30 citations · 51 across the 8 of their papers we have counts for
8 papers
Anomaly Detection in Event-triggered Traffic Time Series via Similarity Learning
Shaoyu Dou, Kai Yang, Yang Jiao +2
Time series analysis has achieved great success in cyber security such as intrusion detection and device identification. Learning similarities among multiple time series is a cruci…
Robust Group Anomaly Detection for Quasi-Periodic Network Time Series
Kai Yang, Shaoyu Dou, Pan Luo +2
Many real-world multivariate time series are collected from a network of physical objects embedded with software, electronics, and sensors. The quasi-periodic signals generated by…
Evaluating Scoring Bias in LLM-as-a-Judge
Qingquan Li, Shaoyu Dou, Kailai Shao +2
The "LLM-as-a-Judge" paradigm, using Large Language Models (LLMs) as automated evaluators, is pivotal to LLM development, offering scalable feedback for complex tasks. However, the…
Fog Intelligence for Network Anomaly Detection
Kai Yang, Hui Ma, Shaoyu Dou
Anomalies are common in network system monitoring. When manifested as network threats to be mitigated, service outages to be prevented, and security risks to be ameliorated, detect…
Multi-Level Association Rule Mining for Wireless Network Time Series Data
Chen Zhu, Chengbo Qiu, Shaoyu Dou +1
Key performance indicators(KPIs) are of great significance in the monitoring of wireless network service quality. The network service quality can be improved by adjusting relevant…
CPMLHO:Hyperparameter Tuning via Cutting Plane and Mixed-Level Optimization
Shuo Yang, Yang Jiao, Shaoyu Dou +2
The hyperparameter optimization of neural network can be expressed as a bilevel optimization problem. The bilevel optimization is used to automatically update the hyperparameter, a…
Task-aware Similarity Learning for Event-triggered Time Series
Shaoyu Dou, Kai Yang, Yang Jiao +2
Time series analysis has achieved great success in diverse applications such as network security, environmental monitoring, and medical informatics. Learning similarities among dif…
TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series
Yang Jiao, Kai Yang, Shaoyu Dou +3
Multivariate time series (MTS) data are becoming increasingly ubiquitous in diverse domains, e.g., IoT systems, health informatics, and 5G networks. To obtain an effective represen…