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Understanding Artificial Intelligence
Understanding ARTIFICIAL INTELLIGENCEPROVIDES STUDENTS ACROSS MAJORS WITH A CLEAR AND ACCESSIBLE OVERVIEW OF NEW ARTIFICIAL INTELLIGENCE TECHNOLOGIES AND APPLICATIONSArtificial intelligence (AI) is broadly defined as computers programmed to simulate the cognitive functions of the human mind. In combination with the Neural Network (NN), Big Data (BD), and the Internet of Things (IoT), artificial intelligence has transformed everyday life: self-driving cars, delivery drones, digital assistants, facial recognition devices, autonomous vacuum cleaners, and mobile navigation apps all rely on AI to perform tasks. With the rise of artificial intelligence, the job market of the near future will be radically different???many jobs will disappear, yet new jobs and opportunities will emerge.Understanding Artificial Intelligence: Fundamentals and Applications covers the fundamental concepts and key technologies of AI while exploring its impact on the future of work. Requiring no previous background in artificial intelligence, this easy-to-understand textbook addresses AI challenges in healthcare, finance, retail, manufacturing, agriculture, government, and smart city development. Each chapter includes simple computer laboratories to teach students how to develop artificial intelligence applications and integrate software and hardware for robotic development. In addition, this text:* Focuses on artificial intelligence applications in different industries and sectors* Traces the history of neural networks and explains popular neural network architectures* Covers AI technologies, such as Machine Vision (MV), Natural Language Processing (NLP), and Unmanned Aerial Vehicles (UAV)* Describes various artificial intelligence computational platforms, including Google Tensor Processing Unit (TPU) and Kneron Neural Processing Unit (NPU)* Highlights the development of new artificial intelligence hardware and architecturesUnderstanding Artificial Intelligence: Fundamentals and Applications is an excellent textbook for undergraduates in business, humanities, the arts, science, healthcare, engineering, and many other disciplines. It is also an invaluable guide for working professionals wanting to learn about the ways AI is changing their particular field.ALBERT CHUN CHEN LIU, PH.D., is the CEO of Kneron and an Adjunct Associate Professor at National Tsing Hua University, National Chiao Tung University, and National Cheng Kung University, Taiwan. OSCAR MING KIN LAW, PH.D., is the director of engineering at Kneron. He has over 20 years of experience in the semiconductor industry and has published more than 70 patents in various areas. IAIN LAW studies Economics and Data Science at the University of California, San Diego. He has worked on several artificial intelligence projects including the LEGO smart robot and DJI Tello smart drone for STEM education. 1 INTRODUCTION 11.1 Overview 11.2 Development History 31.3 Neural Network Model 61.4 Popular Neural Network 71.4.1 Convolutional Neural Network 71.4.2 Recurrent Neural Network 81.4.3 Reinforcement Learning 91.5 Neural Network Classification 91.5.1 Supervised learning 101.5.2 Semi-supervised learning 101.5.3 Unsupervised learning 111.6 Neural Network Operation 111.6.1 Training 111.6.2 Inference 121.7 Application Development 121.7.1 Business Planning 141.7.2 Network Design 141.7.3 Data Engineering 141.7.4 System Integration 15Exercise 162 NEURAL NETWORK 172.1 Convolutional Layer 192.2 Activation Layer 202.3 Pooling Layer 212.4 Batch Normalization 222.5 Dropout Layer 222.6 Fully Connected Layer 23Exercise 243 MACHINE VISION 253.1 Object Recognition 253.2 Feature Matching 273.3 Facial Recognition 283.4 Gesture Recognition 303.5 Machine Vision Applications 313.5.1 Medical Diagnosis 313.5.2 Retail Applications 323.5.3 Airport Security 33Exercise 344 NATURAL LANGUAGE PROCESSING 354.1 Neural Network Model 364.1.1 Convolutional Neural Network 364.1.2 Recurrent Neural Network 374.1.2.1 Long Short-Term Memory Network 384.1.3 Recursive Neural Network 394.1.4 Reinforcement Learning 404.2 Natural Language Processing Applications 414.2.1 Virtual Assistant 414.2.2 Language Translation 424.2.3 Machine Transcription 43Exercise 455 AUTONOMOUS VEHICLE 465.1 Levels of Driving Automation 465.2 Autonomous Technology 485.2.1 Computer Vision 485.2.2 Sensor Fusion 495.2.3 Localization 515.2.4 Path Planning 525.2.5 Drive Control 525.3 Communication Strategies 535.3.1 Vehicle-to-Vehicle Communication 545.3.2 Vehicle-to-Infrastructure Communication 545.3.3 Vehicle-to-Pedestrian Communication 555.4 Law Legislation 565.4.1 Human Behavior 575.4.2 Lability 575.4.3 Regulation 585.5 Future Challenges 585.5.1 Road Rules Variation 585.5.2 Unified Communication Protocol 585.5.3 Safety Standard and Guideline 595.5.4 Weather/Disaster 59Exercise 606 DRONE 616.1 Drone Design 616.2 Drone Structure 626.2.1 Camera 636.2.2 Gyro Stabilization 636.2.3 Collision Avoidance 646.2.4 Global Positioning System 646.2.5 Sensors 646.3 Drone Regulation 656.3.1 Recreational Rules 656.3.2 Commercial Rules 666.4 Applications 666.4.1 Infrastructure Inspection 666.4.2 Civil Construction 676.4.3 Agriculture 686.4.4 Emergency Rescue 69Exercise 707 HEALTHCARE 717.1 Telemedicine 717.2 Medical Diagnosis 727.3 Medical Imaging 737.4 Smart Medical Device 747.5 Electronic Health Record 767.6 Medical Billing 777.7 Drug Development 787.8 Clinical Trial 797.9 Medical Robotics 807.10 Elderly Care 817.11 Future Challenges 82Exercise 848 FINANCE 858.1 Fraud Prevention 858.2 Financial Forecast 888.3 Stock Trading 898.4 Banking 918.5 Accounting 948.6 Insurance 95Exercise 969 RETAIL 979.1 E-Commerce 989.2 Virtual Shopping 1009.3 Product Promotion 1029.4 Store Management 1039.5 Warehouse Management 1049.6 Inventory Management 1069.7 Supply Chain 108Exercise 11010 MANUFACTURING 11110.1 Defect Detection 11210.2 Quality Assurance 11310.3 Production Integration 11410.4 Generative Design 11510.5 Predictive Maintenance 11710.6 Environment Sustainability 11810.7 Manufacturing Optimization 119Exercise 12111 AGRICULTURE 12211.1 Crop and Soil Monitoring 12311.2 Agricultural Robot 12511.3 Pest Control 12611.4 Precision Farming 127Exercise 12912 SMART CITY 13012.1 Smart Transportation 13112.2 Smart Parking 13212.3 Waste Management 13312.4 Smart Grid 13412.5 Environmental Conservation 135Exercise 13713 GOVERNMENT 13813.1 Information Technology 14013.2 Human Service 14113.3 Law Enforcement 14413.3.4 Augmenting Human Movement 14713.4 Homeland Security 14713.5 Legislation 14913.6 Ethics 15213.7 Public Perspective 155Exercise 15914 COMPUTING PLATFORM 16014.1 Central Processing Unit 16014.1.1 System Architecture 16114.1.2 Advanced Vector Extension 16414.1.3 Math Kernel Library for Deep Neural Network 16514.2 Graphics Processing Unit 16514.2.1 Tensor Core Architecture 16714.2.2 NVLink2 Configuration 16714.2.3 High Bandwidth Memory 16914.3 Tensor Processing Unit 17014.3.1 System Architecture 17014.3.2 Brain Floating Point Format 17114.3.3 Cloud Configuration 17214.4 Neural Processing Unit 17314.4.1 System Architecture 17314.4.2 Deep Compression 17414.4.3 Dynamic Memory Allocation 17414.4.4 Edge AI Server 175Exercise 176APPENDIX A KNERON NEURAL PROCESSING UNIT 178APPENDIX B OBJECT DETECTION (OVERVIEW) 179B.1 Kneron Environment Setup 179B.2 Python Installation 180B.3 Library Installation 184B.4 Driver Installation 185B.5 Model Installation 186B.6 Image/Camera Detection 186B.7 Yolo Class List 190APPENDIX C OBJECT DETECTION - HARDWARE 192C.1 Library Setup 192C.2 System Parameters 193C.3 NPU Initialization 194C.4 Image Detection 195C.5 Camera Detection 197APPENDIX D HARDWARE TRANSFER MODE 199D.1 Serial Transfer Mode 199D.2 Pipeline Transfer Mode 201D.3 Parallel Transfer Mode 203APPENDIX E OBJECT DETECTION – SOFTWARE (OPTIONAL) 205E.1 Library Setup 205E.2 Image Detection 207E.3 Video Detection 208Reference 211
From Sequences to Graphs
In order to study living organisms, scientists not only study them at an overall macroscopic scale but also on a more detailed microscopic scale. This observation, pushed to its limits, consists of investigating the very center of each cell, where we find the molecules that determine the way it functions: DNA (deoxyribonucleic acid) and RNA (ribonucleic acid).In an organism, DNA carries the genetic information, which is called the genome. It is represented as four-letter sequences using the letters A, C, G and T; based on these sequences, computer methods described in this book can answer fundamental questions in bioinformatics.This book explores how to quickly find sequences of a few hundred nucleotides within a genome that may be made up of several billion, how to compare those sequences and how to reconstruct the complete sequence of a genome. It also discusses the problems of identifying bacteria in a given environment and predicting the structure of RNA based on its sequence.ANNIE CHATEAU is a lecturer at the University of Montpellier, France. Her research interests include algorithms and combinatorial structures.MIKAËL SALSON is a lecturer at the University of Lille, France. His work focuses mainly on indexing and sequence comparison.Preface xiAuthor Biographies xviiCHAPTER 1 METHODOLOGICAL CONCEPTS: ALGORITHMIC SOLUTIONS OF BIOINFORMATICS PROBLEMS 1Annie CHATEAU and Tom DAVOT-GRANGÉ1.1 Data, Models, Problem Formalism in Bioinformatics 11.1.1 Data 11.1.2 Genome Modeling 41.1.3 Problems in Bioinformatics 51.2 Mathematical Preliminaries 61.2.1 Propositional Logic Preliminaries 61.2.2 Preliminaries on Sets 71.3 Vocabulary in Text Algorithmics 91.4 Graph Theory 101.4.1 Subgraphs 121.4.2 Path in a Graph 131.4.3 Matching 131.4.4 Planarity 141.4.5 Tree Decomposition 151.5 Algorithmic Problems 161.5.1 Definition 161.5.2 Graph Problem 171.5.3 Satisfiability Problems 191.6 Problem Solutions 201.6.1 Algorithm 201.6.2 Complexity 211.6.3 Runtime 241.7 Complexity Classes 261.7.1 Generality 261.7.2 Exact Algorithms 281.7.3 Approximation Algorithms 321.7.4 Solvers 341.8 Some Algorithmic Techniques 351.8.1 Dynamic Programming 351.8.2 Tree Traversal 381.9 Validation 411.9.1 The Different Types of Errors 421.9.2 Quality Measures 441.9.3 And in the Non-Binary Case? 461.10 Conclusion 471.11 References 47CHAPTER 2 SEQUENCE INDEXING 49Thierry LECROQ and Mikaël SALSON2.1 Introduction 492.1.1 What is Indexing? 502.1.2 When to Index? 512.1.3 What to Index? 512.1.4 Indexing Structures and Queries Considered 522.1.5 Basic Notions and Vocabulary 532.2 Word Indexing 542.2.1 Bloom Filters 542.2.2 Inverted List 562.2.3 De Bruijn Graphs 602.2.4 Efficient Structures for Targeted Queries 612.3 Full-Text Indexing 622.3.1 Suffix Tree 622.3.2 (Extended) Suffix Array 642.3.3 Burrows–Wheeler Transform 672.4 Indexing Choice Criteria 762.4.1 Based on the Type of the Necessary Query 772.4.2 Based on the Space-Time and Data Quantity Trade-Off 772.4.3 Based on the Need to Add or Modify Indexed Data 792.4.4 Indexing Choices According to Applications 802.5 Conclusion and Perspectives 812.5.1 Efficient Methods for Indexing a Few Genomes or Sequencing Sets 812.5.2 Methods that Struggle to Take Advantage of Data Redundancy 822.6 References 83CHAPTER 3 SEQUENCE ALIGNMENT 87Laurent NOÉ3.1 Introduction 873.1.1 What is Pairwise Alignment? 