Computer und IT
Beginning Kotlin
This book introduces the Kotlin programming skills and techniques necessary for building applications. You'll learn how to migrate your Java programming skills to Kotlin, a Java Virtual Machine (JVM) programming language.The book starts with a quick tour of the Kotlin language and gradually walks you through the language in greater detail over the course of succeeding chapters. You’ll learn Kotlin fundamentals like generics, functional programming, type system, debugging, and unit testing. Additionally, with the book’s freely downloadable online appendices, you’ll discover how to use Kotlin for building Spring Boot applications, data persistence, and microservices.WHAT YOU WILL LEARN* Learn the Kotlin language, its functions, types, collections, generics, classes, and more* Dive into higher-order functions, generics, debugging, and unit testing* Apply the fundamentals of Kotlin to Spring Boot * Add Hibernate to your Spring Boot application for persistence and data accessibility * Take advantage of functional programming available in KotlinWHO THIS BOOK IS FORJava developers who are new to Kotlin and want to leverage Kotlin, particularly for building Spring Boot apps.TED HAGOS is the CTO and Data Protection Officer of RenditionDigital International (RDI), a software development company based out of Dublin. Before he joined RDI, he had various software development roles and also spent time as a trainer at IBM Advanced Career Education, Ateneo ITI, and Asia Pacific College. He spent many years in software development dating back to Turbo C, Clipper, dBase IV, and Visual Basic. Eventually, he found Java and spent many years working with it. Nowadays, he’s busy with full-stack JavaScript, Android, and Spring applications.Part 1: Kotlin1. Setup2. Tour of the Kotlin language3. Functions4. Types5. Higher order functions6. Collections7. Generics8. Classes9. Unit Testing10. Java InteroperabilityPart 2: Spring Boot11. Spring and SpringBoot12. Setup13. Getting started with a projecta. Using the project initializrb. Auto restarting an appc. Views and backing beansd. Views and controller functionse. Servicesf. Posting to a controllerg. Dependency Injection14. Functional Programminga. Overviewb. Function parametersc. Listsd. Filter and flatMape. Reduce and Foldf. Maps15. Hibernatea. Adding the dependenciesb. Entitiesc. Persisting to a database16. Reflectiona. Overviewb. Ins
Building Browser Extensions
Almost all web developers today have plenty of experience with building regular web page apps, but a lot of that knowledge doesn't transfer over when it comes to creating browser extensions. This book provides a complete reference for how to build modern browser extensions.Creating and deploying a browser extension is more like building a mobile app than a website. When you start building an extension you'll often find there are a large number of new concepts and idiosyncrasies to wrangle with. This book reveals how to successfully navigate around these obstacles and how to take advantage of the limited resources available.You'll see how a browser extensions work, their component pieces, and how to build and deploy them. Additionally, you'll review all the tricky bits of extension development that most developers have to learn through trial and error. The current transition from manifest v2 to v3 is of special interest, and an entire chapter will be dedicated to this subject. By the end of this book, you will have a rich understanding of what browser extensions are, how they work, all the pitfalls to avoid, and the most efficient ways of building them.WHAT YOU’LL LEARN* Examine the different components of browser extensions and how they behave* Review common pitfalls developers encounter when building browser extensions and how to avoid them* Develop, deploy, and manage a published browser extension* Build a browser extension using modern JavaScript frameworksWHO THIS BOOK IS FORDevelopers tasked with building a supplementary browser extension to go alongside their existing product. This book also targets people that have at least a basic understanding of the fundamentals of web development and wish to quickly understand how they can roll out a browser extension.Matt Frisbie has worked in web development for over a decade. During that time, he's been a startup co-founder, an engineer at a Big Four tech company, and the first engineer at a Y Combinator startup that would eventually become a billion-dollar company. As a Google software engineer, Matt worked on both the AdSense and Accelerated Mobile Pages (AMP) platforms; his code contributions run on most of the planet's web browsing devices. Prior to this, Matt was the first engineer at DoorDash, where he helped lay the foundation for a company that has become the leader in online food delivery. Matt has written three books, "Professional JavaScript for Web Developers", "Angular 2 Cookbook", and "AngularJS Web Application Development Cookbook", and recorded two video series, "Introduction to Modern Client-Side Programming" and "Learning AngularJS". He speaks at frontend meetups and webcasts, and is a level 1 sommelier. He majored in Computer Engineering at the University of Illinois Urbana-Champaign. Matt's Twitter handle is @mattfriz.Chapter 1:Introduction to Browser ExtensionChapter 2:Components of Browser ExtensionsChapter 3: Crash CourseChapter 4: Extension ArchitectureChapter 5: Extension ManifestsChapter 6:Manifest v2 versus v3Chapter 7: Background ScriptsChapter 8: Popup and Options PagesChapter 9: Content ScriptsChapter 10: Devtools PagesChapter 11: Extension and browser APIsChapter 12: PermissionChapter 13:NetworkingChapter 14:Extension Development and DeploymentChapter 15: Cross-Browser ExtensionsChapter 16: Tooling and Frameworks.
Beginning Go Programming
Understand and write programs in Go, a multi-paradigm language with built-in features for concurrent programming. This book enables developers to build software that is simple, reliable, and efficient. It'll also help beginners to start programming Go-based applications.Beginning Go Programming begins by explaining the programming fundamentals of the Go language, including basic syntax, data type and structures, and the use of functions and methods. Next, it covers string formatting, Unicode data handling, and how to use regular expressions in Go. Further, it discusses how to encode and decode JSON formatted data for Go applications, and how to work with HTTP in Go. It concludes by exploring concurrency and covering the most powerful features of Go, as well as tips and tricks related to it.After reading this book and working through its practical examples, you will be ready to begin programming your own Go-based applications.WHAT YOU WILL LEARN* Understand the fundamentals of the Go programming language* Master the different features of Go and how to implement real-life scenarios using the language* Work with text in Go, such as string formatting and Unicode data handling* Work with HTTP in GoWHO THIS BOOK IS FORProgrammers and developers looking to learn Go programming language concepts for efficient application building.RUMEEL HUSSAIN, has a Bachelors Degree in Computer Science and is presently working as a Senior Blockchain Developer and Senior Tech Evangelist at BNB Chain (UAE), supporting the development and growth of the ecosystem. He is an information technology enthusiast with more than five years of experience leading and implementing blockchain applications and architectures, analyzing and refactoring modern programming languages like Go, troubleshooting cloud infrastructure, and assessing security risks. His current work is focused on leveraging blockchain technology and crypto to achieve the full potential of Web3 applications.MARYAM ZULFIQAR has four years of research experience and has a Masters Degree in Computer Science. She is currently working as a Tech Martian in BNB Chain (Pakistan Region). She also works as a Senior Researcher and Developer. She is passionate about developer education, especially in sharing her knowledge on topics that are "the talk of the town" in the technology field. She has also worked in the capacity of researcher and team lead roles for HEC-funded projects targeted at community growth and welfare.Chapter 1: Introduction to GoChapter Goal: Provides an overview of the Go programming language in terms of its basic features.No of pages:Sub -Topics:● Is GoLang Static-Typed or Compiled?● Is Go Object-Oriented?● Features that make Go lang the premium choice for programming● Features excluded from Go lang● Go programsChapter 2: Go BasicsChapter Goal: This chapter is intended to cover the programming fundamentals of the Go programming language. Covering basic syntax, program structure, data types, data structures, statements, functions, I/O from files, concurrency, and error handling.No of pages:Sub - Topics○ Overview■ Ancestors of Go○ Go Syntax○ Installing Go○ Go playground○ Using IDE for developing Go applications○ Getting started with programming Go applications■ Hello world!■ Different parts of Go programs■ Executing Go program■ Keywords○ Variables■ Variable data types■ Naming conventions■ Declaring variables■ Taking user input● Using scanf● Using scanln● Using bufio■ Math operators and packages● The math package● Dates and times● Operator precedence in Go○ Memory management & reference values■ New vs make● Incorrect memory allocation example● Correct memory allocation example■ Memory deallocation○ Pointers Data Type■ What is a pointer■ Declaring pointers■ Comparison with Java and C-style languages○ Ordered values in arrays and slices■ Arrays in Go● Declaring arrays● Initialising arrays● Accessing array elements● Querying the size of array● Multi-dimensional arrays [not included yet]■ Slices in Go● Defining a slice● The len() and cap() functions● Nil slice● Sub-slicing● append() and copy() functions● Sorting slices○ Maps■ Defining maps■ Adding entries in a map object■ Deleting entries from a map object■ Iterating over stored values in a map object■○ Structs Data Type■ Defining a struct■ Accessing structure members■ Structures as function arguments■ Pointers to structures○ Program flow■ If statement■ Switch statement■ For statement■ Goto statement○ Functions■ Defining a function■ Calling a function■ Returning multiple values from Function■ Function arguments■ Methods○ Read/Write text files■ Write text files■ Read text files○ HTTP Package○ JSON○ Go Recipes: Basics programming fundamentals■ Overview■ Numbers and slice in Go■ Working with maps in Go■ Go error handling■ Defer and panic recovery○ Hands-On challengeChapter 3: Working with TextChapter Goal: In this chapter, we will discuss how to work with text in Go language. Specifically, we will cover the string formatting, Unicode data handling, and how to use regular expressions in Go language.No of pages:Sub -Topics● Go String formatting and working with unicode● Case insensitive comparisons in Go● Regular expressions and reading text files with Go● Hands-On challengeChapter 4: Structs, Methods, and InterfacesChapter Goal: In this chapter, we will provide exercise related to the usage of structs, methods, and interfaces.No of pages:Sub -Topics:● Overview● Go structs, methods and interfaces○ Structs○ Methods○ Interfaces● Empty interface and working with iota in Go○ JSON Encoding/Decoding○ Generics● Hands-on challengeChapter 5: Working with JSONChapter Goal: In this chapter, we will discuss working with JSON, especially, how to encode and decode the JSON formatted data for use in Go applications.No of pages:Sub -Topics:● Overview● Unmarshalling JSON with GO● Parsing complex JSON with Go● Marshalling JSON with Go● Dealing with zero and missing values in Go● Using mapstructure to handle arbitrary JSONChapter 6: HTTPChapter Goal: In this chapter, we cover on how to work with HTTP in Go language. No of pages:Sub -Topics● Overview● HTTP calls in Go● Authentication and Writing an HTTP server in Go● REST with gorilla/mux● Hands-on challengeChapter 7: ConcurrencyChapter Goal: Go has rich support for concurrency using goroutines and channels. In this chapter, we discuss the most powerful feature of the Go Language, i.e., concurrency.No of pages:Sub -Topics● Understanding goroutines○ Converting sequential code to concurrent in Go● Using Goroutines with shared resources○ Seeing how shared resources impact goroutines○ Accessing shared resources using mutual exclusion○ Using atomic counters for modifying shared resources● Synchronizing Goroutines○ Timeouts in Go○ sync.WaitGroup and sync.Once○ Using a pool of goroutines○ sync/atomic● Hands-on ChallengeChapter 8: Tips & TricksChapter Goal: this chapter we cover different tips and tricks related to the Go language.No of pages:Sub -Topics● Alternate ways to import packages○ goimports○ Organization● Custom constructors● Breaking down code into packages● Sets● Dependency package management● Using errors● Quick look at some compiler’s optimizations● Set the build id using git’s SHA● How to see what packages my app imports● Iota: Elegant Constants○ Auto Increment○ Custom Types
Deep Learning
DEEP LEARNINGA CONCISE AND PRACTICAL EXPLORATION OF KEY TOPICS AND APPLICATIONS IN DATA SCIENCEIn Deep Learning: From Big Data to Artificial Intelligence with R, expert researcher Dr. Stéphane Tufféry delivers an insightful discussion of the applications of deep learning and big data that focuses on practical instructions on various software tools and deep learning methods relying on three major libraries: MXNet, PyTorch, and Keras-TensorFlow. In the book, numerous, up-to-date examples are combined with key topics relevant to modern data scientists, including processing optimization, neural network applications, natural language processing, and image recognition.This is a thoroughly revised and updated edition of a book originally released in French, with new examples and methods included throughout. Classroom-tested and intuitively organized, Deep Learning: From Big Data to Artificial Intelligence with R offers complimentary access to a companion website that provides R and Python source code for the examples offered in the book. Readers will also find:* A thorough introduction to practical deep learning techniques with explanations and examples for various programming libraries* Comprehensive explorations of a variety of applications for deep learning, including image recognition and natural language processing* Discussions of the theory of deep learning, neural networks, and artificial intelligence linked to concrete techniques and strategies commonly used to solve real-world problemsPerfect for graduate students studying data science, big data, deep learning, and artificial intelligence, Deep Learning: From Big Data to Artificial Intelligence with R will also earn a place in the libraries of data science researchers and practicing data scientists.STÉPHANE TUFFÉRY, PHD, is Associate Professor at the University of Rennes 1, France where he teaches courses in data mining, deep learning, and big data methods. He also lectures at the Institute of Actuaries in Paris and has published several books on data mining, deep learning, and big data in English and French. Acknowledgements xiiiIntroduction xv1 FROM BIG DATA TO DEEP LEARNING 11.1 Introduction 11.2 Examples of the Use of Big Data and Deep Learning 61.3 Big Data and Deep Learning for Companies and Organizations 91.3.1 Big Data in Finance 101.3.1.1 Google Trends 101.3.1.2 Google Trends and Stock Prices 111.3.1.3 The quantmod Package for Financial Analysis 111.3.1.4 Google Trends in R 131.3.1.5 Matching Data from quantmod and Google Trends 141.3.2 Big Data and Deep Learning in Insurance 181.3.3 Big Data and Deep Learning in Industry 181.3.4 Big Data and Deep Learning in Scientific Research and Education 201.3.4.1 Big Data in Physics and Astrophysics 201.3.4.2 Big Data in Climatology and Earth Sciences 211.3.4.3 Big Data in Education 211.4 Big Data and Deep Learning for Individuals 211.4.1 Big Data and Deep Learning in Healthcare 211.4.1.1 Connected Health and Telemedicine 211.4.1.2 Geolocation and Health 221.4.1.3 The Google Flu Trends 231.4.1.4 Research in Health and Medicine 261.4.2 Big Data and Deep Learning for Drivers 281.4.3 Big Data and Deep Learning for Citizens 291.4.4 Big Data and Deep Learning in the Police 301.5 Risks in Data Processing 321.5.1 Insufficient Quantity of Training Data 321.5.2 Poor Data Quality 321.5.3 Non-Representative Samples 331.5.4 Missing Values in the Data 331.5.5 Spurious Correlations 341.5.6 Overfitting 351.5.7 Lack of Explainability of Models 351.6 Protection of Personal Data 361.6.1 The Need for Data Protection 361.6.2 Data Anonymization 381.6.3 The General Data Protection Regulation 411.7 Open Data 43Notes 442 PROCESSING OF LARGE VOLUMES OF DATA 492.1 Issues 492.2 The Search for a Parsimonious Model 502.3 Algorithmic Complexity 512.4 Parallel Computing 512.5 Distributed Computing 522.5.1 MapReduce 532.5.2 Hadoop 542.5.3 Computing Tools for Distributed Computing 552.5.4 Column-Oriented Databases 562.5.5 Distributed Architecture and “Analytics" 572.5.6 Spark 582.6 Computer Resources 602.6.1 Minimum Resources 602.6.2 Graphics Processing Units (GPU) and Tensor Processing Units (TPU) 612.6.3 Solutions in the Cloud 622.7 R and Python Software 622.8 Quantum Computing 67Notes 683 REMINDERS OF MACHINE LEARNING 713.1 General 713.2 The Optimization Algorithms 743.3 Complexity Reduction and Penalized Regression 853.4 Ensemble Methods 893.4.1 Bagging 893.4.2 Random Forests 893.4.3 Extra-Trees 913.4.4 Boosting 923.4.5 Gradient Boosting Methods 973.4.6 Synthesis of the Ensemble Methods 1003.5 Support Vector Machines 1003.6 Recommendation Systems 105Notes 1084 NATURAL LANGUAGE PROCESSING 1114.1 From Lexical Statistics to Natural Language Processing 1114.2 Uses of Text Mining and Natural Language Processing 1134.3 The Operations of Textual Analysis 1144.3.1 Textual Data Collection 1154.3.2 Identification of the Language 1154.3.3 Tokenization 1164.3.4 Part-of-Speech Tagging 1174.3.5 Named Entity Recognition 1194.3.6 Coreference Resolution 1244.3.7 Lemmatization 1244.3.8 Stemming 1294.3.9 Simplifications 1294.3.10 Removal of StopWords 1304.4 Vector Representation andWord Embedding 1324.4.1 Vector Representation 1324.4.2 Analysis on the Document-Term Matrix 1334.4.3 TF-IDF Weighting 1424.4.4 Latent Semantic Analysis 1444.4.5 Latent Dirichlet Allocation 1524.4.6 Word Frequency Analysis 1604.4.7 Word2Vec Embedding 1624.4.8 GloVe Embedding 1744.4.9 FastText Embedding 1764.5 Sentiment Analysis 180Notes 1845 SOCIAL NETWORK ANALYSIS 1875.1 Social Networks 1875.2 Characteristics of Graphs 1885.3 Characterization of Social Networks 1895.4 Measures of Influence in a Graph 1905.5 Graphs with R 1915.6 Community Detection 2005.6.1 The Modularity of a Graph 2015.6.2 Community Detection by Divisive Hierarchical Clustering 2025.6.3 Community Detection by Agglomerative Hierarchical Clustering 2035.6.4 Other Methods 2045.6.5 Community Detection with R 2055.7 Research and Analysis on Social Networks 2085.8 The Business Model of Social Networks 2095.9 Digital Advertising 2115.10 Social Network Analysis with R 2125.10.1 Collecting Tweets 2135.10.2 Formatting the Corpus 2155.10.3 Stemming and Lemmatization 2165.10.4 Example 2175.10.5 Clustering of Terms and Documents 2255.10.6 Opinion Scoring 2305.10.7 Graph of Terms with Their Connotation 231Notes 2346 HANDWRITING RECOGNITION 2376.1 Data 2376.2 Issues 2386.3 Data Processing 2386.4 Linear and Quadratic Discriminant Analysis 2436.5 Multinomial Logistic Regression 2456.6 Random Forests 2466.7 Extra-Trees 2476.8 Gradient Boosting 2496.9 Support Vector Machines 2536.10 Single Hidden Layer Perceptron 2586.11 H2O Neural Network 2626.12 Synthesis of “Classical” Methods 267Notes 2687 DEEP LEARNING 2697.1 The Principles of Deep Learning 2697.2 Overview of Deep Neural Networks 2727.3 Recall on Neural Networks and Their Training 2747.4 Difficulties of Gradient Backpropagation 2847.5 The Structure of a Convolutional Neural Network 2867.6 The Convolution Mechanism 2887.7 The Convolution Parameters 2907.8 Batch Normalization 2927.9 Pooling 2937.10 Dilated Convolution 2957.11 Dropout and DropConnect 2957.12 The Architecture of a Convolutional Neural Network 2977.13 Principles of Deep Network Learning for Computer Vision 2997.14 Adaptive Learning Algorithms 3017.15 Progress in Image Recognition 3047.16 Recurrent Neural Networks 3127.17 Capsule Networks 3177.18 Autoencoders 3187.19 Generative Models 3227.19.1 Generative Adversarial Networks 3237.19.2 Variational Autoencoders 3247.20 Other Applications of Deep Learning 3267.20.1 Object Detection 3267.20.2 Autonomous Vehicles 3337.20.3 Analysis of Brain Activity 3347.20.4 Analysis of the Style of a PictorialWork 3367.20.5 Go and Chess Games 3387.20.6 Other Games 340Notes 3418 DEEP LEARNING FOR COMPUTER VISION 3478.1 Deep Learning Libraries 3478.2 MXNet 3498.2.1 General Information about MXNet 3498.2.2 Creating a Convolutional Network with MXNet 3508.2.3 Model Management with MXNet 3618.2.4 CIFAR-10 Image Recognition with MXNet 3628.3 Keras and TensorFlow 3678.3.1 General Information about Keras 3708.3.2 Application of Keras to the MNIST Database 3718.3.3 Application of Pre-Trained Models 3758.3.4 Explain the Prediction of a Computer Vision Model 3798.3.5 Application of Keras to CIFAR-10 Images 3828.3.6 Classifying Cats and Dogs 3938.4 Configuring a Machine’s GPU for Deep Learning 4098.4.1 Checking the Compatibility of the Graphics Card 4108.4.2 NVIDIA Driver Installation 4108.4.3 Installation of Microsoft Visual Studio 4118.4.4 NVIDIA CUDA To34olkit Installation 4118.4.5 Installation of cuDNN 4128.5 Computing in the Cloud 4128.6 PyTorch 4198.6.1 The Python PyTorch Package 4198.6.2 The R torch Package 425Notes 4319 DEEP LEARNING FOR NATURAL LANGUAGE PROCESSING 4339.1 Neural Network Methods for Text Analysis 4339.2 Text Generation Using a Recurrent Neural Network LSTM 4349.3 Text Classification Using a LSTM or GRU Recurrent Neural Network 4409.4 Text Classification Using a H2O Model 4529.5 Application of Convolutional Neural Networks 4569.6 Spam Detection Using a Recurrent Neural Network LSTM 4609.7 Transformer Models, BERT, and Its Successors 461Notes 47910 ARTIFICIAL INTELLIGENCE 48110.1 The Beginnings of Artificial Intelligence 48110.2 Human Intelligence and Artificial Intelligence 48610.3 The Different Forms of Artificial Intelligence 48810.4 Ethical and Societal Issues of Artificial Intelligence 49310.5 Fears and Hopes of Artificial Intelligence 49610.6 Some Dates of Artificial Intelligence 499Notes 502Conclusion 505Note 506Annotated Bibliography 507On Big Data and High Dimensional Statistics 507On Deep Learning 509On Artificial Intelligence 511On the Use of R and Python in Data Science and on Big Data 512Index 515
