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Titlebook: Computer Aided Verification; 33rd International C Alexandra Silva,K. Rustan M. Leino Conference proceedings‘‘‘‘‘‘‘‘ 2021 The Editor(s) (if

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书目名称Computer Aided Verification
副标题33rd International C
编辑Alexandra Silva,K. Rustan M. Leino
视频video
概述This book is open access, which means that you have free and unlimited access
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computer Aided Verification; 33rd International C Alexandra Silva,K. Rustan M. Leino Conference proceedings‘‘‘‘‘‘‘‘ 2021 The Editor(s) (if
描述.This open access two-volume set LNCS 12759 and 12760 constitutes the refereed proceedings of the 33rd International Conference on Computer Aided Verification, CAV 2021, held virtually in July 2021...The 63 full papers presented together with 16 tool papers and 5 invited papers were carefully reviewed and selected from 290 submissions. The papers were organized in the following topical sections:..Part I: invited papers; AI verification; concurrency and blockchain; hybrid and cyber-physical systems; security; and synthesis...Part II: complexity and termination; decision procedures and solvers; hardware and model checking; logical foundations; and software verification..This is an open access book..
出版日期Conference proceedings‘‘‘‘‘‘‘‘ 2021
关键词architecture verification; artificial intelligence; automata theory; computer programming; distributed c
版次1
doihttps://doi.org/10.1007/978-3-030-81685-8
isbn_softcover978-3-030-81684-1
isbn_ebook978-3-030-81685-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s) 2021
The information of publication is updating

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Robustness Verification of Semantic Segmentation Neural Networks Using Relaxed Reachability of robustness for segmentation, such as the percentage of pixels in the output that can be proven robust under different adversarial perturbations, and a robust variant of intersection-over-union (IoU), the typical performance evaluation measure for segmentation tasks. Our approach is based on a ne
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PEREGRiNN: Penalized-Relaxation Greedy Neural Network Verifierns of the usual linear constraints, and combine this with a convex objective function that penalizes the discrepancy between the output of each neuron and its relaxation. This convex function is further structured to force the largest relaxations to appear closest to the input layer; this provides t
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Summing up Smart Transitions automated reasoners do not provide ways to specify summations of arbitrary length..In this paper, we present a generalization of first-order logic which can express the unbounded sum of balances. We prove the decidablity of one of our extensions and the undecidability of a slightly richer one. We i
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Stateless Model Checking Under a Reads-Value-From Equivalencepproaches based on Mazurkiewicz and “reads-from” partitionings. Our experimental evaluation reveals that RVF is quite often a very effective equivalence, as the underlying partitioning is exponentially coarser than other approaches. Moreover, . generates representatives very efficiently, as the redu
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Situation der Materialbeschaffung,hms with respect to a small disturbance of noises, derived from the surrounding environment. The effectiveness of our robust bound and algorithm is confirmed by the experimental results, including quantum bits classification as the “Hello World” example, quantum phase recognition and cluster excitat
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