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Secure Networked Inference with Unreliable Data Sources
The book presents theory and algorithms for secure networked inference in the presence of Byzantines. It derives fundamental limits of networked inference in the presence of Byzantine data and designs robust strategies to ensure reliable performance for several practical network architectures. In particular, it addresses inference (or learning) processes such as detection, estimation or classification, and parallel, hierarchical, and fully decentralized (peer-to-peer) system architectures. Furthermore, it discusses a number of new directions and heuristics to tackle the problem of design complexity in these practical network architectures for inference.
Nanomaterials in Manufacturing Processes
In the manufacturing sector, nanomaterials offer promising outcomes for cost reduction in production, quality improvement, and minimization of environmental hazards. This book focuses on the application of nanomaterials across a wide range of manufacturing areas, including in paint and coatings, petroleum refining, textile and leather industries, electronics, energy storage devices, electrochemical sensors, as well as in industrial waste treatment. This book: Examines nanofluids and nanocoatings in manufacturing and their characterization. Discusses nanomaterial applications in fabricating lightweight structural components, oil refining, smart leather processing and textile industries, and t...
Multifunctional Inorganic Nanomaterials for Energy Applications
Multifunctional Inorganic Nanomaterials for Energy Applications provides deep insight into the role of multifunctional nanomaterials in the field of energy and power generation applications. It mainly focuses on the synthesis, fabrication, design, development, and optimization of novel functional inorganic nanomaterials for energy storage and saving devices. It also covers studies of inorganic electrode materials for supercapacitors, membranes for batteries and fuel cells, and materials for display systems and energy generation. Features: Explores computational and experimental methods of preparing inorganic nanomaterials and their multifunctional applications Includes synthesis and performance analysis of various functional nanomaterials for energy storage and saving applications Reviews current research directions and latest developments in the field of energy materials Discusses importance of computational techniques in designing novel nanomaterials Highlights importance of multifunctional applications of nanomaterials in the energy sector This book is aimed at graduate students and researchers in materials science, electrical engineering, and nanomaterials.
Computer Vision – ECCV 2022
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Holistic Approach to Quantum Cryptography in Cyber Security
This new book discusses the concepts while also highlighting the challenges in the field of quantum cryptography and also covering cryptographic techniques and cyber security techniques, in a single volume. It comprehensively covers important topics in the field of quantum cryptography with applications, including quantum key distribution, position-based quantum cryptography, quantum teleportation, quantum e-commerce, quantum cloning, cyber security techniques’ architectures and design, cyber security techniques management, software-defined networks, and cyber security techniques for 5G communication. The text also discusses the security of practical quantum key distribution systems, applications and algorithms developed for quantum cryptography, as well as cyber security through quantum computing and quantum cryptography. The text will be beneficial for graduate students, academic researchers, and professionals working in the fields of electrical engineering, electronics and communications engineering, computer science, and information technology.
Cooperative and Graph Signal Processing
Cooperative and Graph Signal Processing: Principles and Applications presents the fundamentals of signal processing over networks and the latest advances in graph signal processing. A range of key concepts are clearly explained, including learning, adaptation, optimization, control, inference and machine learning. Building on the principles of these areas, the book then shows how they are relevant to understanding distributed communication, networking and sensing and social networks. Finally, the book shows how the principles are applied to a range of applications, such as Big data, Media and video, Smart grids, Internet of Things, Wireless health and Neuroscience. With this book readers wil...
ECAI 2023
Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrat...
Optimization for Machine Learning
An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessi...
Federated Learning
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.
