Computer und IT
ChatGPT - Mit KI in ein neues Zeitalter
Wie KI-Tools unser Leben und die Gesellschaft verändern.Künstliche Intelligenz hat bereits weitreichende Auswirkungen auf unser Leben und die Gesellschaft. Ulrich und Barbara Engelke diskutieren in diesem Buch die Chancen und Herausforderungen von ChatGPT und anderen KI-Werkzeugen und wie sie verschiedene Bereiche unseres Lebens beeinflussen. Dabei stehen insbesondere die Auswirkungen auf Schule und Studium, Arbeit und Gesellschaft sowie rechtliche Aspekte wie Datenschutz und Urheberschaft im Fokus.Mit dem Buch stellen die Autoren die grundlegenden Fragen zum Einsatz von künstlicher Intelligenz und ermöglichen eine kritische Auseinandersetzung mit aktuellen und künftig möglichen Auswirkungen.ULRICH ENGELKE, Jahrgang 1963, hat das erste Staatsexamen in Germanistik und Anglistik sowie einen Magister mit Schwerpunkt Linguistik. Nach einem kurzen Ausflug in das Verlagswesen und selbstständiger Tätigkeit als Fachautor, hat er eine Internetagentur gegründet. Heute ist er als Unternehmensberater für Onlinemarketing mit Schwerpunkt SEO tätig. Sein besonderes Interesse gilt technischen Innovationen und deren ökonomischen wie gesellschaftlichen Auswirkungen.BARBARA ENGELKE, Jahrgang 1965, hat ein Staatsexamen in Germanistik und Theologie und arbeitet als Lehrerin sowie Fachbetreuerin für Deutsch an einem bayerischen Gymnasium. Es ist ihr ein Anliegen, in ihrer pädagogischen Tätigkeit junge Menschen an neue Herausforderungen heranzuführen und sie für die Zukunft mit den nötigen Kompetenzen auszustatten. Ihre Leidenschaft ist das Theater. Sie leitet eine Theatergruppe und schreibt selbst Stücke.
Understanding Large Language Models
This book will teach you the underlying concepts of large language models (LLMs), as well as the technologies associated with them.The book starts with an introduction to the rise of conversational AIs such as ChatGPT, and how they are related to the broader spectrum of large language models. From there, you will learn about natural language processing (NLP), its core concepts, and how it has led to the rise of LLMs. Next, you will gain insight into transformers and how their characteristics, such as self-attention, enhance the capabilities of language modeling, along with the unique capabilities of LLMs. The book concludes with an exploration of the architectures of various LLMs and the opportunities presented by their ever-increasing capabilities—as well as the dangers of their misuse.After completing this book, you will have a thorough understanding of LLMs and will be ready to take your first steps in implementing them into your own projects.WHAT YOU WILL LEARN* Grasp the underlying concepts of LLMs* Gain insight into how the concepts and approaches of NLP have evolved over the years* Understand transformer models and attention mechanisms* Explore different types of LLMs and their applications* Understand the architectures of popular LLMs* Delve into misconceptions and concerns about LLMs, as well as how to best utilize themWHO THIS BOOK IS FORAnyone interested in learning the foundational concepts of NLP, LLMs, and recent advancements of deep learningTHIMIRA AMARATUNGA is a Senior Software Architect at Pearson PLC Sri Lanka with over 15 years of industry experience. He is also an inventor, author, and researcher in the areas of AI, machine learning, deep learning in education, and computer vision.Thimira holds a Master of Science degree in Computer Science and a bachelor’s degree in information technology from the University of Colombo, Sri Lanka. He has filed three patents in the fields of dynamic neural networks and semantics for online learning platforms. He has published three books on deep learning and computer vision.Chapter 1: Introduction.- Chapter 2: NLP Through the Ages.- Chapter 3: Transformers.- Chapter 4: What Makes LLMs Large?.- Chapter 5: Popular LLMs.- Chapter 6: Threats, Opportunities, and Misconceptions.
Fedora Linux System Administration
Fedora Linux is a free and open-source platform designed for hardware, clouds, and containers that enables software developers and community members to create custom solutions for their customers. This book is a comprehensive guide focusing on workstation configuration for the modern system administrator.The book begins by introducing you to the philosophy underlying the open-source movement, along with the unique attributes of the Fedora Project that set it apart from other Linux distributions. The chapters outline best practices and strategies for essential system administration tasks, including operating system installation, first-boot configuration, storage, and network setup. As you make progress, you’ll get to grips with the selection and usage of top applications and tools in the tech environment. The concluding chapters help you get a clear understanding of the basics of version control systems, enhanced Linux security, automation, virtualization, and containers, which are integral to modern system administration.By the end of this book, you’ll have gained the knowledge needed to optimize day-to-day tasks related to Linux-based system administration.
Python Deep Learning
The field of deep learning has developed rapidly recently and today covers a broad range of applications. This makes it challenging to navigate and hard to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today.The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning.The second part of the book introduces convolutional networks for computer vision. We’ll learn how to solve image classification, object detection, instance segmentation, and image generation tasks.The third part focuses on the attention mechanism and transformers – the core network architecture of large language models. We’ll discuss new types of advanced tasks they can solve, such as chatbots and text-to-image generation.By the end of this book, you’ll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models and adapt existing ones to solve your tasks. You’ll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.
Arduino
* Alle Komponenten der Hardware für Arduino UNO R4 und R3 * Verwendung der digitalen und analogen Ports, Einsatzbeispiele mit Sensoren, Aktoren und Anzeigen * Beispielprojekte wie Gefrierschrankwächter, Miniroboter mit Fernsteuerung, Geschwindigkeitsmesser und Internetanwendungen wie XML-Reader und Wetterstation Arduino besteht aus einem Mikrocontroller und der dazugehörigen kostenlosen Programmierumgebung. Aufgrund der einfachen C-ähnlichen Programmiersprache eignet sich die Arduino-Umgebung für alle Bastler und Maker, die auf einfache Weise Mikrocontroller programmieren möchten, ohne gleich Technik-Freaks sein zu müssen. Dieses Buch ermöglicht einen leichten Einstieg in die Arduino-Plattform. Der Autor bietet Ihnen eine praxisnahe Einführung und zeigt anhand vieler Beispiele, wie man digitale und analoge Signale über die Ein- und Ausgänge verarbeitet. Darüber hinaus lernen Sie, wie man verschiedene Sensoren wie Temperatur-, Umwelt-, Beschleunigungs- und optische Sensoren für Anwendungen mit dem Arduino-Board einsetzen kann. Anschließend werden Servo- und Motoranwendungen beschrieben. Dabei wird ein kleiner Roboter realisiert, der ferngesteuert werden kann. Im Praxiskapitel beschreibt der Autor verschiedene Internetanwendungen mit dem Arduino. Mittels einer Ethernet-Verbindung wird Ihr Arduino Umweltdaten sammeln und verarbeiten können. Als Projekt wird eine Wetterstation realisiert, die Wetterinformationen aus dem Internet abruft und Wetter- und Sensordaten auf einem Display darstellt. Zum Abschluss werden verschiedene Werkzeuge und Hilfsmittel sowie Softwareprogramme für den Basteleinsatz beschrieben und Sie erfahren, wie die Arduino-Anwendung im Miniformat mit ATtiny realisiert werden kann. Mit dem Wissen aus diesem Praxis-Handbuch können Sie Ihre eigenen Ideen kreativ umsetzen. Aus dem Inhalt: * Hardwarekomponenten * Entwicklungsumgebung * Eingänge und Ausgänge verarbeiten * Einsatz von Sensoren, Aktoren und Anzeigen * Serielle Datenübertragung * Daten sammeln und speichern * Softwarebibliotheken und Hardware-Erweiterungen * Praxisanwendungen wie Kompass, Fieberthermometer, Wasserwaage, Netzteil und Roboter * Internetanwendungen mit Arduino wie RSS-Reader und Wetterstation, WiFi mit ESP8266 * Eigene DIY-Boards und Arduino-Clones * Arduino im Miniformat mit ATtiny * Tools für Praktiker Thomas Brühlmann arbeitet als Consultant und hat langjährige Erfahrung in der Hardware- und Softwareentwicklung. Nebenbei realisiert er Projekte mit Open-Source-Hardware, hält Vorträge und führt Workshops durch. In seinem Blog unter arduino-praxis.ch verfolgt er die aktuelle Entwicklung des Arduino-Projektes und publiziert Projekte, Anwendungen, Tipps und Tricks.
Arduino Praxiseinstieg (5. Auflg.)
