Allgemein
Alice and Bob Learn Secure Coding
UNLOCK THE POWER OF SECURE CODING WITH THIS STRAIGHTFORWARD AND APPROACHABLE GUIDE!Discover a game-changing resource that caters to developers of all levels with Alice and Bob Learn Secure Coding. With a refreshing approach, the book offers analogies, stories of the characters Alice and Bob, real-life examples, technical explanations and diagrams to break down intricate security concepts into digestible insights that you can apply right away. Explore secure coding in popular languages like Python, Java, JavaScript, and more, while gaining expertise in safeguarding frameworks such as Angular, .Net, and React. Uncover the secrets to combatting vulnerabilities by securing your code from the ground up!Topics include:* Secure coding in Python, Java, Javascript, C/C++, SQL, C#, PHP, and more* Security for popular frameworks, including Angular, Express, React, .Net, and Spring* Security Best Practices for APIs, Mobile, Web Sockets, Serverless, IOT, and Service Mesh* Major vulnerability categories, how they happen, the risks, and how to avoid them* The Secure System Development Life Cycle, in depth* Threat modeling, testing, and code review* The agnostic fundamentals of creating secure code that apply to any language or frameworkAlice and Bob Learn Secure Coding is designed for a diverse audience, including software developers of all levels, budding security engineers, software architects, and application security professionals. Immerse yourself in practical examples and concrete applications that will deepen your understanding and retention of critical security principles.Alice and Bob Learn Secure Coding illustrates all the included concepts with easy-to-understand examples and concrete practical applications, furthering the reader’s ability to grasp and retain the foundational and advanced topics contained within. Don't miss this opportunity to strengthen your knowledge; let Alice and Bob guide you to a secure and successful coding future.
Mathematics for Digital Science, Volume 1
Over the past century, advancements in computer science have consistently resulted from extensive mathematical work. Even today, innovations in the digital domain continue to be grounded in a strong mathematical foundation. To succeed in this profession, both today's students and tomorrow’s computer engineers need a solid mathematical background.The goal of this book series is to offer a solid foundation of the knowledge essential to working in the digital sector. Across three volumes, it explores fundamental principles, digital information, data analysis, and optimization. Whether the reader is pursuing initial training or looking to deepen their expertise, the Mathematics for Digital Science series revisits familiar concepts, helping them refresh and expand their knowledge while also introducing equally essential, newer topics.GÉRARD-MICHEL COCHARD is Professor Emeritus at Université de Picardie Jules Verne, France, where he has held various senior positions. He has also served at the French Ministry of Education and the CNAM (Conservatoire National des Arts et Métiers). His research is conducted at the Eco-PRocédés, Optimisation et Aide à la Décision (EPROAD) laboratory, France.MHAND HIFI is Professor of Computer Science at Université de Picardie Jules Verne, France, where he heads the EPROAD UR 4669 laboratory and manages the ROD team. As an expert in operations research and NP-hard problem-solving, he actively contributes to numerous international conferences and journals in the field.
Space Piracy
COMPREHENSIVE EXPLORATION OF HUMANITY'S POTENTIAL FOR CRIMINAL ACTIVITIES IN OUTER SPACESpace Piracy: Preparing for a Criminal Crisis in Orbit is a forward-thinking resource that considers, analyzes, and provides solutions to the influence of the ignoble side of humanity in the realm of outer space, where potential for crime, corruption, piracy, and war increases as the exploitation of space as a commercial resource continues to develop. This book blends the authors' knowledge with that of subject matter experts to deliver a holistic understanding of criminality in space and help readers broaden their horizons beyond their own area of specialization. Some of the topics explored in this insightful book include:* Space hacking, from software, data, network, and hardware risks to existing cybersecurity standards and practices in space* Criminal organizations that might pursue criminal activities in space, including cartels, kidnappers and devicenappers, and governments* Laws and treaties relevant to space crime, such as the Communications Satellite Act and the Outer Space Treaty of 1967Filled with practical, thought-provoking knowledge and guidance, Space Piracy: Preparing for a Criminal Crisis in Orbit earns a well-deserved spot on the bookshelves of professionals working in the best of humanity's institutions, including law and intelligence services, finance, insurance and risk management, corporations, and the sciences, who seek to combat what the worst of us may be dreaming up. MARC FELDMAN is Managing Partner at Eonia Capital, an aerospace and defense-based venture capital fund. He has led teams across a variety of industries, including space, life sciences, telecommunications, and more. HUGH TAYLOR is Executive Editor of The Journal of Cyber Policy. He is a Certified Information Security Manager (CSIM) who has worked in cybersecurity and enterprise technology for over twenty years.
Zero to Engineer
A STRAIGHTFORWARD, HANDS-ON STARTER KIT FOR YOUR EXCITING NEW CAREER IN ITZero to Engineer: The Unconventional Blueprint to Securing a 6-Figure IT Career is an intensely practical, no-nonsense guide to starting or rebooting your career in technology. Author and IT industry veteran Terry Kim gives you a first-person view of how to conquer the tech and cybersecurity realms, drawing on his own two decades' experience in the field. You'll learn how to get job-ready in less than four months, regardless of your educational background, and enjoy complimentary NGT Academy bonuses, including unlimited access to the CompTIA Network+ Certification Course, a free one-on-one career strategy session with trained IT career specialists, and membership in the Zero to Engineer Community Group. The book offers:* Common and intuitive IT career pathways, showing you how to get from A-to-Z in the most direct way possible* IT employment contract negotiation tips that make it easier to get your first six-figure job offer* A comprehensive collection of worksheets you can use in your own journey from IT-beginner to subject-matter expertPerfect for anyone interested in starting a brand-new and exciting career in technology, Zero to Engineer is also a must-read for career changers and professionals interested in pivoting to a new job in tech. Whether or not you have a college degree, it's the insider's guide to IT and cybersecurity that you've been waiting for. TERRY KIM has over twenty years’ experience in the IT industry, having worked for prominent tech giants, including Cisco Systems and Arista Networks. He is a veteran of the United States Air Force and is passionate about revolutionizing IT learning as he combines mentorship and rapid, practical skills development.