Deep Learning Approaches for Security Threats in IoT Environments
DEEP LEARNING APPROACHES FOR SECURITY THREATS IN IOT ENVIRONMENTSAN EXPERT DISCUSSION OF THE APPLICATION OF DEEP LEARNING METHODS IN THE IOT SECURITY ENVIRONMENTIn Deep Learning Approaches for Security Threats in IoT Environments, a team of distinguished cybersecurity educators deliver an insightful and robust exploration of how to approach and measure the security of Internet-of-Things (IoT) systems and networks. In this book, readers will examine critical concepts in artificial intelligence (AI) and IoT, and apply effective strategies to help secure and protect IoT networks. The authors discuss supervised, semi-supervised, and unsupervised deep learning techniques, as well as reinforcement and federated learning methods for privacy preservation. This book applies deep learning approaches to IoT networks and solves the security problems that professionals frequently encounter when working in the field of IoT, as well as providing ways in which smart devices can solve cybersecurity issues. Readers will also get access to a companion website with PowerPoint presentations, links to supporting videos, and additional resources. They’ll also find:* A thorough introduction to artificial intelligence and the Internet of Things, including key concepts like deep learning, security, and privacy* Comprehensive discussions of the architectures, protocols, and standards that form the foundation of deep learning for securing modern IoT systems and networks* In-depth examinations of the architectural design of cloud, fog, and edge computing networks* Fulsome presentations of the security requirements, threats, and countermeasures relevant to IoT networksPerfect for professionals working in the AI, cybersecurity, and IoT industries, Deep Learning Approaches for Security Threats in IoT Environments will also earn a place in the libraries of undergraduate and graduate students studying deep learning, cybersecurity, privacy preservation, and the security of IoT networks. MOHAMED ABDEL-BASSET, PHD, is an Associate Professor in the Faculty of Computers and Informatics at Zagazig University, Egypt. He is a Senior Member of the IEEE. NOUR MOUSTAFA, PHD, is a Postgraduate Discipline Coordinator (Cyber) and Senior Lecturer in Cybersecurity and Computing at the School of Engineering and Information Technology at the University of New South Wales, UNSW Canberra, Australia. HOSSAM HAWASH is an Assistant Lecturer in the Department of Computer Science, Faculty of Computers and Informatics at Zagazig University, Egypt. About the Authors xv1 INTRODUCING DEEP LEARNING FOR IOT SECURITY 11.1 Introduction 11.2 Internet of Things (IoT) Architecture 11.2.1 Physical Layer 31.2.2 Network Layer 41.2.3 Application Layer 51.3 Internet of Things’ Vulnerabilities and Attacks 61.3.1 Passive Attacks 61.3.2 Active Attacks 71.4 Artificial Intelligence 111.5 Deep Learning 141.6 Taxonomy of Deep Learning Models 151.6.1 Supervision Criterion 151.6.1.1 Supervised Deep Learning 151.6.1.2 Unsupervised Deep Learning 171.6.1.3 Semi-Supervised Deep Learning 181.6.1.4 Deep Reinforcement Learning 191.6.2 Incrementality Criterion 191.6.2.1 Batch Learning 201.6.2.2 Online Learning 211.6.3 Generalization Criterion 211.6.3.1 Model-Based Learning 221.6.3.2 Instance-Based Learning 221.6.4 Centralization Criterion 221.7 Supplementary Materials 25References 252 DEEP NEURAL NETWORKS 272.1 Introduction 272.2 From Biological Neurons to Artificial Neurons 282.2.1 Biological Neurons 282.2.2 Artificial Neurons 302.3 Artificial Neural Network 312.3.1 Input Layer 342.3.2 Hidden Layer 342.3.3 Output Layer 342.4 Activation Functions 352.4.1 Types of Activation 352.4.1.1 Binary Step Function 352.4.1.2 Linear Activation Function 362.4.1.3 Nonlinear Activation Functions 362.5 The Learning Process of ANN 402.5.1 Forward Propagation 412.5.2 Backpropagation (Gradient Descent) 422.6 Loss Functions 492.6.1 Regression Loss Functions 492.6.1.1 Mean Absolute Error (MAE) Loss 502.6.1.2 Mean Squared Error (MSE) Loss 502.6.1.3 Huber Loss 502.6.1.4 Mean Bias Error (MBE) Loss 512.6.1.5 Mean Squared Logarithmic Error (MSLE) 512.6.2 Classification Loss Functions 522.6.2.1 Binary Cross Entropy (BCE) Loss 522.6.2.2 Categorical Cross Entropy (CCE) Loss 522.6.2.3 Hinge Loss 532.6.2.4 Kullback–Leibler Divergence (KL) Loss 532.7 Supplementary Materials 53References 543 TRAINING DEEP NEURAL NETWORKS 553.1 Introduction 553.2 Gradient Descent Revisited 563.2.1 Gradient Descent 563.2.2 Stochastic Gradient Descent 573.2.3 Mini-batch Gradient Descent 593.3 Gradient Vanishing and Explosion 603.4 Gradient Clipping 613.5 Parameter Initialization 623.5.1 Zero Initialization 623.5.2 Random Initialization 633.5.3 Lecun Initialization 653.5.4 Xavier Initialization 653.5.5 Kaiming (He) Initialization 663.6 Faster Optimizers 673.6.1 Momentum Optimization 673.6.2 Nesterov Accelerated Gradient 693.6.3 AdaGrad 693.6.4 RMSProp 703.6.5 Adam Optimizer 703.7 Model Training Issues 713.7.1 Bias 723.7.2 Variance 723.7.3 Overfitting Issues 723.7.4 Underfitting Issues 733.7.5 Model Capacity 743.8 Supplementary Materials 74References 754 EVALUATING DEEP NEURAL NETWORKS 774.1 Introduction 774.2 Validation Dataset 784.3 Regularization Methods 794.3.1 Early Stopping 794.3.2 L1 and L2 Regularization 804.3.3 Dropout 814.3.4 Max-Norm Regularization 824.3.5 Data Augmentation 824.4 Cross-Validation 834.4.1 Hold-Out Cross-Validation 844.4.2 k-Folds Cross-Validation 854.4.3 Stratified k-Folds’ Cross-Validation 864.4.4 Repeated k-Folds’ Cross-Validation 874.4.5 Leave-One-Out Cross-Validation 884.4.6 Leave-p-Out Cross-Validation 894.4.7 Time Series Cross-Validation 904.4.8 Rolling Cross-Validation 904.4.9 Block Cross-Validation 904.5 Performance Metrics 924.5.1 Regression Metrics 924.5.1.1 Mean Absolute Error (MAE) 924.5.1.2 Root Mean Squared Error (RMSE) 934.5.1.3 Coefficient of Determination (R2) 934.5.1.4 Adjusted R2 944.5.2 Classification Metrics 944.5.2.1 Confusion Matrix 944.5.2.2 Accuracy 964.5.2.3 Precision 964.5.2.4 Recall 974.5.2.5 Precision–Recall Curve 974.5.2.6 F1-Score 974.5.2.7 Beta F1 Score 984.5.2.8 False Positive Rate (FPR) 984.5.2.9 Specificity 994.5.2.10 Receiving Operating Characteristics (ROC) Curve 994.6 Supplementary Materials 99References 1005 CONVOLUTIONAL NEURAL NETWORKS 1035.1 Introduction 1035.2 Shift from Full Connected to Convolutional 1045.3 Basic Architecture 1065.3.1 The Cross-Correlation Operation 1065.3.2 Convolution Operation 1075.3.3 Receptive Field 1085.3.4 Padding and Stride 1095.3.4.1 Padding 1095.3.4.2 Stride 1115.4 Multiple Channels 1135.4.1 Multi-Channel Inputs 1135.4.2 Multi-Channel Output 1145.4.3 Convolutional Kernel 1 × 1 1155.5 Pooling Layers 1165.5.1 Max Pooling 1175.5.2 Average Pooling 1175.6 Normalization Layers 1195.6.1 Batch Normalization 1195.6.2 Layer Normalization 1225.6.3 Instance Normalization 1245.6.4 Group Normalization 1265.6.5 Weight Normalization 1265.7 Convolutional Neural Networks (LeNet) 1275.8 Case Studies 1295.8.1 Handwritten Digit Classification (One Channel Input) 1295.8.2 Dog vs. Cat Image Classification (Multi-Channel Input) 1305.9 Supplementary Materials 130References 1306 DIVE INTO CONVOLUTIONAL NEURAL NETWORKS 1336.1 Introduction 1336.2 One-Dimensional Convolutional Network 1346.2.1 One-Dimensional Convolution 1346.2.2 One-Dimensional Pooling 1356.3 Three-Dimensional Convolutional Network 1366.3.1 Three-Dimensional Convolution 1366.3.2 Three-Dimensional Pooling 1366.4 Transposed Convolution Layer 1376.5 Atrous/Dilated Convolution 1446.6 Separable Convolutions 1456.6.1 Spatially Separable Convolutions 1466.6.2 Depth-wise Separable (DS) Convolutions 1486.7 Grouped Convolution 1506.8 Shuffled Grouped Convolution 1526.9 Supplementary Materials 154References 1547 ADVANCED CONVOLUTIONAL NEURAL NETWORK 1577.1 Introduction 1577.2 AlexNet 1587.3 Block-wise Convolutional Network (VGG) 1597.4 Network in Network 1607.5 Inception Networks 1627.5.1 GoogLeNet 1637.5.2 Inception Network v2 (Inception v2) 1667.5.3 Inception Network v3 (Inception v3) 1707.6 Residual Convolutional Networks 1707.7 Dense Convolutional Networks 1737.8 Temporal Convolutional Network 1767.8.1 One-Dimensional Convolutional Network 1777.8.2 Causal and Dilated Convolution 1807.8.3 Residual Blocks 1857.9 Supplementary Materials 188References 1888 INTRODUCING RECURRENT NEURAL NETWORKS 1898.1 Introduction 1898.2 Recurrent Neural Networks 1908.2.1 Recurrent Neurons 1908.2.2 Memory Cell 1928.2.3 Recurrent Neural Network 1938.3 Different Categories of RNNs 1948.3.1 One-to-One RNN 1958.3.2 One-to-Many RNN 1958.3.3 Many-to-One RNN 1968.3.4 Many-to-Many RNN 1978.4 Backpropagation Through Time 1988.5 Challenges Facing Simple RNNs 2028.5.1 Vanishing Gradient 2028.5.2 Exploding Gradient 2048.5.2.1 Truncated Backpropagation Through Time (TBPTT) 2048.5.2.2 Penalty on the Recurrent Weights Whh2058.5.2.3 Clipping Gradients 2058.6 Case Study: Malware Detection 2058.7 Supplementary Material 206References 2079 DIVE INTO RECURRENT NEURAL NETWORKS 2099.1 Introduction 2099.2 Long Short-Term Memory (LSTM) 2109.2.1 LSTM Gates 2119.2.2 Candidate Memory Cells 2139.2.3 Memory Cell 2149.2.4 Hidden State 2169.3 LSTM with Peephole Connections 2179.4 Gated Recurrent Units (GRU) 2189.4.1 CRU Cell Gates 2189.4.2 Candidate State 2209.4.3 Hidden State 2219.5 ConvLSTM 2229.6 Unidirectional vs. Bidirectional Recurrent Network 2239.7 Deep Recurrent Network 2269.8 Insights 2279.9 Case Study of Malware Detection 2289.10 Supplementary Materials 229References 22910 ATTENTION NEURAL NETWORKS 23110.1 Introduction 23110.2 From Biological to Computerized Attention 23210.2.1 Biological Attention 23210.2.2 Queries, Keys, and Values 23410.3 Attention Pooling: Nadaraya–Watson Kernel Regression 23510.4 Attention-Scoring Functions 23710.4.1 Masked Softmax Operation 23910.4.2 Additive Attention (AA) 23910.4.3 Scaled Dot-Product Attention 24010.5 Multi-Head Attention (MHA) 24010.6 Self-Attention Mechanism 24210.6.1 Self-Attention (SA) Mechanism 24210.6.2 Positional Encoding 24410.7 Transformer Network 24410.8 Supplementary Materials 247References 24711 AUTOENCODER NETWORKS 24911.1 Introduction 24911.2 Introducing Autoencoders 25011.2.1 Definition of Autoencoder 25011.2.2 Structural Design 25311.3 Convolutional Autoencoder 25611.4 Denoising Autoencoder 25811.5 Sparse Autoencoders 26011.6 Contractive Autoencoders 26211.7 Variational Autoencoders 26311.8 Case Study 26811.9 Supplementary Materials 269References 26912 GENERATIVE ADVERSARIAL NETWORKS (GANS) 27112.1 Introduction 27112.2 Foundation of Generative Adversarial Network 27212.3 Deep Convolutional GAN 27912.4 Conditional GAN 28112.5 Supplementary Materials 285References 28513 DIVE INTO GENERATIVE ADVERSARIAL NETWORKS 28713.1 Introduction 28713.2 Wasserstein GAN 28813.2.1 Distance Functions 28913.2.2 Distance Function in GANs 29113.2.3 Wasserstein Loss 29313.3 Least-Squares GAN (LSGAN) 29813.4 Auxiliary Classifier GAN (ACGAN) 30013.5 Supplementary Materials 301References 30114 DISENTANGLED REPRESENTATION GANS 30314.1 Introduction 30314.2 Disentangled Representations 30414.3 InfoGAN 30614.4 StackedGAN 30914.5 Supplementary Materials 316References 31615 INTRODUCING FEDERATED LEARNING FOR INTERNET OF THINGS (IOT) 31715.1 Introduction 31715.2 Federated Learning in the Internet of Things 31915.3 Taxonomic View of Federated Learning 32215.3.1 Network Structure 32215.3.1.1 Centralized Federated Learning 32215.3.1.2 Decentralized Federated Learning 32315.3.1.3 Hierarchical Federated Learning 32415.3.2 Data Partition 32515.3.3 Horizontal Federated Learning 32615.3.4 Vertical Federated Learning 32715.3.5 Federated Transfer Learning 32815.4 Open-Source Frameworks 33015.4.1 TensorFlow Federated 33015.4.2 PySyft and PyGrid 33115.4.3 FedML 33115.4.4 LEAF 33215.4.5 PaddleFL 33215.4.6 Federated AI Technology Enabler (FATE) 33315.4.7 OpenFL 33315.4.8 IBM Federated Learning 33315.4.9 NVIDIA Federated Learning Application Runtime Environment (NVIDIA FLARE) 33415.4.10 Flower 33415.4.11 Sherpa.ai 33515.5 Supplementary Materials 335References 33516 PRIVACY-PRESERVED FEDERATED LEARNING 33716.1 Introduction 33716.2 Statistical Challenges in Federated Learning 33816.2.1 Nonindependent and Identically Distributed (Non-IID) Data 33816.2.1.1 Class Imbalance 33816.2.1.2 Distribution Imbalance 34116.2.1.3 Size Imbalance 34616.2.2 Model Heterogeneity 34616.2.2.1 Extracting the Essence of a Subject 34616.2.3 Block Cycles 34816.3 Security Challenge in Federated Learning 34816.3.1 Untargeted Attacks 34916.3.2 Targeted Attacks 34916.4 Privacy Challenges in Federated Learning 35016.4.1 Secure Aggregation 35116.4.1.1 Homomorphic Encryption (HE) 35116.4.1.2 Secure Multiparty Computation 35216.4.1.3 Blockchain 35216.4.2 Perturbation Method 35316.5 Supplementary Materials 355References 355Index 357
Digital Twin Technology
DIGITAL TWIN TECHNOLOGYTHE BOOK LUCIDLY EXPLAINS THE FUNDAMENTALS OF DIGITAL TWIN TECHNOLOGY ALONG WITH ITS APPLICATIONS AND VARIOUS INDUSTRIAL REAL-WORLD EXAMPLES.Digital twin basically means a replicated model of any object or product in digital form. A digital twin has many advantages as it remains connected with the original object or product it is replicating and receives real-time data. Therefore, the obstacles and issues that could be encountered in a product or object can be known before their actual happening which helps to prevent errors and major losses which otherwise might have been incurred. The various capabilities of digital twin technology make it a powerful tool that can be used to effectively boost various sectors of the healthcare, automotive, and construction industries, among others. Although this technology has been making its way into various sectors, it has not yet received the kind of exposure necessary to increase awareness of its potential in these industries. Therefore, it is critical that a better understanding of digital twin technology is acquired to facilitate growth and to have it implemented in the various sectors so that transformation can be ushered in. Therefore, this book was designed to be a useful resource for those who want to become well acquainted with digital twin technology. AUDIENCEEngineers, researchers, and advanced students in information technology, computer science, and electronics, as well as IT specialists and professionals in various industries such as healthcare, automotive, and transportation. MANISHA VOHRA has a Master of Engineering in electronics and telecommunication and is an independent researcher. She has published various papers in international journals including IEEE Xplore. She has also published various book chapters, authored two books, and edited six books. Preface xv1 OVERVIEW OF DIGITAL TWIN 1Manisha Vohra1.1 A Simplistic Introduction to Digital Twin 11.2 Basic Definition and Explanation of What is Digital Twin 51.3 The History of Digital Twin 71.4 Working 91.5 Features 111.5.1 Replication of Each and Every Aspect of the Original Device or Product 111.5.2 Helps in Product Lifecycle Management 111.5.3 Digital Twin can Prevent Downtime 111.6 Advantages of Digital Twin 111.6.1 Digital Twin is Helpful in Preventing Issues or Errors in the Actual Object, Product or Process 111.6.2 Helps in Well Utilization of Resources 121.6.3 Keeping Vigilance of the Actual Object, Product or Process Through Digital Twin is Possible 121.6.4 Helps in Efficient Handling and Managing of Objects, Device, Equipment, etc. 121.6.5 Reduction in Overall Cost of Manufacturing of Objects, Products, etc. 131.7 Applications 131.8 A Simple Example of Digital Twin Application 131.9 Digital Twin Technology and the Metaverse 141.10 Challenges 151.10.1 Careful Handling of Different Factors Involved in Digital Twin 151.10.2 Expertise Required 151.10.3 Data Security and Privacy 151.11 Conclusion 16References 162 INTRODUCTION, HISTORY, AND CONCEPT OF DIGITAL TWIN 19N. Rajamurugu and M. K. Karthik2.1 Introduction 192.2 History of Digital Twin 212.3 Concept of Digital Twin 232.3.1 DTP 232.3.2 DTI 242.3.3 DTE 242.3.4 Conceptualization 252.3.5 Comparison 252.3.6 Collaboration 252.4 Working Principle 262.5 Characteristics of Digital Twin 272.5.1 Homogenization 272.5.2 Digital Trail 272.5.3 Connectivity 272.6 Advantages 282.6.1 Companies Can Benefit From Digital Twin by Tracking Performance-Related Data 282.6.2 Different Sector’s Progress Can Be Accelerated 282.6.3 Digital Twins Can Be Used for Various Application 282.6.4 Digital Twin Can Help Decide Future Course of Work 282.6.5 Manufacturing Work Can Be Monitored 292.7 Limitations 292.7.1 Data Transmission Could Have Delays and Distortions 292.7.2 Digital Twin Implementation Will Need Required Skills and Sound Knowledge About It 292.8 Example of Digital Twin Application 292.8.1 Digital Twin Application in General Electric (GE) Renewable Energy 292.9 Conclusion 30References 303 AN INSIGHT TO DIGITAL TWIN 33Anant Kumar Patel, Ashish Patel and Kanchan Mona Patel3.1 Introduction 333.2 Understanding Digital Twin 353.3 Digital Twin History 363.4 Essential Aspects From Working Perspectives of Digital Twin 373.5 How Does a Digital Twin Work? 373.6 Insights to Digital Twin Technology Concept 383.6.1 Parts Twins 383.6.2 Product Twins 393.6.3 System Twins 393.6.4 Process Twins 393.7 Types of Digital Twin 393.7.1 Digital Twin Prototype (DTP) 403.7.2 Digital Twin Instance (DTI) 403.7.3 Digital Twin Environment (DTE) 403.8 Traits of Digital Twin 403.8.1 Look Same as the Original Object 403.8.2 Consists Different Details of the Original Object 413.8.3 Behaves Same as the Original Object 413.8.4 Can Predict and Inform in Advance