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                <text>Information Theory for Data Science</text>
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                <text>Information theory deals with mathematical laws that govern the flow, representation, and transmission of information, just as the field of physics concerns laws that govern the behavior of the physical universe. The foundation was made in the context of communication while characterizing the fundamental limits of communication and offering codes (sometimes called algorithms) to achieve them.&#13;
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The most significant achievement of the field is the invention of digital communication, which forms the basis of our daily-life digital products such as smartphones, laptops, and any IoT devices. Recently it has also found important roles in a spotlight field that has been revolutionized during the past decades: data science.&#13;
&#13;
This book aims at demonstrating modern roles of information theory in a widening array of data science applications. The first and second parts of the book cover the core concepts of information theory: basic concepts on several key notions and celebrated source and channel coding theorems, which concern the fundamental limits of communication. The last part focuses on applications that arise in data science, including social networks, ranking, and machine learning.&#13;
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The book is written as a text for senior undergraduate and graduate students working on information theory and communications, and it should also prove to be a valuable reference for professionals and engineers from these fields.</text>
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                <text>Holistic IoT Security, Privacy, and Safety: Integrated Approaches Protecting a Highly Connected World is an authoritative, technical guide authored by Konstantinos Loupos. The book details the architecture, deployment, and practical application of the European Union’s ERATOSTHENES project, providing an enterprise blueprint for building trustworthy, self-defending Internet of Things (IoT) ecosystems.&#13;
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Rather than treating device vulnerabilities, data privacy, and physical safety as isolated issues, this text provides a unified, defense-in-depth framework for highly distributed environments. It bridges the gap between digital asset defense and real-world physical safety, tackling the risks that arise when billions of low-power, heterogeneous edge devices are connected to critical infrastructure.</text>
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                <text>The Internet's exponential growth is driven by the development and emergence of advanced technologies, attended by a flood of new services. The maturity of these technologies is a decisive factor in the progress of the entire telecom industry, leading to a profound transformation of computer networks from traditional, hardware-centric functions to flexible, software-based solutions. Network function virtualization (NFV), together with software-defined networking (SDN) and cloud computing, are not just key enablers, but they are the basis of more programmable networks that are able to adapt to the evolving requirements of diverse customers, ensuring a promising future for the Internet. This future implies the efficient collaboration of different software components, with the aim of improving flexibility, scalability, and the overall agility of the network. Numerous researchers and professionals today focus their research on finding solutions for the resource, service, security, and other orchestration types and further automation of the processes.&#13;
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- Shortcomings in traditional networks and the primary motives and benefits of NFV implementation in network infrastructure&#13;
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- The virtualization techniques and their application in modern computing environments, with an accent on the characteristics and functionality of virtual entities (virtual machines and containers)&#13;
- Containerization technology: creation and characteristics of various containers, and their orchestration and usage of the Kubernetes platform for the management of containerized workloads and services&#13;
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&#13;
The monograph offers a practical blend of theoretical considerations and practical NFV implementation in modern computer environments. It is a valuable resource for students, engineers, and researchers looking for a comprehensive guide to further network softwarization and building a highly programmable, scalable, and software-based network infrastructure.&#13;
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                <text>Differential Privacy in Artificial Intelligence: From Theory to Practice is a comprehensive resource designed to review the principles and applications of differential privacy in a world increasingly driven by data. This book delves into the theoretical underpinnings of differential privacy, its use in machine learning systems, practical implementation details, and its broader social and legal ramifications. Intended as a primer and a deep dive, it lays a solid foundation by introducing essential concepts and mechanisms critical to understanding differential privacy.&#13;
&#13;
From theoretical foundations to practical application, the book is organized into five distinct parts. Part I reviews the foundational notions of differential privacy in the central and local models, delving into composition and privacy amplification. The discussion extends to practical strategies for data release and the creation of synthetic data, which is essential for real-world applications. Part II focuses on the application of differential privacy in optimization and learning, examining the integration of privacy measures in machine learning, including private optimization methods and private federated learning.&#13;
&#13;
Beyond technical applications, the book highlights the use of differential privacy in critical sectors such as healthcare and energy and discusses its implications in image and video analysis in Part III. Part IV provides a thorough look at the tools and challenges in deploying privacy-preserving models, including insights into programming frameworks and machine learning tools. Finally, Part V addresses the societal impact of differential privacy, discussing its intersection with public policy, law, fairness, and bias.&#13;
&#13;
Targeted at researchers, practitioners, and policymakers, Differential Privacy in Artificial Intelligence: From Theory to Practice aims to be an essential guide for anyone committed to advancing privacy in the digital age, providing the knowledge needed to develop and deploy effective and ethical privacy solutions across various domains.&#13;
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                <text>This open access book is a comprehensive examination of how artificial intelligence (AI) is transforming the field of social work across practice, education, and research. This topic is particularly important to the field at this time due to the rapid integration of AI technologies into societal structures and the increasing demand for social services that are efficient, effective, and ethically sound. The book provides timely insights into how AI can be harnessed to address complex social issues while maintaining the core values of social work. It emphasizes the critical need for professionals to understand and navigate the ethical implications of AI, ensuring that technological advancements serve to enhance, rather than compromise, human well-being.&#13;
&#13;
Organized into five parts, the book begins with a foundational overview in Part I, introducing the role of AI in transforming social work. Part II explores AI applications responding to social needs, covering critical areas such as poverty, child welfare, health care, mental health, substance abuse, support for older adults, communities at risk, criminal justice, and human trafficking prevention. Part III delves into AI's impact on social justice in changing contexts, including advocacy, environmental challenges, the evolving workplace, and international issues. Part IV focuses on AI in social work education and research, highlighting how AI tools enhance learning and scholarly inquiry. Part V addresses ethical considerations and future directions, discussing challenges, governance, and strategies for building AI literacy, culminating in a conclusion that outlines opportunities for AI in social work.&#13;
&#13;
Artificial Intelligence in Social Work: Bridging Technology and Humanity equips social work practitioners, educators, researchers, and students with a deep understanding of AI's capabilities and implications within various social work domains. The book offers practical guidance on integrating AI into various aspects of social work practice, enhancing service delivery, and improving client outcomes. For educators and students, it provides a framework for incorporating AI literacy into social work curricula, preparing the next generation of professionals for a technologically advanced landscape. Researchers benefit from insights into innovative methodologies that leverage AI for more robust and impactful studies. By intertwining technological advancements with the human-centric values of social work, the book underscores the necessity of balancing innovation with compassion and ethical responsibility.</text>
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