873.1.2 How to Evaluate an Alignment? 883.2 Exact Alignment 903.2.1 Representation in Edit Graph Form 903.2.2 Global Alignment and Needleman–Wunsch Algorithm 933.2.3 Local Alignment and Smith–Waterman Algorithm 943.2.4 Alignment with Affine Indel Function and the Gotoh Algorithm 963.3 Heuristic Alignment 983.3.1 Seeds 993.3.2 Min-Hash and Global Sampling 1053.3.3 Minimizing and Local Sampling 1063.4 References 109CHAPTER 4 GENOME ASSEMBLY 113Dominique LAVENIER4.1 Introduction 1134.2 Sequencing Technologies 1164.2.1 Short Reads 1174.2.2 Long Reads 1184.2.3 Linked Reads 1184.2.4 Hi-C Reads 1194.2.5 Optical Mapping 1194.3 Assembly Strategies 1204.3.1 The Main Steps 1204.3.2 Cleaning and Correction of Reads 1214.3.3 Scaffold Construction 1224.3.4 Scaffold Ordering 1234.4 Scaffold Construction Methods 1244.4.1 Greedy Assembly 1244.4.2 OLC Assembly 1264.4.3 DBG Assembly 1274.4.4 Constrained Assembly 1304.5 Scaffold-Ordering Methods 1324.5.1 Hi-C Data-Based Methods 1324.5.2 Optical Mapping-Based Methods 1374.6 Assembly Validation 1394.6.1 Metrics 1404.6.2 Read Realignment 1404.6.3 Gene Prediction 1414.6.4 Competitions 1414.7 Conclusion 1424.8 References 143CHAPTER 5 METAGENOMICS AND METATRANSCRIPTOMICS 147Cervin GUYOMAR and Claire LEMAITRE5.1 What is Metagenomics? 1475.1.1 Motivations and Historical Context 1475.1.2 The Metagenomics Data 1485.1.3 Bioinformatics Challenges for Metagenomics 1515.2 “Who Are They”: Taxonomic Characterization of Microbial Communities 1535.2.1 Methods for Targeted Metagenomics 1545.2.2 Whole-Genome Methods with Reference 1555.2.3 Reference-Free Methods 1605.3 “What Are They Able To Do?”: Functional Metagenomics 1665.3.1 Gene Prediction and Annotation 1665.3.2 Metatranscriptomics 1675.3.3 Reconstruction of Metabolic Networks 1685.4 Comparative Metagenomics 1695.4.1 Comparative Metagenomics with Diversity Estimation 1705.4.2 De Novo Comparative Metagenomics 1705.5 Conclusion 1755.6 References 176CHAPTER 6 RNA FOLDING 185Yann PONTY And Vladimir REINHARZ6.1 Introduction 1856.1.1 RNA Folding 1866.1.2 Secondary Structure 1896.2 Optimization for Structure Prediction 1926.2.1 Computing the Minimum Free-Energy (MFE) Structure 1926.2.2 Listing (Sub)optimal Structures 1986.2.3 Comparative Prediction: Simultaneous Alignment/Folding of RNAs 2036.2.4 Joint Alignment/Folding Model 2046.3 Analyzing the Boltzmann Ensemble 2106.3.1 Computing the Partition Function 2106.3.2 Statistical Sampling 2156.3.3 Boltzmann Probability of Structural Patterns 2206.4 Studying RNA Structure in Practice 2256.4.1 The Turner Model 2256.4.2 Tools 2286.5 References 228Conclusion 233List of Authors 237Index 239
Apache Essentials
Apache is the most widely used web server on the Internet today, and that comes as no surprise—it’s open source and therefore free of charge, not to mention gloriously extensible. And it’s much more secure than other web servers.So you’ve picked this book up and youre wondering what exactly makes it different from all those other Apache books? There are hundreds of books about Apache; books that promise to unleash its power, provide a complete reference for it, or delve into the most miniscule corners of its functionality.The problem is that none of them talks to the average web designer or developer. Most books on Apache are written by propellerheads for propellerheads. None of them explain how to configure Apache using plain language, giving examples that matter to you—but _Apache Essentials_ does!In _Apache Essentials_, I’ll show you how to install, configure, and maintain Apache. The key features that you’ll use on a daily basis will be emphasized. You’ll learn how to host secure (SSL) sites, optimize key Apache modules, use Apache with Perl and PHP, work with Server Side Includes (SSI), and take advantage of the information contained in log files.By the time you’re done reading this book, you’ll be an Apache superstar!Like most of the world, Darren James Harkness started his coding career with “Hello world!” on a computer he bought himself at the local office supply store. Originally registered as a computer science major, he quickly learned there might be a different way to participate in technology better suited to his skill set than hard coding. From that point on Darren turned to writing for the web, writing about the web, and managing smart people who do the same. He has never looked back.Darren lives in the lower mainland of British Columbia where he still spends too much time online, much to the chagrin of his partner, son, cat, and scruffy dog.* INTRODUCTION: REVISITING APACHE ESSENTIALSGoals• Learn about Apache and get very brief history of its origins and use.• Discover what you will learn from Apache and what you will be able to accomplish with it.• Question: where to use Apache or another web server, and what the benefits/drawbacks of each are.Chapter 1: Installing ApacheGoals:• Feel confident installing Apache on Windows, Linux, and MacOS for a development environment.• Create a running installation of Apache serving its Hello World static HTML file.Subtopics• Instal Apache.• Question: should to use something other than Apache?• Manage dependencies (PHP, RewriteEngine, etc).Chapter 2: Configuration EssentialsGoals• Set up a basic Apache instance pointing to static HTML on http://localhost/.• Develop an understanding of commonly used Apache directives.• Learn .htaccess files and how/when to use them.Subtopics• Structure of Apache configuration files.• Commonly used configuration directives.• The .htaccess file. Benefits & drawbacks of putting configuration options in .htaccess.• Put it all together.Chapter 3: Scripting languagesGoals:• Learn how the web server and programming languages work together to create websites.• Configure your website securely for PHP or NodeJS.• Configure your localhost to load an index.php instead of the default Apache HTML file.Subtopics• Overview.• Commonly used scripting environments.• PHP on Apache.• NodeJS on Apache.• Put it all together.Chapter 4: setting up virtual hosts for developmentGoals• Learn about Apache virtual hosts and how to configure them.• Set up a local dev domain — using local DNS settings or a tool such as ngrok.• Create two development environments configured and accessible through the browser; one for PHP and another for NodeJS.Subtopics• Overview of virtual hosts.• Set up a local dev domain.• Configure Apache for multiple virtual hosts.• Configure Apache for modern PHP and Node frameworks (example of setting up a NodeJS application).Chapter 5: Securing your setupGoals• Understand the importance of using HTTPS by default.• Recognize what SSL certificates are and how they work to secure communication between the browser and the server.• Install an SSL certificate on their local Apache setup.Subtopics• How HTTPS works to secure data and why this is important.• Get a certificate for your development environment.• Put it all together.Chapter 6: Using log files to troubleshoot your codeGoals• Learn about Log files and what information they provide.• Troubleshoot code problems via Apache’ s log files.Subtopics• About Apache’s log files.• Configure Apache log files.• Common troubleshooting patterns for PHP and NodeJS.• A troubleshooting story (leads reader through analyzing and identifying an issue in a piece of faulty PHP code).Chapter 7: Sample Apache ConfigurationsGoals• Use one the 4-5 example Apache configurations in your own Apache configurations.Subsections• Basic configuration for PHP.• Basic configuration for NodeJS.• Protecting a directory / domain.• Virtual Host configuration.• Configuring for Rewritten URLs (URL redirecting).AfterwordGoals• Revisit main concepts from book. Looking forward to next steps.
Test Automation Fundamentals
Concepts, methods, and techniques—supported with practical, real-world examples* The first book to cover the ISTQB® Certified Test Automation Engineer syllabus* With real-world project examples* – Suitable as a textbook, as a reference book for ISTQB® training courses, and for self-studyThis book provides a complete overview of how to design test automation processes and integrate them into your organization or existing projects. It describes functional and technical strategies and goes into detail on the relevant concepts and best practices. The book's main focus is on functional system testing. Important new aspects of test automation, such as automated testing for mobile applications and service virtualization, are also addressed as prerequisites for creating complex but stable test processes. The text also covers the increase in quality and potential savings that test automation delivers.The book is fully compliant with the ISTQB® syllabus and, with its many explanatory examples, is equally suitable for preparation for certification, as a concise reference book for anyone who wants to acquire this essential skill, or for university-level study.Manfred Baumgartner has more than 30 years of experience in software testing and quality assurance. Since 2001, he has established and expanded the QA consulting and training services of Nagarro GmbH, one of the leading service companies in the field of software testing. He is a board member of the Association for Software Quality and Further Education (ASQF)) and the Association for Software Quality Management Austria (STEV) as well as a member of the Austrian Testing Board (ATB). He shares his extensive experience in numerous presentations at conferences and in articles and books on software testing. Stefan Gwihs is an enthusiastic software developer, software tester and test automation architect for Nagarro GmbH, where he currently focuses on topics related to test automation for Agile software development and DevOps. Richard Seidl has seen and tested a lot of software in his professional career: good and bad, big and small, old and new, chocolate and groats. His credo: Quality is an attitude. If you want to create excellent software today, you have to think holistically about the development process: people, methods, tools, and mindset. As a consultant and coach, he supports companies in living agility and quality and anchoring them in the corporate DNA. Thomas Steirer leads Nagarro's global test automation practice as a test automation architect, test manager and trainer. Since 2010 he is certified as ISTQB® Certified Tester - Full Advanced Level. He is a lecturer for test automation in the master program Software Engineering at the UAS Technikum Vienna and researches the use of artificial intelligence with the goal of making test automation even more efficient. Marc-Florian Wendland is a research associate at the Fraunhofer Institute FOKUS in Berlin. For more than 10 years he has been involved in national and international, cross-domain research and industry projects on topics of test automation in design and execution. He is a member of the German Testing Board (GTB) and a trainer for the various ISTQB® programs. Julian Hartner is an ISTQB® certified quality engineer for Nagarro based in New York City and a passionate software developer and Test Automation Engineer. He currently focuses on streamlining manual and automated testing for CRM applications.