Natural Language Processing for Software Engineering
DISCOVER HOW NATURAL LANGUAGE PROCESSING FOR SOFTWARE ENGINEERING CAN TRANSFORM YOUR UNDERSTANDING OF AGILE DEVELOPMENT, EQUIPPING YOU WITH ESSENTIAL TOOLS AND INSIGHTS TO ENHANCE SOFTWARE QUALITY AND RESPONSIVENESS IN TODAY’S RAPIDLY CHANGING TECHNOLOGICAL LANDSCAPE.Agile development enhances business responsiveness through continuous software delivery, emphasizing iterative methodologies that produce incremental, usable software. Working software is the main measure of progress, and ongoing customer collaboration is essential. Approaches like Scrum, eXtreme Programming (XP), and Crystal share these principles but differ in focus: Scrum reduces documentation, XP improves software quality and adaptability to changing requirements, and Crystal emphasizes people and interactions while retaining key artifacts. Modifying software systems designed with Object-Oriented Analysis and Design can be costly and time-consuming in rapidly changing environments requiring frequent updates. This book explores how natural language processing can enhance agile methodologies, particularly in requirements engineering. It introduces tools that help developers create, organize, and update documentation throughout the agile project process. RAJESH KUMAR CHAKRAWARTI, PHD, is a dean and professor in the Department of Computer Science and Engineering at Sushila Devi Bansal College, Bansal Group of Institutions, India. He has over 20 years of professional experience in academia and industry. Additionally, he has organized and attended over 200 seminars, workshops, and conferences and has published over 100 research papers and book chapters in nationally and internationally revered publications. RANJANA SIKARWAR is currently pursuing a PhD from Amity University, Gwalior. She completed her Bachelor of Engineering in 2006 and Master of Technology in Computer Science and Engineering in 2015. Her research interests include social network analysis, graph mining, machine learning, Internet of Things, and deep learning. SANJAYA KUMAR SARANGI, PHD, is an adjunct professor and coordinator at Utkal University with over 23 years of experience in the academic, research, and industry sectors. He has a number of publications in journals and conferences, has authored many textbooks and book chapters, and has more than 30 national and international patents. He is an active member and life member of many associations, as well as an editor, technical program committee member, and reviewer in reputed journals and conferences. He has dedicated his career to advancing information and communication technology to enhance and optimize worldwide research and information dissemination, leading to improved student learning and teaching methods. SAMSON ARUN RAJ ALBERT RAJ, PHD, is an assistant professor and placement coordinator in the Division of Computer Science and Engineering, School of Computer Science and Technology, Karunya Institute of Technology and Sciences, Tamil Nadu, India. His research is focused on smart city development using drone networks and energy grids with various applications, and his areas of expertise include wireless sensor networks, vehicular ad-hoc networks, and intelligent transportation systems. SHWETA GUPTA is an assistant professor in the Computer Science and Engineering Department at Medicaps University, Indore (M.P.), India. She focuses on natural language processing, data mining, and machine learning. She aims to close the knowledge gap between theory and real-world applications in the tech sector through her passion for research and teaching. Her approach centers on encouraging creativity and motivating students to strive for technological excellence. KRISHNAN SAKTHIDASAN SANKARAN, PHD, is a professor in the Department of Electronics and Communication Engineering at Hindustan Institute of Technology and Science, India. He has been a senior member of the Institute of Electrical and Electronics Engineers for the past ten years and has published more than 70 papers in refereed journals and international conferences. He has also published three books to his credit. His research interests include image processing, wireless networks, cloud computing, and antenna design. ROMIL RAWAT has attended several research programs and received research grants from the United States, Germany, Italy, and the United Kingdom. He has chaired international conferences and hosted several research events, in addition to publishing several research patents. His research interests include cybersecurity, Internet of Things, dark web crime analysis and investigation techniques, and working towards tracing illicit anonymous contents of cyber terrorism and criminal activities.
AWS Certified Developer Study Guide
THE AWS CERTIFIED DEVELOPER EXAM HAS BEEN UPDATED. YOUR STUDY GUIDE SHOULD BE, TOO.The AWS Certified Developer Study Guide–Associate (DVA-C02) Exam is your ultimate preparation resource for the latest exam! Covering the exam objectives, this invaluable resource provides expert guidance, clear explanations, and the wisdom of experience with AWS best practices. You’ll master core services and basic architecture, and equip yourself to develop, deploy, and debug cloud-based applications using AWS.The AWS Developer certification is earned by those who demonstrate the technical knowledge and skill associated with best practices for building secure, reliable cloud-based applications using AWS technology. This book is your exam prep companion, providing everything you need to know to pass with flying colors.* Study the AWS Certified Developer Exam objectives* Gain expert insight on core AWS services and best practices* Test your understanding of key concepts with challenging chapter questions* Access online study tools including practice questions, electronic flashcards, a searchable glossary, and moreWhen you’re ready to get serious about your cloud credentials, the AWS Certified Developer Study Guide–Associate Exam is the resource you need to pass the exam with flying colors.
AWS Certified Developer Study Guide
THE AWS CERTIFIED DEVELOPER EXAM HAS BEEN UPDATED. YOUR STUDY GUIDE SHOULD BE, TOO.The AWS Certified Developer Study Guide–Associate (DVA-C02) Exam is your ultimate preparation resource for the latest exam! Covering the exam objectives, this invaluable resource provides expert guidance, clear explanations, and the wisdom of experience with AWS best practices. You’ll master core services and basic architecture, and equip yourself to develop, deploy, and debug cloud-based applications using AWS.The AWS Developer certification is earned by those who demonstrate the technical knowledge and skill associated with best practices for building secure, reliable cloud-based applications using AWS technology. This book is your exam prep companion, providing everything you need to know to pass with flying colors.* Study the AWS Certified Developer Exam objectives* Gain expert insight on core AWS services and best practices* Test your understanding of key concepts with challenging chapter questions* Access online study tools including practice questions, electronic flashcards, a searchable glossary, and moreWhen you’re ready to get serious about your cloud credentials, the AWS Certified Developer Study Guide–Associate Exam is the resource you need to pass the exam with flying colors.
Julia Quick Syntax Reference
Learn the Julia programming language as quickly as possible. This book is a must-have reference guide that presents the essential Julia syntax in a well-organized format, updated with the latest features of Julia’s APIs, libraries, and packages.This book provides an introduction that reveals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. At a more applied level, you will learn how to use Julia packages for data analysis, numerical optimization, symbolic computation, and machine learning, and how to present your results in dynamic documents.The Second Edition delves deeper into modules, environments, and parallelism in Julia. It covers random numbers, reproducibility in stochastic computations, and adds a section on probabilistic analysis. Finally, it provides forward-thinking introductions to AI and machine learning workflows using BetaML, including regression, classification, clustering, and more, with practical exercises and solutions for self-learners.WHAT YOU WILL LEARN* Work with Julia types and the different containers for rapid development* Use vectorized, classical loop-based code, logical operators, and blocks* Explore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcasts* Build custom structures in Julia* Use C/C++, Python or R libraries in Julia and embed Julia in other code.* Optimize performance with GPU programming, profiling and more.* Manage, prepare, analyse and visualise your data with DataFrames and Plots* Implement complete ML workflows with BetaML, from data coding to model evaluation, and more.WHO THIS BOOK IS FORExperienced programmers who are new to Julia, as well as data scientists who want to improve their analysis or try out machine learning algorithms with Julia.Antonello Lobianco, PhD is a research engineer employed by a French Grande É cole (polytechnic university). He works on the biophysical and economic modelling of the forest sector and is responsible for the lab models portfolio. He does programming in C++, Perl, PHP, Visual Basic, Python, and Julia. He teaches environmental and forest economics at undergraduate and graduate levels and modelling at PhD level. For a few years, he has followed the development of Julia as it fits his modelling needs. He is the author of a few Julia packages, particularly on data analysis and machine learning (search sylvaticus on GitHub).Part 1. Language Core.- 1. Getting Started.- 2. Data Types and Structures.- 3. Control Flow and Functions.- 4. Custom Types.- E1: Shelling Segregation Model - 5. Input – Output.- 6. Metaprogramming and Macros.- 7. Interfacing Julia with Other Languages.- 8. Efficiently Write Efficient Code. - 9 Parallel Computing in Julia - Part 2. Packages Ecosystem.- 10. Working with Data.- 11. Scientific Libraries.- E2: Fitting a forest growth model - 12 – AI with Julia – E3. Predict house values - 13. Utilities. Appendix: Solutions to the exercises.
