Publications (22)
Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training
Zeli Wang, Qingxuan Yang, Shuyin Xia +3
Deep neural networks (DNNs) have emerged as a prominent approach for detecting smart contract vulnerabilities, driven by the growing contract datasets and advanced deep learning te…
Vulseye: Detect Smart Contract Vulnerabilities via Stateful Directed Graybox Fuzzing
Ruichao Liang, Jing Chen, Cong Wu +6
Smart contracts, the cornerstone of decentralized applications, have become increasingly prominent in revolutionizing the digital landscape. However, vulnerabilities in smart contr…
Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective
Shihan Dou, Rui Zheng, Ting Wu +5
Natural language understanding (NLU) models tend to rely on spurious correlations (i.e., dataset bias) to achieve high performance on in-distribution datasets but poor performance…
CC2Vec: Combining Typed Tokens with Contrastive Learning for Effective Code Clone Detection
Shihan Dou, Yueming Wu, Haoxiang Jia +3
With the development of the open source community, the code is often copied, spread, and evolved in multiple software systems, which brings uncertainty and risk to the software sys…
Demystifying the Evolution of Neural Networks with BOM Analysis: Insights from a Large-Scale Study of 55,997 GitHub Repositories
Xiaoning Ren, Yuhang Ye, Xiongfei Wu +2
Neural networks have become integral to many fields due to their exceptional performance. The open-source community has witnessed a rapid influx of neural network (NN) repositories…
A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook
Kaiwen Luo, Zhenhong Zhou, Leo Wang +34
Advances in Large Language Models (LLMs) have paved the way for Multimodal Large Language Models (MLLMs). Among these, Large Audio Language Models (LALMs) are essential for realizi…