Die 5. Auflage aus November 2023. Behandelt Arduino UNO R4 und R3.Arduino besteht aus einem Mikrocontroller und der dazugehörigen kostenlosen Programmierumgebung. Aufgrund der einfachen C-ähnlichen Programmiersprache eignet sich die Arduino-Umgebung für alle Bastler und Maker, die auf einfache Weise Mikrocontroller programmieren möchten, ohne gleich Technik-Freaks sein zu müssen.Dieses Buch ermöglicht einen leichten Einstieg in die Arduino-Plattform. Der Autor bietet Ihnen eine praxisnahe Einführung und zeigt anhand vieler Beispiele, wie man digitale und analoge Signale über die Ein- und Ausgänge verarbeitet.Darüber hinaus lernen Sie, wie man verschiedene Sensoren wie Temperatur-, Umwelt-, Beschleunigungs- und optische Sensoren für Anwendungen mit dem Arduino-Board einsetzen kann. Anschließend werden Servo- und Motoranwendungen beschrieben. Dabei wird ein kleiner Roboter realisiert, der ferngesteuert werden kann.Im Praxiskapitel beschreibt der Autor verschiedene Internetanwendungen mit dem Arduino-Board. Mittels einer Ethernet-Verbindung wird Ihr Arduino Umweltdaten sammeln und verarbeiten können. Als Projekt wird eine Wetterstation realisiert, die Wetterinformationen aus dem Internet abruft und Wetter- und Sensordaten auf einem Display darstellt.Zum Abschluss werden verschiedene Werkzeuge und Hilfsmittel sowie Softwareprogramme für den Basteleinsatz beschrieben und Sie erfahren, wie die Arduino-Anwendung im Miniformat mit ATtiny realisiert werden kann.Mit dem Wissen aus diesem Praxis-Handbuch können Sie Ihre eigenen Ideen kreativ umsetzen.Aus dem Inhalt:HardwarekomponentenEntwicklungsumgebungEingänge und Ausgänge verarbeitenEinsatz von Sensoren, Aktoren und AnzeigenSerielle DatenübertragungDaten sammeln und speichernSoftwarebibliotheken und Hardware-ErweiterungenPraxisanwendungen wie Kompass, Fieberthermometer, Wasserwaage, Netzteil und RoboterInternetanwendungen mit Arduino wie RSS-Reader und Wetterstation, WiFi mit ESP8266Eigene DIY-Boards und Arduino-ClonesArduino im Miniformat mit ATtinyTools für PraktikerInhaltsverzeichnis und Leseprobe (PDF-Link)Downloads zum Buch (zip-Datei von mitp-Seite)Über den Autor:Thomas Brühlmann arbeitet als Consultant und hat langjährige Erfahrung in der Hardware- und Softwareentwicklung. Nebenbei realisiert er Projekte mit Open-Source-Hardware, hält Vorträge und führt Workshops durch. In seinem Blog unter arduino-praxis.ch verfolgt er die aktuelle Entwicklung des Arduino-Projektes und publiziert Projekte, Anwendungen, Tipps und Tricks.
Uncertainty and Artificial Intelligence
Today's information technology, along with Artificial Intelligence (AI), is moving towards total communication between all computerized systems. AI is a representation of human intelligence based on the creation and application of algorithms in specific computer environments. Its aim is to enable computers to act like human beings. For it to work, this type of technology requires computer systems, data with management systems and advanced algorithms, used by AI.In mechanical engineering, AI can offer many possibilities: in mechanical construction, predictive maintenance, plant monitoring, robotics, additive manufacturing, materials, vibration control and agro composites, among many others.This book is dedicated to Artificial Intelligence uncertainties in mechanical problems. Each chapter clearly sets out used and developed illustrative examples. Aimed at students, Uncertainty and Artificial Intelligence is also a valuable resource for practicing engineers and research lecturers.ABDELKHALAK EL HAMI is Full Professor at INSA-Rouen-Normandie, France. He is the author/co-author of over sixty books and is responsible for several European pedagogical projects. He is a specialist in the optimization, reliability and AI of multiphysical systems.
Symmetric Cryptography, Volume 1
Symmetric cryptology is one of the two main branches of cryptology. Its applications are essential and vital in the Information Age, due to the efficiency of its constructions.The scope of this book in two volumes is two-fold. First, it presents the most important ideas that have been used in the design of symmetric primitives, their inner components and their most relevant constructions. Second, it describes and provides insights on the most popular cryptanalysis and proof techniques for analyzing the security of the above algorithms. A selected number of future directions, such as post-quantum security or design of ciphers for modern needs and particular applications, are also discussed.We believe that the two volumes of this work will be of interest to researchers, to master’s and PhD students studying or working in the field of cryptography, as well as to all professionals working in the field of cybersecurity. Christina Boura is an associate professor at the University of Versailles, France, who works on symmetric cryptography. She is a well-recognized member of the cryptographic community, having served on many program committees and as editor-in-chief of the ToSC IACR journal.María Naya-Plasencia is a research director at Inria, France, who also works on symmetric cryptography. She obtained an ERC grant in 2016 and the Young Researcher Prize from Inria-Académie des Sciences in 2019, and has given several invited keynote talks.
Distributed Machine Learning with PySpark
Migrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools.Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Naïve Bayes, and neural networks.After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary to apply these methods using PySpark, the industry standard for building scalable ML data pipelines.WHAT YOU WILL LEARN* Master the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systems* Understand the differences between PySpark, scikit-learn, and pandas* Perform linear regression, logistic regression, and decision tree regression with pandas, scikit-learn, and PySpark* Distinguish between the pipelines of PySpark and scikit-learnWHO THIS BOOK IS FORData scientists, data engineers, and machine learning practitioners who have some familiarity with Python, but who are new to distributed machine learning and the PySpark framework.ABDELAZIZ TESTAS, PH.D., is a data scientist with over a decade of experience in data analysis and machine learning, specializing in the use of standard Python libraries and Spark distributed computing. He holds a Ph.D. in Economics from Leeds University and a Master's degree in Finance from Glasgow University. He has also earned several certificates in computer science and data science.In the last ten years, he has worked for Nielsen in Fremont, California as a Lead Data Scientist focused on improving the company’s audience measurement through planning, initiating, and executing end-to-end data science projects and methodology work. He has created advanced solutions for Nielsen’s digital ad and content rating products by leveraging subject matter expertise in media measurement and data science. He is passionate about helping others improve their machine learning skills and workflows, and is excited to share his knowledge and experience with a wider audience through this book.Chapter 1: An Easy Transition.- Chapter 2: Selecting Algorithms.- Chapter 3: Multiple Linear Regression with Pandas, Scikit-Learn, and PySpark.- Chapter 4: Decision Trees for Regression with Pandas, Scikit-Learn, and PySpark.- Chapter 5: Random Forests for Regression with Pandas, Scikit-Learn, and PySpark.- Chapter 6: Gradient-Boosted Tree Regression with Pandas, Scikit-Learn and PySpark.- Chapter 7: Logistic Regression with Pandas, Scikit-Learn and PySpark.- Chapter 8: Decision Tree Classification with Pandas, Scikit-Learn and PySpark.- Chapter 9: Random Forest Classification with Scikit-Learn and PySpark.- Chapter 10: Support Vector Machine Classification with Pandas, Scikit-Learn and PySpark.- Chapter 11: Naïve Bayes Classification with Pandas, Scikit-Learn and PySpark.- Chapter 12: Neural Network Classification with Pandas, Scikit-Learn and PySpark.- Chapter 13: Recommender Systems with Pandas, Surprise and PySpark.- Chapter 14: Natural Language Processing with Pandas, Scikit-Learn and PySpark.- Chapter 15: K-Means Clustering with Pandas, Scikit-Learn and PySpark.- Chapter 16: Hyperparameter Tuning with Scikit-Learn and PySpark.- Chapter 17: Pipelines with Scikit-Learn and PySpark.- Chapter 18: Deploying Models in Production with Scikit-Learn and PySpark.
Learn Microservices with Spring Boot 3
This book will show you how to build Java-based microservices architecture using the popular Spring Boot framework by evolving a small monolith application to an event-driven architecture composed of several services. This third edition has been updated to cover Spring Boot 3, including its compatibility with Java 17 and Jakarta EE 10, and employs an incremental approach to teach the structure of microservices, test-driven development, and common patterns in distributed systems such as service discovery, load balancing, routing, centralized logs, per-environment configuration, and containerization.Authors Moisés Macero and Tarun Telang get the ball rolling by introducing you to the fundamentals of microservices and Spring Boot before walking you through the development of a basic Spring Boot application. You’ll then see how to build a front end using React, and learn how to use the data layer to read and write data from and to other systems via Spring Boot and its access to Spring Data and its available APIs. Putting together what you've learned thus far, you’ll begin to transform an application from a monolith to a microservice.This pragmatic approach will enable you to better grasp the benefits of using this type of software architecture, instead of keeping you distracted with theoretical concepts. The emphasis is on what matters most, starting with the minimum viable product, while maintaining the ability to adapt and improve your application as needed. After completing this book, you will have the foundational knowledge necessary to build your own microservice-based applications using Spring Boot.WHAT YOU WILL LEARN* Gain a thorough understanding of microservices architecture and how it differs from monolithic architectures.* Discover the step-by-step process of breaking down a monolithic application into smaller, focused services* Build microservices with Spring Boot 3, Spring Data, Spring Cloud, React.js, Docker, Cucumber, and more* Develop Java-based microservices using the latest version of Spring Boot, compatible with Java 17 and Jakarta EE 10.* Discover architecture patterns for distributed systems such as asynchronous processing, eventual consistency, resilience, scalability, and more* Gain insight into event-driven communication patterns and understand how to design and build event-driven microservices* Trace every request from beginning to end with Sleuth and centralized logging* Deploy your microservices anywhere as Docker containersWHO THIS BOOK IS FORThose with at least some prior experience with Java programming. Some prior exposure to Spring Boot recommended but not required.MOISÉS MACERO GARCÍA has been a software developer since he was a kid, when he started playing around with BASIC on his ZX Spectrum. During his career, Moisés has most often worked in development and architecture for small and large projects, and for his own startups as well. He enjoys making software problems simple, and he likes working in teams where he can not only coach others, but also learn from them. Moisés is the author of the blog thepracticaldeveloper.com, where he shares solutions for technical challenges, guides, and his view on different ways of working in IT companies. He also organizes workshops for companies that need a practical approach to software engineering. In his free time, he enjoys traveling and hiking.TARUN TELANG is a seasoned technologist with a wealth of experience in designing and implementing highly scalable software applications. With an impressive career spanning over 18 years, Tarun has been a valuable contributor to renowned companies such as Microsoft, Oracle, Polycom, and SAP. He began his career as an enterprise Java developer at SAP, where he honed his skills in crafting distributed business applications tailored for large enterprises. Through his dedication to continuous learning and professional development, he has become an Oracle Certified Java Programmer and SAP Certified Development Consultant for Java Web Application Servers.