Design Patterns in .NET 6
Implement design patterns in .NET 6 using the latest versions of the C# and F# languages. This book provides a comprehensive overview of the field of design patterns as they are used in today’s developer toolbox. In addition to the functional builder, asynchronous factory method, generic value adapter, and composite proxies, this new edition introduces topics such as Decorator Cycle Policies Functional Commands, a Transformer variation of the Visitor pattern, and factories that can perform Object Tracking and Bulk Replacement.Using the C# and F# programming languages, DESIGN PATTERNS IN .NET 6 explores the classic design pattern implementations and discusses the applicability and relevance of specific language features for implementing patterns. You will learn by example, reviewing scenarios where patterns are applicable. Former C# MVP and patterns expert Dmitri Nesteruk demonstrates possible implementations of patterns, discusses alternatives and pattern relationships, and illustrates the way that a dedicated refactoring tool (JetBrains Rider) can be used to implement design patterns with ease.WHAT YOU WILL LEARN* Become familiar with the latest pattern implementations available in C# 10 and F# 6* Know how to better reason about software architecture* Understand the process of refactoring code to patterns* Refer to researched and proven variations of patterns* Study complete, self-contained examples, including many that cover advanced scenarios* Use the latest versions of C# and Visual Studio/Rider/ReSharperWHO THIS BOOK IS FORDevelopers who have some experience in the C# language and want to expand their comprehension of the art of programming by leveraging design approaches to solve modern problemsDMITRI NESTERUK is a quantitative analyst, developer, course instructor, book author, and occasional conference speaker. His interests lie in software development and integration practices in the areas of computation, quantitative finance, and algorithmic trading. His technological interests include C# and C++ programming as well as high-performance computing using technologies such as CUDA and FPGAs.PART I: INTRODUCTION.-Chapter 1: The SOLID Design PrinciplesChapter 2: The Functional PerspectivePART II: CREATIONAL PATTERNSChapter 3: BuilderChapter 4: FactoriesChapter 5: PrototypeChapter 6: SingletonPART III: STRUCTURAL PATTERNSChapter 7: AdapterChapter 8: BridgeChapter 9: CompositeChapter 10: DecoratorChapter 11: FaçadeChapter 12: FlyweightChapter 13: ProxyChapter 14: Value ObjectPART IV: BEHAVIORAL PATTERNSChapter 15: Chain of ResponsibilityChapter 16: CommandChapter 17: InterpreterChapter 18: IteratorChapter 19: MediatorChapter 20: MementoChapter 21: Null ObjectChapter 22: ObserverChapter 23: StateChapter 24: StrategyChapter 25: Template MethodChapter 26: Visitor
Advanced Excel Formulas
Enhance and upgrade your Excel knowledge with this comprehensive guide to formulas in Excel. OVER 150 OF THE MOST USEFUL EXCEL FUNCTIONS are covered with numerous practical examples of their use. This book is fully updated and includes examples of the most recently released functions in 2022. It is written in a manner that you can read it cover-to-cover or pick it up and learn something new in just 5 minutes.The book begins with a primer on Excel formulas and functions. Starting from a basic level, but diving into intricate detail and building a solid understanding of the fundamentals. It may not always be sexy, but LEARNING WHY, in addition to how, is important to truly understanding Excel formulas. Having strong fundamentals is the most advanced skill.The book covers all the most popular functions in Excel including VLOOKUP, SUMPRODUCT, COUNTIFS, MATCH, SORT and so much more. You will learn how to return multiple results with a single formula. Harness the power of the dynamic array engine and functions such as SEQUENCE, SORTBY, UNIQUE, XLOOKUP and FILTER to create top N Lists, models and reports that would seem impossible without array formulas. In the final chapter, we discover the amazing LAMBDA function in Excel. Create your own Excel functions to simplify future Excel formulas and share them with others.In this book, the formulas are not only written to return values to the grid, but also for use with other Excel features such as charts and Conditional Formatting, to take them to another level. Practice files are provided to follow all examples shown in the book.WHAT WILL YOU LEARN* Gain intimate knowledge of Excel formulas* Understand efficient and practical use of Defined Names and Tables* Master the most popular functions of Excel – VLOOKUP, COUNTIFS, MATCH, SUMPRODUCT, and so much more* Learn to return multiple results from a single formula with the magical array formulas.* Know the best lookup functions for dynamic spreadsheets* Know true power of classic Excel functions such as IF, SUM, and INDEX* Use new Excel functions including XLOOKUP, VSTACK, LET, and LAMBDAWHO IS THIS BOOK FORExcel users who are familiar with formulas but want to improve their current skill levelALAN MURRAY is a Microsoft MVP and Excel trainer. He has been helping people in Excel for over 20 years. He loves training and the joy he gets from knowing he is making peoples working lives easier.Alan runs his own blog - Computergaga (https://computergaga.com) and writes for multiple other websites. His YouTube channel has over 550 videos and over 35 million views.He organizes a free monthly Excel meetup in London where anyone can come learn Excel, chat, and enjoy each other’s company (https://www.meetup.com/London-Excel-Meetup-Group/).CHAPTER 1: EXCEL FORMULAS – A QUICK PRIMERCHAPTER GOAL: Start the journey to mastering formulas in ExcelNO OF PAGES: 25This chapter is the first of many on formulas in Excel. It will start with basic mathematical operations, explain the structure of formulas inside-out, look at some basic functions and cover absolute cell addresses.CHAPTER 2: LOGICAL FORMULASChapter Goal: Learn the important logical functions in Excel.NO OF PAGES: 35This chapter will explain how and when to use logical functions including IF, IFS, AND, OR, XOR, IFERROR, IFNA and SWITCH. These functions are the initial building blocks of automated Excel models and reports. Their importance cannot be understated.CHAPTER 3: DEFINED NAMESCHAPTER GOAL: Understand the use and hidden brilliance of defined names.NO OF PAGES: 25This chapter explores defined names in-depth. This little-known feature has huge benefits in how we use and deploy ranges and formulas in Excel.CHAPTER 4: YOU NEED TO START USING TABLESCHAPTER GOAL: To understand and master the use of TablesNO OF PAGES: 30This chapter will introduce the reader to tables in Excel. It will explain the many benefits to their use and how to use them effectively.CHAPTER 5: MANIPULATING TEXTCHAPTER GOAL: Learn the most useful text functions of ExcelNO OF PAGES: 40This chapter will look at the formulas to extract, combine, replace, and format text. These include but are not limited to LEN, MID, SEARCH, DOLLAR, CONCAT and VALUE.CHAPTER 6: WORKING WITH DATES AND TIMESChapter Goal: Learn the popular date and time functions of ExcelNO OF PAGES: 40This chapter will explain how dates and times are stored, and are to be used, in Excel. We will use different date and time functions to perform transformations and various calculations.CHAPTER 7: THE INFAMOUS VLOOKUP FUNCTIONCHAPTER GOAL: Master this vital functionNO OF PAGES: 35This chapter is dedicated to the third most used function in Excel. It fully explains how to use VLOOKUP in a way people have probably not heard before. It will also cover some insider tricks to avoid common VLOOKUP limitations and mistakes. A variety of examples will be shown.CHAPTER 8: ADVANCED LOOKUP FORMULASCHAPTER GOAL: Learn more advanced lookup functions.NO OF PAGES: 40In this chapter, we explore functions such as INDEX, OFFSET, MATCH, CHOOSE and INDIRECT to build upon the concepts learnt with VLOOKUP. We will see alternative ways to complete a task dependent upon the scenario.CHAPTER 9: THE AWESOME SUMIFS, COUNTIFS AND FRIENDSCHAPTER GOAL: Learn how to use these two exceptional functions.NO OF PAGES: 40This chapter will explain these two functions with various examples. They are two of the best and have come to my aid so many times in the past. The chapter also shows the AVERAGEIFS, MINIFS and MAXIFS functions.CHAPTER 10: NEXT LEVEL AGGREGATION FUNCTIONSCHAPTER GOAL: Learn the SUMPRODUCT and AGGREGATION functions.NO OF PAGES: 30This chapter will look at the SUMPRODUCT and AGGREGATE functions. This chapter follows on nicely from the previous one as these functions have more power than SUMIFS and COUNTIFS.CHAPTER 11: DYNAMIC ARRAY FORMULASChapter Goal: Understand how to efficiently use dynamic array formulasNO OF PAGES: 40This chapter introduces the reader to dynamic array formulas. These formulas are available to 365 users only. Therefore, it is essential that it is understood how these formulas are best used, and how they may affect you if you are not a 365 user.CHAPTER 12: XLOOKUP – THE NEW KID ON THE BLOCKCHAPTER GOAL: Learn how and when to use the XLOOKUP functionNo of pages: 20This chapter is all about the XLOOKUP function. What does it offer that the other lookup function could not? When is the best time to use it? All will be covered in this chapter.CHAPTER 13: FILTER FUNCTION – THE GAME CHANGERCHAPTER GOAL: Learn how to effectively use the FILTER functionNO OF PAGES: 20This chapter focuses on the impressive FILTER function. Several examples are shown to demonstrate the FILTER function performing tasks that other functions cannot do.CHAPTER 14: TABLE RANKINGS AND TOP N LISTSCHAPTER GOAL: Learn to create different dynamic table rankingsNO OF PAGES: 20This chapter uses functions and skills learnt over previous chapters to create different table ranking and top N lists. These are common tasks, but difficult to perform without advanced Excel formula skills.CHAPTER 15: GETTING STATUS INFORMATIONChapter Goal: Learn how to retrieve status of cells and the Excel environmentNO OF PAGES: 20This chapter will look at the CELL, TYPE, and INFO functions of Excel. They are used to return information about cells, data types and the operating environment. We can the utilise this information in other functions.CHAPTER 16: THE LET AND LAMBDA FUNCTIONSCHAPTER GOAL: Understand these two new functionsNO OF PAGES: 30This chapter will look the LET and LAMBDA functions introduced to Excel 365 in 2020/2021. They are freshly released and change how Excel users will write complex formulas. This chapter will explain how and when to use these power functions.
Power Platform and Dynamics 365 CE for Absolute Beginners
This is your complete guide to less-code and no-code theories, along with practical application of Microsoft Power Apps and Dynamics 365 CE/CRM Apps.The book covers topics including the configurations, customizations, and enhancements in Microsoft Power Apps and Dynamics 365 CE/CRM Apps. You will start by learning Microsoft Dataverse concepts followed by Microsoft Canvas Apps, model-driven apps, and PowerApps Portals. You will understand how to work with Power Virtual Agent, Power BI, and Power Automate, and how to use AI in Power Apps. The book provides important integration concepts for Power Apps, Dynamics 365 CE/CRM Apps, and Microsoft Azure. You will know how to customize Dynamics 365 CE/CRM Apps and Power Apps using OOTB capabilities.After reading this book, you will understand how Microsoft Power Apps and Dynamics 365 CE/CRM Apps can be used, configured, and customized for your business needs using customer data. You will be able to increase efficiency in customer data management and cloud app integrations.WHAT YOU WILL LEARN* Get up to speed on the Power platform echo-system and Dynamics 365 architecture* Work with Dataverse* Understand the Power platform building blocks* Select Power Apps to manage customer data* Configure and customize Power Apps* Design robust cloud flows* Integrate Power BI with Dynamics 365 CE/CRM Apps* Understand the core apps in Dynamics 365 CE/CRM* Use AI in Power AppsWHO THIS BOOK IS FORFunctional consultants/business analysts, technical consultants/solution architects in Power Apps and Dynamics 365 CE/CRM; and beginners who want to start a career in Power Apps and Dynamics 365 CE/CRM with easy English termsSANJAYA PRAKASH PRADHAN is a Microsoft Dynamics 365 and Power Apps Business Applications (MVP), and Microsoft Certified Trainer (MCT) in Dynamics 365 CE and Power Apps. He is an experienced senior technical consultant with 13+ years of experience in consulting and training who has worked on numerous business system implementations. Sanjaya is currently working as the research and development industry solutions