The Definitive Guide to Machine Learning Operations in AWS
FOREWORD BY DR. SHREYAS SUBRAMANIAN, PRINCIPAL DATA SCIENTIST, AMAZONThis book focuses on deploying, testing, monitoring, and automating ML systems in production. It covers AWS MLOps tools like Amazon SageMaker, Data Wrangler, and AWS Feature Store, along with best practices for operating ML systems on AWS.This book explains how to design, develop, and deploy ML workloads at scale using AWS cloud's well-architected pillars. It starts with an introduction to AWS services and MLOps tools, setting up the MLOps environment. It covers operational excellence, including CI/CD pipelines and Infrastructure as code. Security in MLOps, data privacy, IAM, and reliability with automated testing are discussed. Performance efficiency and cost optimization, like Right-sizing ML resources, are explored. The book concludes with MLOps best practices, MLOPS for GenAI, emerging trends, and future developments in MLOpsBy the end, readers will learn operating ML workloads on the AWS cloud. This book suits software developers, ML engineers, DevOps engineers, architects, and team leaders aspiring to be MLOps professionals on AWS.What you will learn:● Create repeatable training workflows to accelerate model development● Catalog ML artifacts centrally for model reproducibility and governance● Integrate ML workflows with CI/CD pipelines for faster time to production● Continuously monitor data and models in production to maintain quality● Optimize model deployment for performance and costWho this book is for:This book suits ML engineers, DevOps engineers, software developers, architects, and team leaders aspiring to be MLOps professionals on AWS.NEEL SENDAS is a Principal Technical Account Manager at Amazon Web Services (AWS). In this role, he serves as the AWS Cloud Operations lead for some of the largest enterprises that utilize AWS services. Drawing from his expertise in cloud operations, in this book, Neel presents solutions to common challenges related to ML Cloud Governance, Cloud Finance, and Cloud Operational Resilience & Management at scale. Neel also plays a crucial role as part of the core team of Machine Learning Technical Field Community leaders at AWS, where he contributes to shaping the roadmap of AWS Artificial Intelligence and Machine Learning (AI/ML) Services. Neel is based in the state of Georgia, United States.DEEPALI RAJALE is a former AWS ML Specialist Technical Account Manager, with extensive experience supporting enterprise customers in implementing MLOps best practices across various industries. She is also the founder of Karini AI, a company dedicated to democratizing generative AI for businesses. She enjoys blogging about ML and Generative AI and coaching customers to optimize their AI/ML workloads for operational efficiency and cost optimization. In her spare time, she enjoys traveling, seeking new experiences, and keeping up with the latest technology trends.Chapter 1: Introduction to MLOps.- Chapter 2: Foundations of MLOps on AWS.- Chapter 3: Operational Excellence in MLOps.- Chapter 4: Security in MLOps.- Chapter 5: Reliability in MLOps.- Chapter 6: Performance Efficiency in MLOps.- Chapter 7: Cost Optimization in MLOps.- Chapter 8 MLOps Best Practices and Case Studies.- Chapter 9: MLOps for GenAI.- Chapter 10: Future Trends in MLOps.
Natural Language Processing for Software Engineering
DISCOVER HOW NATURAL LANGUAGE PROCESSING FOR SOFTWARE ENGINEERING CAN TRANSFORM YOUR UNDERSTANDING OF AGILE DEVELOPMENT, EQUIPPING YOU WITH ESSENTIAL TOOLS AND INSIGHTS TO ENHANCE SOFTWARE QUALITY AND RESPONSIVENESS IN TODAY’S RAPIDLY CHANGING TECHNOLOGICAL LANDSCAPE.Agile development enhances business responsiveness through continuous software delivery, emphasizing iterative methodologies that produce incremental, usable software. Working software is the main measure of progress, and ongoing customer collaboration is essential. Approaches like Scrum, eXtreme Programming (XP), and Crystal share these principles but differ in focus: Scrum reduces documentation, XP improves software quality and adaptability to changing requirements, and Crystal emphasizes people and interactions while retaining key artifacts. Modifying software systems designed with Object-Oriented Analysis and Design can be costly and time-consuming in rapidly changing environments requiring frequent updates. This book explores how natural language processing can enhance agile methodologies, particularly in requirements engineering. It introduces tools that help developers create, organize, and update documentation throughout the agile project process. RAJESH KUMAR CHAKRAWARTI, PHD, is a dean and professor in the Department of Computer Science and Engineering at Sushila Devi Bansal College, Bansal Group of Institutions, India. He has over 20 years of professional experience in academia and industry. Additionally, he has organized and attended over 200 seminars, workshops, and conferences and has published over 100 research papers and book chapters in nationally and internationally revered publications. RANJANA SIKARWAR is currently pursuing a PhD from Amity University, Gwalior. She completed her Bachelor of Engineering in 2006 and Master of Technology in Computer Science and Engineering in 2015. Her research interests include social network analysis, graph mining, machine learning, Internet of Things, and deep learning. SANJAYA KUMAR SARANGI, PHD, is an adjunct professor and coordinator at Utkal University with over 23 years of experience in the academic, research, and industry sectors. He has a number of publications in journals and conferences, has authored many textbooks and book chapters, and has more than 30 national and international patents. He is an active member and life member of many associations, as well as an editor, technical program committee member, and reviewer in reputed journals and conferences. He has dedicated his career to advancing information and communication technology to enhance and optimize worldwide research and information dissemination, leading to improved student learning and teaching methods. SAMSON ARUN RAJ ALBERT RAJ, PHD, is an assistant professor and placement coordinator in the Division of Computer Science and Engineering, School of Computer Science and Technology, Karunya Institute of Technology and Sciences, Tamil Nadu, India. His research is focused on smart city development using drone networks and energy grids with various applications, and his areas of expertise include wireless sensor networks, vehicular ad-hoc networks, and intelligent transportation systems. SHWETA GUPTA is an assistant professor in the Computer Science and Engineering Department at Medicaps University, Indore (M.P.), India. She focuses on natural language processing, data mining, and machine learning. She aims to close the knowledge gap between theory and real-world applications in the tech sector through her passion for research and teaching. Her approach centers on encouraging creativity and motivating students to strive for technological excellence. KRISHNAN SAKTHIDASAN SANKARAN, PHD, is a professor in the Department of Electronics and Communication Engineering at Hindustan Institute of Technology and Science, India. He has been a senior member of the Institute of Electrical and Electronics Engineers for the past ten years and has published more than 70 papers in refereed journals and international conferences. He has also published three books to his credit. His research interests include image processing, wireless networks, cloud computing, and antenna design. ROMIL RAWAT has attended several research programs and received research grants from the United States, Germany, Italy, and the United Kingdom. He has chaired international conferences and hosted several research events, in addition to publishing several research patents. His research interests include cybersecurity, Internet of Things, dark web crime analysis and investigation techniques, and working towards tracing illicit anonymous contents of cyber terrorism and criminal activities.
Principles and Applications of Blockchain Systems
TECHNICAL THEORY, KEY TECHNOLOGIES, AND PRACTICAL APPLICATIONS FOR CONSORTIUM BLOCKCHAINS, WITH A SOLUTION TO THE CAP TRILEMMA PROBLEMPrinciples and Applications of Blockchain Systems provides a comprehensive introduction to consortium blockchains, including the physical, network, consensus, and contract layers, covering technical theory, key technologies, and practical applications. Beyond the technical side, this book visually showcases the application potential of consortium blockchains, with information on implementation cases in network management (Multi-Identifier System) and secure storage (Mimic Distributed Storage System). This book thoroughly addresses the CAP trilemma problem for consortium blockchains, a major barrier to scalability, by presenting a novel quantifiable impossibility triangle with a solution. Additionally, optimization techniques in consortium blockchains, such as P2P protocols for future networks and consensus algorithms, are discussed in detail. Written by two highly qualified academics with significant experience in the field, Principles and Applications of Blockchain Systems discusses topics such as:* Peer-to-peer networks in consortium blockchains, covering P2P network architecture and node discovery, data synchronization, and gossip protocols* Basic concepts of distributed consistency, including the SMR model in blockchain systems, assumptions for distributed networks, and the Byzantine Generals problem* Consensus mechanisms evolution process from voting-based, including PBFT, RPCA, SCP, and CoT; to proof-based including PoW, PoS, and PoX; finally optimized by fusion both voting-based and proof-based, including PoV, PPoV, HotStuff* Types of vulnerability for smart contracts, covering solidity code, EVM execution, and blockchain system layers* Historical trend of upgrade from electronic consensus to quantum consensusWith highly comprehensive coverage of the subject, Principles and Applications of Blockchain Systems serves as an ideal textbook for blockchain students and researchers, and a valuable reference book for engineers and business leaders involved in developing real-world blockchain systems. HUI LI, School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, China. Li received the World Leading Internet Scientific and Technological Achievements award at the 6th World Internet Conference in 2019. His research interests include network architecture, cyberspace security, distributed storage, and blockchain. HAN WANG, School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, China.