About Problems That Could Occur 413.9 Value of Digital Twin 413.10 Advantages of Digital Twin 423.11 Real-World Examples of Use of Digital Twin 433.12 Conclusion 44References 454 DIGITAL TWIN SOLUTION ARCHITECTURE 47Suhas D. Joshi4.1 Introduction 474.2 Previous Work 484.2.1 How This Work Differs 494.3 Use Cases 504.4 Architecture Considerations 514.5 Understanding the Physical Object 524.5.1 Modeling Considerations 554.6 Digital Twin and IoT 564.7 Digital Twin Solution Architecture 574.7.1 Conceptual Digital Twin Solution Architecture 574.7.2 Infrastructure Platform and IoT Services 574.7.3 Digital Twin Data and Process Model 574.7.4 Digital Twin Services 604.7.5 Digital Twin Applications 614.7.6 Sample Basic Data Flow through Digital Twin 614.7.7 Sample Data Flow for Exception Handling 634.7.8 Sample Data Flow through Digital Twin Applications 634.7.9 Development Considerations 654.8 Database Considerations 664.9 Messaging 674.10 Interfaces 694.11 User Experience 704.12 Cyber Security 704.13 Use Case Coverage 714.14 Future Direction and Trends 734.15 Conclusion 74References 745 ROLE OF DIGITAL TWIN TECHNOLOGY IN MEDICAL SECTOR—TOWARD ENSURING SAFE HEALTHCARE 77S.N. Kumar, A. Lenin Fred, L.R. Jonisha Miriam, Christina Jane I., H. Ajay Kumar, Parasuraman Padmanabhan and Balazs Gulyas5.1 Introduction to Digital Twin 785.2 Generic Applications of Digital Twin 795.3 Digital Twin Applications in Medical Field 835.3.1 Biosignal and Physiological Parameters Analysis for Body Area Network 845.3.2 Medicinal Drug Delivery 855.3.3 Surgical Preplanning 865.3.4 COVID 19 Screening and Diagnosis 875.4 Ongoing and Future Applications of Digital Twin in Healthcare Sector 895.5 Conclusion 89Acknowledgments 90References 906 DIGITAL TWIN AS A REVAMPING TOOL FOR CONSTRUCTION INDUSTRY 97Greeshma A. S. and Philbin M. Philip6.1 Introduction 976.2 Introduction to Digital Twin 996.3 Overview of Digital Twin in Construction 1006.4 The Perks of Digital Twin 1016.5 The Evolution of Digital Twin 1026.6 Application of Digital Twin Technology in Construction Industry 1036.7 Digital Twins Application for Construction Working Personnel Safety 1066.8 Digital Twin Applications in Smart City Construction 1076.9 Discussion 1076.10 Conclusion 108References 1097 DIGITAL TWIN APPLICATIONS AND CHALLENGES IN HEALTHCARE 111Pavithra S., Pavithra D., Vanithamani R. and Judith Justin7.1 Introduction 1117.2 Digital Twin 1127.3 Applications of Digital Twin 1147.3.1 Smart Cities 1147.3.2 Manufacturing Sector 1157.3.3 Healthcare 1157.3.4 Aviation 1157.3.5 The Disney Park 1157.4 Challenges with Digital Twin 1157.5 Digital Twin in Healthcare 1167.5.1 Digital Twin for Hospital Workflow Management 1167.5.2 Digital Twin for a Healthcare Facility 1177.5.3 Digital Twin for Different Medical Product Manufacturing 1187.5.4 Cardiovascular Digital Twin 1187.5.5 Digital Twin Utilization for Supporting Personalized Treatment 1197.5.6 Digital Twin for Multiple Sclerosis (MS) 1197.6 Digital Twin Challenges in Healthcare 1197.6.1 Need of Training and Knowledge 1207.6.2 Cost Factor 1207.6.3 Trust Factor 1207.7 Conclusion 121References 1228 MONITORING STRUCTURAL HEALTH USING DIGITAL TWIN 125Samaya Pillai, Venkatesh Iyengar and Pankaj Pathak8.1 Introduction 1268.1.1 Digital Twin—The Approach and Uses 1268.2 Structural Health Monitoring Systems (SHMS) 1288.2.1 Criticality and Need for SHMS Approach 1288.2.2 Passive and Active SHMS 1298.3 Sensor Technology, Digital Twin (DT) and Structural Health Monitoring Systems (SHMS) 1308.4 Conclusion 135References 1369 ROLE AND ADVANTAGES OF DIGITAL TWIN IN OIL AND GAS INDUSTRY 141Prakash J.9.1 Introduction 1419.2 Digital Twin 1429.3 Evolution of Digital Twin Technology 1449.4 Various Digital Twins that Can Be Built 1459.4.1 Parts Twins 1459.4.2 Product Twins or Asset Twins 1469.4.3 System Twins or Unit Twins 1469.4.4 Process Twins 1469.5 Advantage of Digital Twin 1469.5.1 Paced Prototypin 1479.5.2 Prediction 1479.5.3 Enhanced Maintenance 1479.5.4 Monitoring 1479.5.5 Safety 1479.5.6 Reduced Waste 1479.6 Applications of Digital Twin 1489.6.1 Aerospace 1489.6.2 Power-Generation Equipment 1489.6.3 Structures and Their Systems 1489.6.4 Manufacturing Operations 1499.6.5 Healthcare Services 1499.6.6 Automotive Industry 1499.6.7 Urban Planning and Construction 1499.6.8 Smart Cities 1499.6.9 Industrial Applications 1499.7 Characteristics of Digital Twin 1509.7.1 High-Fidelity 1509.7.2 Lively 1509.7.3 Multidisciplinary 1509.7.4 Homogenization 1509.7.5 Digital Footprint 1519.8 Digital Twin in Oil and Gas Industry 1519.9 Role of Digital Twin in the Various Areas of Oil and Gas Industry 1529.9.1 Planning of Drilling Process 1539.9.2 Performance Monitoring of Oil Field 1539.9.3 Data Analytics and Simulation for Oil Field Production 1539.9.4 Improving Field Personnel and Workforce Safety 1539.9.5 Predictive Maintenance 1539.10 The Advantages of Digital Twin in the Oil and Gas Industry 1549.10.1 Production Efficacy 1549.10.2 Preemptive Maintenance 1549.10.3 Scenario Development 1549.10.4 Different Processes Monitoring 1559.10.5 Compliance Criteria 1559.10.6 Cost Savings 1559.10.7 Workplace Safety 1559.11 Conclusion 155References 15610 DIGITAL TWIN IN SMART CITIES: APPLICATION AND BENEFITS 159Manisha Vohra10.1 Introduction 15910.2 Introduction of Digital Twin in Smart Cities 16210.3 Applications of Digital Twin in Smart Cities 16410.3.1 Traffic Management 16410.3.2 Construction 16510.3.3 Structural Health Monitoring 16610.3.4 Healthcare 16710.3.5 Digital Twin for Drainage System 16810.3.6 Digital Twin for Power Grid 16910.4 Conclusion 169References 17011 DIGITAL TWIN IN PHARMACEUTICAL INDUSTRY 173Anant Kumar Patel, Ashish Patel and Kanchan Mona Patel11.1 Introduction 17311.2 What is Digital Twin? 17511.2.1 Digital Twin Prototype (DTP) 17611.2.2 Digital Twin Instance 17611.2.3 Parts Twins 17711.2.4 Product Twins 17711.2.5 System Twins 17711.2.6 Process Twins 17811.3 Digital Twin in the Pharmaceutical Industry 17811.4 Digital Twin Applications in Pharmaceutical Industry 18011.4.1 Digital Twin of the Pharmaceutical Manufacturing Process 18011.4.2 Digital Twin for Pharmaceutical Supply Chains 18011.5 Examples of Use of Digital Twin in Pharmaceutical Industry 18111.5.1 Digital Twin Simulator for Supporting Scientific Exchange of Views With Expert Physicians 18111.5.2 Digital Twin for Medical Products 18211.5.3 Digital Twin for Pharmaceutical Companies 18211.6 Advantages of Digital Twin in the Pharmaceutical Industry 18211.6.1 Wastage Can Be Reduced 18211.6.2 Cost Savings 18311.6.3 Faster Time to Market 18311.6.4 Smooth Management 18311.6.5 Remote Monitoring 18411.7 Digital Twin in the Pharmaceutical Industry as a Game-Changer 18411.8 Conclusion 184References 18512 DIFFERENT APPLICATIONS AND IMPORTANCE OF DIGITAL TWIN 189R. Suganya, Seyed M. Buhari and S. Rajaram12.1 Introduction 18912.2 History of Digital Twin 19112.3 Applications of Digital Twin 19212.3.1 Agriculture 19312.3.2 Education 19312.3.3 Healthcare 19412.3.4 Manufacturing and Industry 19512.3.5 Automotive Industry 19712.3.6 Security 19812.3.7 Smart Cities 19912.3.8 Weather Forecasting and Meteorology 19912.4 Importance of Digital Twin 19912.5 Challenges 20012.6 Conclusion 200References 20113 DIGITAL TWIN IN DEVELOPMENT OF PRODUCTS 205Pedro Pablo Chambi Condori13.1 Introduction 20613.2 Digital Twin 20713.2.1 Digital Twin Types 21013.3 Different Aspects of an Organization and Digital Twin in Development of Products in Organizations 21013.4 Implications of Digital Twin in Development of Products in Organizations 21413.5 Advantages 21413.5.1 Digital Twin Helps in Decision Making 21413.5.2 Avoiding Downtine 21513.5.3 Maximizing Efficiency 21513.5.4 Cost Savings 21513.5.5 Optimum Use of Resources 21513.6 Conclusion 215References 21614 POSSIBILITIES WITH DIGITAL TWIN 219Vismay Shah and Anilkumar Suthar14.1 Introduction 21914.2 What is Digital Twin Technology? 22014.3 Possibilities With Digital Twin in Aviation Sector 22414.3.1 Aviation Engineering in Combination With Digital Twin 22414.3.2 Concept of Digital Twin for Aviation Components 22514.3.3 How Important is Digital Twin in the Aviation Industry? 22514.4 Possibilities With Digital Twin in Automotive Industry 22614.4.1 Digital Twin in Automotive Industry 22614.5 How Can Digital Twin Help in Improving Supply Chain Management? 22814.6 Discussion 22914.7 Conclusion 229References 22915 DIGITAL TWIN: PROS AND CONS 233Prakash J.15.1 Introduction 23315.2 Introduction to Digital Twin 23415.3 Pros of Digital Twin 23815.3.1 Digital Twin Can Forecast the Problem in Advance Before Its Arrival 23815.3.2 Digital Twin Can Be Used in Monitoring Work 23915.3.3 Reduction in Waste 24015.3.4 Helps Avoid Hazardous Situations at Work 24015.3.5 Increases Speed of Work Completion 24015.4 Cons of Digital Twin 24015.4.1 Deep Knowledge Will Be Needed for Creating and Handling the Digital Twin 24115.4.2 Issues with Sensors Issue Can Affect the Digital Twin 24115.4.3 Security 24115.5 Application Wise Pros of Digital Twin 24115.5.1 Oil and Gas Sector 24215.5.2 Industrial Sector 24215.5.3 Automotive Sector 24215.5.4 Construction Sector 24215.6 Conclusion 243References 243Index 247
Troubleshooting and Supporting Windows 11
Diagnose, troubleshoot and repair any type of problems on your PC from startup and file access to cloud services and the issues caused by hybrid-work. This book contains everything you need to know to keep PC systems running optimally, and to repair problems quickly and efficiently.This book provides a deep dive into the Windows OS, detailing what everything is, and how it works. You will learn about the in-built, additional, and third-party tools and utilities you can use to create reliable, robust and secure PC systems.Further, you will learn how to configure Windows 11 so as to avoid problems occurring, and how to support every type of end user, working from home, or in any part of the world, speaking any language, and taking into account other factors such as ability or personal barriers.You will discover the support tools and support ecosystem you can use to create and manage effective support tracking and remote access. You will discover how to get detailed events and reliability information, and how to manage update channels. You will deep dive into Windows 11 operating system and folder structure and learn app and software troubleshooting, process and service troubleshooting, network and internet troubleshooting and hardware and peripherals troubleshooting.Finally, you will learn more advanced troubleshooting techniques like security and encryption troubleshooting and using PowerShell scripting to repair problems. Further, you will also learn how to manually remove malware and ransomware, registry troubleshooting and startup and repair troubleshooting. By the end, you will know how to troubleshoot complex problems and diagnose hardware problems in a PC. You will be able to troubleshoot and repair any type of problem on a Windows 11 PC.WHAT WILL YOU LEARN* How to support home and hybrid-workers using their own PCs* Using scripting and PowerShell to troubleshoot and repair systems* Managing networking and internet access to minimize downtime* Managing installation and troubleshoot for updates and patchesWHO IS THIS BOOK FORIT Pros and system administrators who have to maintain small or large networks of connected PCs locally at their organization, or with hybrid workers.MIKE HALSEY is a recognized technical expert. He is the author of more than twenty help and how-to books for Windows 7, 8, 10 and 11, including accessibility, productivity, and troubleshooting. He is also the author of The Green IT Guide (Apress). Mike is well-versed in the problems and issues that PC users experience when setting up, using, and maintaining their PCs and knows how difficult and technical it can appear.He understands that some subjects can be intimidating, so he approaches each subject area in straightforward and easy-to-understand ways. Mike is originally from the UK, but now lives in France with his rescue border collies, Evan and Robbie. CHAPTER 1: INTRODUCING TROUBLESHOOTING IN WINDOWS 11 (25 PAGES)Introducing Windows 11 to the reader. Detailing how it differs from Windows 10 and how it is likely to change over its life. Detailing the different editions of the operating system and how these might affect the support provided to home ad hybrid workers, and looking at the key features that make it unique.1) How Windows 11 Came About2) How Windows 11 differs from Windows 103) How Windows 11 is likely to change during its life4) Windows 11 Editions and Channels5) Key Windows 11 Features6) The Windows Insider ProgramCHAPTER 2: TOOLS AND UTILITIES USED THROUGHOUT THIS BOOK (25 PAGES)A high level view of all of the Windows 11 and Microsoft tools and utilities used throughout this book, so as to avoid repetition in later chapters, and to introduce the reader to some of the advanced reporting and troubleshooting systems available to them.1) Settings2) Control Panel3) Windows Tools4) Windows Terminal5) Recovery Console6) Microsoft SysinternalsCHAPTER 3: BUILDING A ROBUST AND SECURE PC ECOSYSTEM (40 PAGES)What is involved in creating a reliable, robust and secure PC system, and PC / cloud ecosystem. Examining how the reader can create PC systems that are resilient, easy to maintain and restore, and secure from both internal and external threats.1) Understanding System Restore2) Creating a Recovery Drive3) File Backup and Restore4) System Backup and Restore5) Group Policy6) Local Security Policy7) Windows and Cloud Security, and Two-Factor Authentication8) The Windows Firewall and Advanced Firewall9) The Windows Security CenterCHAPTER 4: CONFIGURING WINDOWS 11 (20 PAGES)How can Windows 11 be configured and how are end users likely to change settings and configuration options. Where to look for configuration, what to look for, and what is good to change for users across and organization to mitigate the effects of problems later on.1) Settings2) Control Panel3) Group Policy Configuration4) User Accounts and Shell User Folders5) Handling Remote and Hybrid Workers6) Creating Sustainable PC SystemsCHAPTER 5: SUPPORTING LOCAL AND REMOTE PC USERS (20 PAGES)How to support users who can be of any educational background, from or residing in any part of the world, speaking any language, and taking into account other factors such as disability or personal barriers to technology.1) Understanding User Diversity2) Teaching Fundamentals3) Establishing Effective IT Training4) Supporting Home and Hybrid Workers5) Work Folders and Azure ADCHAPTER 6: WINDOWS 11’S SUPPORT TOOLS USERS (25 PAGES)Detailing all of the support tools and utilities in Windows 11, showing how the end user can help you identify the problem, and how you can help teach the end user how to avoid and repair problems that might occur on their systems.1) Taking Screenshots in Windows 112) Steps Recorder3) Quick Assist4) Windows Remote Assistance5) Remote Desktop6) Third-Party Tools for Remote SupportCHAPTER 7: THE METHODOLOGY OF SUPPORTING USERS (30 PAGES)How to set up and manage an effective IT support ecosystem that takes into account the needs of its end users, and how to manage support reporting in a way that will help and not hamper the job.7) Understanding the Support Ecosystem8) Understanding Different Types of PC User9) Managing Accessibility with IT Support10) Setting Up Effective Support Systems11) Creating and Managing Support ReportingPART 2 – TROUBLESHOOTING WINDOWS 11CHAPTER 8: EVENTS AND RELIABILITY TROUBLESHOOTING (40 PAGES)Examining Windows event and resource monitoring, with in-depth looks at all of the available tools, how they can be used, how the greatest amount of information can be gathered from them, and how reporting can take place across a network.1) Reliability History2) Resource Monitor3) Automatic Maintenance4) System Protection and Backup5) The Event Viewer6) Using PowerShell with Events7) The Blue Screen of Death8) System Information and Settings9) Getting System Information for Remote PCs10) Using PowerShell with System InformationCHAPTER 9: INTEGRITY AND UPDATING TROUBLESHOOTING (25 PAGES)Managing the integrity of the core Windows 11 OS files, during updating and over the lifetime of a PC, using tools to repair and replace damaged kernel files, managing problem updates and Feature Packs, and using scripting to repair system files.1) System File Checker and DISM2) Managing Windows Update and Update Channels3) Troubleshooting Windows Update4) Rolling Back and Uninstalling Updates5) Managing Windows Update with PowerShell6) Reset and RepairCHAPTER 10: USER ACCOUNT AND FILE TROUBLESHOOTING (30 PAGES)Managing user account permissions, and file and document permissions and access. How to troubleshoot locked and inaccessible files and folders. Managing and configuring disks, partitions and virtual disks with appropriate access and security permissions.1) Managing User Accounts and Group Policy2) User File and Folder Permissions3) Ownership and Effective Access4) Troubleshooting Accounts with Sysinternals5) Managing Disks, Partitions, and Virtual Hard Disks6) Using PowerShell to Troubleshoot Disks and PartitionsCHAPTER 11: THE WINDOWS 11 FILE AND FOLDER STRUCTURE IN DEPTH (25 PAGES)A deep dive into the Windows 11 file system, examining legacy components, new store folders, and how Windows manages compatibility with legacy software. Looking at temporary and reporting file stores and how they can be effectively managed on a PC.1) Root Windows Files and Folders2) Win32 and Store App Folders3) Windows Operating System Files and Folders4) User Account Folders5) Windows Log Folders and Reading Log Files6) Windows Temporary Folders7) Windows File Types8) Managing Shell User FoldersCHAPTER 12: APPS AND SOFTWARE TROUBLESHOOTING (25 PAGES)Managing the installation and running of legacy and custom apps in the enterprise. Maintaining compatibility with older websites and Intranets needed by companies, and how to manage new store and Android app usage in Windows 11.1) Managing Legacy App Compatibility2) Managing Store and Third-Party Store Apps3) Managing Browser and Intranet Compatibility in Edge4) Mastering the Windows Task Manager5) Removing Troublesome Apps with SysinternalsCHAPTER 13: PROCESS AND SERVICES TROUBLESHOOTING (20 PAGES)Advanced information on how to manage running processes and services (both Microsoft and third-party) in Windows 11. How to troubleshoot hung and misbehaving processes, manage service usage on a PC, and use scripting and additional tools to get further information on processes and services on the PC.1) Managing Running Processes and Services2) Managing Running Processes and Services with PowerShell3) Troubleshooting Processes and Services with SysinternalsCHAPTER 14: NETWORKING AND INTERNET TROUBLESHOOTING (40 PAGES)Configuring networking settings in Windows 11 to aid productivity and keep people working. Manually configuring advanced networking settings required for specialized security and international environments. Using advanced scripting techniques to manage and repair problems with networks and Wi-Fi.1) Configuring Windows Networking Settings2) The Network and Sharing Center3) Managing Wi-Fi Networks4) Obtaining and Setting Advanced Network Configuration5) Troubleshooting and Configuring Networks with Scripting6) Troubleshooting Networks with SysinternalsCHAPTER 15: HARDWARE AND PERIPHERALS TROUBLESHOOTING (40 PAGES)How to manage problems with external and internal peripherals and hardware. Installing and managing legacy hardware required for specific roles. How to configure and maintain UEFI firmware, and understanding the Windows driver store.1) Managing Hardware and Peripheral Problems2) Troubleshooting USB, Bluetooth, and Other External Peripherals3) Managing UEFI Systems4) Managing Printers and Queues5) Windows Device Manager6) Managing Problem and Unknown Devices7) Managing Legacy Hardware and Devices8) The Windows Driver StorePART 3 – ADVANCED TROUBLESHOOTING TECHNIQUESCHAPTER 16: IT SYSTEMS AND THE WIDER WORLD (20 PAGES)Looking at how the world around us can directly and negatively impact our PC systems, especially for remote workers. Looking at the interaction between