Speichereffizienter Aufbau von binären Entscheidungsdiagrammen
Aufgrund der steigenden Komplexität von Hardwaresystemen besteht die Notwendigkeit, effizientere Datenstrukturen und darauf operierende Algorithmen zu entwickeln, um weiterhin das korrekte Verhalten solcher Systeme zu garantieren. Ein binäres Entscheidungsdiagramm ist eine geeignete Datenstruktur, da sie eine kompakte Repräsentation boolescher Funktionen und effiziente Algorithmen zur Manipulation dieser bietet. Allerdings haben Entscheidungsdiagramme auch Herausforderungen zu bewältigen: Die Praktikabilität hängt von deren Minimierung ab und es besteht ein großer Speicherbedarf für einige komplexe Funktionen. Dieses Buch präsentiert Ansätze, in denen boolesche Normalformen unter Ordnungsdefinitionen und Gesetzen der booleschen Algebra mit dem Ziel angeordnet werden, die Anzahl an Zwischenberechnungen zum Aufbau binärer Entscheidungsdiagramme zu verringern und den Speicher- sowie Zeitbedarf zu reduzieren. Die Methoden werden in ein Softwarepaket integriert, um die Performanz anhand von Benchmark-Instanzen zu untersuchen und mit dem Stand der Forschung zu vergleichen.DER AUTORRUNE KRAUSs ist wissenschaftlicher Mitarbeiter / Promotionsstudent an der Universität Bremen. Seine Forschungs- sowie Lehrschwerpunkte umfassen die Logiksynthese und formale Verifikation von Schaltungen. Die wesentlichen Ziele seiner Arbeit sind deren algorithmische Verbesserung und die Entwicklung neuartiger graphenbasierter Datenstrukturen zur Steigerung der Effizienz des rechnergestützten Entwurfs von integrierten Schaltkreisen in technischen Systemen.einlitung.- grundlagen.- masterbdd ( m b d d ).- ausnutzung von ordnungseigenschaften.- performanz evaluation.- zusammenfassung und ausblick.- literatur.
Introduction to Responsible AI
Learn and implement responsible AI models using Python. This book will teach you how to balance ethical challenges with opportunities in artificial intelligence.The book starts with an introduction to the fundamentals of AI, with special emphasis given to the key principles of responsible AI. The authors then walk you through the critical issues of detecting and mitigating bias, making AI decisions understandable, preserving privacy, ensuring security, and designing robust models. Along the way, you’ll gain an overview of tools, techniques, and code examples to implement the key principles you learn in real-world scenarios.The book concludes with a chapter devoted to fostering a deeper understanding of responsible AI’s profound implications for the future. Each chapter offers a hands-on approach, enriched with practical insights and code snippets, enabling you to translate ethical considerations into actionable solutions.WHAT YOU WILL LEARN* Understand the principles of responsible AI and their importance in today's digital world* Master techniques to detect and mitigate bias in AI* Explore methods and tools for achieving transparency and explainability* Discover best practices for privacy preservation and security in AI* Gain insights into designing robust and reliable AI modelsWHO THIS BOOK IS FORAI practitioners, data scientists, machine learning engineers, researchers, policymakers, and students interested in the ethical aspects of AIAVINASHMANURE is a seasoned machine learning professional with more than ten years of experience in building, deploying, and maintaining state-of-the-art machine learning solutions across different industries. He has more than six years of experience in leading and mentoring high performance teams in developing ML systems catering to different business requirements. He is proficient in deploying complex machine learning and statistical modeling algorithms/ and techniques for identifying patterns and extracting valuable insights for key stakeholders and organizational leadership.He is the author of Learn Tensorflow 2.0 and Introduction to Prescriptive AI, both with Apress.Avinash holds a bachelor’s degree in Electronics Engineering from Mumbai University and earned his Masters in Business Administration (Marketing) from the University of Pune. He resides in Bangalore with his wife and child. He enjoys travelling to new places and reading motivational books.SHALEEN is a machine learning engineer with 4+ years of experience in building, deploying, and managing cutting-edge machine learning solutions across varied industries. He has developed several frameworks and platforms that have significantly streamlined processes and improved efficiency of machine learning teams.SHALEEN BENGANI has authored the book Operationalizing Machine Learning Pipelines as well as three research papers in the deep learning space.He holds a bachelors degree in Computer Science and Engineering from BITS Pilani, Dubai Campus, where he was awarded the Director’s Medal for outstanding all-around performance. In his leisure time, he likes playing table tennis and reading.SARAVANAN S is an AI engineer with more than six years of experience in data science and data engineering. He has developed robust data pipelines for developing and deploying advanced machine learning models, genratinginsightful reports, and ensuring cutting edge solutions for diverse industries.Saravanan earned a masters degree in statistics from Loyola College from Chennai. In his spare time he likes traveling, reading books and playing games.
Roblox Lua Scripting Essentials
Embark on a transformative journey through the exciting world of Roblox Lua scripting with this comprehensive hands-on guide. Tailored to game developers, both seasoned and new, this book serves as your roadmap to mastering the art and science of Lua scripting within the dynamic Roblox Studio environment.You'll explore a wide spectrum of foundational concepts, including variables, functions, loops, tables, arrays, and more. Delve into advanced topics like raycasting, object-oriented programming with humanoids, and intricate leaderboard systems. Each chapter is crafted with real-world examples and step-by-step tutorials, empowering you to create engaging and interactive gameplay experiences.You'll gain profound insights into modularization, efficient coding practices, and techniques to optimize your scripts, paving the way to elevate your game development to an entirely new plane of creativity and complexity. You'll also discover sophisticated scripting concepts, such as custom events, and client-server communication.Invest in your future as a Roblox developer today, and let this book be your guide to crafting extraordinary gaming experiences. Roblox Lua Scripting Essentials provides the insight, tools, and guidance needed to shape your unique path in this thrilling domain of game development.WHAT YOU'LL LEARN* Wield variables, functions, loops, conditionals, arrays, and more to create dynamic gameplay elements* Explore the principles of object-oriented programming, and modularization techniques to develop clean, efficient, and organized code* Understand the complex client-server relationships, custom events, and multiplayer functionalities that bring games to life* Discover the power of modular coding, enabling you to create more organized and maintainable code bases* Lay down a robust foundation in Lua scripting for RobloxWHO THIS BOOK IS FORThis book is meticulously crafted to serve a diverse array of readers, ranging from complete beginners to intermediate developers. CHRISTOPHER COUTINHO is a game developer with an impressive 7-year track record in the domain of Virtual Reality (VR) development. As the visionary founder of Game Works, a cutting-edge game development studio based in Mumbai, he has honed his expertise in creating immersive Virtual Reality experiences. Coutinho's contributions extend beyond the commercial sphere; he has also played a pivotal role in shaping the next generation of game developers. He has been an instrumental educator, sharing his knowledge and insights in Video Game Development through platforms like iDTech, a division of Emeritus. His teachings span popular game engines such as Unity and Roblox. Additionally, Christopher has taught a specialized program on Augmented/Virtual Reality (AR/VR) Design created by the NYU – Tandon School of Engineering, for iDTech. His blend of hands-on experience and pedagogical prowess positions him as a leading figure in the contemporary gaming landscape.
Symmetric Cryptography, Volume 2
Symmetric cryptology is one of the two main branches of cryptology. Its applications are essential and vital in the Information Age, due to the efficiency of its constructions.The scope of this book in two volumes is two-fold. First, it presents the most important ideas that have been used in the design of symmetric primitives, their inner components and their most relevant constructions. Second, it describes and provides insights on the most popular cryptanalysis and proof techniques for analyzing the security of the above algorithms. A selected number of future directions, such as post-quantum security or design of ciphers for modern needs and particular applications, are also discussed.We believe that the two volumes of this work will be of interest to researchers, to master’s and PhD students studying or working in the field of cryptography, as well as to all professionals working in the field of cybersecurity. Christina Boura is an associate professor at the University of Versailles, France, who works on symmetric cryptography. She is a well-recognized member of the cryptographic community, having served on many program committees and as editor-in-chief of the ToSC IACR journal.María Naya-Plasencia is a research director at Inria, France, who also works on symmetric cryptography. She obtained an ERC grant in 2016 and the Young Researcher Prize from Inria-Académie des Sciences in 2019, and has given several invited keynote talks.