Der andere Sport
Nicht nur die Corona-Pandemie hat die Zuschauerzahlen im Esports beflügelt. Weltweit zählt das Publikum inzwischen rund 532 Millionen Menschen, und die Esports-Branche ist zu einer bedeutenden, profitablen Industrie herangewachsen. Im Mittelpunkt steht dabei die Esports-Community, deren Bedürfnisse die Branche stark prägen. Dieses Buch zielt darauf ab, die zentralen Strukturen dieser Zielgruppe zu beleuchten. Die zugrunde liegende Forschungsfrage lautet: „Können durch Künstliche Intelligenz neue Erkenntnisse über die Esports-Zielgruppe gewonnen und gesellschaftliche Strukturen offengelegt werden? Und falls ja, wie beeinflussen diese Ergebnisse das Esport-Marketing?“ Ziel ist es, innovative Ansätze im Esport-Marketing zu fördern, da das Wissen über die facettenreiche Esports-Zielgruppe bislang noch begrenzt ist.
Terraform Made Easy
Explore the transformative benefits of Infrastructure as Code (IaC) and understand why Terraform is the go-to tool for managing cloud infrastructure efficiently. This book is your ultimate guide to mastering Terraform on Google Cloud Platform, providing you with the tools and knowledge to automate and optimize your cloud infrastructure with confidence. You’ll start by reviewing the traditional approach to managing infrastructure, common challenges, and the benefits of adopting IaC and Terraform. You’ll then learn how to install Terraform on various operating systems and get familiar with its configuration language, basic commands, and syntax. The book then turns to provisioning infrastructures on GCP, managing secrets and enhancing security, and concludes with integrating collaboration and DevOps using Terraform. The power of cloud platforms is growing, providing numerous ways to manage infrastructures more efficiently. While the traditional approach to infrastructure management works well on a smaller scale, it becomes a challenge when dealing with complex or extensive projects. From installation and configuration to advanced provisioning and security practices, this book provides a clear, step-by-step approach to mastering Terraform. You will: * Explore providers, variables, modules, state management, and dependencies. * Master encryption methods and IAM policies. * Secure remote state management to protect sensitive data and ensure compliance. * Discover frameworks, tools, and best practices for testing IaC code. * Automate provisioning with CI/CD pipelines. * Provision a comprehensive suite of infrastructure resources on Google Cloud Platform. Explore the transformative benefits of Infrastructure as Code (IaC) and understand why Terraform is the go-to tool for managing cloud infrastructure efficiently. This book is your ultimate guide to mastering Terraform on Google Cloud Platform, providing you with the tools and knowledge to automate and optimize your cloud infrastructure with confidence. You’ll start by reviewing the traditional approach to managing infrastructure, common challenges, and the benefits of adopting IaC and Terraform. You’ll then learn how to install Terraform on various operating systems and get familiar with its configuration language, basic commands, and syntax. The book then turns to provisioning infrastructures on GCP, managing secrets and enhancing security, and concludes with integrating collaboration and DevOps using Terraform. The power of cloud platforms is growing, providing numerous ways to manage infrastructures more efficiently. While the traditional approach to infrastructure management works well on a smaller scale, it becomes a challenge when dealing with complex or extensive projects. From installation and configuration to advanced provisioning and security practices, this book provides a clear, step-by-step approach to mastering Terraform. What You Will Learn * Explore providers, variables, modules, state management, and dependencies. * Master encryption methods and IAM policies. * Secure remote state management to protect sensitive data and ensure compliance. * Discover frameworks, tools, and best practices for testing IaC code. * Automate provisioning with CI/CD pipelines. * Provision a comprehensive suite of infrastructure resources on Google Cloud Platform. Who This Book Is For Cloud engineers and architects, admin engineers, and CTOs familiar with programming languages and basic IT applications. Ivy Wang is a distinguished Data Scientist and Cloud Architect, celebrated for her deep expertise and impactful contributions to the tech industry. As an honored Google Women Techmakers Ambassador, Ivy's leadership and dedication to advancing technology have earned her widespread recognition. With a passion for innovation, Ivy excels in simplifying complex systems and automating processes in big data and AI projects. Her ability to turn intricate challenges into streamlined, efficient solutions consistently drives enhanced performance and operational excellence. Preface.- Chapter 1. Introduction to Infrastructure as Code (IaC) and Terraform.- Chapter 2. Getting started with Terraform.- Chapter 3: Key Concepts of Terraform.- Chapter 4. Provisioning Infrastructure on GCP.- Chapter 5. Managing Secrets, Enhancing Security, and Ensuring Resilience.- Chapter 6. Testing and Automation.
Neural Networks with TensorFlow and Keras
Explore the capabilities of machine learning and neural networks. This comprehensive guidebook is tailored for professional programmers seeking to deepen their understanding of neural networks, machine learning techniques, and large language models (LLMs). The book explores the core of machine learning techniques, covering essential topics such as data pre-processing, model selection, and customization. It provides a robust foundation in neural network fundamentals, supplemented by practical case studies and projects. You will explore various network topologies, including Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Large Language Models (LLMs). Each concept is explained with clear, step-by-step instructions and accompanied by Python code examples using the latest versions of TensorFlow and Keras, ensuring a hands-on learning experience. By the end of this book, you will gain practical skills to apply these techniques to solving problems. Whether you are looking to advance your career or enhance your programming capabilities, this book provides the tools and knowledge needed to excel in the rapidly evolving field of machine learning and neural networks. What You Will Learn * Grasp the fundamentals of various neural network topologies, including DNN, RNN, LSTM, VAE, GAN, and LLMs * Implement neural networks using the latest versions of TensorFlow and Keras, with detailed Python code examples * Know the techniques for data pre-processing, model selection, and customization to optimize machine learning models * Apply machine learning and neural network techniques in various professional scenarios Explore the capabilities of machine learning and neural networks. This comprehensive guidebook is tailored for professional programmers seeking to deepen their understanding of neural networks, machine learning techniques, and large language models (LLMs). The book explores the core of machine learning techniques, covering essential topics such as data pre-processing, model selection, and customization. It provides a robust foundation in neural network fundamentals, supplemented by practical case studies and projects. You will explore various network topologies, including Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Large Language Models (LLMs). Each concept is explained with clear, step-by-step instructions and accompanied by Python code examples using the latest versions of TensorFlow and Keras, ensuring a hands-on learning experience. By the end of this book, you will gain practical skills to apply these techniques to solving problems. Whether you are looking to advance your career or enhance your programming capabilities, this book provides the tools and knowledge needed to excel in the rapidly evolving field of machine learning and neural networks. What You Will Learn * Grasp the fundamentals of various neural network topologies, including DNN, RNN, LSTM, VAE, GAN, and LLMs * Implement neural networks using the latest versions of TensorFlow and Keras, with detailed Python code examples * Know the techniques for data pre-processing, model selection, and customization to optimize machine learning models * Apply machine learning and neural network techniques in various professional scenarios Who This Book Is For Data scientists, machine learning enthusiasts, and software developers who wish to deepen their understanding of neural networks and machine learning techniques Chapter 1: Introduction to Neural Networks.- Chapter 2: Using Tensors.- Chapter 3: How Machines Learn.- Chapter 4: Network Layers.- Chapter 5: The Training Process.- Chapter 6: Generative Models.- Chapter 7: Re-enforcement Learning.- Chapter 8: Using Pre-trained Networks. Philip Hua brings over 30 years of experience in investment, risk management, and IT. He has held senior positions as a partner at a hedge fund, led risk and IT departments at both large and boutique firms, and co-founded a successful fintech company. Alongside Dr. Paul Wilmott, he developed the CrashMetrics methodology, a crucial tool for evaluating severe market risk in portfolios. Philip holds a PhD in Applied Mathematics from Imperial College London, an MBA, and a BSc in Engineering.