our PCs and cloud services, and examining how our own PC usage affects, and impacts business and organization sustainability and climate change policies.1) How environmental affect IT systems2) How construction types affect IT systems3) How cloud service considerations affect IT systems4) Our PCs, the Planet, and Climate ChangeCHAPTER 17: SECURITY AND ENCRYPTION TROUBLESHOOTING (15 PAGES)Managing encryption and security on PC systems to ensure data security and compliance with data security and privacy policies of governments around the world. How to use scripting to manage encryption, and how to recover encrypted files and folders when problems arise.1) Managing Bitlocker, TPM and fTPM Security2) The Encrypting File System3) Best Practice Security for Your Organization4) Troubleshooting Encryption with SysinternalsCHAPTER 18: VIRUS AND MALWARE TROUBLESHOOTING (40 PAGES)How to remove malware and viruses from a PC from beginning to end, starting with preventing infection on PCs, to using the in-built and other available tools for malware removal. Also looking at the steps involved in manual removal of malware from an infected PC.1) The Windows Security Center2) Protecting a PC from Ransomware3) Safe Mode and Diagnostic Mode4) Removing Malware Using Windows 11 Tools5) Manual Removal of Malware from a PC6) Third-Party Malware Removal ToolsCHAPTER 19: REGISTRY TROUBLESHOOTING (25 PAGES)A deep dive into the Windows Registry, looking at what changes you may need to make to the Registry and why you might need to. Examining how to connect to and edit the Registries on PCs across a network, and how to understand these complex databases.1) The Registry Editor and Recovery Console2) Registry Keys and Values3) Working with Registry Files4) Editing Other Users’ and PC’s registries5) The Windows Registry in DepthCHAPTER 20: STARTUP AND REPAIR TROUBLESHOOTING (30 PAGES)Repairing any kind of Windows 11 startup problem, from a non-booting PC using Windows 11’s automatic tools, to using scripting to manually repair startup files. Managing and creating multi-boot systems to use different Windows editions, and Linux on a PC.1) Startup Repair and the Recovery Console2) Repairing UEFI Startup Files3) Rebuilding the Boot Partition4) Working with BCDEdit and BootRec5) Managing Multi-Boot SystemsCHAPTER 21: RESEARCHING AND TROUBLESHOOTING DIFFICULT PROBLEMS (20 PAGES)How to get started troubleshooting the most difficult and complex problems. Where to look for help and support, and how to successfully diagnose hardware problems on a PC.1) Getting Started Troubleshooting Complex Problems2) Reading Windows Log and Dump Files3) Minimal Boot and Jump-Starting a PC4) Microsoft Docs and Microsoft Support5) Microsoft and Third-Party Status Websites6) Using Social Media for Troubleshooting7) Additional Sources of Help and SupportCHAPTER 22: INSTALLING AND RESTORING WINDOWS 11 (20 PAGES)How to Troubleshoot and repair problems with Windows installation, annual feature installation, and recovery. How to create custom out-of-box experiences for new PCs and users, and where and how to obtain up-to-date installation media for PCs.1) Troubleshooting Feature Update Failures2) Using Windows Reset3) Creating and Restoring a System Image Backup4) Nondestructively Reinstalling Windows 115) Using Windows SysPrep6) Obtaining Up to Date Windows 11 Installation Media
Applied Recommender Systems with Python
This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.WHAT YOU WILL LEARN* Understand and implement different recommender systems techniques with Python* Employ popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization * Build hybrid recommender systems that incorporate both content-based and collaborative filtering* Leverage machine learning, NLP, and deep learning for building recommender systemsWHO THIS BOOK IS FORData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.AKSHAY R KULKARNI is an AI and machine learning evangelist and a thought leader. He has consulted several Fortune 500 and global enterprises to drive AI and data science-led strategic transformations. He is a Google developer, Author, and a regular speaker at major AI and data science conferences including Strata, O’Reilly AI Conf, and GIDS. He is a visiting faculty member for some of the top graduate institutes in India. In 2019, he has been also featured as one of the top 40 under 40 Data Scientists in India. In his spare time, he enjoys reading, writing, coding, and helping aspiring data scientists. He lives in Bangalore with his family.ADARSHA SHIVANANDA is Data science and MLOps Leader. He is working on creating world-class MLOps capabilities to ensure continuous value delivery from AI. He aims to build a pool of exceptional data scientists within and outside of the organization to solve problems through training programs, and always wants to stay ahead of the curve. He has worked extensively in the pharma, healthcare, CPG, retail, and marketing domains. He lives in Bangalore and loves to read and teach data science.ANOOSH KULKARNI is a data scientist and an AI consultant. He has worked with global clients across multiple domains and helped them solve their business problems using machine learning (ML), natural language processing (NLP), and deep learning. Anoosh is passionate about guiding and mentoring people in their data science journey. He leads data science/machine learning meet-ups and helps aspiring data scientists navigate their careers. He also conducts ML/AI workshops at universities and is actively involved in conducting webinars, talks, and sessions on AI and data science. He lives in Bangalore with his family.V ADITHYA KRISHNAN is a data scientist and ML Ops Engineer. He has worked with various global clients across multiple domains and helped them to solve their business problems extensively using advanced Machine learning (ML) applications. He has experience across multiple fields of AI-ML, including, Time-series forecasting, Deep Learning, NLP, ML Operations, Image processing, and data analytics. Presently, he is developing a state-of-the-art value observability suite for models in production, which includes continuous model and data monitoring along with the business value realized. He also published a paper at an IEEE conference, “Deep Learning Based Approach for Range Estimation”, written in collaboration with the DRDO. He lives in Chennai with his family. Chapter 1: Introduction to Recommender SystemsChapter Goal: Introduction of recommender systems, along with a high-level overview of how recommender systems work, what are the different existing types, and how to leverage basic and advanced machine learning techniques to build these systems.No of pages: 25Sub - Topics:1. Intro to recommender system2. How it works3. Types and how they worka. Association rule miningb. Content basedc. Collaborative filteringd. Hybrid systemse. ML Clustering basedf. ML Classification basedg. Deep learning and NLP basedh. Graph basedChapter 2: Association Rule MiningChapter Goal: Building one of the simplest recommender systems from scratch, using association rule mining; also called market basket analysis.No of pages: 20Sub - Topics1 APRIORI2 FP GROWTH3 Advantages and DisadvantagesChapter 3: Content and Knowledge-Based Recommender SystemChapter Goal: Building the content and knowledge-based recommender system from scratch using both product content and demographicsNo of pages: 25Sub - Topics 1 TF-IDF2 BOW3 Transformer based4 Advantages and disadvantagesChapter 4: Collaborative Filtering using KNNChapter Goal: Building the collaborative filtering using KNN from scratch, both item-item and user-user basedNo of pages: 25Sub - Topics:1 KNN – item based2 KNN – user based3 Advantages and disadvantagesChapter 5: Collaborative Filtering Using Matrix Factorization, SVD and ALS.Chapter Goal: Building the collaborative filtering using SVM from scratch, both item-item and user-user basedNo of pages: 25Sub - Topics:1 Latent factors2 SVD3 ALS4 Advantages and disadvantagesChapter 6: Hybrid Recommender SystemChapter Goal: Building the hybrid recommender system (Using both content and collaborative methods) which is widely used in the industryNo of pages: 25Sub - Topics:1 Weighted: a different weight given to the recommenders of each technique used to favor some of them.2 Mixed: a single set of recommenders, without favorites.3 Augmented: suggestions from one system are used as input for the next, and so on until the last one.4 Switching: Choosing a random method5 Advantages and disadvantagesChapter 7: Clustering Algorithm-Based Recommender SystemChapter Goal: Building the clustering model for recommender systems.No of pages: 25Sub - Topics:1 K means clustering2 Hierarchal clustering3 Advantages and disadvantagesChapter 8: Classification Algorithm-Based Recommender SystemChapter Goal: Building the classification model for recommender systems.No of pages: 25Sub - Topics:1 Buying propensity model2 Logistic regression3 Random forest4 SVM5 Advantages and disadvantagesChapter 9: Deep Learning and NLP Based Recommender SystemChapter Goal: Building state of art recommender system using advanced topics like Deep learning along with NLP (Natural Language processing).No of pages: 25Sub - Topics:1 Word embedding’s2 Deep neural networks3 Advantages and disadvantagesChapter 10: Graph-Based Recommender SystemChapter Goal: Implementing graph-based recommender system using Python for computation performanceNo of pages: 25Sub - Topics:1 Generating nodes and edges2 Building algorithm3 Advantages and disadvantagesChapter 11: Emerging Areas and Techniques in Recommender SystemChapter Goal: To get an overview of the new and emerging techniques and the areas of research in Recommender systemsNo of pages: 15Sub - Topics:1 Personalized recommendation engine2 Context-based search engine3 Multi-objective recommendations4 Summary
Methods and Techniques in Deep Learning
METHODS AND TECHNIQUES IN DEEP LEARNINGINTRODUCES MULTIPLE STATE-OF-THE-ART DEEP LEARNING ARCHITECTURES FOR MMWAVE RADAR IN A VARIETY OF ADVANCED APPLICATIONSMethods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provides a timely and authoritative overview of the use of artificial intelligence (AI)-based processing for various mmWave radar applications. Focusing on practical deep learning techniques, this comprehensive volume explains the fundamentals of deep learning, reviews cutting-edge deep metric learning techniques, describes different typologies of reinforcement learning (RL) algorithms, highlights how domain adaptation (DA) can be used for improving the performance of machine learning (ML) algorithms, and more. Throughout the book, readers are exposed to product-ready deep learning solutions while learning skills that are relevant for building any industrial-grade, sensor-based deep learning solution. A team of authors with more than 70 filed patents and 100 published papers on AI and sensor processing illustrates how deep learning is enabling a range of advanced industrial, consumer, and automotive applications of mmWave radars. In-depth chapters cover topics including multi-modal deep learning approaches, the elemental blocks required to formulate Bayesian deep learning, how domain adaptation (DA) can be used for improving the performance of machine learning algorithms, and geometric deep learning are used for processing point clouds. In addition, the book:* Discusses various advanced applications and how their respective challenges have been addressed using different deep learning architectures and algorithms* Describes deep learning in the context of computer vision, natural language processing, sensor processing, and mmWave radar sensors* Demonstrates how deep parametric learning reduces the number of trainable parameters and improves the data flow* Presents several human-machine interface (HMI) applications such as gesture recognition, human activity classification, human localization and tracking, in-cabin automotive occupancy sensingMethods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions is an invaluable resource for industry professionals, researchers, and graduate students working in systems engineering, signal processing, sensors, data science, and AI. AVIK SANTRA is Head of Advanced Artificial Intelligence at Infineon Technologies, Munich, Germany. SOUVIK HAZRA is a Senior Staff Machine Learning Engineer at Infineon Technologies, Munich, Germany. LORENZO SERVADEI is a Senior Staff Machine Learning Engineer at Infineon Technologies and a Lecturer at The Technical University of Munich (TU München), Germany. THOMAS STADELMAYER is a Staff Machine Learning Engineer at Infineon Technologies, Munich, Germany. MICHAEL STEPHAN is a PhD candidate at Infineon Technologies, Munich, Germany and Friedrich-Alexander-University of Erlangen-Nürnberg, Germany. ANAND DUBEY is a Staff Machine Learning Engineer at Infineon Technologies. PrefaceAcronyms1 Introduction to Radar Processing & Deep Learning 11.1 Basics of Radar Systems 11.1.1 Fundamentals 21.1.2 Signal Modulation 21.2 FMCW Signal Processing 61.2.1 Frequency-Domain Analysis 71.3 Target Detection & Clustering 141.4 Target Tracking 191.4.1 Track Management 211.4.2 Track Filtering 221.5 Target Representation 281.5.1 Image Representation 301.5.2 Point-Cloud Maps 341.6 Target Recognition 361.6.1 Feedforward Network 371.6.2 Convolutional Neural Networks (CNN) 371.6.3 Recurrent Neural Network (RNN) 431.6.4 Autoencoder & Variational Autoencoder 471.6.5 Generative Adversial Network 511.6.6 Transformer 541.7 Training a Neural Network 561.7.1 Forward Pass & Backpropagation 571.7.2 Optimizers 621.7.3 Loss Functions 651.8 Questions to the Reader 66Bibliography 682 Deep Metric Learning 752.1 Introduction 782.2 Pairwise methods 792.2.1 Contrastive Loss 792.2.2 Triplet Loss 802.2.3 Quadruplet Loss 812.2.4 N-Pair Loss 822.2.5 Big Picture 832.3 End-to-end Learning 842.3.1 Cosine Similarity 862.3.2 Euclidean Distance 952.3.3 Big Picture 1002.4 Proxy methods 1032.5 Advanced Methods 1032.5.1 Statistical Distance 1042.5.2 Structured Metric Learning 1082.6 Application Gesture Sensing 1102.6.1 Radar System Design 1112.6.2 Data Set and Preparation 1122.6.3 Architecture and Metric Learning Procedure 1142.6.4 Results 1232.7 Questions to the Reader 129Bibliography 1303 Deep Parametric Learning 1353.1 Introduction 1353.2 Radar Parametric Neural Network 1403.2.1 2D Sinc Filters 1423.2.2 2D Morlet Wavelets 1433.2.3 Adaptive 2D Sinc Filters 1453.2.4 Complex Frequency Extraction Layer 1463.3 Multilevel Wavelet Decomposition Network 1503.4 Application Activity Classification 1533.4.1 Proposed Parametric Networks 1553.4.2 State-of-art Networks 1583.4.3 Results & Discussion 1603.5 Conclusion 1673.6 Question to Readers 168Bibliography 1684 Deep Reinforcement Learning 1734.1 Useful Notation and Equations 1734.1.1 Markov Decision Process 1734.1.2 Solving the Markov Decision Process 1744.1.3 Bellman Equations 1754.2 Introduction 1754.3 On-Policy Reinforcement Learning 1794.4 Off-Policy Reinforcement Learning 1804.5 Model-Based Reinforcement Learning 1804.6 Model-Free Reinforcement Learning 1814.7 Value-Based Reinforcement Learning 1814.8 Policy-Based Reinforcement Learning 1834.9 Online Reinforcement Learning 1834.10 Offline Reinforcement Learning 1844.11 Reinforcement Learning withDiscrete Actions 1844.12 Reinforcement Learning withContinuous Actions 1854.13 Reinforcement Learning Algorithmsfor Radar Applications 1854.14 Application Tracker’s Parameter Optimization 1894.14.1 Motivation 1904.14.2 Background 1924.14.3 Approach 2024.14.4 Experimental 2084.14.5 Outcomes of the proposed Approach 2194.15 Conclusion 2204.16 Questions to the Reader 220Bibliography 2215 Cross-Modal Learning 2295.1 Introduction 2295.2 Self-Supervised Multi-Modal Learning 2335.2.1 Generating Audio Statistics 2335.2.2 Predicting sounds from images 2345.2.3 Audio Features Clustering 2345.2.4 Binary Coding Model 2355.2.5 Training 2355.2.6 Results 2355.3 Joint Embeddings Learning 2375.3.1 Feature Representations 2375.3.2 Joint-Embedding Learning 2385.3.3 Matching & Ranking 2395.3.4 Training Details & Result 2395.3.5 Discussion 2415.4 Multi-Modal Input 2415.4.1 Multi-modal Compact Bilinear Pooling 2425.4.2 VQA Architecture 2435.4.3 Training Details & Result 2455.4.4 Discussion 2455.5 Cross-Modal Learning 2455.5.1 Data Acquisition 2465.5.2 Cross-Modal Learning for Key-Point Detection 2465.5.3 Training Details & Result 2475.5.4 Discussion 2495.6 Application People Counting 2505.6.1 FMCW Radar System Design 2515.6.2 Data Acquisition 2525.6.3 Solution 1 2535.6.4 Solution 2 2625.7 Conclusion 2655.8 Questions to the Reader 265Bibliography 2676 Signal Processing with Deep Learning 2736.1 Introduction 2736.2 Algorithm Unrolling 2746.2.1 Learning Fast Approximations of Sparse Coding 2756.2.2 Learned ISTA in radar processing 2796.3 Physics-inspired Deep Learning 2826.4 Processing-specific Network Architectures 2846.5 Deep Learning-aided Signal Processing 2886.6 Questions to the Reader 297Bibliography 2977 Domain Adaptation 3037.1 Introduction 3037.2 Transfer Learning and Domain Adaptaton 3047.3 Categories of Domain Adaptation 3077.3.1 Common Data Shifts 3077.3.2 Methods of Domain Adaptation 3087.4 Domain Adaptation in Radar Processing 3157.4.1 Domain Adaptation with a different Sensor Type 3167.4.2 Domain Adaptation with different Radar Settings 3187.5 Summary 3317.6 Questions to the Reader 331Bibliography 3328 Bayesian Deep Learning 3398.1 Learning Theory 3418.2 Bayesian Learning 3438.3 Bayesian Approximations 3528.4 Application VRU Classification 3728.4.1 VAE as Bayesian 373xiii8.4.2 Bayesian Metric Learning 3778.4.3 Kalman as Bayesian 3838.4.4 Results 3878.5 Summary 3918.6 Questions to the Reader 393Bibliography 3939 Geometric Deep Learning 3979.1 Representation Learning in Graph Neural Network 3999.1.1 Fundamentals 3999.1.2 Learning Theory 4019.1.3 Embedding Learning 4069.2 Graph Representation Learning 4079.2.1 Convolution GNN 4089.2.2 Recurrent Graph Neural Networks (RGNN) 4099.2.3 Graph Autoencoders (GAE) 4099.2.4 Spatial–Temporal Graph Neural Networks (STGNN) 4109.2.5 Attention GNN 4109.2.6 Message-passing GNN 4119.3 Applications 4139.3.1 Application 1 Long-Range Gesture Recognition 4139.3.2 Application 2 Bayesian Anchor-Free Target Detection 4269.4 Conclusion 4449.5 Questions to the Reader 445Bibliography 446
Ascii Shrug
Why call the book name ASCII Shrug? The born of ASCII makes almost every computing feature possible. The born of ASCII transforms computing and our lives in such an easier way, sometimes we may finish a job with just a shrug.But all these came not easy, countless computing scientists and engineers have devoted to create a seirs of milestones. Chapter I brings you to hundred years ago, even ancient time when civilization just sprouted. How number is generated? How mathematics and algebra developed? How mathematic related with computing? Chapter II touches many basic concepts. Chapter III goes into a deep further to explain some basic and popular topics in language computing. Have you ever thought about the many basics? What exactly is iteration and recursion? Have you thought about how important floating point is? If philosophy can help us understand the world, we can trace back to Before Christ. Chapter IV tries to illustrate the important programming paradigm from fundamental, from philosophy. What is object in the world? What is object-oriented way of thinking from philosophy point of view? Chapter V accumulates all the contents in my developer notes, it covers data, database, data modeling, SQL server, and the evolvement of windows interface implementation and web services implementation over the years. Have you thought about SQL server architecture? Why the query can run in SQL server? Have you seen those SQL errors before? Chapter VI pictorial tomorrow’s technologies in some computing areas, which directions are for programming languages, big data, and user interface, it also lays out some challenges in the research. If tomorrow comes, we will have something new along with the difficulties, we will have lots of work and challenges, but we are full of hope, we will be looking forward to the coming of each tomorrow.
