36 citations · 45 across the 7 of their papers we have counts for
7 papers
How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation
Cen Zhang, Yaowen Zheng, Mingqiang Bai +6
LLM-based (Large Language Model) fuzz driver generation is a promising research area. Unlike traditional program analysis-based method, this text-based approach is more general and…
Hierarchical Aligned Multimodal Learning for NER on Tweet Posts
Peipei Liu, Hong Li, Yimo Ren +4
Mining structured knowledge from tweets using named entity recognition (NER) can be beneficial for many down stream applications such as recommendation and intention understanding.…
LibAM: An Area Matching Framework for Detecting Third-party Libraries in Binaries
Siyuan Li, Yongpan Wang, Chaopeng Dong +8
Third-party libraries (TPLs) are extensively utilized by developers to expedite the software development process and incorporate external functionalities. Nevertheless, insecure TP…
Multi-Granularity Cross-Modality Representation Learning for Named Entity Recognition on Social Media
Peipei Liu, Gaosheng Wang, Hong Li +4
Named Entity Recognition (NER) on social media refers to discovering and classifying entities from unstructured free-form content, and it plays an important role for various applic…
CEntRE: A paragraph-level Chinese dataset for Relation Extraction among Enterprises
Peipei Liu, Hong Li, Zhiyu Wang +5
Enterprise relation extraction aims to detect pairs of enterprise entities and identify the business relations between them from unstructured or semi-structured text data, and it i…
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis
Peipei Liu, Xin Zheng, Hong Li +4
Modality representation learning is an important problem for multimodal sentiment analysis (MSA), since the highly distinguishable representations can contribute to improving the a…
Threat Detection for General Social Engineering Attack Using Machine Learning Techniques
Zuoguang Wang, Yimo Ren, Hongsong Zhu +1
This paper explores the threat detection for general Social Engineering (SE) attack using Machine Learning (ML) techniques, rather than focusing on or limited to a specific SE atta…