Applied Generative AI for Beginners
This book provides a deep dive into the world of generative AI, covering everything from the basics of neural networks to the intricacies of large language models like ChatGPT and Google Bard. It serves as a one-stop resource for anyone interested in understanding and applying this transformative technology and is particularly aimed at those just getting started with generative AI.Applied Generative AI for Beginners is structured around detailed chapters that will guide you from foundational knowledge to practical implementation. It starts with an introduction to generative AI and its current landscape, followed by an exploration of how the evolution of neural networks led to the development of large language models. The book then delves into specific architectures like ChatGPT and Google Bard, offering hands-on demonstrations for implementation using tools like Sklearn. You’ll also gain insight into the strategic aspects of implementing generative AI in an enterprise setting, with the authors covering crucial topics such as LLMOps, technology stack selection, and in-context learning. The latter part of the book explores generative AI for images and provides industry-specific use cases, making it a comprehensive guide for practical application in various domains.Whether you're a data scientist looking to implement advanced models, a business leader aiming to leverage AI for enterprise growth, or an academic interested in cutting-edge advancements, this book offers a concise yet thorough guide to mastering generative AI, balancing theoretical knowledge with practical insights.WHAT YOU WILL LEARN* Gain a solid understanding of generative AI, starting from the basics of neural networks and progressing to complex architectures like ChatGPT and Google Bard* Implement large language models using Sklearn, complete with code examples and best practices for real-world application* Learn how to integrate LLM’s in enterprises, including aspects like LLMOps and technology stack selection* Understand how generative AI can be applied across various industries, from healthcare and marketing to legal compliance through detailed use cases and actionable insightsWHO THIS BOOK IS FORData scientists, AI practitioners, Researchers and software engineers interested in generative AI and LLMs.AKSHAY KULKARNI is an AI and machine learning evangelist and IT leader. He has assisted numerous Fortune 500 and global firms in advancing strategic transformations using AI and data science. He is a Google Developer Expert, author, and regular speaker at major AI and data science conferences (including Strata, O’Reilly AI Conf, and GIDS). He is also a visiting faculty member for some of the top graduate institutes in India. In 2019, he was featured as one of the top 40 under-40 Data Scientists in India. He enjoys reading, writing, coding, and building next-gen AI products.ADARSHA S is a data science and ML Ops leader. Presently, he is focused on creating world-class ML Ops capabilities to ensure continuous value delivery using AI. He aims to build a pool of exceptional data scientists within and outside the organization to solve problems through training programs, and always wants to stay ahead of the curve. He has worked in the pharma, healthcare, CPG, retail, and marketing industries. He lives in Bangalore and loves to read and teach data science.ANOOSH KULKARNI is a data scientist and ML Ops engineer. He has worked with various global enterprises across multiple domains solving their business problems using machine learning and AI. He has worked at Awok-dot-com, one of the leading e-commerce giants in UAE, where he focused on building state of art recommender systems and deep learning-based search engines. He is passionate about guiding and mentoring people in their data science journey. He often leads data sciences/machine learning meetups, helping aspiring data scientists carve their career road map.DILIP GUDIVADA is a seasoned senior data architect with 13 years of experience in cloud services, big data, and data engineering. Dilip has a strong background in designing and developing ETL solutions, focusing specifically on building robust data lakes on the Azure cloud platform. Leveraging technologies such as Azure Databricks, Data Factory, Data Lake Storage, PySpark, Synapse, and Log Analytics, Dilip has helped organizations establish scalable and efficient data lake solutions on Azure. He has a deep understanding of cloud services and a track record of delivering successful data engineering projects.
Maschinelles Lernen - Grundlagen und Anwendungen
In diesem Fachbuch werden vorwiegend die Grundlagen des Maschinellen Lernens erläutert. Die Hauptthemen sind die mathematischen Grundlagen, Optimierungsmethoden und die ML-Algorithmen. Es wird zu jedem Kapitel mindestens eine Beispiel-Übung durchgeführt. Die Übungen könnten durch Python-Code ergänzt werden. Zusätzlich werden Aufgabenstellungen definiert, dies dient der Festigung des in dem jeweiligen Kapitel gelernten. Spezielle Anwendungen sollen ebenfalls dargestellt werden. Die Zielgruppe sind hauptsächlich Studierende, welche sich in dieses Themengebiet einarbeiten möchten. Ingenieure können allerdings ebenfalls von diesem Fachbuch profitieren, da ein großer Schwerpunkt bei der Anwendung von ML liegt. Besonders die Verwendung in interdisziplinären Fachrichtungen wie der Regelungstechnik, Bildverarbeitung und der Chemie werden aufgezeigt.Mein Name ist Benny Botsch und studierte Maschinenbau an der Technischen Universität in Berlin. Ich arbeite seit einigen Jahren als wissenschaftlicher Mitarbeiter bei der Gesellschaft zur Förderung angewandter Informatik e.V. (GFaI e.V.) im Bereich der Bildverarbeitung / Industrielle Anwendungen. Dabei entwickle ich Software zur Auswertung von 2D-Materialbildern durch klassische Bildverarbeitung (Objekterkennung, Kantenerkennung), aber auch durch neuronale Netze, um Materialkennwerte zu ermitteln.Inhaltsverzeichnis1 Einführung1.1 Was ist maschinelles Lernen1.2 Überwachtes Lernen1.2.1 Klassifikation und Regression1.2.2 Generalisierung, Überanpassung und Unteranpassung1.3 Unüberwachtes Lernen1.4 Bestärkendes Lernen1.5 Teilüberwachte Lernen1.6 Herausforderungen des maschinellen Lernens1.6.1 Unzureichende Menge an Trainingsdaten1.6.2 Nicht repräsentative Trainingsdaten1.6.3 Daten von schlechter Qualität1.6.4 Irrelevante Merkmale1.6.5 Explainable Artificial Intelligence1.7 Bewertung und Vergleich von Algorithmen1.7.1 Kreuzvalidierung1.7.2 Messfehler1.7.3 Intervallschätzung1.7.4 Hypothesenprüfung1.8 Werkzeuge und Ressourcen1.8.1 Installation von Python mit Anaconda1.8.2 Entwicklungsumgebungen1.8.3 Python Bibliotheken1.8.4 Grundlagen in Python2 Lineare Algebra2.1 Skalare, Vektoren und Matrizen2.1.1 Operationen mit Skalaren und Vektoren 2.1.2 Operationen mit Vektoren und Matrizen2.1.3 Die Inverse einer Matrix2.2 Lineare Gleichungssysteme2.2.1 Gauß-Algorithmus2.2.2 Numerische Lösungsmethoden linearer Gleichungssysteme3 Wahrscheinlichkeit und Statistik3.1 Grundbegriffe der Wahrscheinlichkeit3.2 Zufallsgrößen und Verteilungsfunktionen3.3 Momente einer Verteilung3.3.1 Erwartungswert und Streuung3.3.2 Schiefe und Exzess3.4 Bedingte Wahrscheinlichkeiten3.5 Deskriptive Statistik3.6 Einfache statistische Tests3.6.1 Ablauf eines statistischen Tests3.6.2 Parametertests bei normalverteilter Grundgesamtheit3.6.3 Mittelwerttest3.6.4 ����2 Streuungstest4 Optimierung4.1 Grundlagen der Optimierung4.1.1 Univariate Optimierung4.1.2 Bivariate Optimierung4.1.3 Multivariate Optimierung4.2 Gradient Descent4.2.1 Momentum-Based Learning4.2.2 AdaGrad4.2.3 Adam4.3 Newton Methode5 Parametrische Methoden5.1 Regressionsanalyse5.1.1 Lineare Regression5.1.2 Logistische Regression5.2 Lineare Support Vector Machines5.2.1 Die optimale Trennebene5.2.2 Soft-Margin5.2.3 Kernfunktionen5.3 Der Bayessche Schätzer5.3.1 Stochastische Unabhängigkeit5.3.2 Bayessche Netze5.4 Neuronale Netze5.4.1 Das künstliche Neuron5.4.2 Mehrschichtige Neuronale Netze5.4.3 Lernvorgang5.5 Deep Learning5.5.1 Convolutional Neural Networks5.5.2 Rekurrent Neural Networks5.5.3 Generative Modelle6 Nichtparametrische Methoden6.1 Nichtparametrische Dichteschätzung6.1.1 Histogrammschätzer6.1.2 Kernschätzer6.1.3 ����-Nächste-Nachbarn-Schätzer6.2 Entscheidungsbäume6.2.1 Univariate Bäume6.2.2 Multivariate Bäume6.2.3 Pruning6.2.4 Random Forest7 Bestärkendes Lernen7.1 Was ist bestärkendes Lernen7.1.1 Belohnung7.1.2 Der Agent7.1.3 Die Umgebung7.1.4 Aktionen7.1.5 Beobachtungen7.2 Theoretische Grundlagen7.2.1 Markov Entscheidungsprozesse7.2.2 Markov Prozess7.2.3 Markov Belohnungsprozess7.2.4 Policy7.3 Wertebasierte Verfahren7.3.1 Grundlagen der Wertefunktion und der Bellman-Gleichung7.3.2 Q-Learning7.3.3 SARSA7.3.4 Deep Q-Networks (DQN)7.4 Policybasierte Verfahren7.4.1 Policy Gradien7.4.2 Actor-Critic-Verfahren7.4.3 Soft Actor-Critic (SAC)8 Custeranalyse8.1 ����-Means-Clustermethode8.2 Hierarchisches Clustermethode 8.3 Gaußsche Mischmodelle9 Anwendungen9.1 Regelungstechnik9.1.1 Systemidentifikation9.1.2 Neuronaler Regler9.1.3 Regelung eines inversen Pendels9.2 Bildverarbeitung9.2.1 Klassifikation von Zahlen9.2.2 Segmentierung von Bruchflächen9.2.3 Objekterkennung mit Vision Transformers9.2.4 Künstliche Generierung von Bildern9.2.5 Interpretierbarkeit von Vision-Modellen mit Grad-CAM9.3 Chemie9.3.1 Klassifizierung von Wein9.3.2 Vorhersage von Eigenschaften organischer Moleküle9.4 Physik9.4.1 Statistische Versuchsplanung optimieren9.4.2 Vorhersage von RANS-Strömungen9.5 Generierung von Text9.5.1 Textgenerierung mit einem Miniatur-GPT9.5.2 Englisch-Spanisch-Übersetzung mit TensorFlow9.6 Audiodatenverarbeitung9.6.1 Automatische Spracherkennung mit CTC9.6.2 Klassifizierung von Sprechern mit FFTLiteraturverzeichnis