Principles and Applications of Blockchain Systems
Technical theory, key technologies, and practical applications for consortium blockchains, with a solution to the CAP trilemma problem Principles and Applications of Blockchain Systems provides a comprehensive introduction to consortium blockchains, including the physical, network, consensus, and contract layers, covering technical theory, key technologies, and practical applications. Beyond the technical side, this book visually showcases the application potential of consortium blockchains, with information on implementation cases in network management (Multi-Identifier System) and secure storage (Mimic Distributed Storage System). This book thoroughly addresses the CAP trilemma problem for consortium blockchains, a major barrier to scalability, by presenting a novel quantifiable impossibility triangle with a solution. Additionally, optimization techniques in consortium blockchains, such as P2P protocols for future networks and consensus algorithms, are discussed in detail. Written by two highly qualified academics with significant experience in the field, Principles and Applications of Blockchain Systems discusses topics such as: Peer-to-peer networks in consortium blockchains, covering P2P network architecture and node discovery, data synchronization, and gossip protocolsBasic concepts of distributed consistency, including the SMR model in blockchain systems, assumptions for distributed networks, and the Byzantine Generals problemConsensus mechanisms evolution process from voting-based, including PBFT, RPCA, SCP, and CoT; to proof-based including PoW, PoS, and PoX; finally optimized by fusion both voting-based and proof-based, including PoV, PPoV, HotStuffTypes of vulnerability for smart contracts, covering solidity code, EVM execution, and blockchain system layersHistorical trend of upgrade from electronic consensus to quantum consensus With highly comprehensive coverage of the subject, Principles and Applications of Blockchain Systems serves as an ideal textbook for blockchain students and researchers, and a valuable reference book for engineers and business leaders involved in developing real-world blockchain systems. Foreword by Peter Major xv Foreword by Zhang Jing-an xvii Foreword by Yale li xix Foreword by Feng Han xxi Foreword by Ramesh Ramadoss xxv About the Author xxvii Preface xxix Acknowledgments xxxiii Introduction xxxv 1 Fundamentals of Blockchain 1 1.1 Introduction to Blockchain 1 1.2 Evolution of Blockchain 4 1.3 Blockchain-Layered Architecture 13 1.4 Theoretical Constraints of Blockchain Trilemma 16 1.5 Chapter Summary 26 Discussion Questions 27 References 28 2 Physical Topology in Blockchain 31 2.1 Basic Physical Topology of Computer Network 31 2.2 N-Dimensional Hypercube-Based Topology – Making it Possible to Reach CAP Guarantee Bound in Consortium Blockchain 40 2.3 Hierarchical Recursive Physical Topology of N-Dimensional Hypercube 43 2.4 Theoretical Analysis 45 2.5 Chapter Summary 54 Discussion Questions 54 References 56 3 P2P Network in Blockchain 59 3.1 P2P Network Structure 59 3.2 Node Discovery Method 64 3.3 Broadcast Protocol 69 3.4 Chapter Summary 83 Discussion Questions 83 References 84 4 Blockchain Consensus 87 4.1 Basic Concepts of Distributed Consistency 87 4.2 Byzantine Generals Problem 95 4.3 Voting-Based Consensus 100 4.4 Proof-Based Consensus 115 4.5 Consensus Integrating Proof and Voting 130 4.6 Evaluation and Analysis of Blockchain Consensus 155 4.7 Chapter Summary 160 Discussion Questions 163 References 164 5 Smart Contract and Its Security in Blockchain 169 5.1 Concept of Smart Contracts 169 5.2 Vulnerability in Smart Contracts 171 5.3 Taxonomy of Approaches to Detecting Vulnerabilities 175 5.4 Detection Tools for Smart Contract Vulnerability 186 5.5 Chapter Summary 195 Discussion Questions 196 References 197 6 Multi-Identifier System Based on Large-Scale Consortium Blockchain 203 6.1 Background Introduction and Requirement Analysis 203 6.2 System Architecture 204 6.3 Core Functions 215 6.4 Building a Community of Shared Future in Cyberspace with Sovereign Blockchain 222 6.5 Chapter Summary 236 Discussion Questions 237 References 238 7 Integrating Consortium Blockchain and Mimic Security in Distributed Storage System 241 7.1 Background Introduction and Requirement Analysis 241 7.2 Mimic Distributed Secure Storage System 249 7.3 Logging System in Mimic Storage Based on Consortium Blockchain 256 7.4 Chapter Summary 267 Discussion Questions 267 References 268 8 Quantum Blockchain and Its Potential Applications 271 8.1 Quantum Computing and Communication Theory 271 8.2 Quantum Blockchain – Solving Trilemma of Distributed Systems 308 8.3 Scalable Quantum Computer Network 322 8.4 Chapter Summary 342 Discussion Questions 343 References 344 9 Practical Application of Large-Scale Blockchain 347 9.1 Construction of Network Topology 347 9.2 P2P Broadcast Protocol 353 9.3 Solidity Language 357 9.4 Establishment of Blockchain Infrastructure 369 9.5 Smart Contract Security Detection 373 9.6 Chapter Summary 374 Discussion Questions 375 References 375 Index 377 Hui Li, School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, China. Li received the World Leading Internet Scientific and Technological Achievements award at the 6th World Internet Conference in 2019. His research interests include network architecture, cyberspace security, distributed storage, and blockchain. Han Wang, School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, China.
