. While reference-based phishing detectors (RBPDs) achieve high precision by analyzing brand-domain consistency, their real-
Externí odkaz:
http://arxiv.org/abs/2412.09057
Despite inheriting security measures from underlying language models, Vision-Language Models (VLMs) may still be vulnerable to safety alignment issues. Through empirical analysis, we uncover two critical findings: scenario-matched images can signific
Externí odkaz:
http://arxiv.org/abs/2411.18000
Question answering represents a core capability of large language models (LLMs). However, when individuals encounter unfamiliar knowledge in texts, they often formulate questions that the text itself cannot answer due to insufficient understanding of
Externí odkaz:
http://arxiv.org/abs/2411.17993
Autor:
Cao, Tri, Trinh, Minh-Huy, Deng, Ailin, Nguyen, Quoc-Nam, Duong, Khoa, Cheung, Ngai-Man, Hooi, Bryan
Anomaly detection (AD) is a machine learning task that identifies anomalies by learning patterns from normal training data. In many real-world scenarios, anomalies vary in severity, from minor anomalies with little risk to severe abnormalities requir
Externí odkaz:
http://arxiv.org/abs/2411.14515
Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks. Existing GNN-based GFD models are designed to augment graph structure to accom
Externí odkaz:
http://arxiv.org/abs/2412.00020
With the advancement of technology, large language models (LLMs) have achieved remarkable performance across various natural language processing (NLP) tasks, powering LLM-integrated applications like Microsoft Copilot. However, as LLMs continue to ev
Externí odkaz:
http://arxiv.org/abs/2411.00459