Hongsong Zhu

IRPFuzz: Fuzzing Industrial Robot Protocol Via LLM-Driven Traffic Semantic Analysis

CLeVeR: Multi-modal Contrastive Learning for Vulnerability Code Representation

Automated vulnerability detection has become increasingly important. Many existing methods utilize deep learning models to obtain code representations for vulnerability detection. However, these approaches predominantly capture the overall semantics of the code rather than its intrinsic vulnerabilit…

Steering Large Language Models for Vulnerability Detection

Vulnerability detection remains a critical challenge in the field of security. Many existing approaches extract code representations for vulnerability detection. However, these methods often focus on the overall semantics of the code, neglecting to specifically target vulnerability-related semantics…

A Relation-Aware Heterogeneous Graph Transformer on Dynamic Fusion for Multimodal Classification Tasks

Multimodal fusion aims to improve the performance of models for applications by extracting and fusing information in different modalities, including texts, images or others. Recent researches have shown that multimodal fusion is beneficial in many multimedia tasks. In this paper, we study typical mu…

Hierarchical Aligned Multimodal Learning for NER on Tweet Posts

Mining structured knowledge from tweets using named entity recognition (NER) can be beneficial for many downstream applications such as recommendation and intention under standing. With tweet posts tending to be multimodal, multimodal named entity recognition (MNER) has attracted more attention. In…

Improving the Modality Representation with multi-view Contrastive Learning for Multimodal Sentiment Analysis

Modality representation learning is an important problem for multimodal sentiment analysis (MSA), since the highly distinguishable representations can contribute to improving the analysis effect. Previous works of MSA have usually focused on internal fusion strategies for different modalities within…

Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph

Chinese pre-trained language models usually exploit contextual character information to learn representations, while ignoring the linguistics knowledge, e.g., word and sentence information. Hence, we propose a task-free enhancement module termed as Heterogeneous Linguistics Graph (HLG) to enhance Ch…

Discontinuous Named Entity Recognition as Maximal Clique Discovery

Named entity recognition (NER) remains challenging when entity mentions can be discontinuous. Existing methods break the recognition process into several sequential steps. In training, they predict conditioned on the golden intermediate results, while at inference relying on the model output of the…

Maximal Clique Based Non-Autoregressive Open Information Extraction

Open Information Extraction (OpenIE) aims to discover textual facts from a given sentence. In essence, the facts contained in plain text are unordered. However, the popular OpenIE systems usually output facts sequentially in the way of predicting the next fact conditioned on the previous decoded one…

TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking

Extracting entities and relations from unstructured text has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in identifying overlapping relations with shared entities. Prior works show that joint learning can result in a noticeable performance…