Google Cloud Platform for Data Science
This book is your practical and comprehensive guide to learning Google Cloud Platform (GCP) for data science, using only the free tier services offered by the platform.Data science and machine learning are increasingly becoming critical to businesses of all sizes, and the cloud provides a powerful platform for these applications. GCP offers a range of data science services that can be used to store, process, and analyze large datasets, and train and deploy machine learning models.The book is organized into seven chapters covering various topics such as GCP account setup, Google Colaboratory, Big Data and Machine Learning, Data Visualization and Business Intelligence, Data Processing and Transformation, Data Analytics and Storage, and Advanced Topics. Each chapter provides step-by-step instructions and examples illustrating how to use GCP services for data science and big data projects.Readers will learn how to set up a Google Colaboratory account and run Jupyter notebooks, access GCP services and data from Colaboratory, use BigQuery for data analytics, and deploy machine learning models using Vertex AI. The book also covers how to visualize data using Looker Data Studio, run data processing pipelines using Google Cloud Dataflow and Dataprep, and store data using Google Cloud Storage and SQL.WHAT YOU WILL LEARN* Set up a GCP account and project* Explore BigQuery and its use cases, including machine learning* Understand Google Cloud AI Platform and its capabilities * Use Vertex AI for training and deploying machine learning models* Explore Google Cloud Dataproc and its use cases for big data processing* Create and share data visualizations and reports with Looker Data Studio* Explore Google Cloud Dataflow and its use cases for batch and stream data processing * Run data processing pipelines on Cloud Dataflow* Explore Google Cloud Storage and its use cases for data storage * Get an introduction to Google Cloud SQL and its use cases for relational databases * Get an introduction to Google Cloud Pub/Sub and its use cases for real-time data streamingWHO THIS BOOK IS FORData scientists, machine learning engineers, and analysts who want to learn how to use Google Cloud Platform (GCP) for their data science and big data projectsSHITALKUMAR R. SUKHDEVE is an experienced senior data scientist with a strong track record of developing and deploying transformative data science and machine learning solutions to solve complex business problems in the telecom industry. He has notable achievements in developing a machine learning-driven customer churn prediction and root cause exploration solution, a customer credit scoring system, and a product recommendation engine.Shitalkumar is skilled in enterprise data science and research ecosystem development, dedicated to optimizing key business indicators, and adding revenue streams for companies. He is pursuing a doctorate in business administration from SSBM, Switzerland, and an M.Tech in computer science and engineering from VNIT Nagpur.Shitalkumar has authored a book titled Step Up for Leadership in Enterprise Data Science and Artificial Intelligence with Big Data: Illustrations with R and Python and co-authored a book titled Web Application Development with R Using Shiny, 3rd edition. He is a speaker at various technology and business events such as WorldAI Show Jakarta 2021, 2022, and 2023, NXT CX Jakarta 2022, Global Cloud Native Open Source Summit 2022, Cyber Security Summit 2022, and ASEAN Conversational Automation Webinar. You can find him on LinkedIn.SANDIKA S. SUKHDEVE is an expert in Data Visualization and Google-certified Project Management. She previously served as Assistant Professor in a Mechanical Engineering Department and has authored Amazon bestseller titles across diverse markets such as the USA, Germany, Canada, and more. She has a background in Human Resources and a wealth of experience in Branding.As an Assistant Professor, she successfully guided more than 2,000 students and delivered 1,000+ lectures, and mentored numerous projects (including Computational Fluid Dynamics). She excels in managing both people and multiple projects, ensuring timely completion. Her areas of specialization encompass Thermodynamics, Applied Thermodynamics, Industrial Engineering, Product Design and Development, Theory of Machine, Numerical Methods and Optimization, and Fluid Mechanics. She holds a master's degree in Technology (with a Specialization in Heat-Power), and she possesses exceptional skills in visualizing, analyzing, and constructing classification and prediction models using R and MATLAB. You can find her on LinkedIn.Chapter 1: Introduction to GCP.- Chapter 2: Google Colaboratory.- Chapter 3: Big Data and Machine Learning.- Chapter 4: Data Visualization and Business Intelligence.- Chapter 5: Data Processing and Transformation.- Chapter 6: Data Analytics and Storage.- Chapter 7: Advanced Topics.
Building Computer Vision Applications Using Artificial Neural Networks
Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition’s publication. All code used in the book has also been fully updated.This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer vision applications, and you’ll gain a thorough understanding of them. The book’s source code has been updated from TensorFlow 1.x to 2.x, and includes step-by-step examples using both OpenCV and TensorFlow with Python.Upon completing this book, you’ll have the knowledge and skills to build your own computer vision applications using neural networksWHAT YOU WILL LEARN* Understand image processing, manipulation techniques, and feature extraction methods* Work with convolutional neural networks (CNN), single-shot detector (SSD), and YOLO* Utilize large scale model development and cloud infrastructure deployment* Gain an overview of FaceNet neural network architecture and develop a facial recognition systemWHO THIS BOOK IS FORThose who possess a solid understanding of Python programming and wish to gain an understanding of computer vision and machine learning. It will prove beneficial to data scientists, deep learning experts, and students.SHAMSHAD (SAM) ANSARI is an author, inventor, and thought leader in the fields of computer vision, machine learning, artificial intelligence, and cognitive science. He has extensive experience in high scale, distributed, and parallel computing. Sam currently serves as an Adjunct Professor at George Mason University, teaching graduate- level programs within the Data Analytics Engineering department of the Volgenau School of Engineering. His areas of instruction encompass machine learning, natural language processing, and computer vision, where he imparts his knowledge and expertise to aspiring professionals.Having authored multiple publications on topics such as machine learning, RFID, and high-scale enterprise computing, Sam’s contributions extend beyond academia. He holds four US patents related to healthcare AI, showcasing his innovative mindset and practical application of technology.Throughout his extensive 20+ years of experience in enterprise software development, Sam has been involved with several tech startups and early-stage companies. He has played pivotal roles in building and expanding tech teams from the ground up, contributing to their eventual acquisition by larger organizations. At the beginning of his career, he worked with esteemed institutions such as the US Department of Defense (DOD) and IBM, honing his skills and knowledge in the industry.Currently, Sam serves as the President and CEO of Accure, Inc., an AI company that he founded. He is the creator, architect, and a significant contributor to Momentum AI, a no-code platform that encompasses data engineering, machine learning, AI, MLOps, data warehousing, and business intelligence. Throughout his career, Sam has made notable contributions in various domains including healthcare, retail, supply chain, banking and finance, and manufacturing. Demonstrating his leadership skills, he has successfully managed teams of software engineers, data scientists, and DevSecOps professionals, leading them to deliver exceptional results. Sam earned his bachelor’s degree in engineering from Birsa Institute of Technology (BIT) Sindri and subsequently a Master’s degree from the prestigious Indian Institute of Information Technology and Management Kerala (IIITM-K).
