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Titlebook: Computer Aided Verification; 32nd International C Shuvendu K. Lahiri,Chao Wang Conference proceedings‘‘‘‘‘‘‘‘ 2020 The Editor(s) (if applic

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978-3-030-53287-1The Editor(s) (if applicable) and The Author(s) 2020
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Refinement for Structured Concurrent ProgramsThis paper presents a foundation for refining concurrent programs with structured control flow. The verification problem is decomposed into subproblems that aid interactive program development, proof reuse, and automation. The formalization in this paper is the basis of a new design and implementation of the . verifier.
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/233350.jpg
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NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physic systems (NNCS) that instead have nonlinear plant models, NNV supports over-approximate analysis by combining the star set analysis used for FFNN controllers with zonotope-based analysis for nonlinear plant dynamics building on CORA. We evaluate NNV using two real-world case studies: the first is sa
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An Abstraction-Based Framework for Neural Network Verificationxample-guided refinement to adjust the approximation, and then repeat the process. Our approach is orthogonal to, and can be integrated with, many existing verification techniques. For evaluation purposes, we integrate it with the recently proposed Marabou framework, and observe a significant improv
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Synthesis of Super-Optimized Smart Contracts Using Max-SMTblocks with minimal gas cost whose stack functional specification is equal (modulo commutativity) to the extracted one. Our experimental results are very promising: we are able to optimize 55.41 % of the blocks, and prove that 34.28 % were already optimal, for more than 61000 blocks from the most ca
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