lead in an established worldwide business applications practice. Having led software projects in numerous industries, including BFS, healthcare, retail, and the public sector, he works across all areas of the project life cycle from demonstrations to design, architecture, documentation, customization, and development. Sanjaya gets involved in the technical community through leading the Power Platform and Dynamics 365 user group in India, running technical events, and presenting on technical and functional topics at conferences around the world. He is a/an MVP, MCT, community director, UG lead, speaker, trainer, blogger, author, podcaster, business advisor, and senior solution architect.CHAPTER 1: INTRODUCTION: MICROSOFT POWER APPSCHAPTER GOAL: Introduction to book and topics to be coveredNO OF PAGES : 20SUB -TOPICS1. Understanding Environment2. Subscribing 30-days Free Trial Environment3. Power Platform Architecture and Concept4. Difference between Power Apps and Dynamics 365 Apps5. How Power Apps supports Less-code & no-code concept6. PowerFX concept in Power Apps7. Configuration’s v/s Customization in power Platform8. Enhancement Scopes in Power Apps9. Integrations concepts in Power Apps10. Sample Domain and Project AnalysisCHAPTER 2: WORKING WITH MICROSOFT DATAVERSECHAPTER GOAL: WORKING WITH DATAVERSE CONCEPTS IN DETAILSNO OF PAGES: 30SUB – TOPICS1. Understanding Power Apps Solutions2. Working with Table Configurations and settings3. Working with Columns, Data Types, Calculated Fields, Roll-up Fields4. Auto-number columns in Dataverse5. Configuring Relationships and Relationship Behaviors6. Working with Business Rules7. Configure Views for Tables8. Table Forms Design and Layouts9. Chart Configuration and Dashboard pinning10. Configure Table-Specific Dashboards11. Concepts of Alternate Keys12. Working with Data in Dataverse13. Business Scenario, Use cases and ImplementationChapter 3: Working with Microsoft Power AppsCHAPTER GOAL: LEARN MICROSOFT CANVAS-APPS, MODEL-DRIVEN APPS AND POWER PORTALSNO OF PAGES: 30SUB - TOPICS:1. Concept of Power Apps and no-code, less-code platform2. Working with Canvas Apps in details3. PowerFX for Power Apps4. Working with Model-driven apps5. Power Portal Concepts and Design with Deployment6. Manage Power Apps7. Business Scenarios and ImplementationsCHAPTER 4: WORKING WITH POWER AUTOMATECHAPTER GOAL: LEARNING CLOUD FLOW FOR AUTOMATIONSNO OF PAGES: 40SUB - TOPICS:8. Concept of Power Automate and Building Blocks9. Connectors in Power Automate10. Types of Flows and usages11. Dynamic Values and Expressions in Flows12. Using Variables, Loops and Conditions13. Working with Approvals in Power Automate14. Business Scenarios, Use Cases and ImplementationsCHAPTER 5: WORKING WITH POWER VIRTUAL AGENTCHAPTER GOAL: CREATE DIGITAL BOTS IN POWER PLATFORMNo of pages: 20SUB - TOPICS:15. Concept of Power Virtual Agent16. Building Blocks of Power Virtual Agents17. Configure Sample Power Virtual Agent18. Deploy Power Virtual Agent in Public Website/ Portals19. Business Scenarios, Use Cases and ImplementationsCHAPTER 6: WORKING WITH POWER BI AND DYNAMICS 365 APPSCHAPTER GOAL: DESIGN REPORTS AND DASHBOARDS IN CLOUDNO OF PAGES: 20SUB - TOPICS:1. Concept of Power BI2. Building Blocks of Power BI, Data Source, Reports, Dashboards3. Connect Online Data source to use in Power BI4. Connect On-premise Data source to use in Power BI5. Integrate Power BI with Dynamics 365 CE Apps6. Business Scenarios, Use Cases and ImplementationsCHAPTER 7: WORKING WITH AI BUILDERCHAPTER GOAL: LEARN HOW TO USE AI IN POWER APPSNO OF PAGES: 20SUB - TOPICS:1. Concept of AI Builder2. Types of AI Builder and Models3. Form Processing Models4. Object Detection Models5. Use AI Builder in Power Automate and canvas Apps6. Business Scenarios, Use Cases and ImplementationsCHAPTER 8: WORKING WITH CONFIGURATIONSCHAPTER GOAL: LEARN HOW TO USE SETTINGS FOR PERSONALIZATION AND ADVANCED SETTINGS WITH SCENARIOSNO OF PAGES: 30SUB - TOPICS:1. Admin Centers and its usages2. Data import in Power Apps3. Configure Duplicate detection rules4. Configure auditing feature in power apps5. Add users in environments6. Security matrix and configurations7. Configure Workflows and concepts8. Configure Custom Actions9. Configure Reports in Power Apps10. Working with Email Templates and Email Signatures11. Install multiple languages in environments12. Working with Translation13. Working with document templatesCHAPTER 9: WORKING WITH CUSTOMIZATIONSCHAPTER GOAL: HOW TO CUSTOMIZE SYSTEMNO OF PAGES: 30SUB - TOPICS:1. Working with Client-side scripting JavaScript in Power Apps2. Develop Plugins for Power Apps3. Develop Custom Workflow Activities in Power Apps4. Edit command Bars in Power Apps5. Business Scenarios and ImplementationsCHAPTER 10: WORKING WITH INTEGRATIONS FOR POWER APPS AND DYNAMICS 365CHAPTER GOAL: INTEGRATION SCENARIOSNO OF PAGES: 20SUB - TOPICS:6. Outlook Integration in Power Apps7. SharePoint Integration in Power Apps8. One-Drive Integration in Power Apps9. Azure Integration in Power Apps10. Business Scenarios and Implementations11. What NextCHAPTER 11: DYNAMICS 365 CORE APPSCHAPTER GOAL: UNDERSTAND CORE APPS IN DYNAMICS 365 CENO OF PAGES: 30SUB - TOPICS:1. Sales Hub App Life Cycle2. Customer Service Hub App Life Cycle3. Marketing App Concept and Life Cycle4. Field Service App Life Cycle5. Project Service Automation Life Cycle6. What Next in Learning
Systematisches Requirements Engineering
Das umfassende Handbuch zum Requirements Engineering* eingeführtes Standardwerk nun in 7. Auflage!* hoher Praxisbezug* direkt anwendbare Checklisten und PraxistippsDieses Buch beschreibt praxisorientiert und systematisch das Requirements Engineering vom Konzept über Analyse und Realisierung bis zur Wartung und Evolution eines Produkts.Requirements Engineering mit seinen Methoden, Modellen, Notationen und Werkzeugen wird eingeführt. Ein durchgängiges Beispiel sowie viele industrielle Praxiserfahrungen illustrieren die Umsetzung. Direkt anwendbare Checklisten und Praxistipps runden jedes Kapitel ab. Lesen Sie das Buch, um– Requirements Engineering kennenzulernen,– Ihre Projekte und Produkte erfolgreich zu liefern,– agile Entwicklung beispielsweise mit testorientierten Anforderungen umzusetzen,- industrieerprobte Techniken des Requirements Engineering produktiv zu nutzen.Diese 7. Auflage wurde in vielen Aspekten aktualisiert und berücksichtigt den aktuellen Lehrplan des IREB®-Zertifizierungsprogramms.Christof Ebert ist Geschäftsführer von Vector Consulting Services. Er unterstützt Kunden bei Produktstrategie, Entwicklung und agiler Transformation und arbeitet in verschiedenen Aufsichtsgremien von Unternehmen. Zuvor war er zwölf Jahre bei einem IT Konzern in weltweiten Führungsaufgaben. Als Business Angel und Professor an der Universität Stuttgart und der Sorbonne in Paris stimuliert er Innovationen. Er wirkt in den Herausgeber-Komitees von Zeitschriften wie IEEE Software und dem Journal of Systems and Software. In seiner Freizeit spielt er als Musiker Keyboards und engagiert sich im sozialen Bereich.Folgen Sie ihm auf Twitter: @ChristofEbertKontakt: christof.ebert@vector.com, www.christofebert.deHomepage des Buches: www.vector.com/RE-Buch
Web Application Development with Streamlit
Transition from a back-end developer to a full-stack developer with knowledge of all the dimensions of web application development, namely, front-end, back-end and server-side software. This book provides a comprehensive overview of Streamlit, allowing developers and programmers of all backgrounds to get up to speed in as little time as possible.Streamlit is a pure Python web framework that will bridge the skills gap and shorten development time from weeks to hours. This book walks you through the complete cycle of web application development, from an introductory to advanced level with accompanying source code and resources. You will be exposed to developing basic, intermediate, and sophisticated user interfaces and subsequently you will be acquainted with data visualization, database systems, application security, and cloud deployment in Streamlit.In a market with a surplus demand for full stack developers, this skill set could not possibly come at a better time. In one sentence, Streamlit is a means for the empowerment of developers everywhere and all stand to gain from it.WHAT YOU’LL LEARN* Mutate big data in real-time* Visualize big data interactively* Implement web application security and privacy protocols * Deploy Streamlit web applications to the cloud using Streamlit, Linux and Windows serversWHO IS THIS BOOK FOR?Developers with solid programming experience wanting to learn Streamlit; Back-end developers looking to upskill and transition to become a full-stack developers; Those who wish to learn and become more acquainted with data visualization, database systems, security and cloud deployment with SteamlitMOHAMMAD KHORASANI A hybrid of an engineer and a computer scientist with a Bachelor’s of Science in Mechanical Engineering from Texas A&M University, and a Master’s in Computer Science from the University of Illinois at Urbana-Champaign. Mohammad specializes in developing and implementing software solutions for the advancement of renewable energy systems and services at Iberdrola. In addition, he develops robotic devices using embedded systems and rapid prototyping technologies. He is also an avid blog-ger of STEM related topics on Towards Data Science - a Medium publication.MOHAMED ABDOU A Software Engineer with diverse academic and industrial exposure. A graduate of Computer Engineering from Qatar University, and currently a SDE at Amazon. Mohamed has built a variety of open source tools used by tens of thousands in the Streamlit community. He led the first Google Developer Student Club in Qatar, and represented Qatar University in national and international programming contests. He is a a cyber security enthusiast, and was ranked 2nd nationwide in bug bounty hunting in Qatar in 2020 among under 25 year old’s.JAVIER HERNANDEZ FERNANDEZ specializes in the area of technology innovation and brings over twenty years of practical experience in overseeing the design and delivery of technological developments on behalf of multi-national companies in the fields of IT, telecom, and utilities. He publishes extensively, speaks at conferences around the world, and spends his days wading through piles of academic papers in the hope of finding something interesting. He holds Masters’ degrees in both Energy Management and Project Management, in addition to a B.Sc. in Computer Science from the from the Faculty of Engineering of the University of Ottawa.PART I: INTRODUCTION TO STREAMLIT1 GETTING STARTED WITH STREAMLIT1.1 Why Streamlit?1.2 How Streamlit Works1.3 Firing it up2 STREAMLIT BASICS2.1 The Streamlit API2.2 Creating a basic appPART II: DEVELOPING ADVANCED INTERFACES AND APPLICATIONS3 ARCHITECTING STREAMLIT’S FRONT-END DESIGN3.1 Designing the application3.2 Provisioning multi-page applications3.3 Data wrangling4 GRAPHING IN DEPTH4.1 Visualization stack4.2 Exploring Plotly data visualizationsPART III: INTERFACING WITH DATABASE AND BACK-END SYSTEMS5 DATABASE INTEGRATION5.1 Relational Databases5.2 Non-relational databases6 BACK-END SERVERS6.1 The need for back-end servers6.2 Front-end/ Back-end Communication6.3 Working with JSON files6.4 Provisioning a back-end server6.5 Multi-threading and multi-processing request6.6 Connecting Streamlit to a Back-end ServerPART IV: ENFORCING APPLICATION SECURITY AND PRIVACY7 SESSION STATE7.1 Introducing session IDs7.2 Implementing session state persistently7.3 Recording user insights7.4 Implementing session state natively7.5 Cookies management8 AUTHENTICATION AND APPLICATION SECURITY8.1 Developing user accounts8.2 Verifying user credentials8.3 Secrets management8.4 Anti-SQL injection measures with SQL Alchemy8.5 Configuring Git Ignore variablesPART V: DEPLOYING STREAMLIT TO THE CLOUD9 PERSISTENT DEPLOYMENT9.1 Deployment to Streamlit Sharing9.2 Deployment to Linux9.3 Deployment to Windows Server10 EXPOSING LOCAL STREAMLIT TO THE WORLD WIDE WEB10.1 Port forwarding over network gateway10.2 Reverse Port Forwarding using NGROKPART VI: STREAMLIT CUSTOM COMPONENTS11 BUILDING STREAMLIT COMPONENTS WITH REACT.JS11.1 Introduction to Streamlit custom components11.2 Using React.js to create custom HTML components11.3 Deploying components as a Pip package12 EXTRA-STREAMLIT-COMPONENTS PACKAGE12.1 Stepper bar12.2 Splash screen . .12.3 Tab bar12.4 Cookie ManagerPART VII: STREAMLIT CASE STUDIES13 GENERAL USE CASES13.1 Data science & machine learning applications13.2 Dashboards and real-time applications13.3 Time-series applications13.4 Advanced application development14 STEAMLIT AT WORK14.1 Iberdrola Renewables14.2 DummyLearn.com
Just React!