Blockchain and Deep Learning for Smart Healthcare
BLOCKCHAIN AND DEEP LEARNING FOR SMART HEALTHCARETHE BOOK DISCUSSES THE POPULAR USE CASES AND APPLICATIONS OF BLOCKCHAIN TECHNOLOGY AND DEEP LEARNING IN BUILDING SMART HEALTHCARE.The book covers the integration of blockchain technology and deep learning for making smart healthcare systems. Blockchain is used for health record-keeping, clinical trials, patient monitoring, improving safety, displaying information, and transparency. Deep learning is also showing vast potential in the healthcare domain. With the collection of large quantities of patient records and data, and a trend toward personalized treatments. there is a great need for automated and reliable processing and analysis of health information. This book covers the popular use cases and applications of both the above-mentioned technologies in making smart healthcare. AUDIENCEComprises professionals and researchers working in the fields of deep learning, blockchain technology, healthcare & medical informatics. In addition, as the book provides insights into the convergence of deep learning and blockchain technology in healthcare systems and services, medical practitioners as well as healthcare professionals will find this essential reading. AKANSHA SINGH, PHD, is an associate professor in the School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India. Dr. Singh has acquired a BTech, MTech, and PhD (IIT Roorkee) in the area of neural networks and remote sensing. She has to her credit more than 70 research papers, 20 books, and numerous conference papers. She has also national and international patents in the field of machine learning. Her area of interest includes mobile computing, artificial intelligence, machine learning, and digital image processing.ANURADHA DHULL, PHD, is an assistant professor in the Department of Computer Science Engineering, The NorthCap University, Gurugram, India. She has published more than 30 research papers in the area of data mining and machine learning. Dr. Anuradha has acquired a BTech, MTech, and PhD in the area of medical diagnosis and machine learning. KRISHNA KANT SINGH, PHD, is a professor at the Delhi Technical Campus, Greater Noida, India. Dr. Singh has acquired a BTech, MTech, and PhD (IIT Roorkee) in the area of deep learning and remote sensing. He has authored more than 80 technical books and research papers in international conferences and SCIE journals of repute.
Privacy Preservation of Genomic and Medical Data
PRIVACY PRESERVATION OF GENOMIC AND MEDICAL DATADISCUSSES TOPICS CONCERNING THE PRIVACY PRESERVATION OF GENOMIC DATA IN THE DIGITAL ERA, INCLUDING DATA SECURITY, DATA STANDARDS, AND PRIVACY LAWS SO THAT RESEARCHERS IN BIOMEDICAL INFORMATICS, COMPUTER PRIVACY AND ELSI CAN ASSESS THE LATEST ADVANCES IN PRIVACY-PRESERVING TECHNIQUES FOR THE PROTECTION OF HUMAN GENOMIC DATA.Privacy Preservation of Genomic and Medical Data focuses on genomic data sources, analytical tools, and the importance of privacy preservation. Topics discussed include tensor flow and Bio-Weka, privacy laws, HIPAA, and other emerging technologies like Internet of Things, IoT-based cloud environments, cloud computing, edge computing, and blockchain technology for smart applications. The book starts with an introduction to genomes, genomics, genetics, transcriptomes, proteomes, and other basic concepts of modern molecular biology. DNA sequencing methodology, DNA-binding proteins, and other related terms concerning genomes and genetics, and the privacy issues are discussed in detail. The book also focuses on genomic data sources, analyzing tools, and the importance of privacy preservation. It concludes with future predictions for genomic and genomic privacy, emerging technologies, and applications. AUDIENCEResearchers in information technology, data mining, health informatics and health technologies, clinical informatics, bioinformatics, security and privacy in healthcare, as well as health policy developers in public and private health departments and public health. AMIT KUMAR TYAGI, PHD, is an assistant professor, at the National Institute of Fashion Technology, New Delhi, India. He has published more than 100 papers in refereed international journals, conferences, and books. He has filed more than 20 national and international patents in the areas of deep learning, Internet of Things, cyber-physical systems, and computer vision. His current research focuses on smart and secure computing and privacy amongst other interests.
Automated Secure Computing for Next-Generation Systems
AUTOMATED SECURE COMPUTING FOR NEXT-GENERATION SYSTEMSTHIS BOOK PROVIDES CUTTING-EDGE CHAPTERS ON MACHINE-EMPOWERED SOLUTIONS FOR NEXT-GENERATION SYSTEMS FOR TODAY’S SOCIETY.Security is always a primary concern for each application and sector. In the last decade, many techniques and frameworks have been suggested to improve security (data, information, and network). Due to rapid improvements in industry automation, however, systems need to be secured more quickly and efficiently. It is important to explore the best ways to incorporate the suggested solutions to improve their accuracy while reducing their learning cost. During implementation, the most difficult challenge is determining how to exploit AI and ML algorithms for improved safe service computation while maintaining the user’s privacy. The robustness of AI and deep learning, as well as the reliability and privacy of data, is an important part of modern computing. It is essential to determine the security issues of using AI to protect systems or ML-based automated intelligent systems. To enforce them in reality, privacy would have to be maintained throughout the implementation process. This book presents groundbreaking applications related to artificial intelligence and machine learning for more stable and privacy-focused computing. By reflecting on the role of machine learning in information, cyber, and data security, Automated Secure Computing for Next-Generation Systems outlines recent developments in the security domain with artificial intelligence, machine learning, and privacy-preserving methods and strategies. To make computation more secure and confidential, the book provides ways to experiment, conceptualize, and theorize about issues that include AI and machine learning for improved security and preserve privacy in next-generation-based automated and intelligent systems. Hence, this book provides a detailed description of the role of AI, ML, etc., in automated and intelligent systems used for solving critical issues in various sectors of modern society. AUDIENCEResearchers in information technology, robotics, security, privacy preservation, and data mining. The book is also suitable for postgraduate and upper-level undergraduate students. AMIT KUMAR TYAGI, PHD, is an assistant professor, at the National Institute of Fashion Technology, New Delhi, India. He has published more than 100 papers in refereed international journals, conferences, and books. He has filed more than 20 national and international patents in the areas of deep learning, Internet of Things, cyber-physical systems, and computer vision. His current research focuses on smart and secure computing and privacy, amongst other interests.
Automated Secure Computing for Next-Generation Systems
AUTOMATED SECURE COMPUTING FOR NEXT-GENERATION SYSTEMSTHIS BOOK PROVIDES CUTTING-EDGE CHAPTERS ON MACHINE-EMPOWERED SOLUTIONS FOR NEXT-GENERATION SYSTEMS FOR TODAY’S SOCIETY.Security is always a primary concern for each application and sector. In the last decade, many techniques and frameworks have been suggested to improve security (data, information, and network). Due to rapid improvements in industry automation, however, systems need to be secured more quickly and efficiently. It is important to explore the best ways to incorporate the suggested solutions to improve their accuracy while reducing their learning cost. During implementation, the most difficult challenge is determining how to exploit AI and ML algorithms for improved safe service computation while maintaining the user’s privacy. The robustness of AI and deep learning, as well as the reliability and privacy of data, is an important part of modern computing. It is essential to determine the security issues of using AI to protect systems or ML-based automated intelligent systems. To enforce them in reality, privacy would have to be maintained throughout the implementation process. This book presents groundbreaking applications related to artificial intelligence and machine learning for more stable and privacy-focused computing. By reflecting on the role of machine learning in information, cyber, and data security, Automated Secure Computing for Next-Generation Systems outlines recent developments in the security domain with artificial intelligence, machine learning, and privacy-preserving methods and strategies. To make computation more secure and confidential, the book provides ways to experiment, conceptualize, and theorize about issues that include AI and machine