Allgemein
Applied Satisfiability
APPLY SATISFIABILITY TO A RANGE OF DIFFICULT PROBLEMSThe 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 fields* Specific experiments and case studies that demonstrate the effectiveness of satisfiability-aided methods* Chapters covering topics including cryptographic key recovery, various forms of scheduling, coalition structure generation, and many moreApplied 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.
Signals and Systems
INTRODUCTORY COURSE TEXTBOOK ON SIGNALS AND SYSTEMS WITH NUMEROUS EXAMPLES AND CODE SNIPPETS IMPLEMENTED IN PYTHONSupported by code examples, Signals and Systems: Theory and Practical Explorations with Python is a textbook resource for a complete introductory course in systems and signals, enabling readers to run Python programs for convolution, discrete time Fourier transforms and series, sampling, and interpolation for a wide range of functions. Readers are guided step-by-step through basic differential equations, basic linear algebra, and calculus to ensure full comprehension of the exercises. This book is supported by a companion website, hosting interactive material to draw functions, and run programs in Python; it is enriched with audiovisual material via linking to related videos. Links to resources that provide a deeper explanation about the important concepts in the book, such as the systems approach, complex numbers, harmony, the Euler equation, and Hilbert spaces, are also included. Written by two highly qualified academics, topics covered include:* Systems approach for modeling the natural and manmade systems and some application areas* Representation of complex and real signals by basic functions, such as real and complex exponentials, unit step and unit impulse functions* Properties of signals, such as symmetry, harmony, energy, power, continuity and discreteness* Convolution and correlation operations for continuous time and discrete time signals and systems* Representation of systems by impulse response, frequency response, transfer function, block diagram, differential and difference equations * Properties of systems, such as linearity, time invariance, memory, invertibility, stability and causality* Continuous time and discrete time Fourier analysis in Hilbert space and their extension to Laplaca transform and z-transform * Filtering by Linear Time Invariant systems in time and frequency domains, covering low pass, high pass band pass and band reject filters* Sampling theorems for continuous time and discrete time systems, covering A/D and D/A conversion, sampling and interpolationSignals and Systems is an ideal textbook resource for a one semester introductory course on signals and systems for upper level undergraduate and graduate students in computer science, electrical engineering and data science. It is also a useful reference for professionals working in bioinformatics, robotics, remote sensing, and related fields. FATOŞ TUNAY YARMAN VURAL is a Professor in the Department of Computer Engineering at Middle East Technical University, Turkey. She is a Senior Member of the IEEE and received her PhD from Princeton University, USA, in 1981. EMRE AKBAŞ is an Associate Professor in the Department of Computer Engineering at Middle East Technical University, Turkey. Dr. Akbaş received his PhD from the University of Illinois at Urbana-Champaign, USA, in 2011.
Mensch-Computer-Interaktion (3. Auflage)
Jeder hat das schon einmal erlebt: Webseiten, auf denen man die Schrift nicht lesen kann, Informationsterminals, bei denen man nicht erkennt, wo man drücken soll, Programme, die unverständliche Meldungen hervorbringen, kurz: Software, die nicht gebrauchstauglich ist. Dieses Buch enthält Basiswissen, das alle benötigen, die gebrauchstaugliche Software erstellen wollen. Ausgehend von der menschlichen Informationsverarbeitung erläutern die Autoren, wie Benutzungsschnittstellen beschaffen sein müssen und wie bei der Entwicklung von Anwendungen vorzugehen ist, damit Menschen die Software sinnvoll nutzen können. Das Buch berücksichtigt die aktuellen Normen und Vorschriften anhand praktischer Beispiele. Diese dritte erweiterte und komplett überarbeitete Auflage integriert neben aktuellen technologischen Entwicklungen auch die Erfahrungen aus mehr als 20 Jahren Lehre in der Mensch-Computer-Interaktion.Auf der Webseite mci-buch.info sind Lösungen zu den Aufgaben, weitere Beispiele und Übungsaufgaben, inhaltliche Ergänzungen, und weiterführende Links zu finden. Probleme bei der Rechnerbenutzung. Ergonomie in der Mensch-Computer-Interaktion. Die Benutzungsschnittstelle. Entwicklung gebrauchstauglicher Systeme. Physiologie der menschlichen Informationsverarbeitung. Psychologie der menschlichen Informationsverarbeitung. Handlungsprozesse. Standardausgabegeräte. Standardeingabegeräte. Ein- und Ausgabe räumlicher Daten. Nicht visuelle Interaktionsmodalitäten. Gestaltungsprinzipien für Benutzungsschnittstellen. Informationsdarstellung. Visuelle Interaktionselemente. Dialoggestaltung. Benutzerunterstützung. Berücksichtigung spezifischer Benutzerbedürfnisse. DR. ANDREAS M. HEINECKE war bis Ende Februar 2022 hauptamtlich als Professor für interaktive Systeme am Fachbereich Informatik und Kommunikation der Westfälischen Hochschule in Gelsenkirchen tätig. In seinen Lehrveranstaltungen, in der Praxis als Berater und Entwickler sowie in zahlreichen wissenschaftlichen Veröffentlichungen befasst er sich mit Mensch-Computer-Interaktion insbesondere bei multimedialen und mobilen Anwendungen.DR. JENS GERKEN ist seit Juli 2023 Professor für das Fachgebiet Inklusive Mensch-Roboter-Interaktion an der TU Dortmund und beschäftigt sich dort unter anderem mit Fragen der Zugänglichkeit und Barrierefreiheit interaktiver Technologien. Zwischen 2015 und 2023 war er zuvor als Professor für Mensch-Computer-Interaktion am Fachbereich Informatik und Kommunikation der Westfälischen Hochschule in Gelsenkirchen tätig.
Regenerative Zukünfte und künstliche Intelligenz
Der Band basiert auf den 17 Entwicklungszielen (SDGs) der UNO und entwickelt für die Wirtschaft Zukunftsperspektiven zum Zusammenhang von KI und Nachhaltigkeit. Menschen als Subjekt und Objekt von Nachhaltigkeit (Problembeschreibung).- Innovative Lösungen der sozialen Nachhaltigkeitsdimension (Lösungsansätze).- Konzepte zur Erreichung sozialer Entwicklungsziele.- Konkrete Utopien regenerativer Zukünfte 2050 (Schwerpunkt PEOPLE).
Programmieren mit JavaScript
JavaScript hat sich mittlerweile von einer, am Anfang teils nicht ganz ernst genommenen, Skriptsprache im Browser zu einer der wichtigsten Programmiersprachen überhaupt entwickelt. Im Kern immer noch als die Sprache im Webbrowser schlechthin populär, erobert JavaScript nach und nach immer mehr Anwendungsgebiete. Bei der clientseitigen Webentwicklung gibt es im Grunde keine Alternative zu JavaScript für die Entwicklung interaktiver und dynamischer Webanwendungen. Nahezu alle Frameworks basieren zudem auf JavaScript. Aber auch zur Unterstützung vieler weiterer Arten von Software kann man JavaScript einsetzen bzw. gibt es auf JavaScript aufsetzende Frameworks.Insbesondere boomt aber die serverseitige Entwicklung mit JavaScript. Beispielsweise mit Node.js können Entwickler JavaScript auf Serverseite ausführen, was die Entwicklung von skalierbaren und leistungsstarken Webanwendungen ermöglicht. Node.js wird zudem häufig für die Erstellung von APIs, Echtzeit-Anwendungen und Microservices verwendet.Entdecken Sie in diesem Buch nun die mittlerweile fast grenzenlosen Möglichkeiten von JavaScript. Von den Grundlagen bis hin zu fortgeschrittenen Techniken werden alle Aspekte der Sprache abgedeckt. Tauchen Sie ein in die Welt von JavaScript und erweitern Sie Ihr Wissen mit praxisnahen Beispielen und bewährten Methoden. Egal, ob Sie mit JavaScript beginnen und die Grundlagen erlernen möchten, oder als erfahrene(r) Entwicklerin bzw. Entwickler das Können vertiefen wollen. Hier finden Sie das Wissen und die Werkzeuge, um Ihre JavaScript-Fähigkeiten auf das nächste Level zu bringen. Einleitung. Erste Beispiele – Der Sprung ins kalte Wasser. Versionen von JavaScript und Einbindung in Webseiten. Elementare JavaScript-Grundstrukturen. Kontrollflussanweisungen. Arrays, JSON und andere iterierbare Elemente. Funktionen, Prozeduren und Methoden. Module. Objekte und JavaScript. Eingebaute Objektdeklarationen. Der DOM – das Mysterium der JavaScript-Welt. Ausnahmebehandlung. Asynchrone Programmierung. Erweiterte Techniken. Eine Frage der Qualität. RALPH STEYER wurde in Wiesbaden geboren und hat nach dem Abitur in Frankfurt Mathematik studiert. Seit 1996 arbeitet er selbstständig als Diplom-Mathematiker im Bereich EDV-Schulung, Programmierung und Beratung. Seine Spezialgebiete sind Internetprogrammierung und objektorientierte Software-Entwicklung. Unter anderem unterrichtet er an Hochschulen sowie für verschiedene Schulungsanbieter. Ralph Steyer ist zudem Autor zahlreicher Fachbücher und Videotrainings zu verschiedenen Programmierthemen.