Wellness Management Powered by AI Technologies
This book is an essential resource on the impact of AI in medical systems, helping readers stay ahead in the modern era with cutting-edge solutions, knowledge, and real-world case studies. Wellness Management Powered by AI Technologies explores the intricate ways machine learning and the Internet of Things (IoT) have been woven into the fabric of healthcare solutions. From smart wearable devices tracking vital signs in real time to ML-driven diagnostic tools providing accurate predictions, readers will gain insights into how these technologies continually reshape healthcare. The book begins by examining the fundamental principles of machine learning and IoT, providing readers with a solid understanding of the underlying concepts. Through clear and concise explanations, readers will grasp the complexities of the algorithms that power predictive analytics, disease detection, and personalized treatment recommendations. In parallel, they will uncover the role of IoT devices in collecting data that fuels these intelligent systems, bridging the gap between patients and practitioners. In the following chapters, readers will delve into real-world case studies and success stories that illustrate the tangible benefits of this dynamic duo. This book is not merely a technical exposition; it serves as a roadmap for healthcare professionals and anyone invested in the future of healthcare. Readers will find the book: Explores how AI is transforming diagnostics, treatments, and healthcare delivery, offering cutting-edge solutions for modern healthcare challenges;Provides practical knowledge on implementing AI in healthcare settings, enhancing efficiency and patient outcomes;Offers authoritative insights into current AI trends and future developments in healthcare;Features real-world case studies and examples showcasing successful AI integrations in various medical fields. Audience This book is a valuable resource for researchers, industry professionals, and engineers from diverse fields such as computer science, artificial intelligence, electronics and electrical engineering, healthcare management, and policymakers. Bharat Bhushan, PhD, is an assistant professor in the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India. He has published more than 150 research papers, contributed over 30 book chapters, and edited 20 books. Akib Khanday, PhD, is a post-doctoral research fellow in the Department of Computer Science and Software Engineering-CIT, United Arab Emirates University, Abu Dhabi, United Arab Emirates. His research interests include computational social sciences, natural language processing (NLP), and machine/deep learning. Khursheed Aurangzeb, PhD, is an associate professor in the Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. Over his 15 years of research, he has been involved in several projects related to machine/deep learning and embedded systems. His research interests focus on computer architecture, signal processing, and wireless sensor networks. Sudhir Kumar Sharma, PhD, is a professor and head of the Department of Computer Science at the Institute of Information Technology & Management, affiliated with GGSIPU, New Delhi, India. His research interests include machine learning, data mining, and security. He has published more than 60 research papers in various international journals and conferences and is the author of seven books in the fields of IoT, wireless sensor networks (WSN), and blockchain. Parma Nand, PhD, is the dean of the School of Engineering and Technology, Sharda University, Greater Noida, India. His expertise includes wireless and sensor networks, cryptography, algorithms, and computer graphics. He has published more than 85 papers in peer-reviewed journals and filed two patents.
AI in Disease Detection
Comprehensive resource encompassing recent developments, current use cases, and future opportunities for AI in disease detection AI in Disease Detection discusses the integration of artificial intelligence to revolutionize disease detection approaches, with case studies of AI in disease detection as well as insight into the opportunities and challenges of AI in healthcare as a whole. The book explores a wide range of individual AI components such as computer vision, natural language processing, and machine learning as well as the development and implementation of AI systems for efficient practices in data collection, model training, and clinical validation. This book assists readers in assessing big data in healthcare and determining the drawbacks and possibilities associated with the implementation of AI in disease detection; categorizing major applications of AI in disease detection such as cardiovascular disease detection, cancer diagnosis, neurodegenerative disease detection, and infectious disease control, as well as implementing distinct AI methods and algorithms with medical data including patient records and medical images, and understanding the ethical and social consequences of AI in disease detection such as confidentiality, bias, and accessibility to healthcare. Sample topics explored in AI in Disease Detection include: Legal implication of AI in healthcare, with approaches to ensure privacy and security of patients and their dataIdentification of new biomarkers for disease detection, prediction of disease outcomes, and customized treatment plans depending on patient characteristicsAI’s role in disease surveillance and outbreak detection, with case studies of its current usage in real-world scenariosClinical validation processes for AI disease detection models and how they can be validated for accuracy and effectiveness Delivering excellent coverage of the subject, AI in Disease Detection is an essential up-to-date reference for students, healthcare professionals, academics, and practitioners seeking to understand the possible applications of AI in disease detection and stay on the cutting edge of the most recent breakthroughs in the field. Dr. Rajesh Singh, Professor, Electronics & Communication Engineering and Director, Research & Innovation, Uttaranchal University, India. Dr. Singh was featured among the top ten inventors in 2010 to 2020 by Clarivate Analytics in “India’s Innovation Synopsis” in March 2021. Dr. Anita Gehlot, Professor, Electronics & Communication Engineering and Head -Research and Innovation, Uttaranchal University, India. Dr. Navjot Rathour, Associate Professor, Electronics & Communication Engineering, Chandigarh University, Mohali, India. Dr. Shaik Vaseem Akram, Assistant Professor, Electronics & Communication Engineering, S R University, Telangana, India.
Nachhaltige Künstliche Intelligenz
Die aus KI entstehenden Möglichkeiten sind immens. Speziell das maschinelle Lernen ist für viele deutsche Unternehmen mittlerweile kein Fremdwort mehr. In durchweg allen Branchen werden die Einsatzmöglichkeiten von trainierten Modellen evaluiert, die neue Geschäftsfelder entstehen lassen oder bestehende Abläufe optimieren. In der Euphorie werden von vielen Akteurinnen und Akteuren Nachhaltigkeitsaspekte vernachlässigt. Zum Beispiel kann das Training von KI-Algorithmen und der Betrieb der Systeme durchaus ressourcenintensiv sein kann. Die derzeitige Entwicklung zielt darauf ab, bestehende Modelle noch akkurater bzw. performanter zu machen. Dabei müssen Performance und Nachhaltigkeit von KI-Systemen kein Widerspruch sein.Dieses Buch verfolgt das Anliegen, die Chancen nachhaltiger KI-Ansätze darzustellen. Es wird detailliert auf Nachhaltigkeit in der IT, Nachhaltigkeit durch KI sowie auf Digitale Ethik eingegangen. Nicht alle 17 UN-Agenda 2030 Ziele werden behandelt, der Fokus liegt auf der ökologischen Nachhaltigkeit. Das Buch ist kein Theoriewerk. Es beinhaltet diverse konkrete Empfehlungen für zur direkten Anwendung für nachhaltigere KI-Projekte.Aus dem Inhalt: Einleitung und Problemstellung – 6 Problemstellung – 8 UN-Agenda 2030: 17 Nachhaltigkeitsziele und KI – 11 Aufbau des Buches – 14 Literatur – 16 Nachhaltigkeit in KI – 18 Deep Learning: Innovation oder ein wachsendes Problem? – 20 Technische Grundlagen – 23 Technische Möglichkeiten zur Reduzierung des Energieverbrauchs – 26 Lösungen für praktische Anwendungen – 29 Literatur – 32 Nachhaltigkeit durch KI – 33 Einleitung – 33 Stand der Forschung – 35 Typen von KI für Nachhaltigkeit – 40 Decision Trees – 40 Support Vector Machines – 42 K-Nearest Neighbor (KNN) – 43 Clustering – 44 Deep Learning und neuronale Netze – 45 Reinforcement Learning – 47 Few Shot Learning – 47 Long-Short-Term-Memory (LSTM) – 49 KI für (mehr) Nachhaltigkeit – 51 Use-Cases KI für Nachhaltigkeit – 58 Leitfaden für die KI-Implementierung im Unternehmen – 69 Phase 1 – Zielsetzung und Folgenabschätzung – 74 Phase 2 – Planung und Gestaltung – 74 Phase 3 – Vorbereitung und Implementierung – 75 Phase 4 – Evaluation und Anpassung – 78 Literatur – 78 Nachhaltigkeitsethik und Künstliche Intelligenz – 81 Ethische Implikationen der Nachhaltigkeit – 81 Ethische und nachhaltige Maßstäbe des Handelns – 82 Die Verantwortung von Unternehmen – 82 Digitale Ethik – 83 Freiwillige Standards vs. gesetzliche Regulierungen – 84 Freiwillige Standards – 84 Gesetzliche Regulierungen – 85 Digitale Ethik im Unternehmen – 86 Fazit – 88 Literatur – 88 Zusammenfassung und Ausblick – 91 Zusammenfassung – 91 Ausblick – 97
Cracking the Data Code
Why do we continue to struggle with data? With all the powerful tools we have in processing power, data tools, and computer programming, we still search for some elusive truth to pervasive problems. AI hallucinates, 'good data' that we started with is suddenly unintelligible, systems that should talk to each other seamlessly continually experience errors and need correction.What we fail to incorporate into our data world is the fact that DATA IS LANGUAGE and has entwined in that language its own code that does not get captured in databases, APIs, LLMs and the systems we use day in and day out. SO, HOW CAN WE CRACK THIS DATA CODE?By stepping back, we can incorporate the tools that already exist in applied linguistics used to crack the human language code into our approaches in how we tackle the data code challenge. Just because we call it DATA doesn’t mean that it doesn’t suffer from bias or the need for context. But by recognizing these linguistic challenges, and infusing that inside the data, we can create data code that can be cracked, data that tells us its biases, context, and purpose, and for who that data is actually useful to, and for whom it is not.If you are interested in data, and why understanding language and jargon can help you crack the data code, this book is for you. If you’ve had a data challenge and have struggled to find a way to understand it, the practical foundational principles inside can help you frame your problem in a different way. And in doing so, help you crack the data code.
