Yuejun Guo

Publications

• The abstraction for trajectories with different numbers of sampling points
• A New Scheme for Trajectory Visualization
• XaIBO: An Extension of aIB for Trajectory Clustering with Outlier
• A mixed noise removal algorithm based on the maximum entropy principle
• Fast TLS Denoising Algorithm Using Grid Technique
• Trajectory Abstracting with Group-Based Signal Denoising
• Anomaly detection based on trajectory analysis using kernel density estimation and information bottleneck techniques
• Selecting Video Key Frames Based on Relative Entropy and the Extreme Studentized Deviate Test
• Fast Agglomerative Information Bottleneck Based Trajectory Clustering
• Detection and Removal for Impulse Noise in Monte Carlo Global Illumination Rendered Images of Highly Glossy Scenes
• A group-based signal filtering approach for trajectory abstraction and restoration
• Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models
• MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based Ensembles
• Advanced techniques in trajectory data analysis for anomaly detection and map construction
• An Empirical Study on Data Distribution-Aware Test Selection for Deep Learning Enhancement
• Anomaly detection based on the global-local anomaly score for trajectory data
• A scalable method to construct compact road networks from GPS trajectories
• 3D Visualization of Multiscale Video Key Frames
• Trajectory Shape Analysis and Anomaly Detection Utilizing Information Theory Tools
• Multiscale visualization of trajectory data
• IBVis: Interactive Visual Analytics for Information Bottleneck Based Trajectory Clustering
• SHNN-CAD+: An Improvement on SHNN-CAD for Adaptive Online Trajectory Anomaly Detection
• Towards Exploring the Limitations of Active Learning: An Empirical Study
• Trajectory Anomaly Detection Based on the Mean Distance Deviation
• Visualization on agglomerative information bottleneck based trajectory clustering
• SHNN-CAD+: An Improvement on SHNN-CAD for Adaptive Online Trajectory Anomaly Detection
• DRE: density-based data selection with entropy for adversarial-robust deep learning models
• Enhancing Mixup-Based Graph Learning for Language Processing via Hybrid Pooling
• Enhancing Code Classification by Mixup-Based Data Augmentation
• An empirical study on data distribution-aware test selection for deep learning enhancement
• Labeling-Free Comparison Testing of Deep Learning Models
• Robust active learning: Sample-efficient training of robust deep learning models
• Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment
• Efficient Testing of Deep Neural Networks via Decision Boundary Analysis
• CodeS: A Distribution Shift Benchmark Dataset for Source Code Learning
• LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing
• Xaibo: An extension of aib for trajectory clustering with outlier
• The Abstraction for Trajectories with Different Numbers of Sampling Points
• IBVis: Interactive visual analytics for information bottleneck based trajectory clustering
• MixCode: Enhancing Code Classification by Mixup-Based Data Augmentation
• CodeS: Towards Code Model Generalization Under Distribution Shift
• Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation
• Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment
• An empirical study of the imbalance issue in software vulnerability detection
• An Investigation of Quality Issues in Vulnerability Detection Datasets
• MUTEN: Mutant-Based Ensembles for Boosting Gradient-Based Adversarial Attack
• An Empirical Study of Deep Learning-Based SS7 Attack Detection
• LaF: Labeling-free Model Selection for Automated Deep Neural Network Reusing
• KAPE: k NN-based Performance Testing for Deep Code Search
• An Empirical Study of the Imbalance Issue in Software Vulnerability Detection
• Test Optimization in DNN Testing: A Survey
• Active Code Learning: Benchmarking Sample-Efficient Training of Code Models
• SHNN-CAD+: An Improvement on SHNN-CAD for Adaptive Online Trajectory Anomaly Detection
• On the effectiveness of hybrid pooling in mixup-based graph learning for language processing
• Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection
• Poster: Automated Dependency Mapping for Web API Security Testing Using Large Language Models
• Assessing the Robustness of Test Selection Methods for Deep Neural Networks
• Variable Renaming-Based Adversarial Test Generation for Code Model: Benchmark and Enhancement
• SHNN-CAD+: An Improvement on SHNN-CAD for Adaptive Online Trajectory Anomaly Detection
• Mixcode: Enhancing code classification by mixup-based data augmentation
• Towards understanding model quantization for reliable deep neural network deployment
• CodeS: Towards code model generalization under distribution shift
• Evaluating the robustness of test selection methods for deep neural networks
• Aries: Efficient testing of deep neural networks via labeling-free accuracy estimation
• Enhancing mixup-based graph learning for language processing via hybrid pooling
• Towards exploring the limitations of test selection techniques on graph neural networks: An empirical study
• Hazards in Deep Learning Testing: Prevalence, Impact and Recommendations
• Boosting source code learning with text-oriented data augmentation: an empirical study
• A comprehensive analysis on software vulnerability detection datasets: trends, challenges, and road ahead
• Trajectory anomaly detection based on the mean distance deviation
• An investigation of quality issues in vulnerability detection datasets
• MUTEN: Boosting gradient-based adversarial attacks via mutant-based ensembles
• Characterizing and understanding the behavior of quantized models for reliable deployment
• Outside the comfort zone: Analysing llm capabilities in software vulnerability detection
• Evaluating Pre-Trained Models for Multi-Language Vulnerability Patching
• Codes: A distribution shift benchmark dataset for source code learning
• Enhancing Code Classification by Mixup-Based Data Augmentation.
• Boosting source code learning with data augmentation: An empirical study
• CodeLens: An Interactive Tool for Visualizing Code Representations
• TRAON, Yves Le. Active code learning: Benchmarking sample-efficient training of code models.(2024)
• Aries: Efficient testing of deep neural networks via labeling-free accuracy estimation. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
• 2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)| 979-8-3503-0039-0/23/$31.00̧opyright 2023 IEEE| DOI: 10.1109/ICSE-NIER58687. 2023.00032
• CodeS: A Distribution Shift Benchmark Dataset for Source Code Learning:::::::::::::::::::: Classification