AI Integration in Software Development and Operations
Discover how Artificial Intelligence (AI) is transforming the fields of software development, testing, and IT operations by enhancing efficiency, reducing human error, and accelerating processes. This book showcases the practical applications of AI-driven tools, such as automating coding, testing, and operational tasks, predicting potential issues, and optimizing performance.Aimed at digital leaders, practitioners, and customers, this book provides strategic insights and actionable guidance on how to integrate AI technologies to boost productivity, enhance product quality, and streamline development cycles. It serves as a comprehensive guide for those looking to leverage AI to drive innovation, cut costs, and stay competitive in an ever-evolving technological landscape.You’ll explore how AI can be integrated into software development, testing, and IT operations to improve efficiency, accuracy, and speed. Through real-world use cases, you’ll see how AI-driven tools can automate tasks, reduce human error, and improve processes across the development lifecycle. _AI Integration in Software Development and Operations _offers actionable insights on using AI to accelerate innovation, enhance product quality, and optimize costs in your modern software and IT environments.WHAT YOU WILL LEARN* Review the SDLC lifecycle, DevOps, SRE and accompanying topics* Understand machine learning basics, AI techniques, and data preprocessing for DevOps* Explore how AI integration into all phases of SDLC boosts productivity, increases* effectiveness, and reduces human error* Gain a familiarity with AI tools, their use cases, and the value in integrating themWHO THIS BOOK IS FORSoftware engineers, developers, programmers, DevOps engineers, and AI practitioners who are interested in integrating AI into their DevOps practices.ABHINAV KRISHNA KAISER is a highly accomplished professional working as a partner at a prestigious consulting firm, where he plays a pivotal role in leading digital transformation programs for clients across diverse sectors. He is a part of the Distinguished Member of Technical Staff (DMTS) cadre, which represents a select group of best-in-class technologists. With a proven track record in the industry, Abhinav is recognized for his expertise in guiding organizations through complex and innovative changes to stay ahead of the curve in today's dynamic business environment. He takes charge of spearheading various digital transformation initiatives, demonstrating a keen understanding of the unique challenges and opportunities presented by different industries. His portfolio includes successfully steering multiple digital transformation programs, showcasing his ability to navigate and drive change in organizations of varying sizes and complexities. His hands-on experience in implementing cutting-edge technologies and methodologies has contributed to the enhanced efficiency and competitiveness of his clients.In addition, Abhinav Krishna Kaiser is a multifaceted professional with a prolific career as an accomplished writer. He boasts an impressive literary portfolio comprising six published books, each delving into the intricacies of digital transformation, DevOps, GCP and ITIL. Abhinav's written works serve as authoritative guides, offering valuable insights and practical solutions to professionals navigating the complexities of modern business and technology landscapes. Beyond his contributions in the written domain, Abhinav is a panel speaker, captivating audiences with his expertise at industry conferences and events. His commitment to knowledge-sharing ex-tends to digital platforms, where he actively engages as a YouTuber and blogger. Through these mediums, he imparts knowledge, shares best practices, and explores emerging trends, reaching a wider audience eager to enhance their understanding of digital transformation, DevOps, GCP, and ITIL.VAMSHI MEDA is a seasoned partner at a prominent consulting firm, specializing in guiding organizations through digital transformation initiatives. With a wealth of experience in the field, he serves as a trusted advisor to numerous enterprises, helping them identify opportunities for digital innovation and navigate the complexities of implementation. Vamshi has demonstrated track record of leading complex initiatives in Architecture, Cloud, GenAI, SRE, AIOps, Agile, and DevOps. He has completed CTO certification from Wharton and a bachelor’s degree in computer science, blending technical prowess with business acumen.He is Recognized for spearheading digital transformation initiatives across diverse industries, including Insurance, Capital Markets, Retail, and Healthcare. Skilled in strategic planning, solution implementation, and fostering collaboration to achieve organizational objectives. A decisive leader with a knack for problem-solving and a commitment to excellence. Vamshi is highly regarded as a keynote speaker at industry-leading conferences, where he shares his expertise and insights on driving successful digital transformations.Chapter 1: First Steps in AI and DevOps.- Chapter 2: Double Click on Machine Learning.- Chapter 3: Software Development and AI Augmentation.- Chapter 4: Planning and Requirements Management in Projects.- Chapter 5: Integrating Generative AI in Software Design and Architecture.- Chapter 6: AI Infusion in Software Build and Development.- Chapter 7: Infusing AI into Software Testing.- Chapter 8: AI in Continuous Delivery.- Chapter 9: Operations, Observability and Site Reliability Engineering.
Digital Twins and Cybersecurity
THIS BOOK SERVES AS A COMPREHENSIVE GUIDE TO UNDERSTANDING THE COMPLEX RELATIONSHIP BETWEEN DIGITAL TWINS AND CYBERSECURITY, PROVIDING PRACTICAL STRATEGIES FOR SAFEGUARDING CONNECTED SYSTEMS.This book explores the convergence of digital twins and cybersecurity, offering insights, strategies, and best practices for safeguarding connected systems. It examines the definition, evolution, types, and applications of digital twins across industries like manufacturing, healthcare, and transportation. Highlighting growing digital threats, it underscores the need for robust cybersecurity measures to protect the integrity and confidentiality of digital twin ecosystems. The book analyzes key components and infrastructure of digital twins, including data flow, communication channels, vulnerabilities, and security considerations. It also addresses privacy challenges and explores relevant regulations and compliance requirements. Guiding readers through implementing security measures, it presents a comprehensive cybersecurity framework, covering data protection, encryption, and strategies for ensuring data integrity and confidentiality. It also explores incident response and recovery, secure communication protocols, and the roles of gateways and firewalls. Industry-specific challenges and mitigation strategies are examined through real-world case studies, offering valuable insights and lessons learned. Emerging trends in digital twin technology are thoroughly explored, including the impact of advancements such as AI and quantum computing and their associated cybersecurity challenges and solutions. AUDIENCEThis book is an essential resource for professionals in the fields of cybersecurity and industrial and infrastructure sectors, including manufacturing, healthcare, transportation, and other industries that utilize digital twins. Researchers in computer science, cybersecurity, engineering, and technology, as well as policymakers and regulatory bodies, will also find this book highly useful. PALANICHAMY NAVEEN, PHD, is an assistant professor in the Department of Electrical and Electronics Engineering at KPR Institute of Engineering and Technology, Coimbatore, India, and a visiting researcher at the University of Hradec Králové, Czech Republic. He has authored two books and more than 25 research articles in peer-reviewed journals. His research interests include image processing and machine learning. R. MAHESWAR, PHD, is the director in-charge of the Center for Research and Development, head of the Center for IoT and AI (CITI), and a professor in the Department of Electronics and Communication Engineering at KPR Institute of Engineering and Technology, Coimbatore, India. He has published more than 70 papers in international journals and conferences, authored several books, and is affiliated with many journals in wireless communications. His research interests include wireless sensor networks, IoT, queuing theory, and performance evaluation. U.S. RAGUPATHY, PHD, is a professor in the Department of Electronics and Communication Engineering at KPR Institute of Engineering and Technology, Coimbatore, India. He has published more than 100 papers in international journals and conferences and has organized over 30 national level seminars and conferences. His research areas include image processing, VLSI signal processing, wavelets, and soft computing techniques. He has received multiple awards, including the Best Faculty Award and the Best Researcher Award from Kongu Engineering College.
Learning Algorithms for Internet of Things
The advent of Internet of Things (IoT) has paved the way for sensing the environment and smartly responding. This can be further improved by enabling intelligence to the system with the support of machine learning and deep learning techniques. This book describes learning algorithms that can be applied to IoT-based, real-time applications and improve the utilization of data collected and the overall performance of the system.Many societal challenges and problems can be resolved using a better amalgamation of IoT and learning algorithms. “Smartness” is the buzzword that is realized only with the help of learning algorithms. In addition, it supports researchers with code snippets that focus on the implementation and performance of learning algorithms on IoT based applications such as healthcare, agriculture, transportation, etc. These snippets include Python packages such as Scipy, Scikit-learn, Theano, TensorFlow, Keras, PyTorch, and more._Learning Algorithms for Internet of Things _provides you with an easier way to understand the purpose and application of learning algorithms on IoT.WHAT YOU’LL LEARN* Supervised algorithms such as Regression and Classification.* Unsupervised algorithms, like K-means clustering, KNN, hierarchical clustering, principal component analysis, and more.* Artificial neural networks for IoT (architecture, feedback, feed-forward, unsupervised).* Convolutional neural networks for IoT (general, LeNet, AlexNet, VGGNet, GoogLeNet, etc.).* Optimization methods, such as gradient descent, stochastic gradient descent, Adagrad, AdaDelta, and IoT optimization.WHO THIS BOOK IS FORStudents interested in learning algorithms and their implementations, as well as researchers in IoT looking to extend their work with learning algorithmsDR. G. R. KANAGACHIDAMBARESAN is a Professor in the Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology. His main research interests includes IoT, Expert systems and Sensors. He has published several reputed articles and undertaken several consultancy activities for leading MNC companies. He has also guest edited several special issue volumes and books at Springer and serves on the editorial review board for peer reviewed journals.He is currently working on several government-sponsored research projects like ISRO, DBT and DST. He is a TEC committee member in DBT. He is an ASEM-DUO Fellow and has successfully edited several books in EAI Springer. He is currently the Editor-in-Chief for the Next Generation Computer and Communication Engineering Series (Wiley). He received his B.E degree in Electrical and Electronics Engineering from Anna University in 2010 and M.E. Pervasive Computing Technologies in Anna University in 2012. He has completed his Ph.D. in Anna University Chennai in 2017.DR. N. BHARATHI is an Associate Professor in the Computer Science Engineering Department at SRM Institute of Science and Technology in Chennai, India. Her past experiences are Associate Professor at Saveetha School of Engineering, R&D head at Yalamanchili Manufacturing Private Limited, and Assistant professor in SASTRA deemed university. She has good knowledge to work with IoT and embedded system in addition to computer science engineering concepts.She was awarded with a Ph.D. degree in Computer Science in 2014 from SASTRA Deemed University, with 19+ years of work experience as an academic and industrial experience as R&D head involved in ARM platform boards with software development in Ubuntu OS. She completed her M. Tech in Advanced computing in SASTRA deemed University and done her M.Tech project internship at Center for High Performance Embedded System (CHiPES), Nanyang Technological University (NTU), Singapore. She was completed her B.E. in computer science engineering in 2002 at Shanmugha College of Engineering (Bharathidasan University). She published many research papers in reputed journals and conferences along with book chapters, advised many B.Tech. and M.Tech. students in various domains of computer science engineering and embedded systems and is currently advising four research scholars.Chapter 1: Learning Algorithms for IoT.- Chapter 2: Python Packages for Learning Algorithms.- Chapter 3: Supervised Algorithms.- Chapter 4: Unsupervised Algorithms.- Chapter 5: Reinforcement Algorithms.- Chapter 6: Artificial Neural Networks for IoT.- Chapter 7: Convolutional Neural Networks for IoT.- Chapter 8: LSTM, GAN, and RNN.- Chapter 9: Optimization Methods.
