3 citations · 3 across the 10 of their papers we have counts for
11 papers
MixGuard: Towards Detecting and Understanding Mixer Laundering on Ethereum
Qishuang Fu, Hang Zheng, Xihan Xiong +7
Mixers protect privacy by concealing deposit--withdrawal links, but are also abused to launder illicit funds. Existing anti-money laundering studies do not specifically target mixe…
FlowShield: cryptocurrency anti-money laundering with transaction semantics parsing and fund flow tracking
Qishuang Fu, Andreas Deppeler, Joseph K. Liu +5
Cryptocurrency anti-money laundering (Crypto AML) is increasingly challenged by sophisticated laundering behaviors that rapidly fragment stolen assets through diverse semantics and…
From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection
Yixin Liu, Shiyuan Li, Yu Zheng +4
Graph anomaly detection (GAD) is critical for identifying abnormal nodes in graph-structured data from diverse domains, including cybersecurity and social networks. The existing GA…
Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases
Zhao Tan, Yiji Zhao, Shiyu Wang +5
Natural Language Querying for Time Series Databases (NLQ4TSDB) aims to assist non-expert users retrieve meaningful events, intervals, and summaries from massive temporal records. H…
Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses
Lincan Li, Bolin Shen, Chenxi Zhao +4
Graph-structured data, which captures non-Euclidean relationships and interactions between entities, is growing in scale and complexity. As a result, training state-of-the-art grap…
Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models
Zhibiao Wang, Yunlong Zhou, Ziwei Zhang +4
Graph Transformers, leveraging the global attention to capture long-range dependencies in graph structures, have significantly advanced graph machine learning, but face prohibitive…
Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking
Liangliang Zhang, Zhuorui Jiang, Hongliang Chi +8
Knowledge Graph Question Answering (KGQA) systems rely on high-quality benchmarks to evaluate complex multi-hop reasoning. However, despite their widespread use, popular datasets s…
A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment
Kun Wang, Guibin Zhang, Zhenhong Zhou +100
The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communi…
Augmented Commonsense Knowledge for Remote Object Grounding
Bahram Mohammadi, Yicong Hong, Yuankai Qi +3
The vision-and-language navigation (VLN) task necessitates an agent to perceive the surroundings, follow natural language instructions, and act in photo-realistic unseen environmen…
FedPFT: Federated Proxy Fine-Tuning of Foundation Models
Zhaopeng Peng, Xiaoliang Fan, Yufan Chen +5
Adapting Foundation Models (FMs) for downstream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs. Existing methods fi…
GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language Models
Jiaxu Zhao, Meng Fang, Shirui Pan +2
Warning: This paper contains content that may be offensive or upsetting. There has been a significant increase in the usage of large language models (LLMs) in various applications,…