Software Aging in a Real-Time Object Detection System on an Edge Server-Reference-Cited by-同舟云学术

Software Aging in a Real-Time Object Detection System on an Edge Server

Published:2023-03-27 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
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Author:

Watanabe Kengo¹^ORCID,Machida Fumio¹^ORCID,Andrade Ermeson²^ORCID,Pietrantuono Roberto³^ORCID,Cotroneo Domenico³^ORCID

Affiliation:

1. University of Tsukuba, Tsukuba, Japan

2. Federal Rural University of Pernambuco, Recife, Brazil

3. University of Naples Federico II, Naples, Italy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3555776.3577717

Reference36 articles.

1. Software Aging in Image Classification Systems on Cloud and Edge

2. Ermeson Andrade , Roberto Pietrantuono , Fumio Machida , and Domenico Cotroneo . 2021. A Comparative Analysis of Software Aging in Image Classifiers on Cloud and Edge . IEEE Transactions on Dependable and Secure Computing ( 2021 ). Ermeson Andrade, Roberto Pietrantuono, Fumio Machida, and Domenico Cotroneo. 2021. A Comparative Analysis of Software Aging in Image Classifiers on Cloud and Edge. IEEE Transactions on Dependable and Secure Computing (2021).

3. Modeling and Analyzing Server System with Rejuvenation through SysML and Stochastic Reward Nets

4. Benchmark Analysis of Representative Deep Neural Network Architectures

5. Alexey Bochkovskiy , Chien-Yao Wang , and Hong-Yuan Mark Liao . 2020. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 ( 2020 ). Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. 2020. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020).

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