Zobrazeno 1 - 10
of 1 862
pro vyhledávání: '"P. Leckie"'
Autor:
Chang, Ching-Chun, Gao, Kai, Xu, Shuying, Kordoni, Anastasia, Leckie, Christopher, Echizen, Isao
Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic consequences. A backdoor attack
Externí odkaz:
http://arxiv.org/abs/2410.05284
Dreams offer a unique window into the cognitive and affective dynamics of the sleeping and the waking mind. Recent quantitative linguistic approaches have shown promise in obtaining corpus-level measures of dream sentiment and topic occurrence. Howev
Externí odkaz:
http://arxiv.org/abs/2409.14279
Matching in two-sided markets such as ride-hailing has recently received significant attention. However, existing studies on ride-hailing mainly focus on optimising efficiency, and fairness issues in ride-hailing have been neglected. Fairness issues
Externí odkaz:
http://arxiv.org/abs/2407.17839
The proliferation of misinformation and disinformation on social media networks has become increasingly concerning. With a significant portion of the population using social media on a regular basis, there are growing efforts by malicious organizatio
Externí odkaz:
http://arxiv.org/abs/2407.11697
The rise of social media has been accompanied by a dark side with the ease of creating fake accounts and disseminating misinformation through coordinated attacks. Existing methods to identify such attacks often rely on thematic similarities or networ
Externí odkaz:
http://arxiv.org/abs/2407.11690
Large language models (LLMs) are susceptible to social-engineered attacks that are human-interpretable but require a high level of comprehension for LLMs to counteract. Existing defensive measures can only mitigate less than half of these attacks at
Externí odkaz:
http://arxiv.org/abs/2402.13517
Deep neural networks (DNNs) are vulnerable to shortcut learning: rather than learning the intended task, they tend to draw inconclusive relationships between their inputs and outputs. Shortcut learning is ubiquitous among many failure cases of neural
Externí odkaz:
http://arxiv.org/abs/2402.11237
This paper presents a novel and efficient wireless channel estimation scheme based on a tapped delay line (TDL) model of wireless signal propagation, where a data-driven machine learning approach is used to estimate the path delays and gains. The key
Externí odkaz:
http://arxiv.org/abs/2402.07385
Anomaly detection in decision-making sequences is a challenging problem due to the complexity of normality representation learning and the sequential nature of the task. Most existing methods based on Reinforcement Learning (RL) are difficult to impl
Externí odkaz:
http://arxiv.org/abs/2402.04567
This paper considers an important Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to train a detection model using a small number of normal and anomaly nodes (referred to as seen anomalies) to detect both seen anomalies and unseen
Externí odkaz:
http://arxiv.org/abs/2311.06835