1 citations · 1 across the 5 of their papers we have counts for
8 papers
Second-Order Image Characterization with Distortion and Secrecy
Cao Daming
Image-size characterization connects coding constraints to the probability carried by source cells and their output images. At second order, this connection must retain the joint f…
Multi-Modal Anomaly Detection: A Survey
Xudong Mou, Zexin Wu, Chuan Luo +4
Multi-Modal Anomaly Detection (MMAD) detects rare abnormal events from heterogeneous data sources and is increasingly used in safety- and reliability-critical applications such as…
MARD: A Multi-Agent Framework for Robust Android Malware Detection
Xueying Zeng, Youquan Xian, Sihao Liu +4
With the rapid evolution of Android applications, traditional machine learning-based detection models suffer from concept drift. Additionally, they are constrained by shallow featu…
An Improved Time Series Anomaly Detection by Applying Structural Similarity
Tiejun Wang, Rui Wang, Xudong Mou +4
Effective anomaly detection in time series is pivotal for modern industrial applications and financial systems. Due to the scarcity of anomaly labels and the high cost of manual la…
CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments
Xudong Mou, Rui Wang, Tiejun Wang +11
Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability. In production, streams of Key Performance Indicators (KPI) are high-dimensional,…
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data
Xudong Mou, Rui Wang, Bo Li +4
The accumulation of time-series signals and the absence of labels make time-series Anomaly Detection (AD) a self-supervised task of deep learning. Methods based on normality assump…
RobotDiffuse: Diffusion-Based Motion Planning for Redundant Manipulators with the ROP Obstacle Avoidance Dataset
Xudong Mou, Xiaohan Zhang, Tiejun Wang +5
Redundant manipulators, with their higher Degrees of Freedom (DoFs), offer enhanced kinematic performance and versatility, making them suitable for applications like manufacturing,…
Deep Contrastive One-Class Time Series Anomaly Detection
Rui Wang, Chongwei Liu, Xudong Mou +5
The accumulation of time-series data and the absence of labels make time-series Anomaly Detection (AD) a self-supervised deep learning task. Single-normality-assumption-based metho…