2017 IEEE Sensors Applications Symposium (SAS). IEEE Staff最新文献

Error Localization for Sequential Effect Systems (Extended Version) 序列效应系统的错误定位(扩展版)

2017 IEEE Sensors Applications Symposium (SAS). IEEE Staff Pub Date : 2023-07-28 DOI: 10.48550/arXiv.2307.15777

Colin S. Gordon, Chaewon Yun

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引用次数: 0

On Multi-language Abstraction - Towards a Static Analysis of Multi-language Programs 论多语言抽象——面向多语言程序的静态分析

2017 IEEE Sensors Applications Symposium (SAS). IEEE Staff Pub Date : 2023-03-28 DOI: 10.1007/978-3-030-65474-0_14

Samuele Buro, R. Crole, Isabella Mastroeni

引用次数: 3

Stratified Guarded First-Order Transition Systems 分层保护一阶过渡系统

2017 IEEE Sensors Applications Symposium (SAS). IEEE Staff Pub Date : 2022-11-22 DOI: 10.1007/978-3-030-65474-0_6

C. Müller, H. Seidl

引用次数: 0

Automated Synthesis of Asynchronizations 异步的自动合成

2017 IEEE Sensors Applications Symposium (SAS). IEEE Staff Pub Date : 2022-09-14 DOI: 10.48550/arXiv.2209.06648

Sidi Mohamed Beillahi, A. Bouajjani, C. Enea, Shuvendu K. Lahiri

引用次数: 0

Boosting Robustness Verification of Semantic Feature Neighborhoods 增强语义特征邻域鲁棒性验证

2017 IEEE Sensors Applications Symposium (SAS). IEEE Staff Pub Date : 2022-09-12 DOI: 10.48550/arXiv.2209.05446

Anan Kabaha, Dana Drachsler-Cohen

{"title":"Boosting Robustness Verification of Semantic Feature Neighborhoods","authors":"Anan Kabaha, Dana Drachsler-Cohen","doi":"10.48550/arXiv.2209.05446","DOIUrl":"https://doi.org/10.48550/arXiv.2209.05446","url":null,"abstract":"Deep neural networks have been shown to be vulnerable to adversarial attacks that perturb inputs based on semantic features. Existing robustness analyzers can reason about semantic feature neighborhoods to increase the networks' reliability. However, despite the significant progress in these techniques, they still struggle to scale to deep networks and large neighborhoods. In this work, we introduce VeeP, an active learning approach that splits the verification process into a series of smaller verification steps, each is submitted to an existing robustness analyzer. The key idea is to build on prior steps to predict the next optimal step. The optimal step is predicted by estimating the certification velocity and sensitivity via parametric regression. We evaluate VeeP on MNIST, Fashion-MNIST, CIFAR-10 and ImageNet and show that it can analyze neighborhoods of various features: brightness, contrast, hue, saturation, and lightness. We show that, on average, given a 90 minute timeout, VeeP verifies 96% of the maximally certifiable neighborhoods within 29 minutes, while existing splitting approaches verify, on average, 73% of the maximally certifiable neighborhoods within 58 minutes.","PeriodicalId":92164,"journal":{"name":"2017 IEEE Sensors Applications Symposium (SAS). IEEE Staff","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80265985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Invariant Inference With Provable Complexity From the Monotone Theory 单调理论中具有可证明复杂度的不变推理

2017 IEEE Sensors Applications Symposium (SAS). IEEE Staff Pub Date : 2022-08-15 DOI: 10.48550/arXiv.2208.07451

Y. Feldman, Sharon Shoham

{"title":"Invariant Inference With Provable Complexity From the Monotone Theory","authors":"Y. Feldman, Sharon Shoham","doi":"10.48550/arXiv.2208.07451","DOIUrl":"https://doi.org/10.48550/arXiv.2208.07451","url":null,"abstract":"Invariant inference algorithms such as interpolation-based inference and IC3/PDR show that it is feasible, in practice, to find inductive invariants for many interesting systems, but non-trivial upper bounds on the computational complexity of such algorithms are scarce, and limited to simple syntactic forms of invariants. In this paper we achieve invariant inference algorithms, in the domain of propositional transition systems, with provable upper bounds on the number of SAT calls. We do this by building on the monotone theory, developed by Bshouty for exact learning Boolean formulas. We prove results for two invariant inference frameworks: (i) model-based interpolation, where we show an algorithm that, under certain conditions about reachability, efficiently infers invariants when they have both short CNF and DNF representations (transcending previous results about monotone invariants); and (ii) abstract interpretation in a domain based on the monotone theory that was previously studied in relation to property-directed reachability, where we propose an efficient implementation of the best abstract transformer, leading to overall complexity bounds on the number of SAT calls. These results build on a novel procedure for computing least monotone overapproximations.","PeriodicalId":92164,"journal":{"name":"2017 IEEE Sensors Applications Symposium (SAS). IEEE Staff","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77103284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Solving Invariant Generation for Unsolvable Loops 求解不可解循环的不变量生成

2017 IEEE Sensors Applications Symposium (SAS). IEEE Staff Pub Date : 2022-06-14 DOI: 10.48550/arXiv.2206.06943

Daneshvar Amrollahi, E. Bartocci, George Kenison, Laura Kov'acs, Marcel Moosbrugger, Miroslav Stankovivc

引用次数: 8

Local Completeness Logic on Kleene Algebra with Tests Kleene代数上的局部完备逻辑及其检验

2017 IEEE Sensors Applications Symposium (SAS). IEEE Staff Pub Date : 2022-05-17 DOI: 10.48550/arXiv.2205.08128

M. Milanese, Francesco Ranzato

引用次数: 2

Deciding Program Properties via Complete Abstractions on Bounded Domains 在有界域上通过完全抽象决定程序属性

2017 IEEE Sensors Applications Symposium (SAS). IEEE Staff Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-22308-2_9

R. Bruni, R. Gori, N. Manini

引用次数: 0

SecWasm: Information Flow Control for WebAssembly WebAssembly的信息流控制

2017 IEEE Sensors Applications Symposium (SAS). IEEE Staff Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-22308-2_5

Iulia Bastys, Maximilian Algehed, Alexander Sjösten, A. Sabelfeld

引用次数: 2