標題: Titlebook: Computer Aided Verification; 34th International C Sharon Shoham,Yakir Vizel Conference proceedings‘‘‘‘‘‘‘‘ 2022 The Editor(s) (if applicabl [打印本頁] 作者: 法官所用 時間: 2025-3-21 19:15
書目名稱Computer Aided Verification影響因子(影響力)
書目名稱Computer Aided Verification影響因子(影響力)學科排名
書目名稱Computer Aided Verification網(wǎng)絡公開度
書目名稱Computer Aided Verification網(wǎng)絡公開度學科排名
書目名稱Computer Aided Verification被引頻次
書目名稱Computer Aided Verification被引頻次學科排名
書目名稱Computer Aided Verification年度引用
書目名稱Computer Aided Verification年度引用學科排名
書目名稱Computer Aided Verification讀者反饋
書目名稱Computer Aided Verification讀者反饋學科排名
作者: HERE 時間: 2025-3-21 23:59
Program Verification with?Constrained Horn Clauses (Invited Paper)sed in a fragment of First-Order Logic called Constrained Horn Clauses (CHC). This transforms program analysis and verification tasks to the realm of first order satisfiability and into the realm of SMT solvers. In this paper, we give a brief overview of how CHCs capture verification problems for se作者: Congestion 時間: 2025-3-22 03:55 作者: Acclaim 時間: 2025-3-22 06:37 作者: CROW 時間: 2025-3-22 12:27
Does a?Program Yield the?Right Distribution?wledge, . (provided the program terminates almost-surely). The class of distributions that can be specified in our formalism consists of standard distributions (geometric, uniform, etc.) and finite convolutions thereof. Our method relies on representing these (possibly infinite-support) distribution作者: 不如屎殼郎 時間: 2025-3-22 16:42
Abstraction-Refinement for?Hierarchical Probabilistic Modelsdels is subject to the famous state space explosion problem. We alleviate this problem by exploiting a hierarchical structure with repetitive parts. This structure not only occurs naturally in robotics, but also in probabilistic programs describing, e.g., network protocols. Such programs often repea作者: 不如屎殼郎 時間: 2025-3-22 19:29
Shared Certificates for?Neural Network Verificationn of reachable values at each layer. This process is repeated from scratch independently for each input (e.g., image) and perturbation (e.g., rotation), leading to an expensive overall proof effort when handling an entire dataset. In this work, we introduce a new method for reducing this verificatio作者: Pigeon 時間: 2025-3-22 21:14
Example Guided Synthesis of?Linear Approximations for?Neural Network Verificationms involving neural networks. In the latter case, a linear approximation must be hand-crafted for the neural network’s activation functions. This hand-crafting is tedious, potentially error-prone, and requires an expert to prove the soundness of the linear approximation. Such a limitation is at odds作者: 新陳代謝 時間: 2025-3-23 03:31
Verifying Neural Networks Against Backdoor AttacksResearchers also discovered multiple security issues associated with neural networks. One of them is backdoor attacks, i.e., a neural network may be embedded with a backdoor such that a target output is almost always generated in the presence of a trigger. Existing defense approaches mostly focus on作者: 浪費物質 時間: 2025-3-23 07:42
: A CEGAR-Driven Training and?Verification Framework for?Safe Deep Reinforcement Learningustworthy when applied to safety-critical domains, which is typically achieved by formal verification performed after training. This . process has two limits: (i) trained systems are difficult to formally verify due to their continuous and infinite state space and inexplicable AI components (., deep作者: 過度 時間: 2025-3-23 09:57 作者: Exposition 時間: 2025-3-23 16:46
The Lattice-Theoretic Essence of?Property Directed Reachability Analysisf PDR to be an ingenious combination of verification and refutation attempts based on the Knaster–Tarski and Kleene theorems. We introduce four concrete instances of LT-PDR, derive their implementation from a generic Haskell implementation of LT-PDR, and experimentally evaluate them. We also present作者: 鋪子 時間: 2025-3-23 19:17 作者: 忘恩負義的人 時間: 2025-3-24 01:28