Here is your perfect companion for learning about and developing React applications. This book introduces concepts innovatively, using real-world examples based on the most recommended practices to help you establish a firm foundation. This comprehensive approach provides a strong focus on building components by using React hooks.You’ll begin by learning web fundamentals, next-generation JavaScript, and how React fits into this. In the chapters that follow, you’ll build a React application from scratch and learn about JSX, components, props, state management, prop drilling, context, and lifecycle events. Along the way you’ll build a multi-component app and see how the components interact. Debugging and styling React applications are also discussed.You’ll then take an in depth look at React hooks and see how to create a custom hook. There is also a penultimate chapter that explores important concepts such as routing and authentication. The book concludes with a review of some exciting features in the upcoming release of React 18. After reading Just React you will be equipped with the skills necessary to build complex web and mobile user interfaces using this flexible JavaScript library.WHAT YOU’LL LEARN* Examine the details of modern React concepts through example projects* Set up your own React project* Debug and style React components* Take a look at routing, authentication, HTTP requests, Redux, and the new features of React 18WHO THIS BOOK IS FORWeb developers, React developers, and JavaScript developersHari Narayn is a programming enthusiast with over 11 years of experience in building web and mobile applications with React, Microsoft 365, SharePoint, Azure, Teams, Power Platform, .Net, Angular, and JavaScript. He has built web and mobile solutions for various clients across the world. He is a Microsoft 365 Certified Developer Associate, Certified Azure Solutions Architect Expert, and a Certified Power Platform Developer Associate. He is a native of Kerala, India and currently based in Melbourne, Australia. He works as a Senior Technical Specialist in Victorian Public Service.Chapter 1: Time to ReactChapter Goal: The purpose of this chapter is to introduce you to React and to introduce the role it plays in modern web development.No of pages: 20Subtopics1. Think before you React2. How React Reacts compared to JavaScript?3. React Vs Angular.4. Where to React?Chapter 2: JavaScript Before You ReactChapter Goal: Next gen JavaScript (ES6 and above) is the subject of this chapter. The goal of this chapter is to get you familiar with the latest features of this most popular programming language.No of pages: 40Sub - Topics1. Variables, Conditionals, and loops2. Functions and Arrow Functions3. Modules4. Events5. Reference Types6. Async Await7. Template Literals.Chapter 3: Start ReactingChapter Goal: The goal of this chapter is to get you started on your React journey. You will gain an in-depth understanding of React project setup and fundamental concepts.No of pages: 60Sub - Topics:1. Set up an environment to ‘React’2. How to React?3. create-react-app4. Introduction to Components5. JSX6. Reacting to inputs7. Styling your component8. Virtual DOM9. Props and State10. Just React to Child11. React on a ConditionChapter 4: Think ReactChapter Goal: Using the most recommended concepts of React, this chapter creates a fully functional application. You'll gain an understanding of state management, component interactions etc., and you will start thinking the React way.No of pages:50Sub - Topics:1. VS Code Extensions2. Restructuring the React form3. Combining Reactions4. Sibling Reactions5. Component chat continues…6. Reacting to edits7. More Reactions to the parentChapter 5: Rethink ReactChapter Goal: The goal of this chapter is to get you to rethink. It will show you how to identify and fix common problems with React applications. You will learn some advanced concepts such as code splitting and React Context.No of pages:50Sub - Topics:1. React Lazy and Suspense2. Props Drilling3. Multi View React app4. React ContextChapter 6: React to BugsChapter Goal: This chapter covers several ways to debug React applications. It mostly focuses on how you, as a developer, react to bugs in React applications.You will learn about the rich capabilities of Chrome DevTools and React DevTools.No of pages:30Sub - Topics:1. Chrome Reacts2. Don’t React, Debug first3. Console Reactions4. React to Errors5. React Developer Tools6. React to Bugs within VS CodeChapter 7: Reacting in styleChapter Goal: The purpose of this chapter is to present different ways of styling components, their pros and cons, and introduce some tools to assist you in styling React components.No of pages:30Sub - Topics:1. CSS-in-JS2. Styled Components3. CSS Style Sheets4. Sassy CSS (SCSS)5. CSS Modules6. Overview of CodeSandbox and Material UI7. Responsive ReactChapter 8: Hook into ReactChapter Goal: In this chapter, we will cover in depth about React Hooks with examples. You will learn about all the hooks and how to create custom hooks.No of pages:60Sub - Topics:1. Life of a Class2. Life of a Function and the birth of Hooks3. useState4. useEffect4. useRef5. useReducer6. Remember to React7. useMemo8. useCallback9. useContext10. Few more ‘Hookies’11. Custom ‘Hookies’Chapter 9: React moreChapter Goal: This chapter covers basics about Routing, Authentication, sending HTTP requests from a React App, Redux and a few other concepts we haven't talked about so far in other chapters. etc.No of pages:50Sub - Topics:1. React to Routes2. Identify before React3. . HTTP Reactions4. ReduxChapter 10: New ReactionsChapter Goal: This chapter summarizes all the new features in React 18No of pages:20Sub - Topics:1. New Root and the new way to Render2. React Concurrently3. React slowly for faster response4. Server on Suspense5. Automatic Batching6. ’Too Strict’ Mode7. New ‘Hookies’
Building the Snowflake Data Cloud
Implement the Snowflake Data Cloud using best practices and reap the benefits of scalability and low-cost from the industry-leading, cloud-based, data warehousing platform. This book provides a detailed how-to explanation, and assumes familiarity with Snowflake core concepts and principles. It is a project-oriented book with a hands-on approach to designing, developing, and implementing your Data Cloud with security at the center. As you work through the examples, you will develop the skill, knowledge, and expertise to expand your capability by incorporating additional Snowflake features, tools, and techniques. Your Snowflake Data Cloud will be fit for purpose, extensible, and at the forefront of both Direct Share, Data Exchange, and Snowflake Marketplace.BUILDING THE SNOWFLAKE DATA CLOUD helps you transform your organization into monetizing the value locked up within your data. As the digital economy takes hold, with data volume, velocity, and variety growing at exponential rates, you need tools and techniques to quickly categorize, collate, summarize, and aggregate data. You also need the means to seamlessly distribute to release value. This book shows how Snowflake provides all these things and how to use them to your advantage.The book helps you succeed by delivering faster than you can deliver with legacy products and techniques. You will learn how to leverage what you already know, and what you don’t, all applied in a Snowflake Data Cloud context. After reading this book, you will discover and embrace the future where the Data Cloud is central. You will be able to position your organization to take advantage by identifying, adopting, and preparing your tooling for the coming wave of opportunity around sharing and monetizing valuable, corporate data.WHAT YOU WILL LEARN* Understand why Data Cloud is important to the success of your organization* Up-skill and adopt Snowflake, leveraging the benefits of cloud platforms* Articulate the Snowflake Marketplace and identify opportunities to monetize data* Identify tools and techniques to accelerate integration with Data Cloud* Manage data consumption by monitoring and controlling access to datasets* Develop data load and transform capabilities for use in future projectsWHO THIS BOOK IS FORSolution architects seeking implementation patterns to integrate with a Data Cloud; data warehouse developers looking for tips, tools, and techniques to rapidly deliver data pipelines; sales managers who want to monetize their datasets and understand the opportunities that Data Cloud presents; and anyone who wishes to unlock value contained within their data silosANDREW CARRUTHERS is the Director for the Snowflake Corporate Data Cloud at the London Stock Exchange Group. Comprising two Snowflake accounts supporting both ingestion data lake and consumption analytics hub, the Corporate Data Cloud services a growing customer base of over 7,000 end users. He also leads both the Centre for Enablement developing tooling, best practices and training, and the Snowflake Landing Zone provisioning Snowflake Accounts conforming to both internal standards and best practices.PART I. CONTEXT1. The Snowflake Data Cloud2. Breaking Data SiloesPART II. CONCEPTS3. Architecture4. Account Security5. Role Based Access Control (RBAC)6. Account Usage StorePART III. TOOLS7. Ingesting Data8. Data Pipelines9. Data Presentation10. Semi Structured and Unstructured DataPART IV. MANAGEMENT11. Query Optimizer Basics12. Data Management13. Data Modelling14. Snowflake Data Cloud By Example
Pro Data Mashup for Power BI
This book provides all you need to find data from external sources and load and transform that data into Power BI where you can mine it for business insights and a competitive edge. This ranges from connecting to corporate databases such as Azure SQL and SQL Server to file-based data sources, and cloud- and web-based data sources. The book also explains the use of Direct Query and Live Connect to establish instant connections to databases and data warehouses and avoid loading data.The book provides detailed guidance on techniques for transforming inbound data into normalized data sets that are easy to query and analyze. This covers data cleansing, data modification, and standardization as well as merging source data into robust data structures that can feed into your data model. You will learn how to pivot and transpose data and extrapolate missing values as well as harness external programs such as R and Python into a Power Query data flow. You also will see how to handle errors in source data and extend basic data ingestion to create robust and parameterized data load and transformation processes.Everything in this book is aimed at helping you deliver compelling and interactive insight with remarkable ease using Power BI’s built-in data load and transformation tools.WHAT YOU WILL LEARN* Connect Power BI to a range of external data sources* Prepare data from external sources for easy analysis in Power BI * Cleanse data from duplicates, outliers, and other bad values* Make live connections from which to refresh data quickly and easily* Apply advanced techniques to interpolate missing dataWHO THIS BOOK IS FORAll Power BI users from beginners to super users. Any user of the world’s leading dashboarding tool can leverage the techniques explained in this book to turbo-charge their data preparation skills and learn how a wide range of external data sources can be harnessed and loaded into Power BI to drive their analytics. No previous knowledge of working with data, databases, or external data sources is required—merely the need to find, transform, and load data into Power BI..ADAM ASPIN is an independent Business Intelligence consultant based in the United Kingdom. He has worked with SQL Server for over 25 years. During this time, he has developed several dozen reporting and analytical systems based on the Microsoft Data Platform.A graduate of Oxford University, Adam began his career in publishing before moving into IT. Databases soon became a passion, and his experience in this arena ranges from dBase to Oracle, and Access to MySQL, with occasional sorties into the world of DB2. He is, however, most at home in the Microsoft universe when using SQL Server Analysis Services, SQL Server Reporting Services, SQL Server Integration Services, Azure Data Factory, Azure Synapse and, of course, Power BI.Business Intelligence has been Adam's principal focus for 20 years. He has applied his skills for a range of clients in a range of industry sectors. He is the author of SQL Server Data Integration Recipes; Pro Power BI Desktop (now in its third edition); Business Intelligence with SQL Server Reporting Services; High Impact Data Visualization; Data Mashup using Microsoft Excel using Power Query and M, and Pro Power BI Theme Creation—all with Apress.A fluent French speaker, Adam has worked in France and Switzerland for many years.1. Discovering and Loading Data with Power BI Desktop2. Discovering and Loading File-Based Data with Power BI Desktop3. Loading Data From Databases and Data Warehouses4. DirectQuery and Live Connect5. Loading Data from the Web and Cloud6. Loading Data from Other Data Sources7. Power Query8. Structuring Data9. Shaping Data10. Data Cleansing11. Data Transformation12. Complex Data Structures13. Organizing, Managing, and Parameterizing Queries14. The M LanguageAppendix A: Sample Data