Digitally Hijacked: The Age of Influence
In an age when digital media permeates every aspect of our lives, understanding its influence is more critical than ever. This book serves as a compass, guiding readers through the complexities of our interconnected world. From the moment we wake to a flurry of notifications to the late-night scrolling that often accompanies our downtime, we find ourselves enmeshed in a digital landscape that shapes our perceptions, relationships, and routines. The journey ahead will illuminate the dual-edged nature of technology--its ability to connect and empower as well as its potential to isolate and overwhelm. By examining the algorithms that curate newsfeeds and the social media platforms that redefine communication, this book unpacks the intricacies of modern digital life. But beyond the challenges lie opportunities; this book also highlights the ways in which digital media fosters social activism and creative expression, showcasing the remarkable power of collective voices and innovative ideas. Whether digital natives or just beginning to explore this expansive realm, readers will be equipped by this exploration with insights and tools to navigate the digital age thoughtfully. Discover how to harness technology's potential, ensuring it enriches rather than diminishes our lives. Muhammad Atique holds a PhD in digital governance and is a fellow of the Higher Education Academy (HEA-UK). With over fifteen years of experience in the media industry and academia, he specializes in digital media and culture, and technology adoption. His research provides valuable insights into contemporary media trends and the implications of emerging technologies.
Emerging Technologies in Healthcare 4.0
Delve into the evolution of healthcare technologies, exploring their impact on patient care and management. This book provides a comprehensive exploration of the industrial revolution in healthcare. In this book, you'll cover the fundamentals of artificial intelligence (AI) in healthcare, including an overview of AI and machine learning, applications in healthcare domains, and challenges and opportunities in AI implementation. It progresses to explore integration of AI and IoT in Healthcare 4.0, discussing synergies, real-time data analysis, and future trends in telemedicine. The book also addresses critical aspects such as data security and privacy, focusing on regulations, standards, and strategies for ensuring data protection. Practical applications of AI and IoT in remote patient monitoring, disease diagnosis, and healthcare operations management are thoroughly examined, alongside ethical and legal considerations in Healthcare 4.0. The final chapters offer insights into emerging trends, potential challenges, and recommendations for successfully adopting AI and IoT in healthcare. Readers will gain a comprehensive understanding of how AI and IoT are revolutionizing healthcare, from enhancing patient outcomes and operational efficiencies to navigating the ethical and legal landscapes of data privacy. This book equips healthcare professionals, policymakers, and technology enthusiasts with the knowledge to navigate and leverage the transformative potential of Healthcare 4.0 technologies effectively. What You Will Learn * Explore the integration of AI with IoT Technologies in Healthcare 4.0 * Gain insights into the ethical and legal considerations surrounding AI and IoT implementations in healthcare Discover case studies and practical examples illustrating the transformative impact of AI and IoT on patient care Delve into the evolution of healthcare technologies, exploring their impact on patient care and management. This book provides a comprehensive exploration of the industrial revolution in healthcare. In this book, you'll cover the fundamentals of Artificial Intelligence (AI) in healthcare, including an overview of AI and machine learning, applications in healthcare domains, and challenges and opportunities in AI implementation. It progresses to explore integration of AI and IoT in Healthcare 4.0, discussing synergies, real-time data analysis, and future trends in telemedicine. The book also addresses critical aspects such as data security and privacy, focusing on regulations, standards, and strategies for ensuring data protection. Practical applications of AI and IoT in remote patient monitoring, disease diagnosis, and healthcare operations management are thoroughly examined, alongside ethical and legal considerations in Healthcare 4.0. The final chapters offer insights into emerging trends, potential challenges, and recommendations for successfully adopting AI and IoT in healthcare. Readers will gain a comprehensive understanding of how AI and IoT are revolutionizing healthcare, from enhancing patient outcomes and operational efficiencies to navigating the ethical and legal landscapes of data privacy. This book equips healthcare professionals, policymakers, and technology enthusiasts with knowledge to navigate and leverage transformative potential of Healthcare 4.0 technologies effectively. You Will * Explore the integration of AI with IoT technologies in Healthcare 4.0 * Gain insights into the ethical and legal considerations surrounding AI and IoT implementations in healthcare * Learn about emerging trends and future perspectives in Healthcare 4.0, including the potential challenges and recommendations * Discover case studies and practical examples illustrating the transformative impact of AI and IoT on patient care Who Is This Book For Readers with foundational understanding of healthcare systems and technologies will benefit most from this book. Specifically, a basic knowledge of healthcare operations, medical terminology, and information technology would be advantageous. Familiarity with concepts related to AI and IoT in healthcare, though not mandatory, would also enhance comprehension of the advanced topics covered in the book. Dr. Alok Kumar Srivastav is an accomplished Assistant Professor in the Department of Health Science at the University of the People, Pasadena, California, USA. His academic background includes a Ph.D., M.Tech, M.Sc. in Bio-Technology; a Post-Doctoral Fellowship (Research) in Bio-Technology from Lincoln University College, Malaysia; and an MBA in Human Resource Management. He is a distinguished figure in academia and research, honored with the "International Pride of Educationist Award" at AIT, Thailand, in 2022, for pioneering contributions to advancing education in the digital era and receiving a prestigious accolade "Innovative Academic Researcher Award" at HULT, France, UK in 2024 for his exceptional creativity, innovation, and impact in academic research. Dr. Priyanka Das serves as an Assistant Professor in the Department of Health Science at the prestigious University of the People in Pasadena, California, USA. She holds a Ph.D., M.Tech, and M.Sc. in Biotechnology along with an MBA in Human Resource Management. Prior to her current position, she was a Post-Doctoral Fellow (Research) in Biotechnology at Lincoln University College, Malaysia. Dr. Priyanka Das is a dedicated scholar, contributing significantly to the field of Biotechnology. Chapter 1: Introduction to Healthcare 4.0.- Chapter 2: Fundamentals of Artificial Intelligence (AI) in Healthcare.- Chapter 3: Internet of Things (IoT) in Healthcare.- Chapter 4: Integration of AI and IoT in Healthcare 4.0.- Chapter 5: Data Security and Privacy in Healthcare 4.0.- Chapter 6: AI and IoT in Remote Patient Monitoring.- Chapter 7: AI and IoT in Disease Diagnosis and Management.- Chapter 8: AI and IoT in Healthcare Operations Management.- Chapter 9: Ethical and Legal Considerations in Healthcare 4.0.- Chapter 10: Future Perspectives and Challenges.- Bibliography.