Snowflake Recipes
Explore Snowflake’s core concepts and unique features that differentiates it from industry competitors, such as, Azure Synapse and Google BigQuery. This book provides recipes for architecting and developing modern data pipelines on the Snowflake data platform by employing progressive techniques, agile practices, and repeatable strategies.You’ll walk through step-by-step instructions on ready-to-use recipes covering a wide range of the latest development topics. Then build scalable development pipelines and solve specific scenarios common to all modern data platforms, such as, data masking, object tagging, data monetization, and security best practices. Throughout the book you’ll work with code samples for Amazon Web Services, Microsoft Azure, and Google Cloud Platform. There’s also a chapter devoted to solving machine learning problems with Snowflake.Authors Dillon Dayton and John Eipe are both Snowflake SnowPro Core certified, specializing in data and digital services, and understand the challenges of finding the right solution to complex problems. The recipes in this book are based on real world use cases and examples designed to help you provide quality, performant, and secured data to solve business initiatives.WHAT YOU’LL LEARN* Handle structured and un- structured data in Snowflake.* Apply best practices and different options for data transformation.* Understand data application development. * Implement data sharing, data governance and security.WHO THIS BOOK IS FORData engineers, scientists and analysts moving into Snowflake, looking to build data apps. This book expects basic knowledge in Cloud (AWS or Azure or GCP), SQL and PythonDillon Dayton is a senior consultant for CDW. Over the last 10 years he has performed in a data engineering and architect role with numerous companies across a slew of industries including healthcare, retail, and finance. Dillon is Snowflake SnowPro Core certified and participates in the Snowflake SME program. In his spare time, he enjoys applying his data and engineering background to hobbies like motorsports, gardening, and fishing.John Eipe is a Senior Solutions Specialist for CDW and has over 10 years of experience in various roles from enterprise application development to data engineering. He worked primarily with customers from the ecommerce and insurance domain. John is Snowflake SnowPro Core certified and has been working extensively on Snowflake in the recent years. Apart from work, he enjoys cooking and time with his kids.Chapter 1: Introduction to Snowflake.- Chapter 2: Bringing Your Data into Snowflake.- Chapter 3: Handling Atypical Data.- Chapter 4: Data Security and Privacy.- Chapter 5: Handling Near and Real Time Data.- Chapter 6: Programmable Data Pipelines.- Chapter 7: Data Reusability and Monetization.- Chapter 8: Data Recovery and Protection.- Chapter 9: Applications Integration.- Chapter 10: Machine Learning.
Building Scalable Deep Learning Pipelines on AWS
This book is your comprehensive guide to creating powerful, end-to-end deep learning workflows on Amazon Web Services (AWS). The book explores how to integrate essential big data tools and technologies—such as PySpark, PyTorch, TensorFlow, Airflow, EC2, and S3—to streamline the development, training, and deployment of deep learning models.Starting with the importance of scaling advanced machine learning models, this book leverages AWS's robust infrastructure and comprehensive suite of services. It guides you through the setup and configuration needed to maximize the potential of deep learning technologies. You will gain in-depth knowledge of building deep learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment.The book provides insights into setting up an AWS environment, configuring necessary tools, and using PySpark for distributed data processing. You will also delve into hands-on tutorials for PyTorch and TensorFlow, mastering their roles in building and training neural networks. Additionally, you will learn how Apache Airflow can orchestrate complex workflows and how Amazon S3 and EC2 enhance model deployment at scale.By the end of this book, you will be equipped to tackle real-world challenges and seize opportunities in the rapidly evolving field of deep learning with AWS. You will gain the insights and skills needed to drive innovation and maintain a competitive edge in today’s data-driven landscape.WHAT YOU WILL LEARN* Maximize AWS services for scalable and high-performance deep learning architectures* Harness the capacity of PyTorch and TensorFlow for advanced neural network development* Utilize PySpark for efficient distributed data processing on AWS* Orchestrate complex workflows with Apache Airflow for seamless data processing, model training, and deploymentWHO THIS BOOK IS FORData scientists looking to expand their skill set to include deep learning on AWS, machine learning engineers tasked with designing and deploying machine learning systems who want to incorporate deep learning capabilities into their applications, AI practitioners working across various industries who seek to leverage deep learning for solving complex problems and gaining a competitive advantageABDELAZIZ TESTAS, PHD, is a seasoned data scientist with over a decade of experience in data analysis and machine learning. He earned his PhD in Economics from the University of Leeds in England and holds a master’s degree in the same field from the University of Glasgow in Scotland. Additionally, he has earned several certifications in computer science and data science in the United States.For over 10 years, Abdelaziz served as a Lead Data Scientist at Nielsen, where he played a pivotal role in enhancing the company’s audience measurement capabilities. He was instrumental in planning, initiating, and executing end-to-end data science projects and developing methodologies that advanced Nielsen’s digital ad and content rating products. His expertise in media measurement and data science drove the creation of innovative solutions.Recently, Abdelaziz transitioned to the public sector, joining the State of California's Department of Health Care Access and Information (HCAI). In his new role, he leverages his coding and data science leadership skills to make a meaningful impact, supporting HCAI’s mission to ensure quality, equitable, and affordable health care for all Californians.Abdelaziz is also the author of Distributed Machine Learning with PySpark: Migrating Effortlessly from Pandas and Scikit-Learn (Apress).Chapter 1: Overview of Scalable Deep Learning Pipelines on AWS.- Chapter 2: Setting Up a Deep Learning Environment on AWS.- Chapter 3: Data Preparation with PySpark for Deep Learning.- Chapter 4: Deep Learning with PyTorch for Regression.- Chapter 5: Deep Learning with TensorFlow for Regression.- Chapter 6: Deep Learning with PyTorch for Classification.- Chapter 7: Deep Learning with TensorFlow for Classification.- Chapter 8: Scalable Deep Learning Pipelines with Apache Airflow.- Chapter 9: Techniques for Improving Model Performance.- Chapter 10: Deploying and Monitoring Deep Learning Models.
Mastering Enterprise Solution Modeling
Embark on a journey through the Agile-Enterprise Solution Architecture (A-ESA) framework with this in-depth guide designed to provide a structured approach to IT solution modeling. The modeling approach is based on the principles of simplicity, significance, and systematics. It effectively addresses architectural debt issues in today's agile and large-scale IT solutions. Beginning with a foundational overview of A-ESA model specifications, the book introduces readers to the intent and unique methodologies behind A-ESA. It then delves into practical demonstrations through example solution cases, offering real-world context and insights into various architectural styles. Each chapter builds on this knowledge, covering the governing ideas of the thinking framework and essential topics such as key metrics, and modeling considerations for diverse architectural styles, ensuring a thorough understanding of A-ESA's application in different contexts. The book also emphasizes the link between enterprise architecture (EA) and solution architecture (SA), and the importance of governance and measurement in maintaining the integrity and effectiveness of architectural solutions. Readers will explore critical metrics, governance techniques, and the impact of agile modeling on purpose and architectural leading practices. With practical examples, measurement techniques, and governance strategies, this guide equips readers with the cognitive and practical tools necessary for strategic and effective architectural thinking. Concluding with reflections and future outlooks, this comprehensive guide offers valuable insights for mastering IT solution modeling within the A-ESA framework. You Will: Gain hands-on experience with the Agile-Enterprise Solution Architecture (A-ESA) framework through detailed examples and solution cases that illustrate various architectural styles and modeling techniques.Understand the critical metrics and model mappings necessary for evaluating architectural quality and performance.Develop a deep understanding of the A-ESA architectural thinking, including strategic, enterprise, business, data, and cloud architecture considerations This book is for : IT architects, enterprise architects, and solutions architects. PART I: A-ESA Specification and Demo: Chapter 1: Brief Overview of A-ESA.- Chapter 2: A-ESA Modeling by Example.- PART II: A-ESA Governing Ideas: Chapter 3: A-ESA Thinking Framework.- PART III: A-ESA Essentials: Chapter 4: A-ESA Measurement.- Chapter 5: A-ESA Modeling Styles.- Chapter 6: A-ESA Governance Techniques.- Epilogue.- Appendix I: A-ESA Spec Addendum.- Appendix II: A-ESA Tools.- Appendix III: Modeling Language Comparison.- Appendix IV: ESA Architect’s Roles and Skills.- Appendix V: FAQ about A-ESA.
Pro WordPress
_Pro WordPress_ is your ultimate guide to unlocking the full potential of the world's leading content management system. From novice bloggers to seasoned developers, this comprehensive resource offers a step-by-step journey through every aspect of WordPress customization, security and performance optimization.With clear explanations and practical examples, you'll learn how to set up your WordPress environment, choose the right themes and plugins, and customize your site with advanced techniques such as custom post types, widgets, shortcodes, and more. Dive deep into the world of WordPress security and discover how to safeguard your website against cyber threats with strategies like two-factor authentication, secure file permissions, and regular security audits.But that's not all – this book also equips you with the tools and knowledge to optimize your site for lightning-fast performance and high search engine rankings. Learn how to leverage caching mechanisms, minimize HTTP requests, and implement SEO strategies to boost your site's speed and visibility.Whether you're managing a single WordPress site or overseeing a multisite network, you'll find invaluable insights and best practices for scalability and high availability. Real-world case studies provide inspiration and guidance, showcasing successful WordPress implementations and effective strategies for growth.Whether you're a business owner, freelancer, or aspiring web developer, _Pro WordPress_ empowers you to take control of your online presence and build websites that stand out in today's competitive digital landscape. Unlock the full potential of WordPress and elevate your web development skills with this essential resource.YOU WILL LEARN:* The WordPress ecosystem in its entirety, including its history, core features, and community dynamics.* Develop expertise in customizing WordPress themes and plugins using CSS, PHP, and advanced techniques like custom post types and widgets* Implement robust security measures to protect your WordPress site from common vulnerabilities, such as brute force attacks and malicious code injections* Optimize your website's performance through caching mechanisms, image optimization, and other techniques to enhance user experience and SEO rankings* More advanced topics such as managing multisite networks, scalability and high availability to effectively scale your WordPress projects and handle high traffic volumesWHO IS IT FOR:Web designers and developers to business owners looking to develop a webiste of their own as well as bloggers and hobbyists who are looking to design, launch and maintain a website whatever the project.SIVARAJ SELVARAJ work focuses on modern technologies and industry best practices. These topics include frontend development techniques using HTML5, CSS3, and JavaScript frameworks; implementing responsive web design and optimizing user experience across devices; building dynamic web applications with server-side languages such as PHP, WordPress, and Laravel; and Database management and integration using SQL and MySQL databases He loves to share his extensive knowledge and experience to empower readers to tackle complex challenges and create highly functional and visually appealing websites.1: Understanding the WordPress Ecosystem.- 2: Setting Up Your WordPress Environment.- 3: WordPress Customization Fundamentals.- 4: Advanced WordPress Customization Techniques.- 5: Ensuring WordPress Security.- 6: Optimizing WordPress Performance.- 7: WordPress SEO Strategies.- 8: Managing Multisite Networks.- 9: Scalability and High Availability.- 10: Case Studies and Real World Examples.- Conclusion: Becoming a WordPress Pro.- Appendix: Useful Tools and Resources.
