This book focuses on using artificial intelligence (AI) to improve blockchain ecosystems. Gathering the latest advances resulting from AI in blockchain data analytics, it also presents big data research on blockchain ...
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ISBN:
(数字)9789811601279
ISBN:
(纸本)9789811601262;9789811601293
This book focuses on using artificial intelligence (AI) to improve blockchain ecosystems. Gathering the latest advances resulting from AI in blockchain data analytics, it also presents big data research on blockchain systems.;Despite blockchain's merits of decentralisation, immutability, non-repudiation and traceability, the development of blockchain technology has faced a number of challenges, such as the difficulty of data analytics on encrypted blockchain data, poor scalability, software vulnerabilities, and the scarcity of appropriate incentive mechanisms. Combining AI with blockchain has the potential to overcome the limitations, and machine learning-based approaches may help to analyse blockchain data and to identify misbehaviours in blockchain. In addition, deep reinforcement learning methods can be used to improve the reliability of blockchain systems.;This book focuses in the use of AI to improve blockchain systems and promote blockchain intelligence. It describes data extraction, exploration and analytics on representative blockchain systems such as Bitcoin and Ethereum. It also includes data analytics on smart contracts, misbehaviour detection on blockchain data, and market analysis of blockchain-based cryptocurrencies. As such, this book provides researchers and practitioners alike with valuable insights into big data analysis of blockchain data, AI-enabled blockchain systems, and applications driven by blockchain intelligence.
This book gathers the proceedings of the Sixth International Conference on Computational science and Technology 2019 (ICCST2019), held in Kota Kinabalu, Malaysia, on 29–30 August 2019. The respective contributions of...
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ISBN:
(数字)9789811500589
ISBN:
(纸本)9789811500572;9789811500602
This book gathers the proceedings of the Sixth International Conference on Computational science and Technology 2019 (ICCST2019), held in Kota Kinabalu, Malaysia, on 29–30 August 2019. The respective contributions offer practitioners and researchers a range of new computational techniques and solutions, identify emerging issues, and outline future research directions, while also showing them how to apply the latest large-scale, high-performance computational methods.
作者：
DOUGHERTY, JOHN J.U.S.N.
Ret JOHN J. DOUGHERTY
is Director of Personnel and Contract Administration for The Dikewood Corporation a group of computer-oriented consulting scientists research engineers and data processors in University Research Park Albuquerque New Mexico. Mr. Dougherty attended Stevens Institute of Technology from 1940 to 1942. A 1945 Annapolis graduate and a 1948 faculty member there he received the degree of Master of Science in Engineering Electronics from the Naval Postgraduate School in 1953. He is a Professional Electrical Engineer registered in New Mexico and the District of Columbia a Senior Member of the Institute of Electrical and Electronics Engineers Technical Group on Engineering Management a member of ASNE and an Associate Fellow of the American Institute of Aeronautics and Astronautics. In 1962 he received the Navy Bureau of Ships Research and Development Award for Scientific Achievement. He is the author of some twenty technical and management papers and from 1963 to 1965 as a Navy Commander was Assistant Director of Communications Satellite Programs Headquarters NASA Washington D.C.
This book constitutes the joint thoroughly refereed post-proceedings of the Second International Workshop on Modeling Social Media, MSM 2011, held in Boston, MA, USA, in October 2011, and the Second International Work...
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ISBN:
(数字)9783642336843
ISBN:
(纸本)9783642336836
This book constitutes the joint thoroughly refereed post-proceedings of the Second International Workshop on Modeling Social Media, MSM 2011, held in Boston, MA, USA, in October 2011, and the Second International Workshop on Mining Ubiquitous and Social Environments, MUSE 2011, held in Athens, Greece, in September 2011. The 9 full papers included in the book are revised and significantly extended versions of papers submitted to the workshops. They cover a wide range of topics organized in three main themes: communities and networks in ubiquitous social media; mining approaches; and issues of user modeling, privacy and security.
As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile,...
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As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile, machine learning (ML) has been applied to enhance analysis and plan functions of MAPE so that self-adaptive systems have optimal adaption to changing conditions. However, most of ML-based approaches don’t utilize CAVs’ connectivity to collaboratively generate an optimal learner for MAPE, because of sensor data threatened by gradient leakage attack (GLA). In this article, we first design an intelligent architecture for MAPE-based self-adaptive systems on Web 3.0-based CAVs, in which a collaborative machine learner supports the capabilities of managing systems. Then, we observe by practical experiments that importance sampling of sparse vector technique (SVT) approaches cannot defend GLA well. Next, we propose a fine-grained SVT approach to secure the learner in MAPE-based self-adaptive systems, that uses layer and gradient sampling to select uniform and important gradients. At last, extensive experiments show that our private learner spends a slight utility cost for MAPE (e.g., \(0.77\%\) decrease in accuracy) defending GLA and outperforms the typical SVT approaches in terms of defense (increased by \(10\%\sim 14\%\) attack success rate) and utility (decreased by \(1.29\%\) accuracy loss).
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