14 citations · 42 across the 26 of their papers we have counts for
28 papers
DiagChain: A Diagnostic Benchmark for Evaluating LLM Agents on Evidence-Grounded Attack Chain Reconstruction
Xuyang Liu, Yibin Han, Zhenwei Zhang +8
Large Language Model (LLM) agents offer a promising approach to attack chain reconstruction by retrieving and interpreting heterogeneous telemetry to infer ordered attacker actions…
DecoEvo: Score-Decoupled Co-Evolution of Solver and Rubric-Generator Skills in Text Space
Jiangwang Chen, Zixin Song, Junlin Liu +10
Text-space optimization adapts large language models (LLMs) by editing external natural-language artifacts rather than model weights, so the optimized artifacts remain inspectable…
Semantic Code Clone Detection: Are We There Yet?
Zhiwei Xu, Weixian Deng, Xuyang Liu +5
Code clone detection has been extensively studied for decades, and recent approaches have begun reporting remarkably high performance for semantic (Type-4) clones on benchmark data…
Temperature as a Meta-Policy: Adaptive Temperature in LLM Reinforcement Learning
Haoran Dang, Cuiling Lan, Hai Wan +2
Temperature is a crucial hyperparameter in large language models (LLMs), controlling the trade-off between exploration and exploitation during text generation. High temperatures en…
Adversarial Contrastive Learning for LLM Quantization Attacks
Dinghong Song, Zhiwei Xu, Hai Wan +3
Model quantization is critical for deploying large language models (LLMs) on resource-constrained hardware, yet recent work has revealed severe security risks that benign LLMs in f…
Advanced Global Wildfire Activity Modeling with Hierarchical Graph ODE
Fan Xu, Wei Gong, Hao Wu +6
Wildfires, as an integral component of the Earth system, are governed by a complex interplay of atmospheric, oceanic, and terrestrial processes spanning a vast range of spatiotempo…
SCRUTINEER: Detecting Logic-Level Usage Violations of Reusable Components in Smart Contracts
Xingshuang Lin, Binbin Zhao, Jinwen Wang +3
Smart Contract Reusable Components(SCRs) play a vital role in accelerating the development of business-specific contracts by promoting modularity and code reuse. However, the risks…
Unlocking Out-of-Distribution Generalization in Dynamics through Physics-Guided Augmentation
Fan Xu, Hao Wu, Kun Wang +5
In dynamical system modeling, traditional numerical methods are limited by high computational costs, while modern data-driven approaches struggle with data scarcity and distributio…
Breaking the Discretization Barrier of Continuous Physics Simulation Learning
Fan Xu, Hao Wu, Nan Wang +4
The modeling of complicated time-evolving physical dynamics from partial observations is a long-standing challenge. Particularly, observations can be sparsely distributed in a seem…
Ancora: Accurate Intrusion Recovery for Web Applications
Yihao Peng, Biao Ma, Hai Wan +1
Modern web application recovery presents a critical dilemma. Coarse-grained snapshot rollbacks cause unacceptable data loss for legitimate users. Surgically removing an attack's im…
Deep Learning-based Intrusion Detection Systems: A Survey
Zhiwei Xu, Yujuan Wu, Shiheng Wang +5
Intrusion Detection Systems (IDS) have long been a hot topic in the cybersecurity community. In recent years, with the introduction of deep learning (DL) techniques, IDS have made…
Graph-Based Cross-Domain Knowledge Distillation for Cross-Dataset Text-to-Image Person Retrieval
Bingjun Luo, Jinpeng Wang, Wang Zewen +2
Video surveillance systems are crucial components for ensuring public safety and management in smart city. As a fundamental task in video surveillance, text-to-image person retriev…
LESS: Efficient Log Storage System Based on Learned Model and Minimum Attribute Tree
Zhiyang Cheng, Zizhen Zhu, Haoran Dang +2
In recent years, cyber attacks have become increasingly sophisticated and persistent. Detection and investigation based on the provenance graph can effectively mitigate cyber intru…
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection
Fan Xu, Nan Wang, Hao Wu +7
Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs within a graph dataset. However, current methods are constrained by their receptive fields, struggling to…
LTRDetector: Exploring Long-Term Relationship for Advanced Persistent Threats Detection
Xiaoxiao Liu, Fan Xu, Nan Wang +4
Advanced Persistent Threat (APT) is challenging to detect due to prolonged duration, infrequent occurrence, and adept concealment techniques. Existing approaches primarily concentr…
Multi-Energy Guided Image Translation with Stochastic Differential Equations for Near-Infrared Facial Expression Recognition
Bingjun Luo, Zewen Wang, Jinpeng Wang +3
Illumination variation has been a long-term challenge in real-world facial expression recognition(FER). Under uncontrolled or non-visible light conditions, Near-infrared (NIR) can…
Hypergraph-Guided Disentangled Spectrum Transformer Networks for Near-Infrared Facial Expression Recognition
Bingjun Luo, Haowen Wang, Jinpeng Wang +3
With the strong robusticity on illumination variations, near-infrared (NIR) can be an effective and essential complement to visible (VIS) facial expression recognition in low light…
Enhancing Single-Frame Supervision for Better Temporal Action Localization
Changjian Chen, Jiashu Chen, Weikai Yang +6
Temporal action localization aims to identify the boundaries and categories of actions in videos, such as scoring a goal in a football match. Single-frame supervision has emerged a…
Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum
Fan Xu, Nan Wang, Hao Wu +3
Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised node binary classification task. In recent years, Graph Neural Networks (GNN) have been widely ap…
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection
Fan Xu, Nan Wang, Xuezhi Wen +3
Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. It has gained substantial attention in…
Exploring Global and Local Information for Anomaly Detection with Normal Samples
Fan Xu, Nan Wang, Xibin Zhao
Anomaly detection aims to detect data that do not conform to regular patterns, and such data is also called outliers. The anomalies to be detected are often tiny in proportion, con…
TBDetector:Transformer-Based Detector for Advanced Persistent Threats with Provenance Graph
Nan Wang, Xuezhi Wen, Dalin Zhang +7
APT detection is difficult to detect due to the long-term latency, covert and slow multistage attack patterns of Advanced Persistent Threat (APT). To tackle these issues, we propos…
xASTNN: Improved Code Representations for Industrial Practice
Zhiwei Xu, Min Zhou, Xibin Zhao +3
The application of deep learning techniques in software engineering becomes increasingly popular. One key problem is developing high-quality and easy-to-use source code representat…
Grow and Merge: A Unified Framework for Continuous Categories Discovery
Xinwei Zhang, Jianwen Jiang, Yutong Feng +6
Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new c…