learning for improved security and preserve privacy in next-generation-based automated and intelligent systems. Hence, this book provides a detailed description of the role of AI, ML, etc., in automated and intelligent systems used for solving critical issues in various sectors of modern society. AUDIENCEResearchers in information technology, robotics, security, privacy preservation, and data mining. The book is also suitable for postgraduate and upper-level undergraduate students. AMIT KUMAR TYAGI, PHD, is an assistant professor, at the National Institute of Fashion Technology, New Delhi, India. He has published more than 100 papers in refereed international journals, conferences, and books. He has filed more than 20 national and international patents in the areas of deep learning, Internet of Things, cyber-physical systems, and computer vision. His current research focuses on smart and secure computing and privacy, amongst other interests. Preface xviiAcknowledgements xixPART 1: FUNDAMENTALS 11 DIGITAL TWIN TECHNOLOGY: NECESSITY OF THE FUTURE IN EDUCATION AND BEYOND 3Robertas Damasevicius and Ligita Zailskaite-Jakste1.1 Introduction 31.2 Digital Twins in Education 51.3 Examples and Case Studies 81.4 Discussion 121.5 Challenges and Limitations 131.6 Conclusion 172 AN INTERSECTION BETWEEN MACHINE LEARNING, SECURITY, AND PRIVACY 23Hareharan P.K., Kanishka J. and Subaasri D.2.1 Introduction 232.2 Machine Learning 242.3 Threat Model 272.4 Training in a Differential Environment 302.5 Inferring in Adversarial Attack 332.6 Machine Learning Methods That Are Sustainable, Private, and Accountable 362.7 Conclusion 403 DECENTRALIZED, DISTRIBUTED COMPUTING FOR INTERNET OF THINGS-BASED CLOUD APPLICATIONS 43Roopa Devi E.M., Shanthakumari R., Rajadevi R., Kayethri D. and Aparna V.3.1 Introduction to Volunteer Edge Cloud for Internet of Things Utilising Blockchain 443.2 Significance of Volunteer Edge Cloud Concept 453.3 Proposed System 463.4 Implementation of Volunteer Edge Control 493.5 Result Analysis of Volunteer Edge Cloud 523.6 Introducing Blockchain-Enabled Internet of Things Systems Using the Serverless Cloud Platform 533.7 Introducing Serverless Cloud Platforms 543.8 Serverless Cloud Platform System Design 553.9 Evaluation of HCloud 603.10 HCloud-Related Works 613.11 Conclusion 624 ARTIFICIAL INTELLIGENCE–BLOCKCHAIN-ENABLED–INTERNET OF THINGS-BASED CLOUD APPLICATIONS FOR NEXT-GENERATION SOCIETY 65V. Hemamalini, Anand Kumar Mishra, Amit Kumar Tyagi and Vijayalakshmi Kakulapati4.1 Introduction 654.2 Background Work 694.3 Motivation 714.4 Existing Innovations in the Current Society 724.5 Expected Innovations in the Next-Generation Society 724.6 An Environment with Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 734.7 Open Issues in Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 744.8 Research Challenges in Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 754.9 Legal Challenges in Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 764.10 Future Research Opportunities Towards Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 774.11 An Open Discussion 784.12 Conclusion 795 ARTIFICIAL INTELLIGENCE FOR CYBER SECURITY: CURRENT TRENDS AND FUTURE CHALLENGES 83Meghna Manoj Nair, Atharva Deshmukh and Amit Kumar Tyagi5.1 Introduction: Security and Its Types 835.2 Network and Information Security for Industry 4.0 and Society 5.0 865.3 Internet Monitoring, Espionage, and Surveillance 895.4 Cyber Forensics with Artificial Intelligence and without Artificial Intelligence 915.5 Intrusion Detection and Prevention Systems Using Artificial Intelligence 925.6 Homomorphic Encryption and Cryptographic Obfuscation 945.7 Artificial Intelligence Security as Adversarial Machine Learning 955.8 Post-Quantum Cryptography 965.9 Security and Privacy in Online Social Networks and Other Sectors 985.10 Security and Privacy Using Artificial Intelligence in Future Applications/Smart Applications 995.11 Security Management and Security Operations Using Artificial Intelligence for Society 5.0 and Industry 4.0 1015.12 Digital Trust and Reputation Using Artificial Intelligence 1035.13 Human-Centric Cyber Security Solutions 1045.14 Artificial Intelligence-Based Cyber Security Technologies and Solutions 1065.15 Open Issues, Challenges, and New Horizons Towards Artificial Intelligence and Cyber Security 1075.16 Future Research with Artificial Intelligence and Cyber Security 1095.17 Conclusion 110PART 2: METHODS AND TECHNIQUES 1156 AN AUTOMATIC ARTIFICIAL INTELLIGENCE SYSTEM FOR MALWARE DETECTION 117Ahmad Moawad, Ahmed Ismail Ebada, A.A. El-Harby and Aya M. Al-Zoghby6.1 Introduction 1176.2 Malware Types 1196.3 Structure Format of Binary Executable Files 1216.4 Malware Analysis and Detection 1246.5 Malware Techniques to Evade Analysis and Detection 1286.6 Malware Detection With Applying AI 1306.7 Open Issues and Challenges 1346.8 Discussion and Conclusion 1357 EARLY DETECTION OF DARKNET TRAFFIC IN INTERNET OF THINGS APPLICATIONS 139Ambika N.7.1 Introduction 1397.2 Literature Survey 1437.3 Proposed Work 1477.4 Analysis of the Work 1497.5 Future Work 1507.6 Conclusion 1518 A NOVEL AND EFFICIENT APPROACH TO DETECT VEHICLE INSURANCE CLAIM FRAUD USING MACHINE LEARNING TECHNIQUES 155Anand Kumar Mishra, V. Hemamalini, Amit Kumar Tyagi, Piyali Saha and Abirami A.8.1 Introduction 1558.2 Literature Survey 1568.3 Implementation and Analysis 1578.4 Conclusion 1749 AUTOMATED SECURE COMPUTING FOR FRAUD DETECTION IN FINANCIAL TRANSACTIONS 177Kuldeep Singh, Prasanna Kolar, Rebecca Abraham, Vedantam Seetharam, Sireesha Nanduri and Divyesh Kumar9.1 Introduction 1779.2 Historical Perspective 1809.3 Previous Models for Fraud Detection in Financial Transactions 1819.4 Proposed Model Based on Automated Secure Computing 1829.5 Discussion 1849.6 Conclusion 18510 DATA ANONYMIZATION ON BIOMETRIC SECURITY USING IRIS RECOGNITION TECHNOLOGY 191Aparna D. K., Malarkodi M., Lakshmanaprakash S., Priya R. L. and Ajay Nair10.1 Introduction 19110.2 Problems Faced in Facial Recognition 19410.3 Face Recognition 19710.4 The Important Aspects of Facial Recognition 19910.5 Proposed Methodology 20110.6 Results and Discussion 20210.7 Conclusion 20211 ANALYSIS OF DATA ANONYMIZATION TECHNIQUES IN BIOMETRIC AUTHENTICATION SYSTEM 205Harini S., Dharshini R., Agalya N., Priya R. L. and Ajay Nair11.1 Introduction 20511.2 Literature Survey 20711.3 Existing Survey 20911.4 Proposed System 21211.5 Implementation of AI 21911.6 Limitations and Future Works 22011.7 Conclusion 221PART 3: APPLICATIONS 22312 DETECTION OF BANK FRAUD USING MACHINE LEARNING TECHNIQUES 225Kalyani G., Anand Kumar Mishra, Diya Harish, Amit Kumar Tyagi, Sajidha S. A. and Shashank Pandey12.1 Introduction 22512.2 Literature Review 22612.3 Problem Description 22712.4 Implementation and Analysis 22812.5 Results 23812.6 Conclusion 23812.7 Future Works 24013 AN INTERNET OF THINGS-INTEGRATED HOME AUTOMATION WITH SMART SECURITY SYSTEM 243Md. Sayeduzzaman, Touhidul Hasan, Adel A. Nasser and Akashdeep Negi13.1 Introduction 24413.2 Literature Review 24613.3 Methodology and Working Procedure with Diagrams 24913.4 Research Analysis 25213.5 Establishment of the Prototype 25613.6 Results and Discussions 26513.7 Conclusions 27014 AN AUTOMATED HOME SECURITY SYSTEM USING SECURE MESSAGE QUEUE TELEMETRY TRANSPORT PROTOCOL 275P. Rukmani, S. Graceline Jasmine, M. Vergin Raja Sarobin, L. Jani Anbarasi and Soumitro Datta14.1 Introduction 27514.2 Related Works 27714.3 Proposed Solution 27814.4 Implementation 28514.5 Results 29014.6 Conclusion and Future Work 29215 MACHINE LEARNING-BASED SOLUTIONS FOR INTERNET OF THINGS-BASED APPLICATIONS 295Varsha Bhatia and Bhavesh Bhatia15.1 Introduction 29515.2 IoT Ecosystem 29615.3 Importance of Data in IoT Applications 29815.4 Machine Learning 29915.5 Machine Learning Algorithms 30215.6 Applications of Machine Learning in IoT 30415.7 Challenges of Implementing ML for IoT Solutions 31315.8 Emerging Trends in IoT 31415.9 Conclusion 31516 MACHINE LEARNING-BASED INTELLIGENT POWER SYSTEMS 319Kusumika Krori Dutta, S. Poornima, R. Subha, Lipika Deka and Archit Kamath16.1 Introduction 31916.2 Machine Learning Techniques 32116.3 Implementation of ML Techniques in Smart Power Systems 33416.4 Case Study 34016.5 Conclusion 341PART 4: FUTURE RESEARCH OPPORTUNITIES 34517 QUANTUM COMPUTATION, QUANTUM INFORMATION, AND QUANTUM KEY DISTRIBUTION 347Mohanaprabhu D., Monish Kanna S. P., Jayasuriya J., Lakshmanaprakash S., Abirami A. and Amit Kumar Tyagi17.1 Introduction 34717.2 Literature Work 35217.3 Motivation Behind this Study 35317.4 Existing Players in the Market 35417.5 Quantum Key Distribution 35617.6 Proposed Models for Quantum Computing 35617.7 Simulation/Result 36117.8 Conclusion 36518 QUANTUM COMPUTING, QUBITS WITH ARTIFICIAL INTELLIGENCE, AND BLOCKCHAIN TECHNOLOGIES: A ROADMAP FOR THE FUTURE 367Amit Kumar Tyagi, Anand Kumar Mishra, Aswathy S. U. and Shabnam Kumari18.1 Introduction to Quantum Computing and Its Related Terms 36818.2 How Quantum Computing is Different from Security? 37418.3 Artificial Intelligence—Blockchain-Based Quantum Computing? 