Essential Data Analytics, Data Science, and AI
In today’s world, understanding data analytics, data science, and artificial intelligence is not just an advantage but a necessity. This book is your thorough guide to learning these innovative fields, designed to make the learning practical and engaging.The book starts by introducing data analytics, data science, and artificial intelligence. It illustrates real-world applications, and, it addresses the ethical considerations tied to AI. It also explores ways to gain data for practice and real-world scenarios, including the concept of synthetic data. Next, it uncovers Extract, Transform, Load (ETL) processes and explains how to implement them using Python. Further, it covers artificial intelligence and the pivotal role played by machine learning models. It explains feature engineering, the distinction between algorithms and models, and how to harness their power to make predictions. Moving forward, it discusses how to assess machine learning models after their creation, with insights into various evaluation techniques. It emphasizes the crucial aspects of model deployment, including the pros and cons of on-device versus cloud-based solutions. It concludes with real-world examples and encourages embracing AI while dispelling fears, and fostering an appreciation for the transformative potential of these technologies.Whether you’re a beginner or an experienced professional, this book offers valuable insights that will expand your horizons in the world of data and AI.What you will learn:* What are Synthetic data and Telemetry data* How to analyze data using programming languages like Python and Tableau.* What is feature engineering* What are the practical Implications of Artificial IntelligenceWho this book is for:Data analysts, scientists, and engineers seeking to enhance their skills, explore advanced concepts, and stay up-to-date with ethics. Business leaders and decision-makers across industries are interested in understanding the transformative potential and ethical implications of data analytics and AI in their organizations.Maxine Attobrah holds a bachelor’s degree in Electrical Engineering from the University of Massachusetts – Amherst. Maxine’s career began as an Electronic Flight Controls Engineer at a leading global security, defense, and aerospace contractor company, where she was responsible for developing and testing control system software to enhance helicopter piloting. Subsequently, Maxine pursued further education, earning master’s degrees in Electrical & Computer Engineering and Engineering & Technology Innovation Management from Carnegie Mellon University. Maxine started her career after graduating at a major global consulting firm as a Data Scientist and has since transitioned to the role of an AI/ML Engineer. Currently, she serves as a Lead AI/ML Engineer at this firm.This book was prepared by the author in her personal capacity. The views and opinions expressed in this book are those of the author and do not necessarily reflect the official policy, opinion, or position of their present or past employers.Chapter 1: Introduction.- Chapter 2: Obtaining Data.- Chapter 3: ETL Pipeline.- Chapter 4: Exploratory Data Analysis.- Chapter 5: Machine Learning Models.- Chapter 6: Evaluating Models.- Chapter 7: When To Use Machine Learning Models.- Chapter 8: Where Machine Learning Models Live.- Chapter 9: Telemetry.- Chapter 10: Adversaries and Abuse.- Chapter 11: Working With Models.
Natural Language Processing on Oracle Cloud Infrastructure
This book demonstrates how to use Oracle Cloud Infrastructure (OCI) and Hugging Face technologies to develop advanced NLP solutions. Through a practical case study, it addresses common NLP challenges and offers strategies for creating efficient, cost-effective transformer-based models. By the end of this book, you will have the skills and knowledge to create cutting-edge NLP solutions on OCI, customized to meet the needs of various industries and projects.The book takes you through the complete NLP solution life cycle—covering data preparation, model fine-tuning, deployment, and monitoring—while highlighting key topics such as cost-effectiveness and responsible AI for NLP implementations. Drawing from real-world experience and offering practical insights, it bridges the gap between theory and practice, equipping you to design and deploy scalable, cost-efficient NLP solutions.WHAT YOU WILL LEARN* Master key NLP concepts and the OCI ecosystem* Create high-quality datasets using Hugging Face and OCI Data Labeling Service* Fine-tune domain-specific pre-trained models from Hugging Face using OCI Data Science Notebook Sessions* Deploy and operationalize your models with OCI Data Science Model Deployments* Automate the NLP life cycle with OCI Data Science Pipelines* Implement cost-effective strategies throughout the entire NLP life cycle, from dataset preparation to model training and deploymentWHO THIS BOOK IS FORA diverse audience interested in implementing NLP solutions on Oracle Cloud Infrastructure: NLP practitioners, data scientists, and machine learning engineers who want to learn how to leverage Oracle AI and Hugging Face to implement an end-to-end NLP solution life cycle, from data preparation to model deployment; Oracle practitioners who want to expand their Oracle expertise by exploring OCI's advanced capabilities for building and scaling cutting-edge NLP solutions in enterprise environments; business decision makers who want to discover the strategic benefits of NLP solutions on OCI, including cost-effectiveness and responsible AI, while driving business valueHICHAM ASSOUDI is an accomplished IT professional and AI expert with over 30 years of experience, including more than 25 years specializing in Oracle technologies. He holds a PhD in Computer Science and is also an OCI Certified Architect. Hicham has held key roles such as Technology Manager at Oracle and IT Architect at IBM, offering highly specialized technical consulting to major corporations across Canada, the US, and Europe.Hicham’s journey into AI began over a decade ago during his doctoral studies, where he initially focused on intelligent agents and general machine learning. He later specialized in natural language processing (NLP) from the early days of transformer models, positioning him at the forefront of NLP innovation. As the founder of Typica.ai, an AI startup, Hicham applies cutting-edge research to practical NLP solutions, empowering organizations to leverage NLP for significant business impact.In addition to his industry contributions, Hicham maintains strong ties to academia as an External Research Associate at the AI Lab of UQAM University in Montreal, bridging the gap between academic research and real-world applications.Part 1: Foundations and Case Study Introduction.- Chapter 1: NLP Essentials.- Chapter 2: Oracle Cloud for NLP.- Chapter 3: Healthcare NLP Case Study.- Part2: Case Study Implementation.- Chapter 4: Tenancy Preparation.- Chapter 5: Dataset Preparation.- Chapter 6: Model Fine-tuning.- Part 3: Case Study Deployment and Wrap-Up.- Chapter 7: Model Deployment and Monitoring.- Chapter 8: MLOps and Conclusion.
Signals and Systems
INTRODUCTORY COURSE TEXTBOOK ON SIGNALS AND SYSTEMS WITH NUMEROUS EXAMPLES AND CODE SNIPPETS IMPLEMENTED IN PYTHONSupported by code examples, Signals and Systems: Theory and Practical Explorations with Python is a textbook resource for a complete introductory course in systems and signals, enabling readers to run Python programs for convolution, discrete time Fourier transforms and series, sampling, and interpolation for a wide range of functions. Readers are guided step-by-step through basic differential equations, basic linear algebra, and calculus to ensure full comprehension of the exercises. This book is supported by a companion website, hosting interactive material to draw functions, and run programs in Python; it is enriched with audiovisual material via linking to related videos. Links to resources that provide a deeper explanation about the important concepts in the book, such as the systems approach, complex numbers, harmony, the Euler equation, and Hilbert spaces, are also included. Written by two highly qualified academics, topics covered in Signals and Systems include:* Systems approach for modeling the natural and manmade systems and some application areas* Representation of complex and real signals by basic functions, such as, real and complex exponentials, unit step and unit impulse functions* Properties of signals, such as symmetry, harmony, energy, power, continuity and discreteness* Convolution and correlation operations for continuous time and discrete time signals and systems* Representation of systems by impulse response, frequency response, transfer function, block diagram, differential and difference equations* Properties of systems, such as linearity, time invariance , memory, invertibility, stability and causality* Continuous time and discrete time Fourier analysis in Hilbert space and their extension to Laplaca transform and z-transform* Filtering by Linear Time Invariant systems in time and frequency domains, covering low pass, high pass band pass and band reject filters.* Sampling theorems for continuous time and discrete time systems, covering A/D and D/A conversion, sampling and interpolation.Signals and Systems is an ideal textbook resource for a one semester introductory course on signals and systems for upper level undergraduate and graduate students in computer science, electrical engineering and data science. It is also a useful reference for professionals working in bioinformatics, robotics, remote sensing, and related fields. FATOS TUNAY YARMAN VURAL is a Professor in the Department of Computer Engineering at Middle East Technical University, Turkey. She is a Senior Member of the IEEE and received her PhD from Princeton University, USA, in 1981. EMRE AKBAS is an Associate Professor in the Department of Computer Engineering at Middle East Technical University, Turkey. Dr. Akbas received his PhD from the University of Illinois at Urbana-Champaign, USA, in 2011.
AI Solutions for the United Nations Sustainable Development Goals (UN SDGs)
Learn the United Nations Sustainable Development Goals (UN SDGs) and see how machine learning can significantly contribute to their realization. This book imparts both theoretical knowledge and hands-on experience in comprehending and constructing machine learning-based applications for addressing multiple UN SDGs using JavaScript. The reading begins with a delineation of diverse UN SDG targets, providing an overview of previous successful applications of machine learning in solving realistic problems aligned with these targets. It thoroughly explains fundamental concepts of machine learning algorithms for prediction and classification, coupled with their implementation in JavaScript and HTML programming. Detailed case studies examine challenges related to renewable energy, agriculture, food production, health, environment, climate change, water quality, air quality, and telecommunications, corresponding to various UN SDGs. Each case study includes related works, datasets, machine learning algorithms, programming concepts, and comprehensive explanations of JavaScript and HTML codes used for web-based machine learning applications. The results obtained are meticulously analyzed and discussed, showcasing the pivotal role of machine learning in advancing the relevant SDGs. By the end of this book, you’ll have a firm understanding of SDG fundamentals and the practical application of machine learning to address diverse challenges associated with these goals. You will: * Understand the fundamental concepts of the UN SDGs, AI, and machine learning algorithms. * Employ the correct machine learning algorithms to address challenges on the United Nations Sustainable Development Goals (UN SDGs)? * Develop web-based machine learning applications for the UN SDGs using Javascript, and HTML. * Analyze the impact of a machine learning-based solution on a specific UN SDG. Learn the United Nations Sustainable Development Goals (UN SDGs) and see how machine learning can significantly contribute to their realization. This book imparts both theoretical knowledge and hands-on experience in comprehending and constructing machine learning-based applications for addressing multiple UN SDGs using JavaScript. The reading begins with a delineation of diverse UN SDG targets, providing an overview of previous successful applications of machine learning in solving realistic problems aligned with these targets. It thoroughly explains fundamental concepts of machine learning algorithms for prediction and classification, coupled with their implementation in JavaScript and HTML programming. Detailed case studies examine challenges related to renewable energy, agriculture, food production, health, environment, climate change, water quality, air quality, and telecommunications, corresponding to various UN SDGs. Each case study includes related works, datasets, machine learning algorithms, programming concepts, and comprehensive explanations of JavaScript and HTML codes used for web-based machine learning applications. The results obtained are meticulously analyzed and discussed, showcasing the pivotal role of machine learning in advancing the relevant SDGs. By the end of this book, you’ll have a firm understanding of SDG fundamentals and the practical application of machine learning to address diverse challenges associated with these goals. What You’ll Learn * Understand the fundamental concepts of the UN SDGs, AI, and machine learning algorithms. * Employ the correct machine learning algorithms to address challenges on the United Nations Sustainable Development Goals (UN SDGs)? * Develop web-based machine learning applications for the UN SDGs using Javascript, and HTML. * Analyze the impact of a machine learning-based solution on a specific UN SDG. Who This Book Is For Data scientists, machine learning engineers, software professionals, researchers, and graduate students. Dr. Tulsi Pawan Fowdur received his BEng (Hons) degree in Electronic and Communication Engineering with honors from the University of Mauritius in 2004. He was also the recipient of a Gold medal for having produced the best degree project at the Faculty of Engineering in 2004. In 2005 he obtained a full-time PhD scholarship from the Tertiary Education Commission of Mauritius and was awarded his PhD degree in Electrical and Electronic Engineering in 2010 by the University of Mauritius. He is also a Registered Chartered Engineer of the Engineering Council of the UK, Fellow of the Institute of Telecommunications Professionals of the UK, and a Senior Member of the IEEE. He joined the University of Mauritius as an academic in June 2009 and is presently an Associate Professor at the Department of Electrical and Electronic Engineering of the University of Mauritius. His research interests include Mobile and Wireless Communications, Multimedia Communications, Networking and Security, Telecommunications Applications Development, the Internet of Things, and AI. He has published several papers in these areas and is actively involved in research supervision, reviewing papers, and also organizing international conferences. Lavesh Babooram received his BEng (Hons) degree in Telecommunications Engineering with Networking with honors from the University of Mauritius in 2021. He was also awarded a Gold medal for having produced the best degree project at the Faculty of Engineering in 2021. Since 2022, he has been an MSc by Applied Research student at the University of Mauritius. With in-depth knowledge of telecommunications applications design, analytics, and network infrastructure, he aims to pursue research in Networking, Multimedia Communications, Internet of Things, Artificial Intelligence, and Mobile and Wireless Communications. He joined Mauritius Telecom in 2022 and is currently working in the Customer Experience and Service Department as a Pre-Registration Trainee Engineer. Chapter 1: Introduction to Machine Learning Applications Development and the UN SDGs.- Chapter 2: Utilizing Machine Learning Algorithms for Power generation prediction and classification in Wind Farms.- Chapter 3: Crop Recommendation System Using Machine Learning Algorithms for achieving SDGs 2, 9, and 12.- Chapter 4: Aligning Manufacturing Emissions with SDGs 9 and 13 Using Machine Learning Algorithms.- Chapter 5: Water Potability Testing Using Machine Learning.- Applying Machine Learning for Air Quality Monitoring Targeting SDG 3 and 13.- Chapter 7: Clustering the Development of Worldwide Internet Connectivity with Unsupervised Learning for SDGs 7, 9, and 11.
