25 citations · 55 across the 13 of their papers we have counts for
13 papers
Data Compressibility Quantifies LLM Memorization
Yizhan Huang, Zhe Yang, Meifang Chen +3
Large Language Models (LLMs) are known to memorize portions of their training data, sometimes even reproduce content verbatim when prompted appropriately. Despite substantial inter…
KPIRoot+: An Efficient Integrated Framework for Anomaly Detection and Root Cause Analysis in Large-Scale Cloud Systems
Wenwei Gu, Renyi Zhong, Guangba Yu +8
To ensure the reliability of cloud systems, their performance is monitored using KPIs (key performance indicators). When issues arise, root cause localization identifies KPIs respo…
Towards Imperceptible Adversarial Attacks for Time Series Classification with Local Perturbations and Frequency Analysis
Wenwei Gu, Renyi Zhong, Jianping Zhang +1
Adversarial attacks in time series classification (TSC) models have recently gained attention due to their potential to compromise model robustness. Imperceptibility is crucial, as…
Grounded GUI Understanding for Vision-Based Spatial Intelligent Agent: Exemplified by Extended Reality Apps
Shuqing Li, Binchang Li, Yepang Liu +4
In recent years, spatial computing a.k.a. Extended Reality (XR) has emerged as a transformative technology, offering users immersive and interactive experiences across diversified…
Less Cybersickness, Please: Demystifying and Detecting Stereoscopic Visual Inconsistencies in Virtual Reality Apps
Shuqing Li, Cuiyun Gao, Jianping Zhang +5
The quality of Virtual Reality (VR) apps is vital, particularly the rendering quality of the VR Graphical User Interface (GUI). Different from traditional 2D apps, VR apps create a…
Your Code Secret Belongs to Me: Neural Code Completion Tools Can Memorize Hard-Coded Credentials
Yizhan Huang, Yichen Li, Weibin Wu +2
Neural Code Completion Tools (NCCTs) have reshaped the field of software engineering, which are built upon the language modeling technique and can accurately suggest contextually r…
Backpropagation Path Search On Adversarial Transferability
Zhuoer Xu, Zhangxuan Gu, Jianping Zhang +3
Deep neural networks are vulnerable to adversarial examples, dictating the imperativeness to test the model's robustness before deployment. Transfer-based attackers craft adversari…
Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features
Wenwei Gu, Jinyang Liu, Zhuangbin Chen +7
Cloud systems are susceptible to performance issues, which may cause service-level agreement violations and financial losses. In current practice, crucial metrics are monitored per…
On the Robustness of Latent Diffusion Models
Jianping Zhang, Zhuoer Xu, Shiwen Cui +3
Latent diffusion models achieve state-of-the-art performance on a variety of generative tasks, such as image synthesis and image editing. However, the robustness of latent diffusio…
DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities
Tiehua Zhang, Rui Xu, Jianping Zhang +4
Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry. Traditionally, software security is safeguarded b…
Validating Multimedia Content Moderation Software via Semantic Fusion
Wenxuan Wang, Jingyuan Huang, Chang Chen +5
The exponential growth of social media platforms, such as Facebook and TikTok, has revolutionized communication and content publication in human society. Users on these platforms c…
Improving the Transferability of Adversarial Samples by Path-Augmented Method
Jianping Zhang, Jen-tse Huang, Wenxuan Wang +5
Deep neural networks have achieved unprecedented success on diverse vision tasks. However, they are vulnerable to adversarial noise that is imperceptible to humans. This phenomenon…
Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization
Jianping Zhang, Yizhan Huang, Weibin Wu +1
Vision transformers (ViTs) have been successfully deployed in a variety of computer vision tasks, but they are still vulnerable to adversarial samples. Transfer-based attacks use a…