Data-driven Numerical Invariant Synthesis with?Automatic Generation of?Attributeson trees from samples of positive and negative states and implications corresponding to program transitions. The main issue we address is the discovery of relevant attributes to be used in the learning process of numerical invariants. We define a method for solving this problem guided by the data sa作者: 精美食品 時間: 2025-3-24 05:16
Proof-Guided Underapproximation Widening for?Bounded Model Checkingsuch a long history, BMC still faces scalability challenges as programs continue to grow larger and more complex. One approach that has proven to be effective in verifying large programs is called Counterexample Guided Abstraction Refinement (CEGAR). In this work, we propose a complementary approach作者: Temporal-Lobe 時間: 2025-3-24 07:41
Systematic Introduction to Expert Systemsss of its internal systems and providing assurance of correctness to their end-users. In this paper, we focus on how we built abstractions and eliminated specifications to scale a verification engine for AWS access policies, ., to be usable by all AWS users. We present milestones from our journey fr作者: 譏諷 時間: 2025-3-24 10:39 作者: Abbreviate 時間: 2025-3-24 15:48
Conclusions and future research work, to generalize binary state assertions to real-valued ., which can measure expected values of probabilistic program quantities. While loop-free programs can be analyzed by mechanically transforming expectations, verifying loops usually requires finding an ., a difficult task..We propose a new view o作者: INCUR 時間: 2025-3-24 20:48
Fast Source-level Performance Estimation,pecifically, given a non-termination threshold . we aim for certificates proving that the program terminates with probability at least .. The basic idea of our approach is to find a terminating stochastic invariant, i.e.?a subset . of program states such that (i)?the probability of the program ever 作者: Gorilla 時間: 2025-3-25 01:26
Conclusions and future research work,wledge, . (provided the program terminates almost-surely). The class of distributions that can be specified in our formalism consists of standard distributions (geometric, uniform, etc.) and finite convolutions thereof. Our method relies on representing these (possibly infinite-support) distribution作者: laparoscopy 時間: 2025-3-25 04:14 作者: Substance-Abuse 時間: 2025-3-25 09:04 作者: 方便 時間: 2025-3-25 13:49 作者: 他姓手中拿著 時間: 2025-3-25 19:03
Handling of Multidimensional Pareto Curves,Researchers also discovered multiple security issues associated with neural networks. One of them is backdoor attacks, i.e., a neural network may be embedded with a backdoor such that a target output is almost always generated in the presence of a trigger. Existing defense approaches mostly focus on作者: Functional 時間: 2025-3-25 22:14
Fast and Scalable Run-time Scheduling,ustworthy when applied to safety-critical domains, which is typically achieved by formal verification performed after training. This . process has two limits: (i) trained systems are difficult to formally verify due to their continuous and infinite state space and inexplicable AI components (., deep作者: 最高點 時間: 2025-3-26 02:41
https://doi.org/10.1007/978-1-4020-6344-2lness is hampered by their susceptibility to .. Recently, many methods for measuring and improving a network’s robustness to adversarial perturbations have been proposed, and this growing body of research has given rise to numerous explicit or implicit notions of robustness. Connections between thes作者: neologism 時間: 2025-3-26 07:44
Conclusions and future research work,f PDR to be an ingenious combination of verification and refutation attempts based on the Knaster–Tarski and Kleene theorems. We introduce four concrete instances of LT-PDR, derive their implementation from a generic Haskell implementation of LT-PDR, and experimentally evaluate them. We also present作者: 其他 時間: 2025-3-26 09:33 作者: 會議 時間: 2025-3-26 13:34 作者: 易于交談 時間: 2025-3-26 16:49