Architecting Enterprise AI Applications
This book explores how to define, design, and maintain enterprise AI applications, exploring the impacts they will have on the teams who work with them.The book is structured into four parts. In Part 1: Defining Your AI Application, you are introduced to the dynamic interplay between human adaptability and AI specialization, the concept of meta systems, and the mechanics of prediction machines. In Part 2: Designing Your AI Application, the book delves into the anatomy of an AI application, unraveling the intricate relationships among data, machine learning, and reasoners. This section introduces the building blocks and enterprise architectural framework for designing multi-agent systems. Part 3: Maintaining Your AI Application takes a closer look at the ongoing life cycle of AI systems. You are guided through the crucial aspects of testing and test automation, providing a solid foundation for effective development practices. This section covers the critical tasks of security and information curation that ensure the long-term success of enterprise AI applications. The concluding section, Part 4: AI Enabled Teams, navigates the evolving landscape of collaborative efforts between humans and AI. It explores the impact of AI on remote work dynamics and introduces the new roles of the expert persona and the AI handler. This section concludes with a deep dive into the legal and ethical dimensions that AI-enabled teams must navigate.This book is a comprehensive guide that not only equips developers, architects, and product owners with the technical know-how of AI application development, but also delves into the broader implications for teams and society.WHAT YOU WILL LEARN* Understand the algorithms and processes that enable AI to make accurate predictions and enhance decision making* Grasp the concept of metasystems and their role in the design phase of AI applications* Know how data, machine learning, and reasoners drive the functionality and decision-making capabilities of AI applications* Know the architectural components necessary for scalable and maintainable multi-agent AI applications* Understand methodologies for testing AI applications, ensuring their robustness, accuracy, and reliability in real-world applications* Understand the evolving dynamics of human-AI coordination facing teams in the new enterprise working environmentWHO THIS BOOK IS FORA diverse audience, primarily targeting enterprise architects, middle managers, tech leads, and team leads entrenched in the IT sector or possessing a tech-savvy background, including professionals such as digital marketers. Additionally, tech-savvy individual contributors—ranging from digital content creators and data analysts to administrators and programmers—stand to benefit significantly.ANTON CAGLE is a seasoned leader specializing in cloud automation and AI Ops, boasting over two decades of expertise in enterprise architecture and application design. With a passion for delivering democratized, data-driven solutions and automation, Anton focuses on empowering medium to large-sized companies. His dedication extends to mentoring and coaching engineers at all skill levels, fostering a culture of continuous learning and innovation.Recognizing the pivotal role of cloud, data, and AI in shaping the future of business software, Anton is on a mission to guide companies beyond basic automation solutions. His goal is to seamlessly integrate big data and machine learning into organizational frameworks, preparing businesses for the next wave of scalable operations. Anton's approach has led to remarkable transformations for clients, including the reduction of deployment process waste, accelerated feature time to market, and the implementation of cutting-edge cloud data architectures.AHMED CEIFELNASR AHMED is a highly skilled ML engineer, data scientist, and cloud engineer with over six years of experience in developing and deploying data-driven solutions. Ahmed specializes in building and fine-tuning machine learning models, leveraging advanced deep learning techniques, and optimizing cloud-based solutions. His expertise extends to cloud engineering and DevOps practices, where he excels in designing and implementing scalable, efficient cloud architectures and automating deployment processes.With hands-on experience in AWS Cloud environments and a strong background in cloud tools, Ahmed is adept at integrating AI with cloud technologies to create robust, production-ready solutions. He has a proven track record of driving impactful results across various industries, from retail and real estate to fitness and enterprise applications.Ahmed is committed to continuous learning and growth, always seeking to make a significant impact in the fields of AI, data science, and cloud engineering. His career reflects his dedication to advancing technology, optimizing cloud infrastructure, and fostering innovation through data-driven strategies and cutting-edge technology.Part 1: Defining Your AI Application.- Chapter 1: Human Flexibility and AI Specialization.- Chapter 2: Meta Systems.- Chapter 3: Prediction Machines.- Part 2: Designing Your AI Application.- Chapter 4: Anatomy of an AI Application.- Chapter 5: Data, Machine Learning, and Reasoners.- Chapter 6: Large Language Models (LLMs).- Chapter 7: AI Agents.- Part 3: Maintaining Your AI Application.- Chapter 8: Testing Your Enterprise AI Application.- Chapter 9: Testing automation for enterprise ai applications.- Chapter 10: Security.- Chapter 11: Information Curation.- Part 4: AI Enabled Teams.- Chapter 12: Remote Work and Reskilling.- Chapter 13: Expert Personas.- Chapter 14: The Role of the AI Handler.- Chapter 15: Legal and Ethical Considerations.
Digital Twins and Cybersecurity
Digital Twins and Cybersecurity delves into the intricacies of digital twins, covering emerging trends in digital twin technology, and understands the complex relationship between digital twins and cybersecurity. This book highlights the growing threats in the digital landscape and cybersecurity measures to ensure the integrity and confidentiality of digital twin ecosystems. With a focus on the significance of cybersecurity, the book outlines the potential consequences of cyber-attacks on digital twins and emphasizes the importance of robust authentication, access control, and proactive threat detection.The chapters encompass a wide range of topics based on real-world case studies offering valuable insights into digital twin technology, the various types of digital twins, and solutions for future developments. This book also delves into the challenges and mitigation strategies for digital twins in industries such as manufacturing, healthcare, and transportation. In addition to the topics covered in this book, Digital Twins and Cybersecurity aims to bridge the gap between theoretical knowledge and practical implementation. Lastly, this book is accessible to a wide range of readers, including professionals in industries to provide future challenges and developments in this dynamic space.P. NAVEEN, PH.D., is an assistant professor in the Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore, India. He completed his Ph.D. in the field of wireless sensor networks. His research interests include image processing and machine learning. His areas of academic interest include image processing, VLSI signal processing, wavelets, and soft computing techniques. He has authored 2 books titled ‘Understanding the Metaverse and its Technological Marvels: Beyond Reality and Received Signal Strength Based Target Localization and Tracking Using Wireless Sensor Networks.’R. MAHESWAR, PH.D., earned his Ph.D. in the field of wireless sensor networks from Anna University in 2012. He is a professor in the Department of Electronics and Communications Engineering, KPR Institute of Engineering and Technology, Coimbatore, India. His research interests included wireless sensor networks, IoT, queuing theory, and performance evaluation. He authored a book titled ‘Received Signal Strength Based Target Localization and Tracking Using Wireless Sensor Networks.’ He serves as an associate editor for the Alexandria Engineering Journal and a guest editor for the Wireless Network Journal.U.S. RAGUPATHY, PH.D., is a professor in the Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, India. He obtained his Ph.D. in medical image processing from Anna University in 2011. His areas of academic interest include image processing, VLSI signal processing, wavelets, and soft computing techniques. He has published 93 papers in international journals and conferences. He received the Best Faculty Award and the Best Researcher Award from Kongu Engineering College. He has organized more than 30 national-level seminars, workshops, and FDPs. Ragupathy is instrumental in promoting outcome-based education at Kongu Engineering College.M. AKILA, PH.D., is an adjunct professor in the Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, India. Her mission is to enhance the standards of education by providing an efficient and effective learning environment that complements the core values of KPRIET. She has contributed more than 35 research papers to peer-reviewed international journals. She has conducted numerous sponsored educational programs in engineering throughout her career.
