H
Hai Jin
Researcher at Huazhong University of Science and Technology
Publications - 1648
Citations - 26704
Hai Jin is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Cloud computing & Computer science. The author has an hindex of 66, co-authored 1527 publications receiving 21162 citations. Previous affiliations of Hai Jin include University of Hong Kong & University of Southern California.
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Proceedings ArticleDOI
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
TL;DR: The study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features and Experimental results show that VulDeePecker can achieve much fewer false negatives and reasonable false positives than other approaches.
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Performance and energy modeling for live migration of virtual machines
TL;DR: This work constructs application-oblivious models for the cost prediction by using learned knowledge about the workloads at the hypervisor (also called VMM) level and evaluates the models using five representative workloads on a Xen virtualized environment.
Journal ArticleDOI
Color Image Segmentation Based on Mean Shift and Normalized Cuts
TL;DR: A novel approach that provides effective and robust segmentation of color images by incorporating the advantages of the mean shift segmentation and the normalized cut partitioning methods, which requires low computational complexity and is therefore very feasible for real-time image segmentation processing.
Proceedings ArticleDOI
AnySee: Peer-to-Peer Live Streaming
TL;DR: Statistics prove that this design is scalable and robust, and it is believed that the wide deployment of AnySee will soon benefit many more Internet users as it outperforms previous schemes in resource utilization and the QoS of streaming services.
Journal ArticleDOI
Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications
TL;DR: This article makes the first attempt to present a survey of mobile cloud computing from the perspective of its intended usages, and introduces three common mobile cloud architectures and classify comprehensive existing work into two fundamental categories: computation offloading and capability extending.