Factor Analysis in a Mixed-Methods Contextsuch a long history, BMC still faces scalability challenges as programs continue to grow larger and more complex. One approach that has proven to be effective in verifying large programs is called Counterexample Guided Abstraction Refinement (CEGAR). In this work, we propose a complementary approach作者: Infiltrate 時間: 2025-3-26 22:36
Computer Aided Verification978-3-031-13185-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: chronicle 時間: 2025-3-27 02:14 作者: photophobia 時間: 2025-3-27 07:26 作者: 創(chuàng)造性 時間: 2025-3-27 10:34 作者: 忘恩負義的人 時間: 2025-3-27 14:15 作者: intrigue 時間: 2025-3-27 21:26
Systematic Introduction to Expert Systemsom a thousand SMT invocations daily to an unprecedented billion SMT calls in a span of five years. In this paper, we talk about how the cloud is enabling application of formal methods, key insights into what made this scale of a billion SMT queries daily possible, and present some open scientific challenges for the formal methods community.作者: 相容 時間: 2025-3-27 22:32 作者: 猛然一拉 時間: 2025-3-28 03:03 作者: 離開可分裂 時間: 2025-3-28 07:38 作者: Myosin 時間: 2025-3-28 11:16 作者: conceal 時間: 2025-3-28 18:24 作者: paroxysm 時間: 2025-3-28 22:41
The Lattice-Theoretic Essence of?Property Directed Reachability Analysiste instances of LT-PDR, derive their implementation from a generic Haskell implementation of LT-PDR, and experimentally evaluate them. We also present a categorical structural theory that derives these instances.作者: 高興去去 時間: 2025-3-29 00:33 作者: 猛烈責罵 時間: 2025-3-29 04:19
https://doi.org/10.1007/978-1-4020-6344-2ing up general principles for the empirical analysis and evaluation of a network’s robustness as a mathematical property—during the network’s training phase, its verification, and after its deployment. We then apply these principles and conduct a case study that showcases the practical benefits of our general approach.作者: ingenue 時間: 2025-3-29 10:11
Mixed Methods for Research on Open Systemsarator is constructed using an abstract domain representation of convex sets. The generalization mechanism of the decision tree learning from the constraints of the separator allows the inference of general invariants, accurate enough for proving the targeted property. We implemented our algorithm and showed its efficiency.作者: 文字 時間: 2025-3-29 14:14 作者: 深陷 時間: 2025-3-29 18:52
Data-driven Numerical Invariant Synthesis with?Automatic Generation of?Attributesarator is constructed using an abstract domain representation of convex sets. The generalization mechanism of the decision tree learning from the constraints of the separator allows the inference of general invariants, accurate enough for proving the targeted property. We implemented our algorithm and showed its efficiency.作者: figurine 時間: 2025-3-29 21:57 作者: CAB 時間: 2025-3-30 02:31 作者: 獨裁政府 時間: 2025-3-30 07:20
Fast and Scalable Run-time Scheduling,ple inputs to reduce overall verification costs. We perform an extensive experimental evaluation to demonstrate the effectiveness of shared certificates in reducing the verification cost on a range of datasets and attack specifications on image classifiers including the popular patch and geometric perturbations. We release our implementation at ..作者: Interdict 時間: 2025-3-30 09:46
Handling of Multidimensional Pareto Curves, work, we propose an approach to verify whether a given neural network is free of backdoor with a certain level of success rate. Our approach integrates statistical sampling as well as abstract interpretation. The experiment results show that our approach effectively verifies the absence of backdoor or generates backdoor triggers.作者: gerrymander 時間: 2025-3-30 14:46 作者: 小歌劇 時間: 2025-3-30 16:43
Shared Certificates for?Neural Network Verificationple inputs to reduce overall verification costs. We perform an extensive experimental evaluation to demonstrate the effectiveness of shared certificates in reducing the verification cost on a range of datasets and attack specifications on image classifiers including the popular patch and geometric perturbations. We release our implementation at ..作者: graphy 時間: 2025-3-30 23:29