Network Models in Finance
NETWORK MODELS in FINANCE An insightful exploration of the theory and application of networks as applied to investment management Network Models in Finance: Expanding the Tools for Portfolio and Risk Management is a singularly incisive and unique discussion of networks and graph theory as applied to the financial and investment markets. Researchers and authors Gueorgui Konstantinov and Frank Fabozzi walk you through a comprehensive overview of networks in investment management, providing deep insight into their implementation in portfolio and risk management. You’ll discover how to construct diversified and risk-optimized portfolios by linking the price and return movements of different asset classes and factors. You’ll also find out how to better manage risk by properly understanding systematic, counterparty, and systemic risk, and by monitoring changes in the financial system that may indicate a coming financial crisis. Network Models in Finance delivers practical examples of a wide variety of financial data that can be used to visualize, describe, and investigate markets in an entirely new way, and explains the interactions and causal relationships that operate within a network-based framework. This book is a must-read for investors, asset managers, and other finance practitioners with an interest in a largely underexplored area of investing. Expansive overview of theory and practical implementation of networks in investment management Guided by graph theory, Network Models in Finance: Expanding the Tools for Portfolio and Risk Management provides a comprehensive overview of networks in investment management, delivering strong knowledge of various types of networks, important characteristics, estimation, and their implementation in portfolio and risk management. With insights into the complexities of financial markets with respect to how individual entities interact within the financial system, this book enables readers to construct diversified portfolios by understanding the link between price/return movements of different asset classes and factors, perform better risk management through understanding systematic, systemic risk and counterparty risk, and monitor changes in the financial system that indicate a potential financial crisis. With a practitioner-oriented approach, this book includes coverage of: Practical examples of broad financial data to show the vast possibilities to visualize, describe, and investigate markets in a completely new wayInteractions, Causal relationships and optimization within a network-based framework and direct applications of networks compared to traditional methods in financeVarious types of algorithms enhanced by programming language codes that readers can implement and use for their own data Network Models in Finance: Expanding the Tools for Portfolio and Risk Management is an essential read for asset managers and investors seeking to make use of networks in research, trading, and portfolio management. Preface ix Acknowledgments xv About the Authors xvii Part One Chapter 1 Introduction 3 Chapter 2 The Basic Structure of a Network 29 Chapter 3 Network Properties 45 Chapter 4 Network Centrality Metrics 71 Part Two Chapter 5 Network Modeling 95 Chapter 6 Foundations for Building Portfolio Networks – Link Prediction and Association Models 117 Chapter 7 Foundations for Building Portfolio Networks – Statistical and Econometric Models 141 Chapter 8 Building Portfolio Networks – Probabilistic Models 163 Chapter 9 Network Processes in Asset Management 181 Chapter 10 Portfolio Allocation With Networks 227 Part Three Chapter 11 Systematic and Systemic Risk, Spillover, and Contagion 261 Chapter 12 Networks in Risk Management 277 References 313 Index 327 GUEORGUI S. KONSTANTINOV, PHD, has over 17 years’ experience in portfolio management, managing global bond portfolios and currencies for institutional investors and pension funds. He is an advisory board member of the Journal of Portfolio Management and the coauthor of Quantitative Global Bond Portfolio Management. FRANK J. FABOZZI, PHD, is Professor of Practice at John Hopkins University’s Carey Business School. He has authored over 100 books and edited The Handbook of Fixed Income Securities and The Handbook of Mortgage-Backed Securities. He holds the CFA and CPA professional designations.
Applied Satisfiability
Apply satisfiability to a range of difficult problems The Boolean Satisfiability Problem (SAT) is one of the most famous and widely-studied problems in Boolean logic. Optimization versions of this problem include the Maximum Satisfiability Problem (MaxSAT) and its extensions, such as partial MaxSAT and weighted MaxSAT, which assess whether, and to what extent, a solution satisfies a given set of problems. Numerous applications of SAT and MaxSAT have emerged in fields related to logic and computing technology. Applied Satisfiability: Cryptography, Scheduling, and Coalitional Games outlines some of these applications in three specific fields. It offers a huge range of SAT applications and their possible impacts, allowing readers to tackle previously challenging optimization problems with a new selection of tools. Professionals and researchers in this field will find the scope of their computational solutions to otherwise intractable problems vastly increased. Applied Satisfiability readers will also find: Coding and problem-solving skills applicable to a variety of fieldsSpecific experiments and case studies that demonstrate the effectiveness of satisfiability-aided methodsChapters covering topics including cryptographic key recovery, various forms of scheduling, coalition structure generation, and many more Applied Satisfiability is ideal for researchers, graduate students, and practitioners in these fields looking to bring a new skillset to bear in their studies and careers. Xiaojuan Liao, PhD, is an Associate Professor in the College of Computer and Cyber Security, Chengdu University of Technology, Chengdu, China. Miyuki Koshimura, PhD, is an Assistant Professor in the Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan.
The Complete Engineering Manager
Take a 360-degree tour of the engineering manager’s role and responsibilities. This book brings them to life with practical scenarios and references and ensures their relevance to your daily work.From upkeeping technical skills, to managing people and stakeholders, to ensuring timely deliverables, the job of the engineering manager is fast-paced, complex, and often short on learning resources. Fear not, this book has you covered with tips on managing evolving processes, delivering impactful projects in a timely manner, setting goals and priorities among product and technical initiatives, and helping your team focus and deliver.Business priorities are changing at a much faster pace than ever before with new technologies being introduced and adopted regularly. This book will help managers adopt modern practices to meet this moment and aid them in helping engineering teams succeed. _The Complete Engineering Manager_ will leave you with a broader perspective and deeper skill set to apply to engineering management.WHAT YOU WILL LEARN* Employ the SELF framework for self-management and learn to build trust with team members* Manage performance and craft individualized growth plans for employee success* Evolve your team’s development, delivery, and technical processes to improve their efficiency* Drive impact for your organization through prioritization, strategy and value delivery* Adopt modern engineering management practices such as utilizing AIWHO THIS BOOK IS FORNew, aspiring, and experienced engineering managers who are looking for resources to address challenges in their role.Ananth Ramachandran is a seasoned engineering leader who has experience in building happy, productive and high-performing engineering teams. He started his career in a Fortune 500 company as a software engineer and later found his passion in startups and building engineering teams from the ground up. He’s passionate about scaling up people, product and technology strategy and ultimately contributing to an organization’s success.He runs a newsletter for Engineering Managers, techmanagerguide.substack.com, where he writes about day-to-day experiences, challenges, and modern engineering management practices and techniques. He speaks on podcasts and at meetups, and mentors and trains software professionals and aspiring leaders.PART 1: Congratulations, You're an Engineering Manager.- Chapter1: Engineering Manager's Starter Kit.- PART 2: Managing People.- Chapter 2: Self-Management.- Chapter 3: It’s All About Trust.- Chapter 4: Mindful One-on-Ones.- Chapter 5: Managing Performance.- Chapter 6: Working with Your Manager.- PART 3: Managing Processes.- Chapter 7: Evolving Processes and Bringing Change.- Chapter 8: Development and Delivery Processes. Chapter 9: Technical Processes.- PART 4: Mastering Prioritization.- Chapter 10: The Bigger Picture.- Chapter 11: Pragmatic Approach To Prioritization.- Chapter 12: Prioritizing Technical Initiatives.- PART 5: Delivering Impactful Projects.- Chapter 13: Modern Delivery Practices.- Chapter 14: Measuring Delivery Effectiveness.- Chapter 15: Managing Stakeholders, Blockers and Progress.- PART 6: Building High-Performing Teams.- Chapter 16: Make Your Team Great Again.- Chapter 17: Building a Strong Engineering Culture.- Chapter 18: Becoming an Organizational Leader.