Автор: Janech Название: Proceedings of the 2015 Federated Conference on Software Development and Object Technologies ISBN: 3319465341 ISBN-13(EAN): 9783319465340 Издательство: Springer Рейтинг: Цена: 186330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents the proceedings of the International Conference SDOT which was organized at the University in ?ilina, Faculty of Management Sciences and Informatics, Slovak Republic in November 19, 2015. The conference was truly international both in terms of the amount of foreign contributions and in terms of composition of steering and scientific committees. The book and the conference serves as a platform of professional exchange of knowledge and experience for the latest trends in software development and object-oriented technologies (theory and practice). This proceedings present information on the latest developments and mediate the exchange of experience between practitioners and academia.
Автор: Leiva Luis A., Pruski Cйdric, Markovich Rйka Название: Artificial Intelligence and Machine Learning: 33rd Benelux Conference on Artificial Intelligence, BNAIC/Benelearn 2021, Esch-sur-Alzette, Luxembourg, ISBN: 3030938417 ISBN-13(EAN): 9783030938413 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book contains a selection of the best papers of the 33rd Benelux Conference on Artificial Intelligence, BNAIC/ BENELEARN 2021, held in Esch-sur-Alzette, Luxembourg, in November 2021. The 14 papers presented in this volume were carefully reviewed and selected from 46 regular submissions. They address various aspects of artificial intelligence such as natural language processing, agent technology, game theory, problem solving, machine learning, human-agent interaction, AI and education, and data analysis.
Автор: Rehman Название: Federated Learning Systems ISBN: 3030706060 ISBN-13(EAN): 9783030706067 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development.
Автор: Chockler Название: Computer Aided Verification ISBN: 3319961446 ISBN-13(EAN): 9783319961446 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018.
The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications.
Автор: Chockler Название: Computer Aided Verification ISBN: 3319961411 ISBN-13(EAN): 9783319961415 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018.
The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications.
Автор: Klaus Havelund Название: formal methods ISBN: 3319955810 ISBN-13(EAN): 9783319955810 Издательство: Springer Рейтинг: Цена: 83850.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the 22nd International Symposium on Formal Methods, FM 2018, held in Oxford, UK, in July 2018. The 44 full papers presented together with 2 invited papers were carefully reviewed and selected from 110 submissions.
Автор: Galmiche Название: Automated Reasoning ISBN: 3319942042 ISBN-13(EAN): 9783319942049 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the 9th International Joint Conference on Automated Reasoning, IJCAR 2018, held in Oxford, United Kingdom, in July 2018, as part of the Federated Logic Conference, FLoC 2018.
Автор: Beyersdorff Название: Theory and Applications of Satisfiability Testing – SAT 2018 ISBN: 3319941437 ISBN-13(EAN): 9783319941431 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the 21st International Conference on Theory and Applications of Satisfiability Testing, SAT 2018, held in Oxford, UK, in July 2018.The 20 revised full papers, 4 short papers, and 2 tool papers were carefully reviewed and selected from 58 submissions.
Название: Interactive theorem proving ISBN: 3319948202 ISBN-13(EAN): 9783319948201 Издательство: Springer Рейтинг: Цена: 83850.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the 9th International Conference on Interactive Theorem Proving, ITP 2018, held in Oxford, UK, in July 2018. The 32 full papers and 5 short papers presented were carefully reviewed and selected from 65 submissions.
Автор: Ahmed Bouajjani; Alexandra Silva Название: Formal Techniques for Distributed Objects, Components, and Systems ISBN: 3319602241 ISBN-13(EAN): 9783319602240 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Session types for Link failures.- Learning-based compositional parameter synthesis for event-recording automata.- Modularising opacity verification for Hybrid Transactional Memory.- Proving opacity via linearizability: a sound and complete method.- On futures for streaming data in ABS.- Session-based concurrency, reactively.- Procedural choreographic programming.- An observational approach to defining linearizability on weak memory models.- Applying a dependency mechanism in the formal development of voting protocol models using event-B.- Weak simulation quasimetric in a gossip scenario.- Reasoning about distributed secrets.- Classical higher-order processes.- Weak nominal modal logic.- Type inference of simulink hierarchical block diagrams in Isabelle.- Creating Bьchi automata for multi-valued model checking.- Privacy assessment using static taint analysis.- EPTL - a temporal logic for weakly consistent systems.
Автор: Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu Название: Federated Learning ISBN: 1681736993 ISBN-13(EAN): 9781681736990 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 103490.00 T Наличие на складе: Нет в наличии. Описание: How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
Автор: Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu Название: Federated Learning ISBN: 1681736977 ISBN-13(EAN): 9781681736976 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 82230.00 T Наличие на складе: Нет в наличии. Описание: How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
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