Building Intelligence Identification System via Large Language Model Watermarking: A Survey and Beyond

no code implementations • 15 Jul 2024 • Xuhong Wang, Haoyu Jiang, Yi Yu, Jingru Yu, Yilun Lin, Ping Yi, Yingchun Wang, Yu Qiao, Li Li, Fei-Yue Wang

Large Language Models (LLMs) are increasingly integrated into diverse industries, posing substantial security risks due to unauthorized replication and misuse.

Language Modelling Large Language Model

IR Design for Application-Specific Natural Language: A Case Study on Traffic Data

no code implementations • 13 Jul 2023 • Wei Hu, Xuhong Wang, Ding Wang, Shengyue Yao, Zuqiu Mao, Li Li, Fei-Yue Wang, Yilun Lin

In the realm of software applications in the transportation industry, Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their ease of use and various other benefits.

TransWorldNG: Traffic Simulation via Foundation Model

1 code implementation • 25 May 2023 • Ding Wang, Xuhong Wang, Liang Chen, Shengyue Yao, Ming Jing, Honghai Li, Li Li, Shiqiang Bao, Fei-Yue Wang, Yilun Lin

To the best of our knowledge, this is the first traffic simulator that can automatically learn traffic patterns from real-world data and efficiently generate accurate and realistic traffic environments.

Decision Making Management

Building Transportation Foundation Model via Generative Graph Transformer

no code implementations • 24 May 2023 • Xuhong Wang, Ding Wang, Liang Chen, Yilun Lin

This data-driven and model-free simulation method addresses the challenges faced by traditional systems in terms of structural complexity and model accuracy and provides a foundation for solving complex transportation problems with real data.

Graph Generation Management +1

CEP3: Community Event Prediction with Neural Point Process on Graph

no code implementations • 21 May 2022 • Xuhong Wang, Sirui Chen, Yixuan He, Minjie Wang, Quan Gan, Yupu Yang, Junchi Yan

Many real world applications can be formulated as event forecasting on Continuous Time Dynamic Graphs (CTDGs) where the occurrence of a timed event between two entities is represented as an edge along with its occurrence timestamp in the graphs. However, most previous works approach the problem in compromised settings, either formulating it as a link prediction task on the graph given the event time or a time prediction problem given which event will happen next.

Link Prediction

APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding

1 code implementation • 23 Nov 2020 • Xuhong Wang, Ding Lyu, Mengjian Li, Yang Xia, Qi Yang, Xinwen Wang, Xinguang Wang, Ping Cui, Yupu Yang, Bowen Sun, Zhenyu Guo

Limited by the time complexity of querying k-hop neighbors in a graph database, most graph algorithms cannot be deployed online and execute millisecond-level inference.

Fraud Detection Graph Embedding

One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks

3 code implementations • 22 Feb 2020 • Xuhong Wang, Baihong Jin, Ying Du, Ping Cui, Yupu Yang

Since traditional anomaly detection methods are stable, robust and easy to use, it is vitally important to generalize them to graph data.

General Classification Graph Anomaly Detection +2

adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection

2 code implementations • 3 Mar 2019 • Xuhong Wang, Ying Du, Shi-Jie Lin, Ping Cui, Yuntian Shen, Yupu Yang

We assume that both the anomalous and the normal prior distribution are Gaussian and have overlaps in the latent space.

Unsupervised Anomaly Detection