37518.4 Process to Build a Quantum Computer 37818.5 Popular Issues with Quantum Computing in this Smart Era 37918.6 Problems Faced with Artificial Intelligence–Blockchain-Based Quantum Computing 37918.7 Challenges with the Implementation of Quantum Computers in Today's Smart Era 38018.8 Future Research Opportunities with Quantum Computing 38118.9 Future Opportunities with Artificial Intelligence–Blockchain-Based Quantum Computing 38218.10 Conclusion 38319 QUBITS, QUANTUM BITS, AND QUANTUM COMPUTING: THE FUTURE OF COMPUTER SECURITY SYSTEM 385Harini S., Dharshini R., Praveen R., Abirami A., Lakshmanaprakash S. and Amit Kumar Tyagi19.1 Introduction 38519.2 Importance of Quantum Computing 38719.3 Literature Survey 38819.4 Quantum Computing Features 39019.5 Quantum Algorithms 39419.6 Experimental Results 39919.7 Conclusion 40020 FUTURE TECHNOLOGIES FOR INDUSTRY 5.0 AND SOCIETY 5.0 403Mani Deepak Choudhry, S. Jeevanandham, M. Sundarrajan, Akshya Jothi, K. Prashanthini and V. Saravanan20.1 Introduction 40420.2 Related Work 40720.3 Comparative Analysis of I4.0 to I5.0 and S4.0 to S5.0 40920.4 Risks and Prospects 41220.5 Conclusion 41221 FUTURISTIC TECHNOLOGIES FOR SMART MANUFACTURING: RESEARCH STATEMENT AND VISION FOR THE FUTURE 415Amit Kumar Tyagi, Anand Kumar Mishra, Nalla Vedavathi, Vijayalakshmi Kakulapati and Sajidha S. A.21.1 Introduction About Futuristic Technologies 41521.2 Related Work Towards Futuristic Technologies 41821.3 Related Work Towards Smart Manufacturing 41921.4 Literature Review Towards Futuristic Technology 42021.5 Motivation 42121.6 Smart Applications 42221.7 Popular Issues with Futuristic Technologies for Emerging Applications 42421.8 Legal Issues Towards Futuristic Technologies 42721.9 Critical Challenges with Futuristic Technology for Emerging Applications 42821.10 Research Opportunities for Futuristic Technologies Towards Emerging Applications 43021.11 Lesson Learned 43321.12 Conclusion 434References 434Index 443
Fashion Tech Applied
Provide a more tactile experience for your customers, who won’t even need to physically visit stores while optimizing conventional production processes and eradicating the tenuous tasks that nobody really likes to do. Reevaluate all parts of the value chain. You’ll see the ways technology has been used by fashion brands so far within design, production, marketing, and retail. Then discover the market opportunity that technologies such as 3D printing, augmented reality, and more bring into the chain. Challenge the ways in which you implement basic functions in your own practices. Despite the dominance of brick & mortar stores, digital platforms have emerged to pave the way for more diverse retail experiences with the adoption of technology in the fashion industry.We’re talking about more than just online shopping. Tap into NFTs, online fitting rooms, and tech solutions for better customer acquisition and increased sales. Fashion tech is more than just clothing covered in LEDs. It is the adoption of value chain optimizations, customer experience enhancing tools, and advanced operations management into existing business models. When taken seriously by the industry, it will be the future of how we all sell and purchase our products and interact with them. With a particular focus on apparel, each section of this book will put a lens on the existing conventions practiced in the industry at all stages of the value chain and how the help of technology could completely transform fashion for a more cost efficient, sustainable and time efficient industry.Use augmented reality, virtual reality, NFTs, body scanning, 3D design, and more to completely revolutionize how you approach fashion. Both as a consumer and as an amazing designer!WHAT YOU'LL LEARN* Become more creative in the way you hack any part of the fashion value chain* Spark inspiration for implementations of new technologies in fashion for both customers and designers* Adopt and stay forward-thinking by diving into where the industry currently is and where it is goingWHO THIS BOOK IS FORDecision makers in fashion corporate companies and emerging fashion brands wondering how to innovate in the saturated fashion market, as well as, fashion students with an interest in building the future of fashion. No prior technology knowledge is needed.VON N. RUZIVE began chasing fashion tech before she even knew it would be a thing. She’s journeyed from being a young fashion designer exploring the ways in which customers could better envision how her fashion designs would be worn, through frustration of the usual sketches and images not being able to do the trick. Of course the classic video was an option but would not fully illustrate the state of innovation of the garments. This was key to demonstrating her unconventional designs of zip openings in unconventional places on the garments to increase wearability for all women including those with disabilities—something she continues to work on along with teaching 3D printing & laser cutting and leading the partnerships department at a fashion tech venture studio in Paris. All of which have picked her brain in this field of fashion tech.PETER JEUN HO TSANG is the founder & CEO of Beyond Form, a fashion tech venture studio. Since obtaining a master’s degree in Digital Fashion from London College of Fashion in the late 2000’s, he has developed several fashion tech initiatives including a retail store of the future in London, a lab in Paris, and an MBA program. At Beyond Form, he now works with entrepreneurs globally to build and scale technology solutions for the fashion industry.CHAPTER 1: INTRODUCTION TO FASHION TECHNOLOGYI. Defining the fashion value chainII. Defining the garment industry (The different market levels in the industry: who is likely to adopt it or not)III. What is technology?IIII. Why synergise technology with fashion?CHAPTER 2: NEXT EVOLUTION OF DESIGN2. A. HYPERREALITY: DIGITAL DESIGN2. A. I. What is fashion design? Digital 3D design softwares: DC Suite, CLO 3D, Browzwear2. A. II. Digital 3D design: How brands have used the softwares so far: Nike2. B. CAN ANYONE NOW BECOME A DESIGNER?2. B. I. Emergence of digital design platforms: LaunchMart2. b. ii. Generative design with AI: T-Fashion & IBM2. c. THE ROLE OF DESIGN IN THE FUTURE2. c. i. What you need to learn: NFTs2. c. ii. Future design workforceCHAPTER 3: PRODUCTION AND SUPPLY CHAIN3. A. GRABBING THE FABRICS & FASTENINGS: ON-DEMAND MANUFACTURING & MATERIAL SOURCING3. A. I. Emerging supplier platforms: RoundRack3. A. II. Block chain: Supplier communication: IBM3. B. THE MAKING3. B. I. Digital pattern making: Lectra3. B. II. Digital lay-planning for sustainability: N-Hega, Marine Serre3. c. IDENTIFY THE GARMENT3. c. i. Tracing stocks3. c. ii. Digital IDs: EON3. D. TRANSPARENCY & TRACEABILITY3. d. i. Blockchain: Qbrics; ArianeCHAPTER 4: MARKETING4. A. THE NEW TECH APPROACH4. A. I. FASHION IN YOUR LIVING ROOM: Augmented reality: Cappasity & Instagram (Gucci)4. A. II. Digital fashion shows: The original with a slight twist - Balenciaga Simpsons Show4. A. III. Digital fashion shows: Purely rendered for hyperreality - Hanifa4. A. IIII. Virtual reality: virtual environments & interactivityCHAPTER 5: RETAIL5. A. THE INSTORE EXPERIENCE5. A. I. The magic mirror: IBM5. B. THE VIRTUAL EXPERIENCE5. B. I. The immersive experience: virtual reality marketplace5. B. II. The immersive screen: Haptic technology for clearer tactile product expectations5. C. THE FITTING ISSUE: ONLINE5. C. I. Virtual try on - Gucci & Farfetch5. C. II. Body scanning - Artificial intelligence & machine learning: TG3D Studio 3. b. iii. Reaching (or not) a new customer: Disabled individuals5. D. ONLINE ONLY ZONE5. D. I. NFTs: Purely online garments – DressXCHAPTER 6: MINIMUM EFFORT AND MAXIMUM OUTPUTI. Redundancy is realII. Safeguard your workforceIIII. Good Ol’ CorporatesV. Tiptoeing around the ultimate decision maker in all of this: the customerAudience: Intermediate
The Human Firewall
Nicht nur im privaten Umfeld, auch im beruflichen sind Computer, Smartphones und das Internet Begleiter unseres täglichen Lebens. Dabei spielt der Schutz von Informationen eine wichtige Rolle. Über 70% aller Cyber-Angriffe zielen auf den Nutzer ab, lediglich ein kleiner Teil auf die tatsächlichen Systeme. Daher sind geschulte und aufmerksame Mitarbeiter ein unabdingbarer Bestandteil der allgemeinen Sicherheitsstrategie zum Schutz der Informationen. Das Ziel ist dabei der Aufbau einer Kultur der Cyber-Sicherheit, einer sogenannten human firewall.Florian Jörgens ist Chief Information Security Officer der Vorwerk Gruppe. Zusätzlich ist er als Keynote-Speaker, Dozent, Autor und wissenschaftlicher Mitarbeiter an verschiedenen Hochschulen tätig. Darüber hinaus hält er Fachvorträge zu den Themen Informationssicherheit, Awareness und Cybersicherheit.Florian Jörgens wurde im September 2020 vom CIO Magazin mit dem Digital Leader Award in der Kategorie "Cyber-Security" ausgezeichnet.Einführung.- Aufmerksame Mitarbeiter: der beste Schutz.- Pre-Phase.- Durchführung.- Post-Phase.- Kennzahlen und dauerhafte Implementierung.- Fazit.