Data & AI Imperative
UNLOCK PREDICTABLE BOTTOM LINE GROWTH THROUGH TAILORED DATA AND AI STRATEGIES.In The Data & AI Imperative: Designing Strategies for Exponential Growth, celebrated data-driven growth leader, Lillian Pierson, delivers a masterclass in developing custom strategies to harness the full potential of data and AI within your organization. This book offers a clear, actionable roadmap for leveraging your company's data and technology assets to drive significant, reliable growth. With over two decades of experience, Pierson unveils her proprietary STAR framework through which you'll learn to survey, take stock of, and assess your company's current state. Finally, you'll be guided on how to recommend strategies that drive growth via the execution of optimally positioned data- and AI- intensive projects or products that directly improve your business bottom line. From conception to execution, learn to:* Identify high-impact opportunities for data or AI interventions within your business.* Assess your organization's readiness and data literacy to ensure successful outcomes.* Implement practical, effective tactics for overseeing your data-intensive projects, from strategic plans to profitable realities.* Develop and deploy AI and data strategies that exceed your business goals.While ideal for executives, managers, and other leaders of data- or AI-intensive companies, The Data & AI Imperative is also invaluable to data and technical professionals who aspire to elevate their impact by turning technical expertise into strategic leadership success. LILLIAN PIERSON, P.E., is an industry-renowned data- and AI-driven growth strategist, advisor and fractional CMO for B2B technology companies. She’s also a licensed professional engineer who’s supported the expansion of 10% of the Fortune 100 whilst educating over 2 million learners on topics of data strategy, data science, AI, and growth marketing. Acknowledgments ixAbout the Author xiIntroduction xiiiPART I THE DATA & AI ADVANTAGE IN MODERN BUSINESS 1Chapter 1 Leveling the Playing Field with Data and AI 3Chapter 2 Introduction to Data Strategy 17Chapter 3 Types of Data-Intensive Use Cases Based on Business Objectives 32Chapter 4 Data- and AI-Driven Product-Led Growth 49Chapter 5 Amplifying Growth Marketing Outcomes with Data and AI 66Chapter 6 Validating Product-Market Fit for Commercial Data and AI Products and Services 83PART II THE DATA & AI TRIFECTA: ETHICAL CONSIDERATIONS, DEPLOYMENT TACTICS, AND COMPETITIVE ANALYSIS 105Chapter 7 Complying with Regulatory and Ethical Standards 107Chapter 8 Practical Tactics for Successful AI Deployments 121PART III THE TECHNICAL FOUNDATION FOR GROWTH 139Chapter 9 Surveying Your Industry and Organization 141Chapter 10 Perform a Technical Assessment 160Chapter 11 Stakeholder Engagement and Data Literacy 176Chapter 12 Assessing Your Current State Organization 193Chapter 13 Assessing Your Current State AI Ethics and Data Privacy 215PART IV FORMULATING AND IMPLEMENTING AN AI STRATEGY 235Chapter 14 Selecting and Scoping a Winning Use Case 237Chapter 15 Evaluating All Relevant Resources 249Chapter 16 Data Strategy Recommendations for Reaching Future State Goals 277Chapter 17 Finalizing Your Strategic Plan 299Index 320
Artificial Intelligence for Future Networks
AN EXPLORATION OF CONNECTED INTELLIGENT EDGE, ARTIFICIAL INTELLIGENCE, AND MACHINE LEARNING FOR B5G/6G ARCHITECTUREArtificial Intelligence for Future Networks illuminates how artificial intelligence (AI) and machine learning (ML) influence the general architecture and improve the usability of future networks like B5G and 6G through increased system capacity, low latency, high reliability, greater spectrum efficiency, and support of massive internet of things (mIoT). The book reviews network design and management, offering an in-depth treatment of AI oriented future networks infrastructure. Providing up-to-date materials for AI empowered resource management and extensive discussion on energy-efficient communications, this book incorporates a thorough analysis of the recent advancement and potential applications of ML and AI in future networks. Each chapter is written by an expert at the forefront of AI and ML research, highlighting current design and engineering practices and emphasizing challenging issues related to future wireless applications. Some of the topics include:* Signal processing and detection, covering preprocess and level signals, transform signals and extract features, and training and deploying AI models and systems* Channel estimation and prediction, covering channel characteristics, modeling, and classic learning-aided and AI-aided estimation techniques* Resource allocation, covering resource allocation optimization and efficient power consumption for different computing paradigms such as Cloud, Edge, Fog, IoT, and MEC* Antenna design using AI, covering basics of antennas, EM simulator/optimization algorithms, and surrogate modelingIdentifying technical roadblocks and sharing cutting-edge research on developing methodologies, Artificial Intelligence for Future Networks is an essential reference on the subject for professionals and researchers involved in the field of wireless communications and networks, along with graduate and PhD students in electrical and computer engineering programs of study. MOHAMMAD A. MATIN is a Professor and Chairman in the Department of Electrical and Computer Engineering at North South University, Dhaka, Bangladesh. SOTIRIOS K. GOUDOS is a Professor in the Department of Physics at the Aristotle University of Thessaloniki, Greece and the Director of the ELEDIA@AUTH lab member of the ELEDIA Research Center Network. GEORGE K. KARAGIANNIDIS is a Professor in the Department of Electrical and Computer Engineering of Aristotle University of Thessaloniki, Greece, and the Head of the Wireless Communications and Information Processing (WCIP) Group. About the Editors xvList of Contributors xviiAcknowledgments xxi1 INTELLIGENT BEAM PREDICTION AND TRACKING 1Christos Masouros, Jianjun Zhang, and Yongming Huang1.1 Introduction 11.2 Challenge of Beam Prediction Modeling in Wireless Communications 51.3 Prior Identification – Perspective of Function Space 71.3.1 Perspective of Function Space 81.3.2 Useful Priors for Beam Process Modeling 91.3.2.1 High-speed Train Communication 91.3.2.2 Indoor Environment 91.3.2.3 City Street Environment 91.4 Methodology from Stochastic Process 121.5 Stochastic Continuity – Beam Index Difference 161.5.1 Beam Index Difference Technique 161.5.2 BPT Solution via Beam Index Difference 171.5.3 Theoretical Analysis for Beam Index Difference 211.6 Stochastic Smoothness – Hybrid Data-induced Kalman Filtering 251.6.1 Theoretical Foundation 261.6.2 Implicit Dynamics Learning via Multitask Learning 281.6.3 SDE Representation and Efficient Inference 311.7 Beam Width Optimization 331.7.1 Stochastic Continuity – Locality Principle of Beam Change and Data Transmission with Multiresolution Beam 331.7.2 Stochastic Smoothness – Low-frequency Sounding via BWO and Long-term Prediction 351.8 Numerical Results 361.8.1 Simulation Results for Stochastic Continuity 371.8.2 Simulation Results for Stochastic Smoothness 391.9 Conclusion 45References 462 SIGNAL DETECTION WITH MACHINE LEARNING 51Jayakrishnan Vijayamohanan, Arjun Gupta, Manel Martínez-Ramón, and Christos Christodoulou2.1 Introduction 512.2 Symbol Detection 522.2.1 The Viterbi Algorithm 522.2.2 Channel Equalization Through Machine Learning 542.2.3 Machine Learning Implementations of the Viterbi Algorithm 572.3 Modulation Detection 602.3.1 Signal Model 612.3.2 Feature Selection 622.3.3 Maximum Likelihood Estimation 642.3.4 Neural Modulation Detection 642.3.4.1 Convolutional Neural Network 652.3.4.2 CNN Modulation Detection 672.4 Source Detection 742.4.1 Array Signal Model 742.4.2 Conventional Source Detection 772.4.3 Neural Source Detection 792.4.3.1 CNN Detector 802.4.3.2 RadioNet 822.5 Conclusion 84References 853 AI-AIDED CHANNEL PREDICTION 93Oscar Stenhammar, Gábor Fodor, and Carlo FischioneAcronyms 933.1 Introduction 943.1.1 Channel Aging 943.1.2 Channel Estimation 963.1.3 Channel Prediction 963.2 Preliminaries 983.2.1 Multilayer Perceptron 983.2.2 Convolutional Neural Network 1003.2.3 Recurrent Neural Network 1013.2.3.1 Long Short-Term Memory 1013.2.3.2 Gated Recurrent Units 1033.2.4 Transformer 1033.3 Previous Work 1053.3.1 Previous Work in Channel Estimation 1053.3.2 Conventional Channel Prediction 1073.3.3 Previous Work in AI-Aided Channel Prediction 1093.4 Experimental Evaluations 1133.4.1 Simulation Setup 1133.4.2 Neural Network Setup 1153.4.3 Experimental Results 1183.5 Discussion 1213.6 Summary 123References 1244 SEMANTIC COMMUNICATIONS 131Qiyang Zhao, Hang Zou, Mehdi Bennis, and Merouane Debbah4.1 Introduction 1314.2 Semantic Information and Semantic-Native Communication 1344.2.1 Semantic Information Theory 1344.2.2 Semantic-Native Communication 1374.3 Interplay of AI and Semantic Communication 1404.3.1 AI for Semantic Communication 1404.3.2 Semantic-Native Collective Intelligence 1434.4 Conclusion 145References 1465 FEDERATED LEARNING FOR WIRELESS COMMUNICATIONS 151Ahmet M. Elbir and Wei Shi5.1 Introduction 1515.2 Channel Models 1555.2.1 mmWave Channel Model 1555.2.2 THz Channel Model 1575.2.2.1 Near-Field Array Model 1585.2.2.2 Near-Field Beam Squint 1605.3 Federated Learning for Channel Estimation 1625.3.1 Training Data Collection 1625.3.2 FL-Based Model Training 1635.3.3 FL for mmWave Channel Estimation in Massive MIMO 1655.3.4 FL for mmWave Channel Estimation in RIS-Assisted Massive Mimo 1695.3.5 FL for THz Channel Estimation 1725.4 FL For