Mapping Data Flows in Azure Data Factory
Build scalable ETL data pipelines in the cloud using Azure Data Factory’s Mapping Data Flows. Each chapter of this book addresses different aspects of an end-to-end data pipeline that includes repeatable design patterns based on best practices using ADF’s code-free data transformation design tools. The book shows data engineers how to take raw business data at cloud scale and turn that data into business value by organizing and transforming the data for use in data science projects and analytics systems.The book begins with an introduction to Azure Data Factory followed by an introduction to its Mapping Data Flows feature set. Subsequent chapters show how to build your first pipeline and corresponding data flow, implement common design patterns, and operationalize your result. By the end of the book, you will be able to apply what you’ve learned to your complex data integration and ETL projects in Azure. These projects will enable cloud-scale big analytics and data loading and transformation best practices for data warehouses.WHAT YOU WILL LEARN* Build scalable ETL jobs in Azure without writing code* Transform big data for data quality and data modeling requirements* Understand the different aspects of Azure Data Factory ETL pipelines from datasets and Linked Services to Mapping Data Flows* Apply best practices for designing and managing complex ETL data pipelines in Azure Data Factory* Add cloud-based ETL patterns to your set of data engineering skills* Build repeatable code-free ETL design patternsWHO THIS BOOK IS FORData engineers who are new to building complex data transformation pipelines in the cloud with Azure; and data engineers who need ETL solutions that scale to match swiftly growing volumes of dataMARK KROMER has been in the data analytics product space for over 20 years and is currently a Principal Program Manager for Microsoft’s Azure data integration products. Mark often writes and speaks on big data analytics and data analytics and was an engineering architect and product manager for Oracle, Pentaho, AT&T, and Databricks prior to Microsoft Azure. IntroductionPART I. GETTING STARTED WITH AZURE DATA FACTORY AND MAPPING DATA FLOWS1. Introduction to Azure Data Factory2. Introduction to Mapping Data FlowsPART II. DESIGNING SCALABLE ETL JOBS WITH ADF MAPPING DATA FLOWS3. Build Your First Pipeline4. Common Pipeline Patterns5. Design Your First Mapping Data Flow6. Common Data Flow Patterns7. Debugging Mapping Data Flows8. Data Pipelines with Data FlowsPART III. OPERATIONALIZE YOUR ETL DATA PIPELINES9. CI/CD and Scheduling10. Monitoring, Management, and SecurityPART IV. SAMPLE PROJECT11. Build a New ETL Project in ADF using Mapping Data Flows12. End-to-End Review of the ADF Project
Learn JavaFX Game and App Development
Understand real-world game development concepts using JavaFX game engine called FXGL. The core focus of the book is on developing a standalone game or application with FXGL.We will start with an overview of the book followed by requisite concepts from Java and JavaFX that will be used throughout this book. Next, we will learn about the FXGL game engine and its wide range of real-world game development techniques. In the following chapter, we learn about entity-component model used in FXGL to create a powerful abstraction of the game world. The next chapter builds on this, where we develop a platformer game using the physics engine and a popular external tool called Tiled. An important concept of games AI is covered in the following chapter. Visually complex features related to graphics and rendering as well as UI elements and animation system in FXGL will be discussed in the next chapter. The following chapter is dedicated to non-game applications that can be developed using FXGL. The last two chapters cover packaging and deployment of JavaFX and FXGL applications and discussion on future projects.The key take-away skill from this book is the ability to develop professional-level applications and games with FXGL. During the course of this book, you will have produced a range of cross-platform applications and games using FXGL, reinforcing the game development concepts covered throughout.WHAT YOU WILL LEARN• Understand use of advanced Java and JavaFX concepts• Learn about real-world game development concepts in a general-purpose programming language• Master professional cross-platform, desktop and mobile, games using the FXGL game engineWHO IS THIS BOOK FORThis book is for beginners in Java and/or JavaFX who wish to develop apps and games with FXGL, while improving Java and JavaFX skills.Dr Almas Baimagambetov is a Principal Lecturer in Computer Science at the University of Brighton, UK. He has considerable software development experience and is a huge fan of open source. His prominent contributions to the JVM community on GitHub include the FXGL game engine, collaborations on numerous JavaFX projects, a wide range of open-source games, and a collection of practical tutorials. Almas also has a YouTube channel focused on Java, Kotlin, JavaFX, Unity, and Unreal Engine. Chapter 1: IntroductionChapter Goal: Sets the scene for the book, provides an overview and sets expectationsChapter 2: Requisite Java and JavaFX ConceptsChapter Goal: Covers fundamental knowledge required to understand the book contentSub-topics: Java programmingJavaFX scene graphJavaFX model of programmingJavaFX conceptsChapter 3: FXGL ArchitectureChapter Goal: Provides an overview of the FXGL architecture, features, and capabilitiesChapter 4: Entity-Component Case Study: Develop Arcade GamesChapter Goal: Introduction to entity-component model used for abstracting game worldsSub-topics:Game worldEntity-Component modelPong and Breakout style gamesChapter 5: Physics Case Study: Develop a Platformer GameChapter Goal: Introduction to lightweight and heavyweight physics engines in FXGLSub-topics:Collision detectionRigid body dynamicsMario style gameChapter 6: AI Case Study: Develop a Maze Action GameChapter Goal: Provides a foundation for using and developing AI agents in FXGLSub-topics:A* pathfindingGraph theoryComponent-driven behaviorPac-man style gameChapter 7: Graphics and UI Case Study: Develop a Top-Down Shooter GameChapter Goal: Introduction to the particle and animation systems used in FXGLSub-topics:Particle systemMulti-layer renderingAnimationsInterpolationsGeometry wars style gameChapter 8: Developing General-Purpose ApplicationsChapter Goal: Provide information on how FXGL can be used in non-game contextsChapter 9: Cross-platform DeploymentChapter Goal: Demonstrates the package and deployment process with FXGLSub-topics:jlinkNative imagesGluon toolsMobile developmentChapter 10: ConclusionChapter Goal: Recap what was covered in the chapters, provides external resources and ideas for future projects
Blockchain and Ethereum Smart Contract Solution Development
Build decentralized applications with smart contract programming. Following the curriculum from an active blockchain course taught by the author at the McCombs School of Business at the University of Texas, this book fills the gaps for you from learning about basic cryptocurrency uses of blockchain to understanding smart contracts and dapps.You’ll first start by understanding the basics of blockchain technology. Take a business point of view to discover general concepts about blockchains and dapps or “decentralized apps” built off of smart contracts. Next, learn about the token economy, how to design tokens, and relevant client technologies, such as web3, metamask, and UI/UX design. Then, install a blockchain node yourself.With a basic understanding of blockchain applications and business uses, you’ll move further into hands-on development. There are ten modules for hands-on smart contract programming covered to build your own decentralized applications. Several team projects built end-to-end from concept to deployment to operation are also provided. Using these models and your own original work, you’ll build a smart contract development environment, practice Solidity programming, compile source code, perform security reviews, and deploy bytecode to blockchains.The breakthrough in blockchain technology has empowered novel ecosystems and applications in the areas of Decentralized Finance (DeFi), Central Bank Digital Currency (CBDC), Non-Fungible Tokens (NFT), Decentralized Autonomous Organization (DAO), and more. Blockchain and Ethereum Smart Contract Solution Development will prepare you to create fantastic applications using Ethereum’s smart contracts and solid concepts of decentralized programming!WHAT YOU'LL LEARN* Become familiar with Blockchain technology, both in theory and in practice* Understand architectural components of blockchain and the underlying computer science* Implement blockchain smart contract solutions using both public and enterprise Ethereum blockchainsWHO THIS BOOK IS FORIT professionals and mid-level managers interested in smart contract development. Blockchain Consultants who want to have a handbook of smart contract development methodologies. And enterprise technologiests helping companies through the transformation to blockchain technologies.DR.WEIJIA ZHANG teaches a smart contract development course at the University of Texas. Dr. Weijia Zhang has extensive R&D knowledge and engineering experience in blockchain, cognitive sciences, mental modeling, Computational Fluid Dynamics (CFD), software modeling, computer technologies, and industrial standards. Weijia has published over thirty research and technical papers and is named as an inventor for over twenty patents, granted and pending, in computer and digital technology. He has also served as a technical committee voting member to publish the Solution Deployment Descriptor (SDD) by the Organization for the Advancement of Structured Information Standards (OASIS).DR TEJ ANAND is an award-winning business-technology strategist, consultant, and innovator with a passion for conceiving and successfully implementing transformative data-driven business initiatives. He’s known for being a charismatic leader who effectively collaborates across silos to create committed and impactful cross-functional teams. As a published author, adjunct professor, and educator, Dr. Anand also holds multiple patents in healthcare business processes and business intelligence.CHAPTER 1: BUSINESS AND ECONOMIC MOTIVATION FOR BLOCKCHAIN● Origin of moneyEvolution of fiat currencyComplications with multiparty transactionsAdvantages and disadvantages of paper currents● Current economic inefficiencies● Blockchain potential● QuizzesCHAPTER 2: THE CORE TECHNOLOGIES SUPPORTING BLOCKCHAIN● Cryptology● Distributed systems● Peer-to-peer networking● QuizzesCHAPTER 3: BLOCKCHAIN COMPONENTS AND ARCHITECTURE● Notion of distributed ledgers● Transactions, blocks, mining● Smart contracts● QuizzesCHAPTER 4: BLOCKCHAIN BUSINESS APPLICATION GUIDELINES● Selecting a use case● Design issues● QuizzesCHAPTER 5: BITCOIN BLOCKCHAIN IMPLEMENTATION AND ECONOMICS● Bitcoin system setup● Programming Assignments● QuizzesCHAPTER 6: ETHEREUM OVERVIEW AND ARCHITECTURE● Blockchain Ecosystem and Dapps● Assignment: Ethereum Smart contract setup with environments● geth client, Besu client, Metamask, Remix, Truffle, web3CHAPTER 7: PROGRAMMING SMART CONTRACT WITH SOLIDITY● Module 1 Hello World and syntax● Module 2 data structure● Module 3 event● Module 4 security● Module 5 (Tools, Test, Debug)● Module 6 (Client consideration)CHAPTER 8: SECURITY CONSIDERATIONSCHAPTER 9: LAYER 2, SHARDING, ETH2 TECHNOLOGIES PROJECT DESIGNSCHAPTER 10: FUNDING A PROJECTCHAPTER 11: BUILDING TEAM PROJECTS● Brainstorming● User stories● Architecture● Token and smart contract Design● Client consideration● Security review● Testnet deployment● Mainnet deployment● Operation and upgrade considerationAudience: Intermediate
Data Science with Semantic Technologies