Deep Learning Approaches for Security Threats in IoT Environments
DEEP LEARNING APPROACHES FOR SECURITY THREATS IN IOT ENVIRONMENTSAN EXPERT DISCUSSION OF THE APPLICATION OF DEEP LEARNING METHODS IN THE IOT SECURITY ENVIRONMENTIn Deep Learning Approaches for Security Threats in IoT Environments, a team of distinguished cybersecurity educators deliver an insightful and robust exploration of how to approach and measure the security of Internet-of-Things (IoT) systems and networks. In this book, readers will examine critical concepts in artificial intelligence (AI) and IoT, and apply effective strategies to help secure and protect IoT networks. The authors discuss supervised, semi-supervised, and unsupervised deep learning techniques, as well as reinforcement and federated learning methods for privacy preservation. This book applies deep learning approaches to IoT networks and solves the security problems that professionals frequently encounter when working in the field of IoT, as well as providing ways in which smart devices can solve cybersecurity issues. Readers will also get access to a companion website with PowerPoint presentations, links to supporting videos, and additional resources. They’ll also find:* A thorough introduction to artificial intelligence and the Internet of Things, including key concepts like deep learning, security, and privacy* Comprehensive discussions of the architectures, protocols, and standards that form the foundation of deep learning for securing modern IoT systems and networks* In-depth examinations of the architectural design of cloud, fog, and edge computing networks* Fulsome presentations of the security requirements, threats, and countermeasures relevant to IoT networksPerfect for professionals working in the AI, cybersecurity, and IoT industries, Deep Learning Approaches for Security Threats in IoT Environments will also earn a place in the libraries of undergraduate and graduate students studying deep learning, cybersecurity, privacy preservation, and the security of IoT networks. MOHAMED ABDEL-BASSET, PHD, is an Associate Professor in the Faculty of Computers and Informatics at Zagazig University, Egypt. He is a Senior Member of the IEEE. NOUR MOUSTAFA, PHD, is a Postgraduate Discipline Coordinator (Cyber) and Senior Lecturer in Cybersecurity and Computing at the School of Engineering and Information Technology at the University of New South Wales, UNSW Canberra, Australia. HOSSAM HAWASH is an Assistant Lecturer in the Department of Computer Science, Faculty of Computers and Informatics at Zagazig University, Egypt. Author BiographyAbout the Companion Website1. Chapter 1: INTRODUCING DEEP LEARNING FOR IoT SECURITY1.1. Introduction1.2. Internet of Things (IoT) Architectures1.2.1. Physical layer1.2.2. Network layer1.2.3. Application Layer1.3. Internet of Things Vulnerabilities and attacks1.3.1. Passive attacks1.3.2. Active attacks1.4. Artificial Intelligence1.5. Deep Learning1.6. Taxonomy of Deep Learning Models1.6.1. Supervision criterion1.6.1.1. Supervised deep learning1.6.1.2. Unsupervised deep learning.1.6.1.3. Semi-supervised deep learning.1.6.1.4. Deep reinforcement learning.1.6.2. Incrementality criterion1.6.2.1. Batch Learning1.6.2.2. Online Learning1.6.3. Generalization criterion1.6.3.1. model-based learning1.6.3.2. instance-based learning1.7. Supplementary Materials2. Chapter 2: Deep Neural Networks2.1. Introduction2.2. From Biological Neurons to Artificial Neurons2.2.1. Biological Neurons2.2.2. Artificial Neurons2.3. Artificial Neural Network (ANN)2.4. Activation Functions2.4.1. Types of Activation2.4.1.1. Binary Step Function2.4.1.2. Linear Activation Function2.4.1.3. Non-Linear Activation Functions2.5. The Learning process of ANN2.5.1. Forward Propagation2.5.2. Backpropagation (Gradient Descent)2.6. Loss Functions2.6.1. Regression Loss Functions2.6.1.1. Mean Absolute Error (MAE) Loss2.6.1.2. Mean Squared Error (MSE) Loss2.6.1.3. Huber Loss2.6.1.4. Mean Bias Error (MBE) Loss2.6.1.5. Mean Squared Logarithmic Error (MSLE)2.6.2. Classification Loss Functions2.6.2.1. Binary Cross Entropy (BCE) Loss2.6.2.2. Categorical Cross Entropy (CCE) Loss2.6.2.3. Hinge Loss2.6.2.4. Kullback Leibler Divergence (KL) Loss2.7. Supplementary Materials3. Chapter 3: Training Deep Neural Networks3.1. Introduction3.2. Gradient Descent revisited3.2.1. Gradient Descent3.2.2. Stochastic Gradient Descent3.2.3. Mini-batch Gradient Descent3.2.4.3.3. Gradients vanishing and exploding3.4. Gradient Clipping3.5. Parameter initialization3.5.1. Random initialization3.5.2. Lecun Initialization3.5.3. Xavier initialization3.5.4. Kaiming (He) initialization3.6. Faster Optimizers3.6.1. Momentum optimization3.6.2. Nesterov Accelerated Gradient3.6.3. AdaGrad3.6.4. RMSProp3.6.5. Adam optimizer3.7. Model training issues3.7.1. Bias3.7.2. Variance3.7.3. Overfitting issues3.7.4. Underfitting issues3.7.5. Model capacity3.8. Supplementary Materials4. Chapter 4: Evaluating Deep Neural Networks4.1. Introduction4.2. Validation dataset4.3. Regularization methods4.3.1. Early Stopping4.3.2. L1 & L2 Regularization4.3.3. Dropout4.3.4. Max-Norm Regularization4.3.5. Data Augmentation4.4. Cross-Validation4.4.1. Hold-out cross-validation4.4.2. K-folds cross-validation4.4.3. Repeated K-folds cross-validation4.4.4. Leave-one-out cross-validation4.4.5. Leave-p-out cross-validation4.4.6. Time series cross-validation4.4.7. Block cross-validation4.5. Performance Metrics.4.5.1. Regression Metrics4.5.1.1. Mean Absolute Error (MAE)4.5.1.2. Root Mean Squared Error (RMSE)4.5.1.3. Coefficient of determination (R-Squared)4.5.1.4. Adjusted R24.5.1.5.4.5.2. Classification Metrics4.5.2.1. Confusion Matrix.4.5.2.2. Accuracy4.5.2.3. Precision4.5.2.4. Recall4.5.2.5. Precision-Recall Curve4.5.2.6. F1-score4.5.2.7. Beta F1-score4.5.2.8. False Positive Rate (FPR)4.5.2.9. Specificity4.5.2.10. Receiving operating characteristics (ROC) curve4.6. Supplementary Materials5. Chapter 55.1. Introduction5.2. Shift from full connected to convolutional5.3. Basic Architecture5.3.1. The Cross-Correlation Operation5.3.2. Convolution operation5.3.3. Receptive Field5.3.4. Padding and Stride5.3.4.1. Padding5.3.4.2. Stride5.4. Multiple Channels5.4.1. Multi-channel Inputs5.4.2. Multi-channels Output5.4.3. Convolutional kernel 1×1.5.5. Pooling Layers5.5.1. Max Pooling5.5.2. Average Pooling5.6. Normalization Layers5.6.1. Batch Normalization5.6.2. Layer Normalization5.6.3. Instance Normalization5.6.4. Group Normalization5.6.5. Weight Normalization5.7. Convolutional Neural Networks (LeNet)5.8. Case studies5.8.1. Handwritten Digit Classification (one channel input)5.8.2. Dog vs Cat Image Classification (Multi-channel input)5.9. Supplementary Materials6. Chapter 6: Dive into Convolutional Neural Networks6.1. Introduction6.2. One-dimensional Convolutional Network6.2.1. One-dimensional Convolution6.2.2. One-dimensional pooling6.3. Three-dimensional Convolutional Network6.3.1. Three-dimension convolution6.3.2. Three-dimensional pooling6.4. Transposed Convolution Layer6.5. Atrous/Dilated Convolution6.6. Separable Convolutions6.6.1. Spatially Separable Convolutions6.6.2. Depth-wise Separable (DS) Convolutions6.7. Grouped Convolution6.8. Shuffled Grouped Convolution6.9. Supplementary Materials7. Chapter 7: Advanced Convolutional Neural Network7.1. Introduction7.2. AlexNet7.3. Block-wise Convolutional Network (VGG)7.4. Network-in Network7.5. Inception Networks7.5.1. GoogLeNet7.5.2. Inception Network V2(Inception V2)7.5.3. Inception Network V3 (Inception V3)7.6. Residual Convolutional Networks7.7. Dense Convolutional Networks7.8. Temporal Convolutional Network7.8.1. One-dimensional Convolutional Network7.8.2. Causal and Dilated Convolution7.8.3. Residual blocks7.9. Supplementary Materials8. Chapter 8: Introducing Recurrent Neural Networks8.1. Introduction8.2. Recurrent neural networks8.2.1. Recurrent Neurons8.2.2. Memory Cell8.2.3. Recurrent Neural Network8.3. Different Categories of RNNs8.3.1. One-to-one RNN8.3.2. One-to-many RNN8.3.3. Many-to-one RNN8.3.4. Many-to-many RNN8.4. Backpropagation Through Time8.5. Challenges facing simple RNNs8.5.1. Vanishing Gradient8.5.2. Exploding gradient.8.5.2.1. Truncated Backpropagation through time (TBPTT)8.5.3. Clipping Gradients8.6. Case study: Malware Detection8.7. Supplementary Materials9. Chapter 9: Dive into Recurrent Neural Networks9.1. Introduction9.2. Long Short-term Memory (LSTM)9.2.1. LSTM gates9.2.2. Candidate Memory Cells9.2.3. Memory Cell9.2.4. Hidden state9.3. LSTM with Peephole Connections9.4. Gated Recurrent Units (GRU)9.4.1. CRU cell gates9.4.2. Candidate State9.4.3. Hidden state9.5. ConvLSTM9.6. Unidirectional vs Bi-directional Recurrent Network9.7. Deep Recurrent Network9.8. Insights9.9. Case study of Malware Detection9.10. Supplementary Materials10. Chapter 10: Attention Neural Networks10.1. Introduction10.2. From biological to computerized attention10.2.1. Biological Attention10.2.2. Queries, Keys, and Values10.3. Attention Pooling: Nadaraya-Watson Kernel Regression10.4. Attention Scoring Functions10.4.1. Masked Softmax Operation10.4.2. Additive Attention (AA)10.4.3. Scaled Dot-Product Attention10.5. Multi-Head Attention (MHA)10.6. Self-Attention Mechanism10.6.1. Self-Attention (SA) mechanism10.6.2. Positional encoding10.7. Transformer Network10.8. Supplementary Materials11. Chapter 11: Autoencoder Networks11.1. Introduction11.2. Introducing Autoencoders11.2.1. Definition of Autoencoder11.2.2. Structural Design11.3. Convolutional Autoencoder11.4. Denoising Autoencoder11.5. Sparse autoencoders11.6. Contractive autoencoders11.7. Variational autoencoders11.8. Case study11.9. Supplementary Materials12. Chapter 12: Generative Adversarial Networks (GANs)12.1. Introduction12.2. Foundation of Generative Adversarial Network12.3. Deep Convolutional GAN12.4. Conditional GAN12.5. Supplementary Materials13. Chapter 13: Dive into Generative Adversarial Networks13.1. Introduction13.2. Wasserstein GAN13.2.1. Distance functions13.2.2. Distance function in GANs13.2.3. Wasserstein loss13.3. Least-squares GAN (LSGAN)13.4. Auxiliary Classifier GAN (ACGAN)13.5. Supplementary Materials14. Chapter 14: Disentangled Representation GANs14.1. Introduction14.2. Disentangled representations14.3. InfoGAN14.4. StackedGAN14.5. Supplementary Materials15. Chapter 15: Introducing Federated Learning for Internet of Things (IoT)15.1. Introduction15.2. Federated Learning in Internet of Things.15.3. Taxonomic view of Federated Learning15.3.1. Network Structure15.3.1.1. Centralized Federated Learning15.3.1.2. Decentralized Federated Learning15.3.1.3. Hierarchical Federated Learning15.3.2. Data Partition15.3.3. Horizontal Federated Learning15.3.4. Vertical Federated Learning15.3.5. Federated Transfer learning15.4. Open-source Frameworks15.4.1. TensorFlow Federated15.4.2. FedML15.4.3. LEAF15.4.4. Paddle FL15.4.5. Federated AI Technology Enabler (FATE)15.4.6. OpenFL15.4.7. IBM Federated Learning15.4.8. NVIDIA FLARE15.4.9. Flower15.4.10. Sherpa.ai15.5. Supplementary Materials16. Chapter 16: Privacy-Preserved Federated Learning16.1. Introduction16.2. Statistical Challenges in Federated Learning16.2.1. Non-Independent and Identically Distributed (Non-IID) Data16.2.1.1. Class Imbalance16.2.1.2. Distribution Imbalance16.2.1.3. Size Imbalance16.2.2. Model Heterogeneity16.2.3. Block Cycles16.3. Security Challenge in Federated Learning16.3.1. Untargeted Attacks16.3.2. Targeted Attacks16.4. Privacy Challenges in Federated Learning16.4.1. Secure Aggregation16.4.1.1. Homomorphic Encryption (HE)16.4.1.2. Secure Multiparty Computation16.4.1.3. Blockchain16.4.2. Perturbation Method16.5. Supplementary Materials
A Roadmap for Enabling Industry 4.0 by Artificial Intelligence
A ROADMAP FOR ENABLING INDUSTRY 4.0 BY ARTIFICAIAL INTELLIGENCETHE BOOK PRESENTS COMPREHENSIVE AND UP-TO-DATE TECHNOLOGICAL SOLUTIONS TO THE MAIN ASPECTS REGARDING THE APPLICATIONS OF ARTIFICIAL INTELLIGENCE TO INDUSTRY 4.0. The industry 4.0 vision has been discussed for quite a while and the enabling technologies are now mature enough to turn this vision into a grand reality sooner rather than later. The fourth industrial revolution, or Industry 4.0, involves the infusion of technology-enabled deeper and decisive automation into manufacturing processes and activities. Several information and communication technologies (ICT) are being integrated and used towards attaining manufacturing process acceleration and augmentation. This book explores and educates the recent advancements in blockchain technology, artificial intelligence, supply chains in manufacturing, cryptocurrencies, and their crucial impact on realizing the Industry 4.0 goals. The book thus provides a conceptual framework and roadmap for decision-makers for implementing this transformation. AUDIENCEComputer and artificial intelligence scientists, information and communication technology specialists, and engineers in electronics and industrial manufacturing will find this book very useful. JYOTIR MOY CHATTERJEE is an assistant professor in the Information Technology department at Lord Buddha Education Foundation (LBEF), Kathmandu, Nepal. He has published more than 60 research papers in international publications, three conference papers, three authored books, 10 edited books, 16 book chapters, two Master’s theses converted into books, and one patent. HARISH GARG, PHD, is an associate professor at Thapar Institute of Engineering & Technology, Deemed University, Patiala, Punjab, India. His research interests include soft computing, decision-making, aggregation operators, evolutionary algorithm, expert systems, and decision support systems. He has published more than 300 papers published in refereed international journals. Dr. Garg is the Editor-in-Chief of Annals of Optimization Theory and Practice.R N THAKUR, PHD, is a senior lecturer in the Information Technology Department, Lord Buddha Education Foundation (LBEF), Kathmandu, Nepal. He has published about 20 research articles in various journals. Preface xv1 ARTIFICIAL INTELLIGENCE—THE DRIVING FORCE OF INDUSTRY 4.0 1Hesham Magd, Henry Jonathan, Shad Ahmad Khan and Mohamed El Geddawy1.1 Introduction 21.2 Methodology 21.3 Scope of AI in Global Economy and Industry 4.0 31.3.1 Artificial Intelligence—Evolution and Implications 41.3.2 Artificial Intelligence and Industry 4.0—Investments and Returns on Economy 51.3.3 The Driving Forces for Industry 4.0 71.4 Artificial Intelligence—Manufacturing Sector 81.4.1 AI Diversity—Applications to Manufacturing Sector 91.4.2 Future Roadmap of AI—Prospects to Manufacturing Sector in Industry 4.0 121.5 Conclusion 13References 142 INDUSTRY 4.0, INTELLIGENT MANUFACTURING, INTERNET OF THINGS, CLOUD COMPUTING: AN OVERVIEW 17Sachi Pandey, Vijay Laxmi and Rajendra Prasad Mahapatra2.1 Introduction 172.2 Industrial Transformation/Value Chain Transformation 182.2.1 First Scenario: Reducing Waste and Increasing Productivity Using IIoT 192.2.2 Second Scenario: Selling Outcome (User Demand)– Based Services Using IIoT 202.3 IIoT Reference Architecture 202.4 IIoT Technical Concepts 222.5 IIoT and Cloud Computing 262.6 IIoT and Security 27References 293 ARTIFICIAL INTELLIGENCE OF THINGS (AIOT) AND INDUSTRY 4.0– BASED SUPPLY CHAIN (FMCG INDUSTRY) 31Seyyed Esmaeil Najafi, Hamed Nozari and S. A. Edalatpanah3.1 Introduction 323.2 Concepts 333.2.1 Internet of Things 333.2.2 The Industrial Internet of Things (IIoT) 343.2.3 Artificial Intelligence of Things (AIoT) 353.3 AIoT-Based Supply Chain 363.4 Conclusion 40References 404 APPLICATION OF ARTIFICIAL INTELLIGENCE IN FORECASTING THE DEMAND FOR SUPPLY CHAINS CONSIDERING INDUSTRY 4.0 43Alireza Goli, Amir-Mohammad Golmohammadi and S. A. Edalatpanah4.1 Introduction 444.2 Literature Review 454.2.1 Summary of the First Three Industrial Revolutions 454.2.2 Emergence of Industry 4.0 454.2.3 Some of the Challenges of Industry 4.0 474.3 Application of Artificial Intelligence in Supply Chain Demand Forecasting 484.4 Proposed Approach 504.4.1 Mathematical Model 504.4.2 Advantages of the Proposed Model 514.5 Discussion and Conclusion 52References 535 INTEGRATING IOT AND DEEP LEARNING—THE DRIVING FORCE OF INDUSTRY 4.0 57Muhammad Farrukh Shahid, Tariq Jamil Saifullah Khanzada and Muhammad Hassan Tanveer5.1 Motivation and Background 585.2 Bringing Intelligence Into IoT Devices 605.3 The Foundation of CR-IoT Network 625.3.1 Various AI Technique in CR-IoT Network 635.3.2 Artificial Neural Network (ANN) 635.3.3 Metaheuristic Technique 645.3.4 Rule-Based System 645.3.5 Ontology-Based System 655.3.6 Probabilistic Models 655.4 The Principles of Deep Learning and Its Implementation in CR-IoT Network 655.5 Realization of CR-IoT Network in Daily Life Examples 695.6 AI-Enabled Agriculture and Smart Irrigation System—Case Study 705.7 Conclusion 75References 756 A SYSTEMATIC REVIEW ON BLOCKCHAIN SECURITY TECHNOLOGY AND BIG DATA EMPLOYED IN CLOUD ENVIRONMENT 79Mahendra Prasad Nath, Sushree Bibhuprada B. Priyadarshini, Debahuti Mishra and Brojo Kishore Mishra6.1 Introduction 806.2 Overview of Blockchain 836.3 Components of Blockchain 856.3.1 Data Block 856.3.2 Smart Contracts 876.3.3 Consensus Algorithms 876.4 Safety Issues in Blockchain Technology 886.5 Usage of Big Data Framework in Dynamic Supply Chain System 916.6 Machine Learning and Big Data 946.6.1 Overview of Shallow Models 956.6.1.1 Support Vector Machine (SVM) 956.6.1.2 Artificial Neural Network (ANN) 956.6.1.3 K-Nearest Neighbor (KNN) 956.6.1.4 Clustering 966.6.1.5 Decision Tree 966.7 Advantages of Using Big Data for Supply Chain and Blockchain Systems 966.7.1 Replenishment Planning 966.7.2 Optimizing Orders 976.7.3 Arranging and Organizing 976.7.4 Enhanced Demand Structuring 976.7.5 Real-Time Management of the Supply Chain 976.7.6 Enhanced Reaction 986.7.7 Planning and Growth of Inventories 986.8 IoT-Enabled Blockchains 986.8.1 Securing IoT Applications by Utilizing Blockchain 996.8.2 Blockchain Based on Permission 1016.8.3 Blockchain Improvements in IoT 1016.8.3.1 Blockchain Can Store Information Coming from IoT Devices 1016.8.3.2 Secure Data Storage with Blockchain Distribution 1016.8.3.3 Data Encryption via Hash Key and Tested by the Miners 1026.8.3.4 Spoofing Attacks and Data Loss Prevention 1026.8.3.5 Unauthorized Access Prevention Using Blockchain 1036.8.3.6 Exclusion of Centralized Cloud Servers 1036.9 Conclusions 103References 1047 DEEP LEARNING APPROACH TO INDUSTRIAL ENERGY SECTOR AND ENERGY FORECASTING WITH PROPHET 111Yash Gupta, Shilpi Sharma, Naveen Rajan P. and Nadia Mohamed Kunhi7.1 Introduction 1127.2 Related Work 1137.3 Methodology 1147.3.1 Splitting of Data (Test/Train) 1167.3.2 Prophet Model 1167.3.3 Data Cleaning 1197.3.4 Model Implementation 1197.4 Results 1207.4.1 Comparing Forecast to Actuals 1217.4.2 Adding Holidays 1227.4.3 Comparing Forecast to Actuals with the Cleaned Data 1227.5 Conclusion and Future Scope 122References 1258 APPLICATION OF NOVEL AI MECHANISM FOR MINIMIZING PRIVATE DATA RELEASE IN CYBER-PHYSICAL SYSTEMS 127Manas Kumar Yogi and A.S.N. Chakravarthy8.1 Introduction 1288.2 Related Work 1318.3 Proposed Mechanism 1338.4 Experimental Results 1358.5 Future Directions 1378.6 Conclusion 138References 1389 ENVIRONMENTAL AND INDUSTRIAL APPLICATIONS USING INTERNET OF THINGS (IOT) 141Manal Fawzy, Alaa El Din Mahmoud and Ahmed M. Abdelfatah9.1 Introduction 1429.2 IoT-Based Environmental Applications 1469.3 Smart Environmental Monitoring 1479.3.1 Air Quality Assessment 1479.3.2 Water Quality Assessment 1489.3.3 Soil Quality Assessment 1509.3.4 Environmental Health-Related to COVID- 19Monitoring 1509.4 Applications of Sensors Network in Agro-Industrial System 1519.5 Applications of IoT in Industry 1539.5.1 Application of IoT in the Autonomous Field 1539.5.2 Applications of IoT in Software Industries 1559.5.3 Sensors in Industry 1569.6 Challenges of IoT Applications in Environmental and Industrial Applications 1579.7 Conclusions and Recommendations 159Acknowledgments 159References 15910 AN INTRODUCTION TO SECURITY IN INTERNET OF THINGS (IOT) AND BIG DATA 169Sushree Bibhuprada B. Priyadarshini, Suraj Kumar Dash, Amrit Sahani, Brojo Kishore Mishra and Mahendra Prasad Nath10.1 Introduction 17010.2 Allusion Design of IoT 17210.2.1 Stage 1—Edge Tool 17210.2.2 Stage 2—Connectivity 17210.2.3 Stage 3—Fog Computing 17310.2.4 Stage 4—Data Collection 17310.2.5 Stage 5—Data Abstraction 17310.2.6 Stage 6—Applications 17310.2.7 Stage 7—Cooperation and Processes 17410.3 Vulnerabilities of IoT 17410.3.1 The Properties and Relationships of Various IoT Networks 17410.3.2 Device Attacks 17510.3.3 Attacks on Network 17510.3.4 Some Other Issues 17510.3.4.1 Customer Delivery Value 17510.3.4.2 Compatibility Problems With Equipment 17610.3.4.3 Compatibility and Maintenance 17610.3.4.4 Connectivity Issues in the Field of