PROCEEDINGS OF THE 22ND CONFERENCE ON FORMAL METHODS IN COMPUTER-AIDED DESIGN – FMCAD 2022 PDF Download
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Author: Alberto Griggio
Publisher: TU Wien Academic Press
ISBN: 3854480539
Category : Computers
Languages : en
Pages : 405
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Book Description
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system testing.
Author: Alberto Griggio
Publisher: TU Wien Academic Press
ISBN: 3854480539
Category : Computers
Languages : en
Pages : 405
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Book Description
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system testing.
Author: Alexander Nadel
Publisher: TU Wien Academic Press
ISBN: 3854480601
Category : Computers
Languages : en
Pages : 332
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Book Description
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system testing.
Author: Pin-Yu Chen
Publisher: Academic Press
ISBN: 0128242574
Category : Computers
Languages : en
Pages : 300
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Book Description
Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and veri?cation. Sections cover adversarial attack, veri?cation and defense, mainly focusing on image classi?cation applications which are the standard benchmark considered in the adversarial robustness community. Other sections discuss adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. For researchers, this book provides a thorough literature review that summarizes latest progress in the area, which can be a good reference for conducting future research. In addition, the book can also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning. While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems. - Summarizes the whole field of adversarial robustness for Machine learning models - Provides a clearly explained, self-contained reference - Introduces formulations, algorithms and intuitions - Includes applications based on adversarial robustness
Author: Doron A. Peled
Publisher: Springer Science & Business Media
ISBN: 1475735405
Category : Computers
Languages : en
Pages : 344
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Book Description
This book presents current methods for dealing with software reliability, illustrating the advantages and disadvantages of each method. The description of the techniques is intended for a non-expert audience with some minimal technical background. It also describes some advanced techniques, aimed at researchers and practitioners in software engineering. This reference will serve as an introduction to formal methods and techniques and will be a source for learning about various ways to enhance software reliability. Various projects and exercises give readers hands-on experience with the various formal methods and tools.
Author: Dang Van Hung
Publisher: Springer Nature
ISBN: 3030591522
Category : Computers
Languages : en
Pages : 574
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Book Description
This book constitutes the refereed proceedings of the 18th International Symposium on Automated Technology for Verification and Analysis, ATVA 2020, held in Hanoi, Vietnam, in October 2020. The 27 regular papers presented together with 5 tool papers and 2 invited papers were carefully reviewed and selected from 75 submissions. The symposium is dedicated to promoting research in theoretical and practical aspects of automated analysis, verification and synthesis by providing an international venue for the researchers to present new results. The papers focus on neural networks and machine learning; automata; logics; techniques for verification, analysis and testing; model checking and decision procedures; synthesis; and randomization and probabilistic systems.
Author: Alexander Ivrii
Publisher: TU Wien Academic Press
ISBN: 3854480423
Category : Technology & Engineering
Languages : en
Pages : 284
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Book Description
Formal Methods in Computer-Aided Design (FMCAD) is a conference series on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing ground-breaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing.
Author: G. De Giacomo
Publisher: IOS Press
ISBN: 164368101X
Category : Computers
Languages : en
Pages : 3122
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Book Description
This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.
Author: Katy Warr
Publisher: "O'Reilly Media, Inc."
ISBN: 1492044903
Category : Computers
Languages : en
Pages : 233
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Book Description
As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come
Author: Shuvendu K. Lahiri
Publisher: Springer Nature
ISBN: 3030532887
Category : Computers
Languages : en
Pages : 682
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Book Description
The open access two-volume set LNCS 12224 and 12225 constitutes the refereed proceedings of the 32st International Conference on Computer Aided Verification, CAV 2020, held in Los Angeles, CA, USA, in July 2020.* The 43 full papers presented together with 18 tool papers and 4 case studies, were carefully reviewed and selected from 240 submissions. The papers were organized in the following topical sections: Part I: AI verification; blockchain and Security; Concurrency; hardware verification and decision procedures; and hybrid and dynamic systems. Part II: model checking; software verification; stochastic systems; and synthesis. *The conference was held virtually due to the COVID-19 pandemic.
Author: Cezara Dragoi
Publisher: Springer Nature
ISBN: 303124950X
Category : Computers
Languages : en
Pages : 401
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Book Description
This book constitutes the proceedings of the 24th International Conference on Verification, Model Checking, and Abstract Interpretation, VMCAI 2023, which took place in Boston, USA, in January 2023. The 17 full papers presented in this book were carefully reviewed and selected from 34 submissions. The contributions deal with program verification, model checking, abstract interpretation, program synthesis, static analysis, type systems, deductive methods, decision procedures, theorem proving, program certification, debugging techniques, program transformation, optimization, and hybrid and cyber-physical systems.