DATA SCIENCE WITH SEMANTIC TECHNOLOGIESTHIS BOOK WILL SERVE AS AN IMPORTANT GUIDE TOWARD APPLICATIONS OF DATA SCIENCE WITH SEMANTIC TECHNOLOGIES FOR THE UPCOMING GENERATION AND THUS BECOMES A UNIQUE RESOURCE FOR SCHOLARS, RESEARCHERS, PROFESSIONALS, AND PRACTITIONERS IN THIS FIELD. To create intelligence in data science, it becomes necessary to utilize semantic technologies which allow machine-readable representation of data. This intelligence uniquely identifies and connects data with common business terms, and it also enables users to communicate with data. Instead of structuring the data, semantic technologies help users to understand the meaning of the data by using the concepts of semantics, ontology, OWL, linked data, and knowledge-graphs. These technologies help organizations to understand all the stored data, adding the value in it, and enabling insights that were not available before. As data is the most important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. Data Science with Semantic Technologies provides a roadmap for the deployment of semantic technologies in the field of data science. Moreover, it highlights how data science enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book provides answers to various questions like: Can semantic technologies be able to facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is knowledge data science? How does knowledge data science relate to other domains? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of researchers? AUDIENCEResearchers in the fields of data science, semantic technologies, artificial intelligence, big data, and other related domains, as well as industry professionals, software engineers/scientists, and project managers who are developing the software for data science. Students across the globe will get the basic and advanced knowledge on the current state and potential future of data science. ARCHANA PATEL, PHD, is a faculty of the Department of Software Engineering, School of Computing and Information Technology, Binh Duong Province, Vietnam. She completed her Postdoc from the Freie Universität Berlin, Berlin, Germany. Dr. Patel is an author or co-author of more than 30 publications in numerous refereed journals and conference proceedings. She has been awarded the Best Paper award (three times) at international conferences. Her research interests are ontological engineering, semantic web, big data, expert systems, and knowledge warehouse.NARAYAN C. DEBNATH, PHD, is the Founding Dean of the School of Computing and Information Technology at Eastern International University, Vietnam. He is also serving as the Head of the Department of Software Engineering at Eastern International University, Vietnam. Dr. Debnath has been the Director of the International Society for Computers and their Applications (ISCA), USA since 2014. Formerly, Dr. Debnath served as a Full Professor of Computer Science at Winona State University, Minnesota, USA for 28 years. BHARAT BHUSAN, PHD, is an assistant professor in the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, India. In the last three years, he has published more than 80 research papers in various renowned international conferences and SCI indexed journals and edited 11 books. Preface xv1 A BRIEF INTRODUCTION AND IMPORTANCE OF DATA SCIENCE 1Karthika N., Sheela J. and Janet B.1.1 What is Data Science? What Does a Data Scientist Do? 21.2 Why Data Science is in Demand? 21.3 History of Data Science 41.4 How Does Data Science Differ from Business Intelligence? 91.5 Data Science Life Cycle 111.6 Data Science Components 131.7 Why Data Science is Important 141.8 Current Challenges 151.8.1 Coordination, Collaboration, and Communication 161.8.2 Building Data Analytics Teams 161.8.3 Stakeholders vs Analytics 171.8.4 Driving with Data 171.9 Tools Used for Data Science 191.10 Benefits and Applications of Data Science 281.11 Conclusion 28References 292 EXPLORATION OF TOOLS FOR DATA SCIENCE 31Qasem Abu Al-Haija2.1 Introduction 322.2 Top Ten Tools for Data Science 352.3 Python for Data Science 352.3.1 Python Datatypes 362.3.2 Helpful Rules for Python Programming 372.3.3 Jupyter Notebook for IPython 372.3.4 Your First Python Program 382.4 R Language for Data Science 392.4.1 R Datatypes 392.4.2 Your First R Program 412.5 SQL for Data Science 442.6 Microsoft Excel for Data Science 482.6.1 Detection of Outliers in Data Sets Using Microsoft Excel 482.6.2 Regression Analysis in Excel Using Microsoft Excel 502.7 D3.JS for Data Science 572.8 Other Important Tools for Data Science 582.8.1 Apache Spark Ecosystem 582.8.2 MongoDB Data Store System 602.8.3 MATLAB Computing System 622.8.4 Neo4j for Graphical Database 632.8.5 VMWare Platform for Virtualization 652.9 Conclusion 66References 683 DATA MODELING AS EMERGING PROBLEMS OF DATA SCIENCE 71Mahyuddin K. M. Nasution and Marischa Elveny3.1 Introduction 723.2 Data 723.2.1 Unstructured Data 743.2.2 Semistructured Data 743.2.3 Structured Data 763.2.4 Hybrid (Un/Semi)-Structured Data 773.2.5 Big Data 783.3 Data Model Design 793.4 Data Modeling 813.4.1 Records-Based Data Model 813.4.2 Non–Record-Based Data Model 843.5 Polyglot Persistence Environment 87References 884 DATA MANAGEMENT AS EMERGING PROBLEMS OF DATA SCIENCE 91Mahyuddin K. M. Nasution and Rahmad Syah4.1 Introduction 924.2 Perspective and Context 924.2.1 Life Cycle 934.2.2 Use 954.3 Data Distribution 984.4 CAP Theorem 1004.5 Polyglot Persistence 101References 1025 ROLE OF DATA SCIENCE IN HEALTHCARE 105Anidha Arulanandham, A. Suresh and Senthil Kumar R.5.1 Predictive Modeling—Disease Diagnosis and Prognosis 1065.1.1 Supervised Machine Learning Models 1075.1.2 Clustering Models 1105.1.2.1 Centroid-Based Clustering Models 1105.1.2.2 Expectation Maximization (EM) Algorithm 1105.1.2.3 DBSCAN 1115.1.3 Feature Engineering 1115.2 Preventive Medicine—Genetics/Molecular Sequencing 1115.2.1 Technologies for Sequencing 1135.2.2 Sequence Data Analysis with BioPython 1145.2.2.1 Sequence Data Formats 1145.2.2.2 BioPython 1175.3 Personalized Medicine 1215.4 Signature Biomarkers Discovery from High Throughput Data 1225.4.1 Methodology I — Novel Feature Selection Method with Improved Mutual Information and Fisher Score 1235.4.1.1 Algorithm for the Novel Feature Selection Method with Improved Mutual Information and Fisher Score 1245.4.1.2 Computing F-Score Values for the Features 1255.4.1.3 Block Diagram for the Method-1 1255.4.1.4 Data Set 1265.4.1.5 Identification of Biomarkers Using the Feature Selection Technique-I 1275.4.2 Feature Selection Methodology-II — Entropy Based Mean Score with mRMR 1285.4.2.1 Algorithm for the Feature Selection Methodology-II 1305.4.2.2 Introduction to mRMR Feature Selection 1325.4.2.3 Data Sets 1325.4.2.4 Identification of Biomarkers Using Rank Product 1335.4.2.5 Fold Change Values 133Conclusion 136References 1366 PARTITIONED BINARY SEARCH TREES (P(H)-BST): A DATA STRUCTURE FOR COMPUTER RAM 139Pr. D.E Zegour6.1 Introduction 1406.2 P(h)-BST Structure 1416.2.1 Preliminary Analysis 1436.2.2 Terminology and Conventions 1436.3 Maintenance Operations 1436.3.1 Operations Inside a Class 1456.3.2 Operations Between Classes (Outside a Class) 1486.4 Insert and Delete Algorithms 1536.4.1 Inserting a New Element 1536.4.2 Deleting an Existing Element 1576.5 P(h)-BST as a Generator of Balanced Binary Search Trees 1606.6 Simulation Results 1626.6.1 Data Structures and Abstract Data Types 1646.6.2 Analyzing the Insert and Delete Process in Random Case 1646.6.3 Analyzing the Insert Process in Ascending (Descending) Case 1686.6.4 Comparing P(2)-BST/P(∞)-BST to Red-Black/AVL Trees 1746.7 Conclusion 175Acknowledgments 176References 1767 SECURITY ONTOLOGIES: AN INVESTIGATION OF PITFALL RATE 179Archana Patel and Narayan C. Debnath7.1 Introduction 1797.2 Secure Data Management in the Semantic Web 1847.3 Security Ontologies in a Nutshell 1877.4 InFra_OE Framework 1897.5 Conclusion 193References 1938 IOT-BASED FULLY-AUTOMATED FIRE CONTROL SYSTEM 199Lalit Mohan Satapathy8.1 Introduction 2008.2 Related Works 2018.3 Proposed Architecture 2038.4 Major Components 2058.4.1 Arduino UNO 2058.4.2 Temperature Sensor 2078.4.3 LCD Display (16X2) 2088.4.4 Temperature Humidity Sensor (DHT11) 2098.4.5 Moisture Sensor 2108.4.6 CO2 Sensor 2118.4.7 Nitric Oxide Sensor 2128.4.8 CO Sensor (MQ-9) 2128.4.9 Global Positioning System (GPS) 2128.4.10 GSM Modem 2138.4.11 Photovoltaic System 2148.5 Hardware Interfacing 2168.6 Software Implementation 2188.7 Conclusion 222References 2239 PHRASE LEVEL-BASED SENTIMENT ANALYSIS USING PAIRED INVERTED INDEX AND FUZZY RULE 225Sheela J., Karthika N. and Janet B.9.1 Introduction 2269.2 Literature Survey 2289.3 Methodology 2339.3.1 Construction of Inverted Wordpair Index 2349.3.1.1 Sentiment Analysis Design Framework 2359.3.1.2 Sentiment Classification 2369.3.1.3 Preprocessing of Data 2379.3.1.4 Algorithm to Find the Score 2409.3.1.5 Fuzzy System 2409.3.1.6 Lexicon-Based Sentiment Analysis 2419.3.1.7 Defuzzification 2429.3.2 Performance Metrics 2439.4 Conclusion 244References 24410 SEMANTIC TECHNOLOGY PILLARS: THE STORY SO FAR 247Michael DeBellis, Jans Aasman and Archana Patel10.1 The Road that Brought Us Here 24810.2 What is a Semantic Pillar? 24910.2.1 Machine Learning 24910.2.2 The Semantic Approach 25010.3 The Foundation Semantic Pillars: IRI’s, RDF, and RDFS 25210.3.1 Internationalized Resource Identifier (IRI) 25410.3.2 Resource Description Framework (RDF) 25410.3.2.1 Alternative Technologies to RDF: Property Graphs 25610.3.3 RDF Schema (RDFS) 25710.4 The Semantic Upper Pillars: OWL, SWRL, SPARQL, and SHACL 25910.4.1 The Web Ontology Language (OWL) 26010.4.1.1 Axioms to Define Classes 26210.4.1.2 The Open World Assumption 26310.4.1.3 No Unique Names Assumption 26310.4.1.4 Serialization 26410.4.2 The Semantic Web Rule Language 26410.4.2.1 The Limitations of Monotonic Reasoning 26710.4.2.2 Alternatives to SWRL 26710.4.3 SPARQL 26810.4.3.1 The SERVICE Keyword and Linked Data 26810.4.4 SHACL 27110.4.4.1 The Fundamentals of SHACL 27210.5 Conclusion 274References 27411 EVALUATING RICHNESS OF SECURITY ONTOLOGIES FOR SEMANTIC WEB 277Ambrish Kumar Mishra, Narayan C. Debnath and Archana Patel11.1 Introduction 27711.2 Ontology Evaluation: State-of-the-Art 28011.2.1 Domain-Dependent Ontology Evaluation Tools 28111.2.2 Domain-Independent Ontology Evaluation Tools 28211.3 Security Ontology 28411.4 Richness of Security Ontologies 28711.5 Conclusion 295References 29512 HEALTH DATA SCIENCE AND SEMANTIC TECHNOLOGIES 299Haleh Ayatollahi12.1 Health Data 30012.2 Data Science 30112.3 Health Data Science 30112.4 Examples of Health Data Science Applications 30412.5 Health Data Science Challenges 30612.6 Health Data Science and Semantic Technologies 30812.6.1 Natural Language Processing (NLP) 30912.6.2 Clinical Data Sharing and Data Integration 31012.6.3 Ontology Engineering and Quality Assurance (QA) 31112.7 Application of Data Science for COVID-19 31312.8 Data Challenges During COVID-19 Outbreak 31412.9 Biomedical Data Science 31512.10 Conclusion 316References 31713 HYBRID MIXED INTEGER OPTIMIZATION METHOD FOR DOCUMENT CLUSTERING BASED ON SEMANTIC DATA MATRIX 323Tatiana Avdeenko and Yury Mezentsev13.1 Introduction 32413.2 A Method for Constructing a Semantic Matrix of Relations Between Documents and Taxonomy Concepts 32713.3 Mathematical Statements for Clustering Problem 33013.3.1 Mathematical Statements for PDC Clustering Problem 33013.3.2 Mathematical Statements for CC Clustering Problem 33413.3.3 Relations between PDC Clustering and CC Clustering 33613.4 Heuristic Hybrid Clustering Algorithm 34013.5 Application of a Hybrid Optimization Algorithm for Document Clustering 34213.6 Conclusion 344Acknowledgment 344References 34414 ROLE OF KNOWLEDGE DATA SCIENCE DURING COVID-19 PANDEMIC 347Veena Kumari H. M. and D. S. Suresh14.1 Introduction 34814.1.1 Global Health Emergency 35014.1.2 Timeline of the COVID-19 35114.2 Literature Review 35414.3 Model Discussion 35614.3.1 COVID-19 Time Series Dataset 35714.3.2 FBProphet Forecasting Model 35814.3.3 Data Preprocessing 36014.3.4 Data Visualization 36014.4 Results and Discussions 36214.4.1 Analysis and Forecasting: The World 36214.4.2 Performance Metrics 37114.4.3 Analysis and Forecasting: The Top 20 Countries 37714.5 Conclusion 388References 38915 SEMANTIC DATA SCIENCE IN THE COVID-19 PANDEMIC 393Michael DeBellis and Biswanath Dutta15.1 Crises Often Are Catalysts for New Technologies 39315.1.1 Definitions 39415.1.2 Methodology 39515.2 The Domains of COVID-19 Semantic Data Science Research 39715.2.1 Surveys 39815.2.2 Semantic Search 39915.2.2.1 Enhancing the CORD-19 Dataset with Semantic Data 39915.2.2.2 CORD-19-on-FHIR – Semantics for COVID-19 Discovery 40015.2.2.3 Semantic Search on Amazon Web Services (AWS) 40015.2.2.4 COVID*GRAPH 40215.2.2.5 Network Graph Visualization of CORD-19 40315.2.2.6 COVID-19 on the Web 40415.2.3 Statistics 40515.2.3.1 The Johns Hopkins COVID-19 Dashboard 40515.2.3.2 The NY Times Dataset 40615.2.4 Surveillance 40615.2.4.1 An IoT Framework for Remote Patient Monitoring 40615.2.4.2 Risk Factor Discovery 40815.2.4.3 COVID-19 Surveillance in a Primary Care Network 40815.2.5 Clinical Trials 40915.2.6 Drug Repurposing 41115.2.7 Vocabularies 41415.2.8 Data Analysis 41515.2.8.1 CODO 41515.2.8.2 COVID-19 Phenotypes 41615.2.8.3 Detection of “Fake News” 41715.2.8.4 Ontology-Driven Weak Supervision for Clinical Entity Classification 41715.2.9 Harmonization 41815.3 Discussion 41815.3.1 Privacy Issues 42015.3.2 Domains that May Currently be Under Utilized 42115.3.2.1 Detection of Fake News 42115.3.2.2 Harmonization 42115.3.3 Machine Learning and Semantic Technology: Synergy Not Competition 42215.3.4 Conclusion 423Acknowledgment 423References 423Index 427