Hybrid Beamforming 1765.5 Conclusions 178Acknowledgment 179References 1796 FEDERATED LEARNING IN MESH NETWORKS 185Xu Wang, Yuanzhu Chen, and Octavia A. Dobre6.1 Introduction 1856.1.1 Federated Learning 1856.1.2 Mesh Networks 1866.1.3 The Convergence: Federated Learning on Mesh Networks 1876.2 Decentralized Federated Learning 1886.2.1 Traditional Federated Learning versus Decentralized Federated Learning 1896.2.2 Core Principles of Decentralized Federated Learning 1916.2.3 Advantages of Decentralization in Federated Learning 1916.2.4 Architecture Variants for Decentralized Federated Learning 1926.2.5 Challenges of Decentralization in Federated Learning 1926.3 Mesh Networks 1926.3.1 Why Mesh Networks 1936.3.2 Fundamental Concepts and Terminologies 1936.3.3 Topological Structures 1936.3.4 Advantages of Mesh Networks 1946.3.5 Challenges and Limitations 1956.3.6 Integration with Federated Learning 1956.4 The Intersection: Decentralized Federated Learning over Mesh Networks 1966.4.1 Natural Synergy Between Federated Learning and Mesh Networks 1966.4.2 Potential Benefits of the Convergence 1966.4.3 Enabling Technologies 1986.4.4 Challenges at the Intersection 1986.4.4.1 Communication Overhead 1986.4.4.2 Data Heterogeneity and Non-IID Data 1996.4.4.3 Model Aggregation in Decentralized Networks 1996.4.4.4 Network Latency and Asynchrony 1996.4.4.5 Security and Privacy Concerns 1996.4.4.6 Scalability Concerns 2006.4.4.7 Fault Tolerance and Robustness 2006.4.4.8 Resource Constraints 2006.5 Solutions 2006.5.1 Communication Overhead 2006.5.2 Data Heterogeneity and Non-IID Data 2016.5.3 Model Aggregation in Decentralized Networks 2016.5.4 Latency and Asynchrony 2026.5.5 Security and Privacy Concerns 2026.5.6 Scalability Concerns 2026.5.7 Fault Tolerance and Robustness 2036.5.8 Resource Constraints 2036.6 State-of-the-Art and Noteworthy Implementations 2046.6.1 Decentralized Federated Learning Techniques 2046.6.1.1 Network Topology 2046.6.1.2 Communication Protocols 2046.6.1.3 Privacy Enhancements 2056.6.2 Advances in Mesh Networking Technologies 2056.6.2.1 Low-Latency Protocols 2056.6.2.2 Scalable Architectures 2066.6.2.3 Security Enhancements 2066.6.3 Decentralized Federated Learning on Mesh Networks: Integrated Approaches 2066.6.4 Toolkits and Platforms 2076.6.5 Benchmarks and Evaluation 2086.7 Future Directions and Open Research Challenges 2096.7.1 Advanced Algorithms 2096.7.2 Enhanced Security Mechanisms 2096.7.3 Network Optimization 2106.7.4 Interoperability and Standardization 2106.7.5 Energy Efficiency and Sustainability 2116.7.6 User-Centric Approaches 2116.7.7 Real-time Decentralized Federated Learning 2126.7.8 Codesigning Hardware and Software 2126.7.9 Ethical and Regulatory Considerations 2136.7.10 Interdisciplinary Research 2136.8 Concluding Remarks 213References 2147 ANTENNA DESIGN USING ARTIFICIAL INTELLIGENCE 227Sotirios K. Goudos, Mohammad A. Matin, and George K. Karagiannidis7.1 Introduction 2277.2 Evolutionary Algorithms 2297.2.1 Mainstream Algorithms 2297.2.1.1 Genetic Algorithms 2297.2.1.2 Particle Swarm Optimization 2307.2.1.3 Differential Evolution 2317.2.1.4 Ant Colony Optimization 2327.2.2 Emerging Algorithms 2357.2.2.1 Biogeography-Based Optimization 2357.2.2.2 Grey Wolf Optimizer 2357.2.2.3 Wind-Driven Optimization 2357.2.2.4 Salp Swarm Algorithm 2357.2.2.5 Artificial Bee Colony (ABC) 2367.2.2.6 Harmony Search (HS) 2367.2.2.7 Shuffled Frog-Leaping Algorithm 2377.2.3 Antenna Optimization Using Evolutionary Algorithms 2377.2.3.1 Problem Formulation 2377.2.3.2 Numerical Results 2397.3 Machine Learning 2447.3.1 Artificial Neural Networks (ANNs) 2447.3.2 Support Vector Machines 2447.3.3 Gaussian Process (GP) 2457.3.4 Deep Learning (DL) 2457.3.5 ANFIS 2457.3.6 Surrogate Modeling 2467.3.6.1 Surrogate Modeling Example 2487.4 Knowledge Representation 2527.5 Conclusion 253References 2538 AI-DRIVEN APPROACHES FOR SOLVING ELECTROMAGNETIC INVERSE PROBLEMS 257Marco Salucci, Maokun Li, and Andrea Massa8.1 Introduction 2578.2 Mathematical Formulation 2588.3 AI-Based EM–IP Solution Strategies 2628.3.1 3-Step Learning-by-Examples (LBE) Framework 2638.3.2 System-by-Design (SbD) Framework 2678.3.3 Deep Learning (DL) Framework 2698.4 Applications 2718.4.1 Microwave Imaging of Free-Space and Buried Objects 2718.4.2 Biomedical Imaging 2728.4.3 Non-destructive Testing and Evaluation (NDT/NDE) 2748.4.4 Wireless Detection, Localization, and Tracking of Targets 2758.5 Conclusions 276Acknowledgments 276References 2779 RA-BASED RIS-1 DESIGN USING SUPPORT VECTOR MACHINES TO ENHANCE MMWAVE 5G COVERAGE 283Álvaro F.Vaquero, Eduardo Martinez-de-Rioja, Jesús A. López-Fernández, and Manuel Arrebola9.1 Introduction 2839.1.1 RA-Based Reflective Intelligent Surface 2859.1.2 Considerations of RA-Based RIS Design 2879.2 RIS-1 Unit-Cell Characterization Using SVR 2899.2.1 Passive Unit Cell for RIS-1 Design 2899.2.2 SVR-Based Models of RA Unit Cells 2919.2.2.1 SVM Theoretical Background 2939.2.2.2 Model Selection, Expected Accuracy, and Training 2979.2.2.3 Efficient Grid Search 2999.3 RIS-1: Analysis and Optimization 3029.3.1 Radiated Field by a RIS 3049.3.1.1 Electric Field on the RIS Aperture 3049.3.1.2 Radiated Field of an RIS 3079.3.2 Intersection Approach Framework 3119.3.3 Generalized Intersection Approach 3159.4 SVR-Based Design of RIS-1 to Enhance 5G mmWave NF Coverage 3179.4.1 Definition of Scenario and Single-Layer Unit Cell 3179.4.2 Unit-Cell Modeling Based on SVR 3209.4.2.1 Discussion on the Number of Training Patterns, Time Cost and Achieved Precision 3219.4.2.2 Reflection Coefficients 3239.4.3 RIS-1 Designed Based on Intersection Approach Framework 3259.4.4 RIS-1 Design Process 3299.5 Conclusions and Road Map 332References 33410 AI AT THE PHYSICAL LAYER FOR WIRELESS NETWORK SECURITY AND PRIVACY 341Aly S. Abdalla, Bo Tang, and Vuk Marojevic10.1 Introduction 34110.2 Network Security and Privacy Threats and Vulnerabilities 34210.2.1 Security Threats 34210.2.2 Identifying and Assessing Network Security and Privacy Threats 34310.2.3 Exploiting Vulnerabilities: Techniques and Attack Vectors 34410.3 Fundamentals of AI for Network Security and Privacy 34610.3.1 Supervised Learning 34710.3.2 Unsupervised Learning 34910.3.3 Reinforcement Learning 35010.3.4 Generative Adversarial Networks 35110.3.5 Federated Learning 35210.3.6 Ensemble Learning 35310.4 AI-Driven Physical Layer Security Solutions 35510.4.1 Intelligent Beamforming 35610.4.2 AI-Based Radio Frequency Fingerprinting Techniques 35710.4.3 AI-Assisted Power Control 35810.5 Case Study: UAV-Assisted PLS for Terrestrial Wireless Communications Networks 35910.6 Practical Considerations and Challenges of Implementing AI-Based Security Solutions 36610.6.1 Scalability and Performance Optimization of AI Models 36610.6.2 Privacy Considerations of AI-Enhanced Wireless Network Security 36710.7 Conclusions and Outlook 369References 370Index 381
Erstellung hochwertiger Shader für Unity®
Shader gelten oft als mystisch und schwer zu entwickeln, selbst für erfahrene Programmierer, Künstler und Entwickler aus anderen Bereichen des Spieldesigns. Dieses Buch räumt mit dieser Vorstellung auf, indem es Ihr Wissen über in Stufen aufbaut. Es beginnt mit grundlegenden Shader-Mathematiken, erklärt, wie sich die Denkweise der Shader-Entwicklung von anderen Arten von Kunst und Programmierung unterscheidet, und geht dann langsam auf Themen wie Vertex- und Fragment-Shader, Beleuchtung, Tiefenbasierte Effekte, Texture Mapping und Shader Graph ein.Jedes dieser Themen wird mit einer umfassenden Aufschlüsselung, der benötigten Theorie und einigen praktischen Anwendungen für die im jeweiligen Kapitel gelernten Techniken präsentiert. Der HLSL (High Level Shading Language)-Code und Shader Graphs werden für jeden relevanten Abschnitt bereitgestellt, ebenso wie zahlreiche Screenshots.Am Ende des Buches werden Sie ein gutes Verständnis für den Shader-Entwicklungsprozess haben und bestens gerüstet sein, um Ihre eigenen ästhetischen und leistungsstarken Shader-Effekte für Ihre Spiele zu erstellen!WAS SIE IN DIESEM BUCH LERNEN Shaders in den Rendering-Pipelines von Unity zu verwenden Shader zu schreiben und ihr Verhalten mit C#-Skripting zu ändern Shader Graph für eine Entwicklung ohne Code zu nutzen Die wichtige Mathematik hinter Shadern zu verstehen, insbesondere Raumtransformationen Die Leistung von Shadern zu analysieren, um Optimierungspotenziale zu identifizieren ZUM INHALT Kapitel 1: Einführung in Shader in Unity. Kapitel 2: Mathematik für die Shader-Entwicklung. Kapitel 3: Ihr allererster Shader. Kapitel 4: Shader-Grafik. Kapitel 5: Texturen, UV-Koordinaten & Normal Mapping. Kapitel 6: Transparenz. Kapitel 7: Der Tiefenpuffer. Kapitel 8: Transparenz und Alpha. Kapitel 9: Weitere Shader-Grundlagen. Kapitel 10: Beleuchtung und Schatten. Kapitel 11: Bildeffekte und Nachbearbeitung. Kapitel 12: Erweiterte Shader. Kapitel 13: Profiling und Optimierung. Kapitel 14: Shader-Rezepte für Ihre Spiele. ZIELGRUPPENDieses Buch richtet sich an Anfänger in der Shader-Entwicklung oder Leser, die den Schritt von Shader-Code zum Shader Graph machen möchten. Es enthält auch einen Abschnitt über Shader-Beispiele für diejenigen, die bereits die Grundlagen von Shadern kennen und nach spezifischen Anwendungsfällen suchen.DANIEL ILETT ist Doktorand an der University of Warwick. Als leidenschaftlicher Spieleentwickler hat er sich auf Shader und technische Kunst spezialisiert. Er ist Autor verschiedener Lehr- und Tutorial-Inhalte, darunter Videos und schriftliche Arbeiten, die sich an Anfänger und fortgeschrittene Entwickler richten. Darüber hinaus arbeitet er freiberuflich an Shadern und visuellen Effekten für Spiele.
