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Titlebook: Computer Safety, Reliability, and Security; 40th International C Ibrahim Habli,Mark Sujan,Friedemann Bitsch Conference proceedings 2021 Spr

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發(fā)表于 2025-3-21 19:46:19 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Computer Safety, Reliability, and Security
副標(biāo)題40th International C
編輯Ibrahim Habli,Mark Sujan,Friedemann Bitsch
視頻videohttp://file.papertrans.cn/234/233721/233721.mp4
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Computer Safety, Reliability, and Security; 40th International C Ibrahim Habli,Mark Sujan,Friedemann Bitsch Conference proceedings 2021 Spr
描述This book constitutes the proceedings of the 40th International Conference on Computer Safety, Reliability and Security, SAFECOMP 2021, which took place in York, UK, in September 2021..The 17 full papers included in this volume were carefully reviewed and selected from 76 submissions. They were organized in topical sections as follows: machine learning safety assurance; security engineering; safety and assurance cases; machine learning applications; safety validation and simulation; and fault tolerance..
出版日期Conference proceedings 2021
關(guān)鍵詞artificial intelligence; computer hardware; computer networks; computer programming; computer science; co
版次1
doihttps://doi.org/10.1007/978-3-030-83903-1
isbn_softcover978-3-030-83902-4
isbn_ebook978-3-030-83903-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

書(shū)目名稱(chēng)Computer Safety, Reliability, and Security影響因子(影響力)




書(shū)目名稱(chēng)Computer Safety, Reliability, and Security影響因子(影響力)學(xué)科排名




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書(shū)目名稱(chēng)Computer Safety, Reliability, and Security被引頻次




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https://doi.org/10.1007/978-94-011-6361-3l/command or navigation systems. Recently, as an example, many research papers have focused on the ACAS Xu LUT compression. In this work, we explore how to make such a compression while preserving the system safety and offering adequate means of certification.
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https://doi.org/10.1007/978-3-030-83903-1artificial intelligence; computer hardware; computer networks; computer programming; computer science; co
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978-3-030-83902-4Springer Nature Switzerland AG 2021
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https://doi.org/10.1007/978-94-011-6361-3d changes in image contrast. While the robustness of DNN classifiers has been the subject of intense research in recent years, the solutions delivered by this research focus on verifying DNN robustness to small perturbations in the images being classified, with perturbation magnitude measured using
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The Beecham Manual for Family Practiceritical systems. However, current AI-based models are data-driven, so we cannot assure that they will provide the intended outcome for any input. To obtain information about the uncertainty remaining in their outcome, uncertainty estimation capabilities can be integrated during model building. Howev
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