Data 17610.3.4.5 Incorrect Data Collection and Difficulties 17710.3.4.6 Security Concern 17710.3.4.7 Problems in Computer Confidentiality 17710.4 Challenges in Technology 17810.4.1 Skepticism of Consumers 17810.5 Analysis of IoT Security 17910.5.1 Sensing Layer Security Threats 18010.5.1.1 Node Capturing 18010.5.1.2 Malicious Attack by Code Injection 18010.5.1.3 Attack by Fake Data Injection 18010.5.1.4 Sidelines Assaults 18110.5.1.5 Attacks During Booting Process 18110.5.2 Network Layer Safety Issues 18110.5.2.1 Attack on Phishing Page 18110.5.2.2 Attacks on Access 18210.5.2.3 Attacks on Data Transmission 18210.5.2.4 Attacks on Routing 18210.5.3 Middleware Layer Safety Issues 18210.5.3.1 Attack by SQL Injection 18310.5.3.2 Attack by Signature Wrapping 18310.5.3.3 Cloud Attack Injection with Malware 18310.5.3.4 Cloud Flooding Attack 18310.5.4 Gateways Safety Issues 18410.5.4.1 On-Boarding Safely 18410.5.4.2 Additional Interfaces 18410.5.4.3 Encrypting End-to-End 18410.5.5 Application Layer Safety Issues 18510.5.5.1 Theft of Data 18510.5.5.2 Attacks at Interruption in Service 18510.5.5.3 Malicious Code Injection Attack 18510.6 Improvements and Enhancements Needed for IoT Applications in the Future 18610.7 Upcoming Future Research Challenges with Intrusion Detection Systems (IDS) 18910.8 Conclusion 192References 19311 POTENTIAL, SCOPE, AND CHALLENGES OF INDUSTRY 4.0 201Roshan Raman and Aayush Kumar11.1 Introduction 20211.2 Key Aspects for a Successful Production 20211.3 Opportunities with Industry 4.0 20411.4 Issues in Implementation of Industry 4.0 20611.5 Potential Tools Utilized in Industry 4.0 20711.6 Conclusion 210References 21012 INDUSTRY 4.0 AND MANUFACTURING TECHNIQUES: OPPORTUNITIES AND CHALLENGES 215Roshan Raman and Aditya Ranjan12.1 Introduction 21612.2 Changing Market Demands 21712.2.1 Individualization 21812.2.2 Volatility 21812.2.3 Efficiency in Terms of Energy Resources 21812.3 Recent Technological Advancements 21912.4 Industrial Revolution 4.0 22112.5 Challenges to Industry 4.0 22412.6 Conclusion 225References 22613 THE ROLE OF MULTIAGENT SYSTEM IN INDUSTRY 4.0 227Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan and Rudra Pratap Ojha13.1 Introduction 22813.2 Characteristics and Goals of Industry 4.0 Conception 22813.3 Artificial Intelligence 23113.3.1 Knowledge-Based Systems 23213.4 Multiagent Systems 23413.4.1 Agent Architectures 23413.4.2 Jade 23813.4.3 System Requirements Definition 23913.4.4 HMI Development 24013.5 Developing Software of Controllers Multiagent Environment Behavior Patterns 24013.5.1 Agent Supervision 24013.5.2 Documents Dispatching Agents 24113.5.3 Agent Rescheduling 24213.5.4 Agent of Executive 24213.5.5 Primary Roles of High-Availability Agent 24313.6 Conclusion 244References 24414 AN OVERVIEW OF ENHANCING ENCRYPTION STANDARDS FOR MULTIMEDIA IN EXPLAINABLE ARTIFICIAL INTELLIGENCE USING RESIDUE NUMBER SYSTEMS FOR SECURITY 247Akeem Femi Kadri, Micheal Olaolu Arowolo, Ayisat Wuraola Yusuf-Asaju, Kafayat Odunayo Tajudeen and Kazeem Alagbe Gbolagade14.1 Introduction 24814.2 Reviews of Related Works 25014.3 Materials and Methods 25814.3.1 Multimedia 25814.3.2 Artificial Intelligence and Explainable Artificial Intelligence 26114.3.3 Cryptography 26214.3.4 Encryption and Decryption 26514.3.5 Residue Number System 26614.4 Discussion and Conclusion 268References 26815 MARKET TRENDS WITH CRYPTOCURRENCY TRADING IN INDUSTRY 4.0 275Varun Khemka, Sagar Bafna, Ayush Gupta, Somya Goyal and Vivek Kumar Verma15.1 Introduction 27615.2 Industry Overview 27615.2.1 History (From Barter to Cryptocurrency) 27615.2.2 In the Beginning Was Bitcoin 27815.3 Cryptocurrency Market 27915.3.1 Blockchain 27915.3.1.1 Introduction to Blockchain Technology 27915.3.1.2 Mining 28015.3.1.3 From Blockchain to Cryptocurrency 28115.3.2 Introduction to Cryptocurrency Market 28115.3.2.1 What is a Cryptocurrency? 28115.3.2.2 Cryptocurrency Exchanges 28315.4 Cryptocurrency Trading 28315.4.1 Definition 28315.4.2 Advantages 28315.4.3 Disadvantages 28415.5 In-Depth Analysis of Fee Structures and Carbon Footprint in Blockchain 28515.5.1 Need for a Fee-Driven System 28515.5.2 Ethereum Structure 28615.5.3 How is the Gas Fee Calculated? 28715.5.3.1 Why are Ethereum Gas Prices so High? 28715.5.3.2 Carbon Neutrality 28715.6 Conclusion 291References 29216 BLOCKCHAIN AND ITS APPLICATIONS IN INDUSTRY 4.0 295Ajay Sudhir Bale, Tarun Praveen Purohit, Muhammed Furqaan Hashim and Suyog Navale16.1 Introduction 29616.2 About Cryptocurrency 29616.3 History of Blockchain and Cryptocurrency 29816.4 Background of Industrial Revolution 30016.4.1 The First Industrial Revolution 30116.4.2 The Second Industrial Revolution 30116.4.3 The Third Industrial Revolution 30216.4.4 The Fourth Industrial Revolution 30216.5 Trends of Blockchain 30316.6 Applications of Blockchain in Industry 4.0 30416.6.1 Blockchain and the Government 30416.6.2 Blockchain in the Healthcare Sector 30416.6.3 Blockchain in Logistics and Supply Chain 30616.6.4 Blockchain in the Automotive Sector 30716.6.5 Blockchain in the Education Sector 30816.7 Conclusion 309References 310Index 315
Programming for Absolute Beginners
New programmers start here...this book introduces students or aspiring professionals to the world of computer programming using JavaScript and related technologies. This book doesn't just teach the basics of programming, but also all of the tools that new programmers need to get started, including the basics of making web pages and how the Internet works.Programming for Absolute Beginners offers practice problems, activities, and a host of resources to get new programmers started, plus a large glossary of terms introduced in the book and that a new programmer might encounter when learning on their own. No special software is required; this book will help you regardless of what your computer setup is, and source code will be freely available via GitHub.WHAT YOU WILL LEARN* How computers work* How computers communicate over networks* How web pages are built with HTML and CSS* How JavaScript works* How JavaScript interacts with web pages* Intermediate JavaScript topics such as recursion and scoping* Using JavaScript for network communicationWHO THIS BOOK IS FORAnyone wanting to begin computer programming, including students who need to learn the fundamentals and early professionals who want to go back and revisit the basics.JONATHAN BARTLETT is a software developer, researcher, and writer. His first book, Programming from the Ground Up, has been required reading in computer science programs from DeVry to Princeton. He has been the sole or lead author for eight books on topics ranging from computer programming to calculus. He is a technical lead for ITX, where his specialty is getting stuck projects unstuck. He is a senior software developer for McElroy Manufacturing, spearheading projects in web, mobile, and embedded software. He is now the author of several Apress books including Electronics for Beginners and more.Chapter 1: Introduction.-Part I: Computers, Data, and Communication.-Chapter 2: A Short History of Computers.-Chapter 3: How Computers Communicate.-Chapter 4: How a Computer Looks at Data.-Chapter 5: How Computers Work.- Part II: Basic Ingredients for Web Programming.-Chapter 6: The HTML File Format.-Chapter 7: Introduction to Cascading Style Sheets.-Chapter 8: Your First JavaScript Program.-Part III: JavaScript Fundamentals.-9: Basic JavaScript Syntax.-Chapter 10: Introducing Functions and Scope.-Chapter 11: Grouping Values Together with Objects and Arrays.-Chapter 12: Interacting with Web Pages.-Part IV: Intermediate JavaScript.-Chapter 13: Recursive Functions and the Stack.-Chapter 14: Manipulating Functions and Scopes.-Chapter 15: Intermediate Objects.-Part V: Programming Applications.-Chapter 16: Modernizing JavaScript.-Chapter 17: Working With Remote Services (APIs).-Chapter 18: Writing Server-Side JavaScript.-Chapter 19: Conclusion.-Appendix A: Glossary.-Appendix B: Operating System and Browser Specifics.-Appendix C: The JavaScript Toolbox on Docker.-Appendix D: Character Encoding Issues.-Appendix E: Additional Machine Language Programs.
Asymmetric Cryptography
Public key cryptography was introduced by Diffie and Hellman in 1976, and it was soon followed by concrete instantiations of public-key encryption and signatures; these led to an entirely new field of research with formal definitions and security models. Since then, impressive tools have been developed with seemingly magical properties, including those that exploit the rich structure of pairings on elliptic curves.Asymmetric Cryptography starts by presenting encryption and signatures, the basic primitives in public-key cryptography. It goes on to explain the notion of provable security, which formally defines what "secure" means in terms of a cryptographic scheme. A selection of famous families of protocols are then described, including zero-knowledge proofs, multi-party computation and key exchange.After a general introduction to pairing-based cryptography, this book presents advanced cryptographic schemes for confidentiality and authentication with additional properties such as anonymous signatures and multi-recipient encryption schemes. Finally, it details the more recent topic of verifiable computation.DAVID POINTCHEVAL obtained a PhD in Computer Science and has since worked on the Cryptography Team at the École Normale Supérieure in France. His research focuses on provable security of cryptographic primitives and protocols.Foreword xiDavid POINTCHEVALCHAPTER 1 PUBLIC-KEY ENCRYPTION AND SECURITY NOTIONS 1Nuttapong ATTRAPADUNG and Takahiro MATSUDA1.1. Basic definitions for PKE 21.1.1. Basic notation 21.1.2. Public-key encryption 21.1.3. IND-CPA and IND-CCA security 21.1.4. Other basic security notions and relations 41.2. Basic PKE schemes 51.2.1. Game-based proofs 51.2.2. ElGamal encryption 61.2.3. Simplified CS encryption 81.2.4. Cramer–Shoup encryption 111.2.5. Other specific PKE schemes 141.3. Generic constructions for IND-CCA secure PKE 161.3.1. Hybrid encryption 171.3.2. Naor–Yung construction and extensions 191.3.3. Fujisaki–Okamoto and other transforms in the RO model 211.3.4. Other generic constructions for IND-CCA secure PKE 231.4. Advanced topics 251.4.1. Intermediate notions related to CCA 251.4.2. IND-CCA security in multi-user setting and tight security 261.4.3. Key-dependent message security 281.4.4. More topics on PKE 301.5. References 31CHAPTER 2 SIGNATURES AND SECURITY NOTIONS 47Marc FISCHLIN2.1. Signature schemes 472.1.1. Definition 472.1.2. Examples of practical schemes 492.2. Unforgeability 512.2.1. Discussion 512.2.2. Existential unforgeability under chosen-message attacks 532.2.3. Unforgeability of practical schemes 542.3. Strong unforgeability 562.3.1. Discussion 562.3.2. Strong existential unforgeability under chosen-message attacks 572.3.3. Strong unforgeability of practical schemes 582.3.4. Building strongly unforgeable schemes 592.4. Summary 602.5. References 60CHAPTER 3 ZERO-KNOWLEDGE PROOFS 63Ivan VISCONTI3.1. Introduction 633.2. Notation 643.3. Classical zero-knowledge proofs 643.3.1. Zero knowledge 653.4. How to build a zero-knowledge proof system 683.4.1 ZK proofs for all NP 703.4.2. Round complexity 713.5. Relaxed security in proof systems 723.5.1. Honest-verifier ZK 723.5.2. Witness hiding/indistinguishability 733.5.3. Σ-Protocols 743.6. Non-black-box zero knowledge 753.7. Advanced notions 753.7.1. Publicly verifiable zero knowledge 763.7.2. Concurrent ZK and more 773.7.3. ZK with stateless players 783.7.4. Delayed-input proof systems 793.8. Conclusion 803.9. References 80CHAPTER 4 SECURE MULTIPARTY COMPUTATION 85Yehuda LINDELL4.1. Introduction 854.1.1. A note on terminology 874.2. Security of MPC 874.2.1. The definitional paradigm 874.2.2. Additional definitional parameters 894.2.3. Adversarial power 894.2.4. Modular sequential and concurrent composition 914.2.5. Important definitional implications 924.2.6. The ideal model and using MPC in practice 924.2.7. Any inputs are allowed 924.2.8. MPC secures the process, but not the output 924.3. Feasibility of MPC 934.4. Techniques 944.4.1. Shamir secret sharing 944.4.2. Honest-majority MPC with secret sharing 954.4.3. Private set intersection 974.4.4. Threshold cryptography 994.4.5. Dishonest-majority MPC 1004.4.6. Efficient and practical MPC 1004.5. MPC use cases 1014.5.1. Boston wage gap (Lapets et al. 2018) 1014.5.2. Advertising conversion (Ion et al. 2017) 1014.5.3. MPC for cryptographic key protection (Unbound Security; Sepior; Curv) 1014.5.4. Government collaboration (Sharemind) 1024.5.5. Privacy-preserving analytics (Duality) 1024.6. Discussion 1024.7. References 103CHAPTER 5 PAIRING-BASED CRYPTOGRAPHY 107Olivier BLAZY5.1. Introduction 1085.1.1. Notations 1085.1.2. Generalities 1085.2. One small step for man, one giant leap for cryptography 1095.2.1. Opening Pandora’s box, demystifying the magic 1105.2.2. A new world of assumptions 1125.3. A new world of cryptographic protocols at your fingertips 1165.3.1. Identity-based encryption made easy 1175.3.2. Efficient deterministic compact signature 1185.4. References 119CHAPTER 6 BROADCAST ENCRYPTION AND TRAITOR TRACING 121Duong HIEU PHAN6.1. Introduction 1216.2. Security notions for broadcast encryption and TT 1236.3. Overview of broadcast encryption and TT 1256.4. Tree-based methods 1296.5. Code-based TT 1326.6. Algebraic schemes 1356.7. Lattice-based approach with post-quantum security 1426.8. References 143CHAPTER 7 ATTRIBUTE-BASED ENCRYPTION 151Romain GAY7.1. Introduction 1517.2. Pairing groups 1527.2.1. Cyclic groups 1527.2.2. Pairing groups 1527.3. Predicate encodings 1537.3.1. Definition 1537.3.2. Constructions 1547.4. Attribute-based encryption 1567.4.1. Definition 1567.4.2. A modular construction 1587.5. References 165CHAPTER 8 ADVANCED SIGNATURES 167Olivier SANDERS8.1. Introduction 1678.2. Some constructions 1698.2.1. The case of scalar messages 1698.2.2. The case of non-scalar messages 1718.3. Applications 1738.3.1. Anonymous credentials 1738.3.2. Group signatures 1768.3.3. Direct anonymous attestations 1808.4. References 184CHAPTER 9 KEY EXCHANGE 187Colin BOYD9.1. Key exchange fundamentals 1879.1.1. Key exchange parties 1889.1.2. Key exchange messages 1899.1.3. Key derivation functions 1899.2. Unauthenticated key exchange 1919.2.1. Formal definitions and security models 1919.2.2. Constructions and examples 1929.3. Authenticated key exchange 1949.3.1. Non-interactive key exchange 1959.3.2. AKE security models 1969.3.3. Constructions and examples 2009.4. Conclusion 2069.5. References 207CHAPTER 10 PASSWORD AUTHENTICATED KEY EXCHANGE: PROTOCOLS AND SECURITY MODELS 213Stanislaw JARECKI10.1. Introduction 21310.2. First PAKE: EKE 21510.3. Game-based model of PAKE security 21810.3.1. The BPR security model 21810.3.2. Implicit versus explicit authentication 22110.3.3. Limitations of the BPR model 22110.3.4. EKE instantiated with Diffie–Hellman KE 22310.3.5. Implementing ideal cipher on arbitrary groups 22410.4. Simulation-based model of PAKE security 22510.4.1. The BMP security model 22510.4.2. Advantages of BMP definition: arbitrary passwords, tight security 22910.4.3. EKE using RO-derived one-time pad encryption 23010.4.4. BMP model for PAKE with explicit authentication (pake-ea) 23110.5. Universally composable model of PAKE security 23210.6. PAKE protocols in the standard model 23610.7. PAKE efficiency optimizations 23910.8. Asymmetric PAKE: PAKE for the client-server setting 24210.9. Threshold PAKE 24410.10. References 246CHAPTER 11 VERIFIABLE COMPUTATION AND SUCCINCT ARGUMENTS FOR NP 257Dario FIORE11.1. Introduction 25711.1.1. Background 25811.2. Preliminaries 25911.3. Verifiable computation 26011.4. Constructing VC 26111.4.1. VC for circuits in three steps 26111.4.2. Succinct non-interactive arguments for non-deterministic computation 26311.4.3. Verifiable computation from SNARG 26411.5. A modular construction of SNARGs 26411.5.1. Algebraic non-interactive linear proofs 26511.5.2. Bilinear groups 26711.5.3. SNARGs from algebraic NILPs with degree-2 verifiers using bilinear groups 26911.6. Constructing algebraic NILPs for arithmetic circuits 27111.6.1. Arithmetic circuits 27111.6.2. Quadratic arithmetic programs 27111.6.3. Algebraic NILP for QAPs 27411.7. Conclusion 27911.8. References 279List of Authors 283Index 285
Solving Identity Management in Modern Applications
Know how to design and use identity management to protect your application and the data it manages.At a time when security breaches result in increasingly onerous penalties, it is paramount that application developers and owners understand identity management and the value it provides when building applications. This book takes you from account provisioning to authentication to authorization, and covers troubleshooting and common problems to avoid. The authors include predictions about why this will be even more important in the future. Application best practices with coding samples are provided.SOLVING IDENTITY AND ACCESS MANAGEMENT IN MODERN APPLICATIONS gives you what you need to design identity and access management for your applications and to describe it to stakeholders with confidence. You will be able to explain account creation, session and access management, account termination, and more.This expanded edition has been revised to provide an overview of the new version of OAuth (2.1)―the primary changes in this version, including features that were removed from 2.1 that were in 2.0 and why they were removed. The discussion of the book's accompanying sample application has been revised to cover in more depth the approach for developing the application (also revised). A new section has been added on the OAuth 2.0 Device Authorization Grant (RFC 8628) specification, which is useful for devices with limited UI capability. Minor additions include the topics of identity proofing, the need to capture and organize consent information, the impact of tracking prevention technology on certain identity protocols, and the availability of additional options for authorization requests such as OAuth 2.0 Rich Authorization Requests and JWT-Secured Authorization Requests (RFC 9101).WHAT YOU’LL LEARN• Understand key identity management concepts• Incorporate essential design principles• Design authentication and access control for a modern application• Know the identity management frameworks and protocols used today (OIDC/OAuth 2.0/2.1, SAML 2.0)• Review historical failures and know how to avoid themWHO THIS BOOK IS FORDevelopers, enterprise or application architects, business application or product owners, and anyone involved in an application's identity management solutionYVONNE WILSON is co-founder and Chief Strategy Officer for XploitDefense. She has had many roles in the software industry related to security and identity management as a security and identity architect; enterprise architect; director of developer success working with identity customers; sr. director of security governance, risk, and compliance (GRC); Chief Strategy Officer; and founder of cloud identity services. Yvonne was responsible for IT security strategy and architecture at Sun Microsystems, founded and designed the identity management services offered through Oracle Managed Cloud Services, created a GRC team at Auth0 and founded a world-wide developer success team for Auth0, working with customers and overseeing the creation of an identity management training program for customer-facing support and professional services engineers. Yvonne is currently Chief Strategy Officer at XploitDefense.In working with business teams at Sun, designing and deploying identity systems for customers at Oracle, and while founding a developer success team at Auth0, Yvonne had the opportunity of working with many customers, from small startups to large enterprises. Her experience spans the implementation of SSO, identity federation, directory services, adaptive knowledge-based authentication, and identity provisioning as well as multilevel authentication systems with certificate-based authentication. She has worked with OIDC, SAML 2.0, WS-Fed, OAuth2.0/2.1, and OpenID. From this depth of experience, Yvonne realized the growing need for a basic overview of identity management concepts that is understandable to business application owners as well as architects and developers.ABHISHEK HINGNIKAR is at Okta, the identity provider for the internet. He has several years of experience designing and demonstrating Identity Management solutions to customers using Auth0 using OAuth 2.0/2.1, OpenID Connect and SAML 2.0. His current focus areas involve Consumer IoT, Device Based Identity and designing solutions that explore web based identity in peripheral domains.