Private Intelligence Services
Die Einführung des Buches legt den Grundstein für die nachfolgenden Kapitel, indem sie die Definitionen und Grundlagen der Wirtschaftskriminalität klärt und ein solides Verständnis für die Dimensionen und die Tragweite dieser Herausforderung schafft. Sie legt dar, wie Wirtschaftskriminalität nicht nur finanzielle Verluste verursacht, sondern auch das Vertrauen in Märkte und Institutionen untergräbt, was weitreichende soziale und wirtschaftliche Konsequenzen nach sich zieht.Im ersten Kapitel wird die Zusammenarbeit zwischen privaten Intelligence-Unternehmen und staatlichen Behörden beleuchtet. Es werden Fallstudien und Beispiele präsentiert, die demonstrieren, wie eine erfolgreiche Partnerschaft aussehen kann, und welche Herausforderungen und Spannungsfelder dabei auftreten können. Das Kapitel unterstreicht die Bedeutung von Vertrauen, Transparenz und gegenseitigem Respekt für den Erfolg dieser Kooperationen.Das zweite Kapitel widmet sich den spezifischen Herausforderungen und Anforderungen, die verschiedene Branchen und Sektoren an private Intelligence-Unternehmen stellen. Es wird detailliert aufgezeigt, wie unterschiedlich die Bedürfnisse von Unternehmen, je nach Größe, Branche und geografischer Lage sein können, und wie maßgeschneiderte Lösungen aussehen können. Es wird deutlich, dass eine tiefgehende Branchenkenntnis und Spezialisierung essentiell sind, um effektive Dienstleistungen zu bieten.Das dritte Kapitel ist in mehrere Unterkapitel gegliedert und deckt ein breites Spektrum an Themen ab, darunter die besonderen Anforderungen bestimmter Zielgruppen, Methoden und Techniken zur Sicherung von Vermögenswerten und Informationen, sowie praxisnahe Fallbeispiele und Best Practices. Hier wird auch aufgezeigt, wie Unternehmen wie Foreus als Dienstleister in der Praxis agieren und welche Rolle sie im Gesamtkontext spielen.Das vierte Kapitel setzt sich mit den rechtlichen und ethischen Aspekten von Private Intelligence auseinander. Es werden aktuelle Gesetzgebungen und Rechtsprechungen diskutiert, ethische Dilemmata beleuchtet und klare Empfehlungen und Richtlinien für verantwortungsvolles Handeln präsentiert.Das fünfte Kapitel blickt in die Zukunft und versucht eine Prognose über die nächsten 10 Jahre im Bereich Private Intelligence und Wirtschaftskriminalität abzugeben. Es werden technologische Entwicklungen, Ausbildungs- und Weiterbildungsmöglichkeiten sowie potentielle Herausforderungen und Chancen diskutiert.Das abschließende sechste Kapitel fasst die zentralen Erkenntnisse zusammen, bietet konkrete Handlungsempfehlungen für verschiedene Fachleute und endet mit abschließenden Gedanken und einem Ausblick in die Zukunft.STEFAN EMBACHER blickt auf eine 13-jährige intensive Laufbahn im Sicherheitssektor zurück. Seine Erfahrungen reichen von der Zusammenarbeit mit Rechtsanwaltskanzleien, Staatsanwaltschaften bis hin zu internationalen Behörden. Nach mehreren Jahren beim österreichischen Bundesheer trat er eine Tätigkeit bei einem des geheimnisvollsten Unternehmens der Welt in den USA an.Einleitung.- Private Intelligence für Rechtsanwälte und Steuerberater.- Private Intelligence für Wirtschaftsprüfer, Detektive und Lobbyisten.- Private Intelligence in Privatbanken und Family Offices.- Rechtliche und ethische Aspekte von Private Intelligence.- Zukunft von Private Intelligence im Kampf gegen Wirtschaftskriminalität.- Schlusswort und Handlungsempfehlungen.
Ökonomische Nachhaltigkeit - Interdisziplinäre Perspektiven
Dieses Buch bietet als Band 4 einer zunächst vier Bände umfassenden Reihe grundlegende Informationen zum Thema „Ökonomische Nachhaltigkeit“. Die weiteren Bände der Reihe sind Band 1 (Allgemeine Grundlagen), Band 2 (Ökologie) und Band 3 (Soziales). Band 4 erläutert nach einer Darstellung der Grundlagen der Wirtschaft folgende Themenbereiche: Wirtschaftssystem, Finanzierung, Infrastruktur, Produktion und Konsum, Handel sowie Arbeit. In jedem dieser Kapitel werden zunächst die wesentlichen Fakten dargestellt und darauf aufbauend die Herausforderungen und aktuellen Entwicklungen erörtert. Das Kapitel „Wirtschaftssystem“ behandelt unter anderem die Themen Postwachstumsökonomie und Gemeinwohl-Ökonomie, Unterschiede im Wohlstand in verschiedenen Ländern sowie die Grenzen des Wirtschaftswachstums. Im Kapitel „Finanzierung“ geht es beispielsweise um Besteuerung, Entwicklungshilfe und Auslandsverschuldung. Das Kapitel „Infrastruktur“ behandelt nach einer Erläuterung der Bedeutung wirtschaftlicher Infrastruktur exemplarisch die Themen Infrastrukturdefizite und nachhaltige Innovationen. Wandel des Konsumverhaltens, Ressourcenknappheit, Megatrends im Handel, globaler Handel, Zukunft der Arbeit sowie informeller Arbeitssektor sind ausgewählte Stichworte in den Kapiteln „Produktion und Konsum“, „Handel“ sowie „Arbeit“. Das Buch unterstützt Führungskräfte, Spezialisten in Unternehmen und Politik sowie Studierende dabei, die vielfältigen Herausforderungen der ökonomischen Nachhaltigkeit besser zu verstehen. Neben Ökonomen werden auch Informatiker, Juristen und andere Berufsgruppen angesprochen, die über ihre Fachperspektive hinaus ein umfassendes Verständnis von ökonomischer Nachhaltigkeit erwerben möchten. Prof. Dr. Michael Jacob lehrt und forscht im Fachbereich Betriebswirtschaft der Hochschule Kaiserslautern. Nach einer Bankausbildung und einem Studium der Betriebswirtschaftslehre war er unter anderem als Mitglied der Geschäftsführung bei PricewaterhouseCoopers Consulting S.à r.l. Luxembourg und als wissenschaftlicher Leiter am Institut für Betriebswirtschaft und Wirtschaftsinformatik tätig. Als Autor von bisher sieben Fachbüchern und vielen anderen Veröffentlichungen versucht er, die Theorie mit seinen umfangreichen Praxiserfahrungen zu verknüpfen und Themen fachübergreifend zu vernetzen.Grundlagen der ökonomischen Dimension.- Wirtschaftssystem.- Finanzierung.- Infrastruktur.- Produktion und Konsum.- Handel.- Arbeit.- Gesamtbetrachtung.
Azure-Sicherheitshandbuch
Wussten Sie, dass die häufigsten Cloud-Sicherheitsbedrohungen auf Fehlkonfigurationen von Cloud-Diensten und nicht auf Angriffe von außen zurückzuführen sind? Wenn Sie das nicht wussten, sind Sie nicht allein. In der lokalen Welt waren die Cybersicherheitsrisiken auf das Netzwerk des Unternehmens beschränkt, aber im Zeitalter des Cloud Computing sind sowohl die Auswirkungen als auch die Wahrscheinlichkeit potenzieller Risiken deutlich höher. Mit dem Aufkommen der DevOps-Methodik liegt die Verantwortung für die Sicherheit nun bei allen, die am Lebenszyklus der Anwendungsentwicklung beteiligt sind, und nicht nur bei den Sicherheitsspezialisten. Durch die Anwendung der klaren und pragmatischen Empfehlungen in diesem Buch können Sie die Sicherheitsrisiken von Cloud-Anwendungen in Ihrem Unternehmen reduzieren.Dies ist das Buch, das jeder Azure-Lösungsarchitekt, Entwickler und IT-Fachmann zur Hand haben sollte, wenn er mit dem Lernen über Azure-Sicherheit beginnt. Es entmystifiziert die Vielzahl von Sicherheitskontrollen und bietet zahlreiche Richtlinien für Azure, wodurch stundenlange Lernmüdigkeit und Verwirrung vermieden werden. Im Laufe des Buches lernen Sie, wie Sie Ihre Anwendungen mit den nativen Sicherheitskontrollen von Azure schützen können. Nach der Lektüre dieses Buches wissen Sie, welche Sicherheitsleitplanken zur Verfügung stehen, wie effektiv sie sind und wie hoch die Kosten für ihre Implementierung sein werden. Die Szenarien in diesem Buch sind real und stammen aus der Sicherung von Unternehmensanwendungen und Infrastrukturen, die auf Azure laufen.WAS SIE LERNEN WERDEN* Behebung der Sicherheitsrisiken von Azure-Anwendungen durch Implementierung der richtigen Sicherheitskontrollen zur richtigen Zeit* Erreichen Sie ein Sicherheitsniveau und bleiben Sie in Ihrer Azure-Umgebung sicher, indem Sie Leitplanken zur Automatisierung sicherer Konfigurationen festlegen.* Schutz der gängigsten Referenz-Workloads gemäß bewährter Sicherheitsverfahren* Entwerfen Sie sichere Zugangskontrolllösungen für Ihren administrativen Azure-Zugang sowie für den Zugang zu Azure-AnwendungenFÜR WEN DIESES BUCH BESTIMMT ISTCloud-Sicherheitsarchitekten, Cloud-Anwendungsentwickler und Cloud-Lösungsarchitekten, die mit Azure arbeiten. Es ist auch eine wertvolle Ressource für IT-Experten, die für die Sicherung von Azure-Workloads im Unternehmen verantwortlich sind.KARL OTS ist ein führender Cloud- und Cybersicherheitsexperte mit mehr als einem Jahrzehnt Erfahrung im Bereich Microsoft Azure-Sicherheit. Er hat mit großen Unternehmen in Bereichen wie Technologie, Fertigung und Finanzen zusammengearbeitet. Karl Ots ist ein anerkannter Microsoft Regional Director, ein patentierter Erfinder, ein LinkedIn Learning-Dozent und ein Microsoft Azure MVP. Er besitzt die Zertifizierungen Azure Security Engineer, SABSA Foundation SCF und CISSP. Karl ist ein häufiger Redner zu Cloud-Sicherheitsthemen auf globalen Konferenzen wie Microsoft Ignite oder (ISC)2 Security Congress. Er ist Gastgeber des Cloud Gossip-Podcasts.Kapitel 1. Einführung in die Cloud-Sicherheitsarchitektur - Kapitel 2. Identitäts- und Zugriffsmanagement . - Kapitel 3. Protokollierung und Überwachung - Kapitel 4. Netzwerksicherheit.- Kapitel 5. Workload-Schutz- Daten.- Kapitel 6. Workload-Schutz- Platform-as-a-Service.- Kapitel 7. Workload-Schutz - Container - Kapitel 8. Workload-Schutz - IaaS.