1 citations · 2 across the 6 of their papers we have counts for
6 papers
AIPsychoBench: Understanding the Psychometric Differences between LLMs and Humans
Wei Xie, Shuoyoucheng Ma, Zhenhua Wang +4
Large Language Models (LLMs) with hundreds of billions of parameters have exhibited human-like intelligence by learning from vast amounts of internet-scale data. However, the unint…
Graph Federated Learning for Personalized Privacy Recommendation
Ce Na, Kai Yang, Dengzhao Fang +6
Federated recommendation systems (FedRecs) have gained significant attention for providing privacy-preserving recommendation services. However, existing FedRecs assume that all use…
Towards Generalized and Stealthy Watermarking for Generative Code Models
Haoxuan Li, Jiale Zhang, Xiaobing Sun +1
Generative code models (GCMs) significantly enhance development efficiency through automated code generation and code summarization. However, building and training these models req…
MalGuard: Towards Real-Time, Accurate, and Actionable Detection of Malicious Packages in PyPI Ecosystem
Xingan Gao, Xiaobing Sun, Sicong Cao +5
Malicious package detection has become a critical task in ensuring the security and stability of the PyPI. Existing detection approaches have focused on advancing model selection,…
"No Matter What You Do": Purifying GNN Models via Backdoor Unlearning
Jiale Zhang, Chengcheng Zhu, Bosen Rao +5
Recent studies have exposed that GNNs are vulnerable to several adversarial attacks, among which backdoor attack is one of the toughest. Similar to Deep Neural Networks (DNNs), bac…
Coca: Improving and Explaining Graph Neural Network-Based Vulnerability Detection Systems
Sicong Cao, Xiaobing Sun, Xiaoxue Wu +4
Recently, Graph Neural Network (GNN)-based vulnerability detection systems have achieved remarkable success. However, the lack of explainability poses a critical challenge to deplo…