Pro Encryption in SQL Server 2022
This in-depth look at the encryption tools available in SQL Server shows you how to protect data by encrypting it at rest with Transparent Data Encryption (TDE) and in transit with Transport Level Security (TLS). You will know how to add the highest levels of protection for sensitive data using Always Encrypted to encrypt data also in memory and be protected even from users with the highest levels of access to the database. The book demonstrates actions you can take today to start protecting your data without changing any code in your applications, and the steps you can subsequently take to modify your applications to support implementing a gold standard in data protection.The book highlights work that Microsoft has been doing since 2016 to make encryption more accessible, by making TDE available in the standard edition, and the introduction of Always Encrypted that requires minimal work on your part to implement powerful and effective encryption, protecting your data and meeting regulatory requirements. The book teaches you how to work with the encryption technologies in SQL Server with the express goal of helping you understand those technologies on an intuitive level. You’ll come away with a deep level of understanding that allows you to answer questions and speak as an expert. The book’s aim is to make you as comfortable in deploying encryption in SQL Server as you would be in driving your car to buy groceries.Those with a data security mindset will appreciate the discussion of how each feature protects you and what it protects you from, as well as how to implement things in the most secure manner. Database administrators will appreciate the high level of detail around managing encryption over time and the effect of encryption on database performance. All readers will appreciate the advice on how to avoid common pitfalls, ensuring that your projects to implement encryption run smoothly.WHAT YOU WILL LEARN* Architect an effective encryption strategy for new applications* Retrofit encryption into your existing applications* Encrypt data at rest, in memory, and in transit* Manage key and certificate life cycles, including backup and restore* Recover encrypted databases in case of server failure* Work with encryption in cloud-based scenariosWHO THIS BOOK IS FORDatabase developers, architects, and administrators who want to work with encryption in SQL Server; those who want to maintain encryption whether data is at rest or being transmitted over the network; and those who wish to encrypt their data even when in the server’s own memory. Readers should be familiar with SQL Server, but no existing knowledge of encryption is assumed.MATTHEW MCGIFFEN is a Data Architect with over 20 years’ experience working on SQL Server and associated technologies. Matthew has also had the opportunity to collaborate with Microsoft during the development of some of the latest enhancements in encryption. He is the author of a popular blog on SQL Server and has written articles for SQL Server Central. In his spare time, Matthew is an amateur chess player and pianist. IntroductionPART I. UNDERSTANDING THE LANDSCAPE1. Purpose of Encryption and Available Tools.PART II. TRANSPARENT DATA ENCRYPTION (TDE)2. Introducing Transparent Data Encryption3. Setting Up TDE4. Managing TDE5. Backup EncryptionPART III. ALWAYS ENCRYPTED6. What Is Always Encrypted?7. Setting Up Always Encrypted8. Executing Queries Using Always Encrypted9. Encrypting Existing Data with Always Encrypted10. Limitations with Always Encrypted11. Key Rotation with Always Encrypted12. Considerations When Implementing Always EncryptedPART IV. ALWAYS ENCRYPTED WITH ENCLAVES13. Introducing Always Encrypted With Enclaves14. Setting Up Always Encrypted With Enclaves15. In-Place Encryption With Always EncryptedPART V. COMPLETING THE PICTURE16. Rich Querying With Always Encrypted Enclaves17. Setting Up TPM Attestation18. Encryption In Transit Using Transport Level Security19. Hashing and Salting of Passwords20. Extensible Key Management (EKM)APPENDIXESA. Glossary of TermsB. Encryption in the CloudC. Encryption Algorithms.
Introduction to Infrastructure as Code
Get inspired to explore the depths of the DevOps field. In today’s rapidly transforming world, Infrastructure as Code (IaC) has emerged as an effective approach to maintain, scale, and deploy software systems. This book offers a mixture of foundational IaC concepts and practical examples to give you hands-on experience.You will first gain an understanding of DevOps culture as well as how to adapt to IaC. Introduction to Infrastructure as Code begins by reviewing the innovative features that DevOps in general, and IaC in particular, have to offer for adoption and growth for different verticals. With this solid base established, you will then learn the importance, processes, and outcome of building infrastructure solutions.Authors Sneh Pandya and Riya Guha Thakurta then provide hands-on examples utilizing IaC platforms, open source tools, and essential considerations such as security, scalability, and deployments. Each chapter focuses on one vertical (i.e., foundations, architecture patterns, securing infrastructure, preparing for deployment), how it impacts the DevOps toolchain in a holistic manner, and how it can be used to build solutions specific to that vertical, with a detailed walkthrough of code, environments, and other tools.After completing this book, you’ll have launched your own infrastructure solution through an open source stack consisting of platforms and tools such as Terraform, Chef, and Puppet.WHAT YOU WILL LEARN* Understand the fundamentals of DevOps and Infrastructure as Code* Prepare for the ever-evolving ecosystem of modular infrastructure and the needs of the future* Avoid potential pitfalls and breakdowns while working with infrastructure* Build scalable and efficient IaC solutions that work at a small, medium, and large scale in a real-life environment* Understand and be responsibly aware of security concerns related to the domain, and how to address themWHO IS THIS BOOK FORBeginners interested in building a career in DevOps as well as professionals looking to gain expertise and advance their career with greater knowledge of IaC. including Technical Product Managers, and Architects.SNEH PANDYA is an emerging Product Management leader with specialization in strategic leadership. He advocates for product strategy, digital transformation, and sustainable innovation.His qualifications and certifications include a bachelor's degree in Computer Science and Engineering and further studies with majors in Strategy Management and Leadership from The Wharton School, University of Pennsylvania.Sneh is also a Developer Community Leader at Google Developers Group Baroda and has given public talks at several worldwide developer conferences. He is a co-founder of the NinjaTalks podcast, which brings together experiences from the world's leaders, changemakers, and innovators to make knowledge accessible to all.With extensive experience in the field of technology, including mobile and web software applications, DevOps, Cloud, infrastructure automation, and software architecture, he has articles published in various technology publications.RIYA GUHA THAKURTA is a graduate student in Computer Information Systems at Boston University. Her undergraduate education includes a bachelor's degree in Computer Science Application from the Institute of Engineering and Management.She was formerly a Scrum Master in the realm of technology management, and her previous experiences with Johnson Controls include technology development across several business products and software verticals. Riya also leads Women Techmakers Kolkata, a diversity, equality, and inclusion community that encourages women in technology. She is also an Intel Software Innovator for the Internet of Things.She is also a co-founder of the NinjaTalks podcast, which seeks to share experiences from the world's most prominent leaders, changemakers, and innovators in order to make knowledge accessible to all.Her diverse interests include technology and project management, sustainability, public speaking, and Research & Development.PART 1: CONCEPTSChapter 1: Introduction to Infrastructure as CodeChapter Goal: Understand DevOps culture and fundamentals of Infrastructure as CodeChapter 2: Patterns and Principles of IaCChapter Goal: Learn about every layer of Infrastructure as Code stackChapter 3: Infrastructure ManagementChapter Goal: Explains management of infrastructure in a holistic mannerChapter 4: Production Complexity ManagementChapter Goal: Learn how to maintain, deploy, and scale infrastructure with respect to various environmentsChapter 5: Business SolutionsChapter Goal: Helps you familiarize and understand business aspects and future scope of IaCPART 2: HANDS-ON EXPERIENCEChapter 6: Hands-on IaC with Hashicorp TerraformChapter Goal: Helps you gain hands-on experience with popular open-source IaC platform - Hashicorp's TerraformChapter 7: Hands-on IaC with PuppetChapter Goal: Take you through with another popular open-source IaC tool - PuppetChapter 8: Hands-on IoC with ChefChapter Goal: Helps you with hands-on experience with another popular open-source IaC tool - Chef
Pro Freeware and Open Source Solutions for Business
This book will point the way to numerous free, low-cost, and open-source software solutions that could provide viable alternatives to their paid counterparts. Pro Freeware and Open Source Solutions for Business is now in its Second Edition; it has been thoroughly revised and updated. This book covers the most up-to-date software versions. Software described in the First Edition that is no longer available has been replaced with comparable titles when possible.The book starts with an office productivity tool known as OfficeLibre and goes on to explain CRM and compression software. You will then learn about desktop publishing, illustration, 3D modeling, and photo editing software. As we progress further, you will learn more about audio-video capture and editing software along with Openshot, an easy-to-use free video editor. You will also learn about available project planning and time tracking software, and much more. By the end of the book, you will have also gained knowledge about security programs, as well as how to use Linux on Windows and MacOS.With the challenging economic times we find ourselves in, this book may be more important than ever to help small business owners eliminate and reduce costs, and keep more money in their business. .WHAT YOU WILL LEARN* Understand the important differences between freeware and open-source software.* Discern which paid commercial software the free version replaces (when applicable).* Gain insight into how organizations and municipalities around the world adopting open-source software to save money on licensing fees.WHO THIS BOOK IS FORPrimarily small business owners, solo entrepreneurs or freelancers on a budget, and cost efficiency experts. PHILLIP W. WHITT is a professional digital retoucher,restoration artist, and author. . As a small business owner, he’s had a good deal of experience in seeking out and using free or inexpensive alternatives to expensive, paid software in his business (starting with his discovery of Open Office in 2009). Even now, most of his work is accomplished using free programs such as LibreOffice for document creation, GIMP or Paint.NET for editing images, and Inkscape for vector drawing. Mr. Whitt has authored several books pertaining to image editing using free software for Apress Publishing. He’s also produced several video tutorials for Apress as well.CHAPTER 1: OFFICE PRODUCTIVITY, NOTE TAKING, ACCOUNTING, AND PDF CREATIONLIBREOFFICE: THE POWERFUL FREE OFFICE SUITELibreOffice ModulesWriterCalcImpressBaseDrawLibreOffice SupportGOOGLE DOCS: CREATE DOCUMENTS AND COLLABORATE ONLINEGoogle DocsGoogle SheetsGoogle SlidesGoogle FormsMoreGoogle Docs SupportGOOGLE KEEP: CREATE, SYNC, AND SHARE NOTESFeature HighlightsGoogle Keep SupportZIM: OPEN-SOURCE NOTE TAKINGFeature HighlightsZim SupportGNUCASH: OPEN-SOURCE ACCOUNTING SOFTWAREFeature HighlightsGnuCash SupportMANAGER: FREE SMALL BUSINESS ACCOUNTING SOFTWAREFeature HighlightsManager SupportPDF REDIRECT: BASIC PDF CREATION FREEWAREFeature HighlightsPDF reDirect SupportChapter SummaryCHAPTER 2: POINT-OF-SALE, CRM, BACKUP, AND COMPRESSION SOFTWAREIMONGO (FREE VERSION): A POINT-OF-SALE UTILITY FOR THE SMALL SHOP OR BOUTIQUEFeature HighlightsImonggo SupportPOS/CASH REGISTER: TURN YOUR OLD PC INTO A CASH REGISTERFeature HighlightsPOS/Cash Register SupportBITRX24 (FREE OPTION): BASIC CRM FOR SMALL BUSINESSFeature HighlightsBitrx24 SupportFBACKUP (FREE OPTION): A FREE BASIC BACKUP UTILITYFeature HighlightsFBackup SupportZZIP: AN OPEN-SOURCE ALTERNATIVE TO WINZIPFeature Highlights7Zip SupportChapter SummaryCHAPTER 3: DESKTOP PUBLISHING, ILLUSTRATION, PAINTING, AND 3D MODELINGGOOGLE DOCS: EASY, BASIC DESKTOP PUBLISHINGFeature HighlightsGoogle Docs SupportSCRIBUS: THE POWERFUL, PROFESSIONAL, OPEN-SOURCE DESKTOP PUBLISHING PROGRAMFeature HighlightsDesign CapabilitiesScribus SupportVECTR:A FREE, BASIC ON-LINE VECTOR DRAWING PROGRAMFeature HighlightsDesign CapabilitiesVectr SupportINKSCAPE: PRO-QUALITY OPEN-SOURCE VECTOR DRAWING SOFTWAREFeature HighlightsGraphics CreationInkscape SupportKRITA: THE POWERFUL OPEN SOURCE DIGITAL DRAWING AND PAINTING PROGRAMFeature HighlightsDocument CreationBrush PresetsKrita SupportFREECAD: OPEN-SOURCE PARAMETRIC 3D MODELING SOFTWAREFeature HighlightsDrafting CapabilitiesFreeCAD SupportBLENDER: THE ULTIMATE OPEN-SOURCE 3D CREATION SOFTWAREFeature HighlightsRendering CapabilitiesBlender SupportChapter SummaryCHAPTER 4: PHOTO EDITING SOFTWAREPHOTOSCAPE: AN EASY-TO-USE PHOTO EDITOR FOR BEGINNERSFeature HighlightsToolsFiltersObjectsPhotoScape SupportPAINT.NET: BASIC IMAGE EDITING FOR WINDOWSFeature HighlightsEditing CapabilitiesGraphics CreationPaint.NET SupportGIMP: THE PREMIER OPEN-SOURCE IMAGE EDITORFeature HighlightsEditing CapabilitiesGraphics CreationGIMP SupportPIXLR: WEB-BASED AND MOBILE DEVICE PHOTO EDITINGFeature HighlightsEditing CapabilitiesDARKTABLE: AN OPEN-SOURCE PHOTOGRAPHY WORKFLOW PROGRAMFeature HighlightsEditing Capabilitiesdarktable SupportFOTOSKETCHER: AUTOMATICALLY TURN PHOTOS INTO DIGITAL ARTFeature HighlightsEditing CapabilitiesFotoSketcher Support SupportChapter SummaryCHAPTER 5: AUDIO-VIDEO CAPTURE, CONVERSION, AND EDITING SOFTWAREFRE:AC: (FREE AUDIO CONVERTER)AN OPEN-SOURCE AUDIO CONVERTER AND CD RIPPERFeature Highlightsfre:ac SupportAUDACITYⓇ: A POWERFUL OPEN-SOURCE AUDIO EDITORFeature HighlightsEditing CapabilitiesAudacityⓇ SupportMPEG STREAMCLIP: A HANDY FREE VIDEO CONVERSION TOOLFeature HighlightsEditing CapabilitiesMPEG Streamclip SupportVIRTUALDUB: OPEN-SOURCE VIDEO PROCESSING FOR WINDOWSFeature HighlightsEditing CapabilitiesVirtualDubSupportOPENSHOT: A SIMPLE, POWERFUL FREE VIDEO EDITORFeature HighlightsEditing CapabilitiesOpenShot SupportKDENLIVE: OPEN-SOURCE VIDEO EDITING FROM BASIC TO PROFeature HighlightsEditing CapabilitiesKdenlive SupportChapter SummaryCHAPTER 6: PROJECT PLANNING, INVENTORY MANAGEMENT, AND TIME TRACKING SOFTWAREPROJECTLIBRE AN OPEN SOURCE ALTERNATIVE TO MICROSOFT PROJECTFeature HighlightsProjectLibre SupportMONDAY.COM: FREE BASIC PROJECT MANAGEMENT SOFTWAREFeature Highlightsmonday.com SupportABC INVENTORY: A FREE OPTION FOR SMALL AND MID-SIZED BUSINESSFeature HighlightsABC Inventory SupportHOMEBASE: FREE ONLINE SCHEDULING AND HR SOFTWAREFeature HighlightsHomebase SupportChapter SummaryCHAPTER 7: WEBSITE CREATION SOFTWARE AND WEB BROWSERSWORDPRESS: FREE, OPEN-SOURCE TOOL AND CONTENT MANAGEMENT SYSTEMFeature HighlightsMulti-Use and Multi-BloggingWordPress SupportWIX: BUILD A BASIC SITE FOR FREEFeature HighlightsWix SupportAVG SECURE BROWSER:BROWSE MORE SECURELY AND PRIVATELYFeature HighlightsFIREFOX: THE BROWSER THAT RESPECTS PRIVACYFeature HighlightsChapter SummaryCHAPTER 8: CONTENT MANAGEMENT SOLUTIONSDRUPAL: AN OPEN-SOURCE, COMMUNITY BASED ALTERNATIVEFeature HighlightsDrupal SupportCONCRETECMS (FORMERLY CONCRETE5): AN OPEN SOURCE CONTENT MANAGEMENT SYSTEMFeature Highlights Concrete CMS SupportGETSIMPLE CMS: A SIMPLE, OPEN-SOURCE CONTENT MANAGEMENT SYSTEMFeature Highlights GetSimple CMS SupportChapter SummaryCHAPTER 9: SECURITY PROGRAMSADVANCED IP SCANNER: FREE SCANNER FOR IP ADDRESSESFeature Highlights Advanced IP Scanner SupportKEEPASS: A FREE AND OPEN-SOURCE PASSWORD MANAGERFeature HighlightsKeepass SupportTCPDUMP: A POWERFUL COMMAND LINE PACKET ANALYZERFeature Highlightstcpdump SupportAVIRA: POWERFUL, FREE ANTIVIRUS PROTECTION FOR PERSONAL PCS AND MACSFeature HighlightsAvira SupportCLAMAV: AN OPEN-SOURCE CROSS PLATFORM ANTI-VIRUS PROGRAMFeature HighlightsClamAV SupportSIGNAL (FORMERLY OPEN WHISPER SYSTEMS): OPEN-SOURCE SECURITY FOR MOBILE DEVICESFeature HighlightsSignal SupportChapter SummaryCHAPTER 10: LINUX: THE FREE ALTERNATIVE TO WINDOWS AND MAC OSWhat is Linux?A Brief History of LinuxThe Advantages of Using LinuxUBUNTU: POWERING MILLIONS OF LAPTOP AND DESKTOP COMPUTERS AROUND THE WORLDEase of UseUbuntu Dashboard and DesktopDownloading and Installing UbuntuUbuntu Software and Software CenterRunning Windows Applications on WINESecurityAccessory CompatibilityUbuntu SupportZORIN OS: ESPECIALLY FOR NEWCOMERS TO LINUXThe Look ChangerDownloading and Installing Zorin OSZorin OS SupportLINUX MINT: A MODERN, ELEGANT OPERATING SYSTEMDownloading and Installing Linux MintLinux Mint SupportChapter Summary
Creating Business Applications with Microsoft 365
Learn how to automate processes, visualize your data, and improve productivity using Power Apps, Power Automate, Power BI, SharePoint, Forms, Teams, and more. This book will help you build complete solutions that often involve storing data in SharePoint, creating a front-end application in Power Apps or Forms, adding additional functionality with Power Automate, and effective reports and dashboards in Power BI.This new edition greatly expands the focus on Power Apps, Power BI, Power Automate, and Teams, along with SharePoint and Microsoft Forms. It starts with the basics of programming and shows how to build a simple email application in .NET, HTML/JavaScript, Power Apps on its own, and Power Apps and Power Automate in combination. It then covers how to connect Power Apps to SharePoint, create an approval process in Power Automate, visualize surveys in Power BI, and create your own survey solution with the combination of a number of Microsoft 365 tools. You’ll work with an extended example that shows how to use Power Apps and SharePoint together to create your own help ticketing system.This book offers a deep dive into Power BI, including working with JSON, XML, and Yes/No data, as well as visualizing learning data and using it to detect inconsistencies between Excel files. You’ll also see how to connect to Remedy and to the help system you will have created. Under author Jeffrey Rhodes’s guidance, you’ll delve into the Power Apps collection to learn how to avoid dreaded "delegation" issues with larger data sets. Back on applications, you will create a training class sign-up solution to only allow users to choose classes with available seats. Digging deeper into Teams, you’ll learn how to send chats, posts, and "adaptive cards" from Power Automate. Rounding things out, you’ll save Forms attachments to SharePoint with Power Automate, create your own "Employee Recognition" app with all of the Power Platform and Teams, add or edit weekly status reports, and learn how to create reservation and scoring applications.After reading the book, you will be able to build powerful applications using Power Apps, Power Automate, Power BI, SharePoint, Forms, and Teams.WHAT YOU WILL LEARN* Create productivity-enhancing applications with Power Apps, Power Automate, SharePoint, Forms, and/or Teams* Transform and visualize data with Power BI to include custom columns, measures, and pivots* Avoid delegation issues and tackle complicated Power Apps issues like complex columns, filtering, and ForAll loops* Build scheduled or triggered Power Automate flows to schedule Teams Meetings, send emails, launch approvals, and much moreWHO THIS BOOK IS FORBusiness and application developers. JEFFREY RHODES is a founder and Chief Technical Officer of Platte Canyon Multimedia Software Corporation, a leader in developing commercial e-learning software. He graduated at the top of his class at the Air Force Academy, where he earned a bachelor's degree in electrical engineering. Jeff received a master’s degree in economics from the London School of Economics, which he attended under a British Marshall Scholarship. He is the author of Creating Business Applications with Office 365: Techniques in SharePoint, PowerApps, Power BI, and More, Programming for e-Learning Developers: ToolBook, Flash, JavaScript, and Silverlight, VBTrain.Net: Creating Computer and Web Based Training with Visual Basic .NET. He also co-wrote The ToolBook Companion. He lives in Colorado Springs with his wife Sue and is the proud father of his sons Derek and Michael.CHAPTER 1. PROGRAMMING IN THE POWER PLATFORMIn this chapter, we will cover the basics of programming: properties, methods, and events. We will then look at how their implementation differs between in each of the Power Platform applications compared with traditional environments like .NET (Windows Forms and ASP.NET) and JavaScript. For Power Apps, we will see how you can set the properties of other objects directly but instead need to make the value of what you want to change (such as the text of a button) a variable and then change the value of that variable elsewhere in the application. We will also explore its Visual Basic-like syntax. In Power Automate, we will look at the different types of actions (variables, loop, parallel branches, conditions, etc.). Finally, in Power BI we will look at the Power Query M formula language.CHAPTER 2. UPDATING A SHAREPOINT LIST USING POWER APPSCHAPTER 3. CREATING AN APPROVAL PROCESS WITH POWER AUTOMATECHAPTER 4. CREATING A SURVEY RESPONSE DASHBOARD WITH MICROSOFT POWER BICHAPTER 5. CREATING A SURVEY SOLUTION WITH MICROSOFT FORMS, FLOW, SHAREPOINT, AND POWER BICHAPTER 6. POWER BI CHALLENGES WITH JSON, XML, AND YES/NO DATACHAPTER 7. POWER BI CASE STUDY: MONITORING BMC REMEDY HELP TICKETSCHAPTER 8. BUILDING A HELP TICKETING SYSTEM IN POWERAPPS AND SHAREPOINT – NEW TICKET FORMCHAPTER 9. CONTINUING THE HELP TICKETING SYSTEM – TECHNICIAN FORMCHAPTER 10. USING POWER BI FOR THE HELP TICKETING SYSTEMCHAPTER 11. OVERCOMING POWER APPS DELEGATION ISSUES WITH SHAREPOINT DATA SOURCESIn this chapter, we look at how to use the technique described in https://tinyurl.com/twzvbgl to overcome delegation limits in Power Apps using a SharePoint data source. We also implement a corresponding Power Automate Flow to copy the ID value to a numeric column each time we create a record.CHAPTER 12. CREATING A CLASS SIGN-UP SOLUTION IN SHAREPOINT AND POWER APPSCHAPTER 13. VISUALIZING LEARNING MANAGEMENT DATA FROM SQL SERVER USING POWER BIThis chapter gives several examples of connecting to multiple tables in a SQL Server database in order to visualize test score, completion, assignments and similar learning management data. It demonstrates custom columns, merging tables, slicers, and much more.CHAPTER 14. DYNAMIC INFORMATION IN POWER APPS AND SENDING AN ADAPTIVE CARD TO TEAMS USING POWER AUTOMATEIn this chapter, we create linked SharePoint lists that display status levels, colors, and associated steps. We then read these lists from PowerApps to create a status display that we display in a tab in Teams. Finally, we create a Power Automate flow so that each time the status changes, we automatically post that to our Teams channel.CHAPTER 15. DYNAMICALLY SETTING OBJECT PROPERTIES IN POWER APPS BASED ON A SHAREPOINT LISTIn this chapter, we explore how to approximate dynamic object references/reflection in Power Apps. We take an "Actions" list in SharePoint and use it to set Text, Tooltip, and Visible properties of each corresponding button in Power Apps.CHAPTER 16. UPLOADING FILES FROM POWERAPPS TO SHAREPOINT AND EMAILING LINKS USING POWER AUTOMATEIn this chapter, we see how to upload multiple attachments from Power Apps to a SharePoint document library using Power Automate. While we are in Power Automate, we create an email of links to these documents and email it to the designated recipient.CHAPTER 17. WORKING WITH SHAREPOINT LOOKUP COLUMNS IN POWER BIIn this chapter, we explore how to use the FieldValuesAsText functionality in Power BI to get the data from within a SharePoint Lookup column. We also explore creating custom columns and filtering by Content Type.CHAPTER 18. JOINING SHAREPOINT/EXCEL TABLES IN POWER BIThis chapter shows two different examples on how to join data in Power BI to make effective visualizations. The first example shows how to access lookup columns in SharePoint lists by doing a join on the lists once you bring them into Power BI. The second one is an extended example on how to verify data between two Excel spreadsheets that share a common value. We first show how to accomplish the task with Microsoft Access with a join query and a set of custom columns that reflect whether columns between the two spreadsheets actually match. We then show how it is easier and more reproducible with later data to do the same thing with Power BI using a merge query.CHAPTER 19. COPYING MICROSOFT FORMS ANSWERS TO SHAREPOINT USING POWER AUTOMATE AND THEN SHOWING THE MOST CURRENT SUBMISSION IN POWER BIIn this chapter, we take a simple Microsoft Form, copy each entry to SharePoint with Power Automate, and then visualize the data in Power BI. The main insight on the Power BI side is to show only the most recent form submission by grouping within Power BI, creating a MaxDate column, and then filtering.CHAPTER 20. COPYING MICROSOFT FORMS ATTACHMENTS TO A SHAREPOINT LIST ITEM USING POWER AUTOMATEIn this chapter, we see how to create a group form in Microsoft Forms, create an associated SharePoint List to hold the data, use Power Automate to copy the form responses to the list, and, most importantly, copy each file uploaded with the form and attach it to the corresponding list item.CHAPTER 21. CREATING AN EMPLOYEE RECOGNITION APP IN POWER APPS, POWER AUTOMATE, POWER BI, TEAMS, AND SHAREPOINTIn this chapter, we demonstrate how to create a Power Apps and Power Automate employee recognition solution that can post the recognition to a Teams channel, send a Teams chat, and/or send via email. We try to make it optional for submitters to include their information, finding it works for chats and email but not posts. We store the information in SharePoint and then use Power BI to visualize the values demonstrated and other data.CHAPTER 22. CREATING A RESERVATIONS BOOKING SOLUTION IN POWER APPS AND SHAREPOINTIn this chapter, we demonstrate how to create a SharePoint list of available appointments and then use Power Apps to allow users to select an available appointment and make that not available to anyone else. It also shows how to allow users to edit or delete their appointments (or those created on their behalf).CHAPTER 23. CREATING A SCORING APPLICATION IN POWER APPS AND SHAREPOINTIn this chapter, we create a scoring application where we patch three different SharePoint records at the same time. Along the way, we use cascading drop-down lists, collections, data tables, and variables.
