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Yuqun Zhang
Researcher at Southern University of Science and Technology
Publications - 40
Citations - 1324
Yuqun Zhang is an academic researcher from Southern University of Science and Technology. The author has contributed to research in topics: Computer science & Software as a service. The author has an hindex of 12, co-authored 28 publications receiving 744 citations. Previous affiliations of Yuqun Zhang include University of Rochester & South University of Science and Technology of China.
Papers
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Proceedings ArticleDOI
DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous driving systems
TL;DR: The experimental results demonstrate that DeepRoad can detect thousands of inconsistent behaviors for DNN-based autonomous driving systems, and effectively validate input images to potentially enhance the system robustness as well.
Proceedings ArticleDOI
DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization
TL;DR: This work proposes DeepFL, a deep learning approach to automatically learn the most effective existing/latent features for precise fault localization and results show DeepFL can significantly outperform state-of-the-art TraPT/FLUCCS and is also surprisingly effective for cross-project prediction.
Journal ArticleDOI
A survey on security issues in services communication of Microservices-enabled fog applications
TL;DR: A survey of different security risks that pose a threat to the Microservices‐based fog applications is presented and an ideal solution for security issues in services communication of Micro services‐based Fog Services architecture is proposed.
Posted Content
DeepRoad: GAN-based Metamorphic Autonomous Driving System Testing
TL;DR: DeepRoad is proposed, an unsupervised framework to automatically generate large amounts of accurate driving scenes to test the consistency of DNN-based autonomous driving systems across different scenes, and can detect thousands of behavioral inconsistencies in these systems.
Proceedings ArticleDOI
DeepBillboard: systematic physical-world testing of autonomous driving systems
TL;DR: In this paper, the authors proposed a robust and resilient printable adversarial billboard test, which works under dynamic changing driving conditions including viewing angle, distance, and lighting, to maximize the possibility, degree, and duration of the steering-angle errors of an autonomous vehicle driving by the generated adversarial billboards.