Workload Automation Using HWA
Apply best practices for deploying and administering HCL Workload automation (HWA) to meet the automation requirements of the digitally transformed platform. This book will provide detailed architecture and deployment options to achieve this goal.Workload automation focuses on real-time processing, predefined event-driven triggers, and situational dependencies. It offers centralized control of managing multiple tasks, making it possible to schedule enterprise-wide tasks. You'll see how it supports the timely completion of tasks and is beneficial for processes that need to happen at a specific time or need to occur as a result of another event.HWA increases efficiency, reduces the turnaround time for workflows, and reduces errors along with delays in end-to-end processes. You'll review proven ways to deliver batch optimization and modernization requirements, and see how solutions can be aligned with the DevSecOps delivery model. Workload Automation Using HWA presents information on how to use the tool and has numerous use cases and implementation procedures to guide every workload automation deployment requirement.WHAT YOU'LL LEARN* Automate and integrate your complex workload, workflow, and business processes across automation platforms, ERP systems, and business applications* Understand event-driven batch automation* Practice alignment of the workload automation solution with the DevSecOps principlesWHO THIS BOOK IS FORSolution Architects, Infrastructure Architects, Technical Architects, Enterprise Architects, Workload Automation Tool Administrators or SME’s, Schedulers, Application owners, Automation Specialists, Service Delivery ManagersNAVIN SABHARWAL, currently is the Chief Architect and Head of Strategy for Autonomics, named ‘DRYiCE’ at HCL Technologies. He is responsible for innovation, presales, and delivery of award-winning autonomics platforms for HCL Technologies. Navin is an innovator, thought leader, author and a consultant in areas of AI and Machine Learning, Cloud Computing, Big Data Analytics, Software Product Development, Engineering and R&D. He is responsible for IP Development & Service Delivery in the Areas of AI and Machine Learning, Automation products, Cloud Computing, Public Cloud AWS, Microsoft Azure, VMWare Private Cloud, Microsoft Private Cloud, Data Center Automation, Analytics for IT Operations, IT Service Management.SUBRAMANI KASIVISWANATHAN is the Solution architect for Application Performance Management and Workload Automation Solutions, also leading the Engineering and R&D function, having overall 16 years of IT experience and 6 years of experience in Academics, currently working as Practice Lead in HCL DRYiCE, responsible for creating solutions catering to APM and Workload Automation, responsible and accountable for the Research and Development in autonomics platform.Chapter 1: Introduction to Workload AutomationSub –Topics1. Workload Automation Concepts2. Introduction to HCL Workload Automation3. HCL Workload Automation strengths4. Common HCL Workload Automation terminologyChapter 2: HCL Workload Automation ArchitectureSub –Topics1. HWA components2. HWA communication path3. Architecture TypesChapter 3: HCL Workload Automation DeploymentsSub - Topics1. Deployment Options4. Planning Deployments5. Stand-Alone Architecture6. High Availability Architecture7. Disaster Recovery Architecture8. Containerized deployments9. Deployment on Kubernetes clusters10. Workload Automation on HCL SofyChapter 4: Workload Design and Monitoring using DWC and CLIChapter 5: Use Case: HWA for managed file transfersChapter 6: Use Case: HWA integration with SAPChapter 7: Use Case: Automate job executions on Microsoft SQL serverChapter 8: Use Case: Working with RESTful Web ServicesChapter 9: Use Case: Submit, orchestrate and monitor jobs on a Kubernetes clusterChapter 10: Use Case: HWA Integration with Hadoop Distributed File SystemChapter 11: Use Case: HWA Integration with Apache SparkChapter 12: Use Case 1: HWA Integration with ServiceNow Use Case 2: Auto Remediation of Job failuresChapter 13: Tool Administration and best practicesChapter 14: Alerting and Troubleshooting issuesChapter 15: HWA ReportingChapter 16: HWA SecurityChapter 17: HWA tuning for best performanceChapter 18: Alignment of HWA with DevSecOps Delivery model.
Krypto-Mining für Dummies (2. Auflg.)
Kryptowährungen versprechen schnelles Geld und Reichtum. Anders als die Goldsucher im vorletzten Jahrhundert brauchen Sie als Investor aber sehr viel mehr technisches Know-how, um in das Krypto-Mining einzusteigen. Dieses Buch wurde von zwei Insidern geschrieben. Sie erläutern, welche Hard- und Software Sie brauchen und wie Sie bei der Gewinnung von Bitcoin, Ethereum, Monero, LiteCoin und Dash am besten vorgehen - und zwar so, dass Sie der Konkurrenz voraus sind und Ihren Return on Investment maximieren. Peter Kent ist langjähriger Tech-Autor, der hauptberuflich Online-Unternehmen aufbaut und entwickelt. Tyler Bain ist Elektroingenieur mit Schwerpunkt Stromnetze und außerdem zertifizierter Bitcoin Professional. Er vertraut der Widerstandsfähigkeit der Blockchain und weiß alles über Mining-Mechanik.Über die Autoren 11Einleitung 23TEIL I: ERSTE SCHRITTE MIT KRYPTO-MINING 27Kapitel 1: Kryptowährungen kurz erklärt 29Kapitel 2: Krypto-Mining verstehen 49Kapitel 3: Die Reise der Transaktion zur Blockchain 57Kapitel 4: Die Arten und Wege des Minings 71TEIL II: DIE EVOLUTION DES KRYPTO-MININGS 89Kapitel 5: Die Evolution des Minings 91Kapitel 6: Die Zukunft des Krypto-Minings 101TEIL III: EIN KRYPTO-MINER WERDEN 113Kapitel 7: Mining leicht gemacht: Einen Pool finden und ein Benutzerkonto einrichten 115Kapitel 8: Eine Kryptowährung auswählen 137Kapitel 9: Die Ausrüstung zusammenstellen 163Kapitel 10: Die Mining-Hardware einrichten 183TEIL IV: BETRIEBSWIRTSCHAFTLICHE ASPEKTE DES MININGS 209Kapitel 11: Rechnen Sie nach: Lohnt es sich? 211Kapitel 12: Kosten senken: Immer einen Schritt voraus 235Kapitel 13: Ihr Kryptowährungs-Business betreiben 253TEIL V: DER TOP-TEN-TEIL 273Kapitel 14: Etwa zehn Tipps für den Fall, dass der Markt einbricht 275Kapitel 15: Zehn Möglichkeiten zur Steigerung der Kapitalrendite 293Kapitel 16: Zehn Arten von Kryptowährungs-Ressourcen 303Kapitel 17: Zehn Kritikpunkte an Kryptowährungen und am Mining 309Abbildungsverzeichnis 321Stichwortverzeichnis 327
Modern Full-Stack Development
Explore what React, Node, Python, Django, TypeScript, Webpack, and Docker have to offer individually, and how they all fit together in modern app development. This updated version will show you how to build apps with React, Node.js or Django, and TypeScript, and how Webpack can be used to optimize and organize your code for deployment.You’ll begin by building a solid foundation of knowledge and quickly expand it by constructing three different real-world apps. These aren’t just simple, contrived examples but real apps that you can choose to install on your servers and use for real. You’ll also understand how Docker can be used to run the apps you build in a clear and well-defined way, all of which will be able to springboard you into creating more advanced apps on your own.You'll see why React is one of the most popular web development tools available today, and why Node.js is also frequently used for server-side development. The fact that both utilize JavaScript is a big selling point, but there are shortcomings. Modern Full-Stack Development highlights how adding Django, Webpack, and Docker to the mix resolves any issues by creating a potent full development stack on which to build applications – two possible stacks, really!!WHAT YOU'LL LEARN:* Review the basics of TypeScript and writing both React and Node apps with it* Construct a project with NPM and Webpack, configuration and usage* Utilize client-side and server-side development* Employ REST APIs and database usage across two tech stacks, Node and Python/Django* Deploy apps using DockerWHO THIS BOOK IS FOR:Web developers and software architects.Frank Zammetti is a Principal Full-Stack Developer for a major financial firm with nearly 27 years of professional experience (plus almost 15 years of nonprofessional experience before that). He is an author of, including this one, 13 technical books for Apress. Frank has also authored over two dozen certification exams for SHL as well as several independent articles for various publications. He is also a fiction author (shameless plug: look him up on Amazon if you like sci-fi) and a musician of some renown (and here, “some” should be taken to mean very little). Frank has been married for 27 years (to the same woman even!) and they have two children together. When not doing any of the afermentioned things, Frank can be found sleeping, ‘cause that’s about all there’s time for after all that, and not nearly enough of it either!1. Server-Side Action: Node.js and NPM2. A Few More Words: Advanced Node and NPM3. Client-Side Adventures: React4. A Few More Words: Advanced React5. Building a Strong Foundation: TypeScript6. A Few More Words: Advanced TypeScript7. Tying it up in a Bow: Webpack8. Delivering the Goods: MailBag, the Server9. Delivering the Goods: MailBag, the Client10. Time for Fun: BattleJong, the Server11. Time for Fun: BattleJong, the Client12. Bringing the Dev Ship into Harbor: Docker13. Feed Your Face: Fooderator, The Server14. Feed Your Face: Fooderator, The Client
Künstliche Intelligenz heute
Künstliche Intelligenz wird schon heute in vielen Unternehmen angewendet. Und es werden immer mehr. Schon bald werden KI-Anwender starke Wettbewerbsvorteile erzielen, weil sie bessere Produkte anbieten, die haltbarer sind, effizienter produziert wurden und wahrscheinlich auch nachhaltiger sein werden. Diese Unternehmen haben also gleich mehrere Vorteile. Aber: Dies sind nur selten deutsche Unternehmen. Und es sind noch seltener deutsche Startups. In diesem Buch spricht Gunnar Brune mit Anwendern, Forschern und Investoren aus Wirtschaft, Medizin und Wissenschaft, um zu zeigen, wie Machine Learning und Künstliche Intelligenz erfolgreich und oft überraschend einfach zum Einsatz kommen (Applied AI). Die Lektüre bietet Informationen, Ansätze und Inspiration für den nutzbringenden Einsatz von Künstlicher Intelligenz heute.GUNNAR BRUNE ist Strategieberater und Autor für Wirtschaftsthemen. Gemeinsam mit dem Netzwerk AI.HAMBURG unterstützt er die Anwendung von Künstlicher Intelligenz in der Wirtschaft, damit deren strategische Chancen genutzt werden können.Einführung in die Welt aktueller Anwendungen Künstlicher Intelligenz.- Applied AI und das AI-Paradox.- Mit Künstlicher Intelligenz mehr Market Intelligence generieren.- Mehr Qualität, geringere Kosten, höhere Effizienz. KI in der Produktion von Nahrungsmitteln.- Mit Enthusiasmus für Daten kann jeder Mehrwert für Unternehmen und Mitarbeitende schaffen.- Mit Künstlicher Intelligenz besseren Content produzieren.- Künstliche Intelligenz und der Mensch – together forever.- Mit Künstlicher Intelligenz das Wissen der Chefärzte konservieren.- Mit KI-Unterstützung kann man Krebs spezifischer therapieren und Nebenwirkungen vermeiden.- Künstliche Intelligenz: Wir erkunden eine Terra Incognita für das Marketing.- In Fußball, Wirtschaft und Gesellschaft: Neue Algorithmen lösen Probleme, die man vorher auf diese Art nicht lösen konnte.- Der Umgang mit lernenden Maschinensystemen und Künstlicher Intelligenz ist eine wichtige Bildungsaufgabe.- Mit Künstlicher Intelligenz lassen sich wie nie zuvor technische Verfahren und Produkte optimieren.-Mehr Impact für Künstliche Intelligenz mit mehr KI-Startups aus Deutschland.- Künstliche Intelligenz anwenden. Jetzt.
GameMaker Fundamentals
Master the fundamental programming skills needed to create your own computer games in GameMaker. This book shows how to use GameMaker to build and publish cross-platform games.Each chapter covers a certain programming element, including layers, variables, and so on. You will also learn how to design levels in your games, draw sprites to populate your virtual worlds, and build GUIs for your menus and game interfaces. GameMaker Fundamentals also provides a thorough introduction to the GameMaker Language (GML). Practical example projects reinforce the concept discussed in each chapter.On completing this book, you will have a thorough understanding of how to create games from scratch using game design and programming principles using GameMaker and GML.WHAT YOU WILL LEARN* Review core programming features required for sound knowledge of GameMaker* Master how to combine GML to orchestrate game actions* Utilize GameMaker's layers to create exciting games* Set up player controlWHO IS THIS BOOK FORThose new to GameMaker or game programming in general; it assumes no prior knowledge or skill set.BEN TYERS is an expert GameMaker user, developer, coder, and trainer. He has authored a number of books on GameMaker for game application developers.Chapter 1: Instance LayersSub - Topics● What are layers● Layer types● Layer orders● ProjectsChapter 2: VariablesSub –Topics● Built in variables● Drawing variables● Variable types● ProjectsChapter 3: ConditionalsSub - Topics● What are conditoinals● Examples● ProjectsChapter 4: Drawing ShapesSub - Topics:● Drawing shapes● ProjectsChapter 5: Drawing ContinuedSub - Topics:● Drawing variables● Using fonts● Formatting text● Drawing sprites● Formatting sprites● ProjectsChapter 6: Keyboard Input & Basic ControlsSub - Topics:● Keyboard input● Mouse input● Moving a player instance● ProjectsChapter 7: Objects & EventsSub - Topics:● Alarm event● Create event● Draw event● Step event● Input events● Collision event● Draw GUI event● ProjectsChapter 8: SpritesSub - Topics:● Importing sprites● Strip images● Formatting sprites● ProjectsChapter 9: Health & LivesSub - Topics:● Health● Lives● Score● ProjectsChapter 10: MouseSub - Topics:● Mouse Buttons● Interaction with mouse● ProjectsChapter 11: AlarmsSub - Topics:● Usage● Setting alarms● Example usage● ProjectsChapter 12: CollisionsSub - Topics:● Usage● Events● Collisions using code● Examples● ProjectsChapter 13: RoomsSub - Topics:● Setting a background● Views● ProjectsChapter 14: BackgroundsSub - Topics:● Moving backgrounds● ProjectsChapter 15: SoundsSub - Topics:● Importing audio● Playing sounds● Playing music● Audio control● ProjectsChapter 16: Splash Screens & MenusSub - Topics:● Why use a splash screen● Unlockable levels● ProjectsChapter 17: RandomizationSub - Topics:● Using random values● Random variables examples● ProjectsChapter 18: AISub - Topics: ● Moving towards the player● Bullets● ProjectsChapter 19: INI filesSub - Topics:● What are INI files● Writing data● Reading data● ProjectsChapter 20: EffectsSub - Topics:● Built in effects● Effect layers● ProjectsChapter 21: LoopsSub - Topics:● Types of loops● Effect layers● Projects● Examples● ProjectsChapter 22: ArraysSub - Topics:● Example usage● Two dimensional arrays● Drawing array data● Using for weapon control● ProjectsChapter 23: DS ListsSub - Topics:● Example usage● Adding data● Organizing data● ProjectsChapter 24: PathsSub - Topics:● Creating a path● Manipulating a path● ProjectsChapter 25: FunctionsSub - Topics:● Setting up● Examples● Projects