Cognitive Intelligence and Big Data in Healthcare
COGNITIVE INTELLIGENCE AND BIG DATA IN HEALTHCAREAPPLICATIONS OF COGNITIVE INTELLIGENCE, ADVANCED COMMUNICATION, AND COMPUTATIONAL METHODS CAN DRIVE HEALTHCARE RESEARCH AND ENHANCE EXISTING TRADITIONAL METHODS IN DISEASE DETECTION AND MANAGEMENT AND PREVENTION. As health is the foremost factor affecting the quality of human life, it is necessary to understand how the human body is functioning by processing health data obtained from various sources more quickly. Since an enormous amount of data is generated during data processing, a cognitive computing system could be applied to respond to queries, thereby assisting in customizing intelligent recommendations. This decision-making process could be improved by the deployment of cognitive computing techniques in healthcare, allowing for cutting-edge techniques to be integrated into healthcare to provide intelligent services in various healthcare applications. This book tackles all these issues and provides insight into these diversified topics in the healthcare sector and shows the range of recent innovative research, in addition to shedding light on future directions in this area. AUDIENCEThe book will be very useful to a wide range of specialists including researchers, engineers, and postgraduate students in artificial intelligence, bioinformatics, information technology, as well as those in biomedicine. D. SUMATHI, PHD, is an associate professor at VIT-AP University, Andhra Pradesh. She has an overall experience of 21 years out of which six years in the industry, and 15 years in the teaching field. Her research interests include cloud computing, network security, data mining, natural language processing, and the theoretical foundations of computer science.T. POONGODI, PHD, is an associate professor in the Department of Computer Science and Engineering at Galgotias University, Delhi – NCR, India. She has more than 15 years of experience working in teaching and research. B. BALAMURUGAN, PHD, is a professor in the School of Computing Science and Engineering at Galgotias University, Delhi – NCR, India. His focus is on engineering education, blockchain, and data sciences. He has published more than 30 books on various technologies and more than 150 research articles in SCI journals, conferences, and book chapters. LAKSHMANA KUMAR RAMASAMY, PHD, is leading the Machine Learning for Cyber Security team at Hindusthan College of Engineering and Technology, Coimbatore. Tamil Nadu, India. He is also allied with a company conducting specific training for Infosys Campus Connect, Oracle WDP, and Palo Alto Networks. He holds the Gold level partnership award from Infosys, India for bridging the gap between industry and academia in 2017. Preface xv1 ERA OF COMPUTATIONAL COGNITIVE TECHNIQUES IN HEALTHCARE SYSTEMS 1Deependra Rastogi, Varun Tiwari, Shobhit Kumar and Prabhat Chandra Gupta1.1 Introduction 21.2 Cognitive Science 31.3 Gap Between Classical Theory of Cognition 41.4 Cognitive Computing’s Evolution 61.5 The Coming Era of Cognitive Computing 71.6 Cognitive Computing Architecture 91.6.1 The Internet-of-Things and Cognitive Computing 101.6.2 Big Data and Cognitive Computing 111.6.3 Cognitive Computing and Cloud Computing 131.7 Enabling Technologies in Cognitive Computing 131.7.1 Reinforcement Learning and Cognitive Computing 131.7.2 Cognitive Computing with Deep Learning 151.7.2.1 Relational Technique and Perceptual Technique 151.7.2.2 Cognitive Computing and Image Understanding 161.8 Intelligent Systems in Healthcare 171.8.1 Intelligent Cognitive System in Healthcare (Why and How) 201.9 The Cognitive Challenge 321.9.1 Case Study: Patient Evacuation 321.9.2 Case Study: Anesthesiology 321.10 Conclusion 34References 352 PROPOSAL OF A METAHEURISTIC ALGORITHM OF COGNITIVE COMPUTING FOR CLASSIFICATION OF ERYTHROCYTES AND LEUKOCYTES IN HEALTHCARE INFORMATICS 41Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur and Yuzo Iano2.1 Introduction 422.2 Literature Concept 442.2.1 Cognitive Computing Concept 442.2.2 Neural Networks Concepts 472.2.3 Convolutional Neural Network 492.2.4 Deep Learning 522.3 Materials and Methods (Metaheuristic Algorithm Proposal) 552.4 Case Study and Discussion 572.5 Conclusions with Future Research Scopes 60References 613 CONVERGENCE OF BIG DATA AND COGNITIVE COMPUTING IN HEALTHCARE 67R. Sathiyaraj, U. Rahamathunnisa, M.V. Jagannatha Reddy and T. Parameswaran3.1 Introduction 683.2 Literature Review 703.2.1 Role of Cognitive Computing in Healthcare Applications 703.2.2 Research Problem Study by IBM 733.2.3 Purpose of Big Data in Healthcare 743.2.4 Convergence of Big Data with Cognitive Computing 743.2.4.1 Smart Healthcare 743.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare 753.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification 763.3.1 EEG Pathology Diagnoses 763.3.2 Cognitive–Big Data-Based Smart Healthcare 773.3.3 System Architecture 793.3.4 Detection and Classification of Pathology 803.3.4.1 EEG Preprocessing and Illustration 803.3.4.2 CNN Model 803.3.5 Case Study 813.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud 833.4.1 Cloud Computing with Big Data in Healthcare 863.4.2 Heart Diseases 873.4.3 Healthcare Big Data Techniques 883.4.3.1 Rule Set Classifiers 883.4.3.2 Neuro Fuzzy Classifiers 893.4.3.3 Experimental Results 913.5 Conclusion 92References 934 IOT FOR HEALTH, SAFETY, WELL-BEING, INCLUSION, AND ACTIVE AGING 97R. Indrakumari, Nilanjana Pradhan, Shrddha Sagar and Kiran Singh4.1 Introduction 984.2 The Role of Technology in an Aging Society 994.3 Literature Survey 1004.4 Health Monitoring 1014.5 Nutrition Monitoring 1054.6 Stress-Log: An IoT-Based Smart Monitoring System 1064.7 Active Aging 1084.8 Localization 1084.9 Navigation Care 1114.10 Fall Monitoring 1134.10.1 Fall Detection System Architecture 1144.10.2 Wearable Device 1144.10.3 Wireless Communication Network 1144.10.4 Smart IoT Gateway 1154.10.5 Interoperability 1154.10.6 Transformation of Data 1154.10.7 Analyzer for Big Data 1154.11 Conclusion 115References 1165 INFLUENCE OF COGNITIVE COMPUTING IN HEALTHCARE APPLICATIONS 121Lucia Agnes Beena T. and Vinolyn Vijaykumar5.1 Introduction 1225.2 Bond Between Big Data and Cognitive Computing 1245.3 Need for Cognitive Computing in Healthcare 1265.4 Conceptual Model Linking Big Data and Cognitive Computing 1285.4.1 Significance of Big Data 1285.4.2 The Need for Cognitive Computing 1295.4.3 The Association Between the Big Data and Cognitive Computing 1305.4.4 The Advent of Cognition in Healthcare 1325.5 IBM’s Watson and Cognitive Computing 1335.5.1 Industrial Revolution with Watson 1345.5.2 The IBM’s Cognitive Computing Endeavour in Healthcare 1355.6 Future Directions 1375.6.1 Retail 1385.6.2 Research 1395.6.3 Travel 1395.6.4 Security and Threat Detection 1395.6.5 Cognitive Training Tools 1405.7 Conclusion 141References 1416 AN OVERVIEW OF THE COMPUTATIONAL COGNITIVE FROM A MODERN PERSPECTIVE, ITS TECHNIQUES AND APPLICATION POTENTIAL IN HEALTHCARE SYSTEMS 145Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur and Yuzo Iano6.1 Introduction 1466.2 Literature Concept 1486.2.1 Cognitive Computing Concept 1486.2.1.1 Application Potential 1516.2.2 Cognitive Computing in Healthcare 1536.2.3 Deep Learning in Healthcare 1576.2.4 Natural Language Processing in Healthcare 1606.3 Discussion 1626.4 Trends 1636.5 Conclusions 164References 1657 PROTECTING PATIENT DATA WITH 2F- AUTHENTICATION 169G. S. Pradeep Ghantasala, Anu Radha Reddy and R. Mohan Krishna Ayyappa7.1 Introduction 1707.2 Literature Survey 1757.3 Two-Factor Authentication 1777.3.1 Novel Features of Two-Factor Authentication 1787.3.2 Two-Factor Authentication Sorgen 1787.3.3 Two-Factor Security Libraries 1797.3.4 Challenges for Fitness Concern 1807.4 Proposed Methodology 1817.5 Medical Treatment and the Preservation of Records 1867.5.1 Remote Method of Control 1877.5.2 Enabling Healthcare System Technology 1877.6 Conclusion 189References 1908 DATA ANALYTICS FOR HEALTHCARE MONITORING AND INFERENCING 197Gend Lal Prajapati, Rachana Raghuwanshi and Rambabu Raghuwanshi8.1 An Overview of Healthcare Systems 1988.2 Need of Healthcare Systems 1988.3 Basic Principle of Healthcare Systems 1998.4 Design and Recommended Structure of Healthcare Systems 1998.4.1 Healthcare System Designs on the Basis of these Parameters 2008.4.2 Details of Healthcare Organizational Structure 2018.5 Various Challenges in Conventional Existing Healthcare System 2028.6 Health Informatics 2028.7 Information Technology Use in Healthcare Systems 2038.8 Details of Various Information Technology Application Use in Healthcare Systems 2038.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below 2048.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems 2058.11 Healthcare Data Analytics 2068.12 Healthcare as a Concept 2068.13 Healthcare’s Key Technologies 2078.14 The Present State of Smart Healthcare Application 2078.15 Data Analytics with Machine Learning Use in Healthcare Systems 2088.16 Benefit of Data Analytics in Healthcare System 2108.17 Data Analysis and Visualization: COVID-19 Case Study in India 2108.18 Bioinformatics Data Analytics 2228.18.1 Notion of Bioinformatics 2228.18.2 Bioinformatics Data Challenges 2228.18.3 Sequence Analysis 2228.18.4 Applications 2238.18.5 COVID-19: A Bioinformatics Approach 2248.19 Conclusion 224References 2259 FEATURES OPTIMISTIC APPROACH FOR THE DETECTION OF PARKINSON’S DISEASE 229R. Shantha Selva Kumari, L. Vaishalee and P. Malavikha9.1 Introduction 2309.1.1 Parkinson’s Disease 2309.1.2 Spect Scan 2319.2 Literature Survey 2329.3 Methods and Materials 2339.3.1 Database Details 2339.3.2 Procedure 2349.3.3 Pre-Processing Done by PPMI 2359.3.4 Image Analysis and Features Extraction 2359.3.4.1 Image Slicing 2359.3.4.2 Intensity Normalization 2379.3.4.3 Image Segmentation 2399.3.4.4 Shape Features Extraction 2409.3.4.5 SBR Features 2419.3.4.6 Feature Set Analysis 2429.3.4.7 Surface Fitting 2429.3.5 Classification Modeling 2439.3.6 Feature Importance Estimation 2469.3.6.1 Need for Analysis of Important Features 2469.3.6.2 Random Forest 2479.4 Results and Discussion 2489.4.1 Segmentation 2489.4.2 Shape Analysis 2499.4.3 Classification 2499.5 Conclusion 252References 25310 BIG DATA ANALYTICS IN HEALTHCARE 257Akanksha Sharma, Rishabha Malviya and Ramji Gupta10.1 Introduction 25810.2 Need for Big Data Analytics 26010.3 Characteristics of Big Data 26410.3.1 Volume 26410.3.2 Velocity 26510.3.3 Variety 26510.3.4 Veracity 26510.3.5 Value 26510.3.6 Validity 26510.3.7 Variability 26610.3.8 Viscosity 26610.3.9 Virality 26610.3.10 Visualization 26610.4 Big Data Analysis in Disease Treatment and Management 26710.4.1 For Diabetes 26710.4.2 For Heart Disease 26810.4.3 For Chronic Disease 27010.4.4 For Neurological Disease 27110.4.5 For Personalized Medicine 27110.5 Big Data: Databases and Platforms in Healthcare 27910.6 Importance of Big Data in Healthcare 28510.6.1 Evidence-Based Care 28510.6.2 Reduced Cost of Healthcare 28510.6.3 Increases the Participation of Patients in the Care Process 28510.6.4 The Implication in Health Surveillance 28510.6.5 Reduces Mortality Rate 28510.6.6 Increase of Communication Between Patients and Healthcare Providers 28610.6.7 Early Detection of Fraud and Security Threats in Health Management 28610.6.8 Improvement in the Care Quality 28610.7 Application of Big Data Analytics 28610.7.1 Image Processing 28610.7.2 Signal Processing 28710.7.3 Genomics 28810.7.4 Bioinformatics Applications 28910.7.5 Clinical Informatics Application 29110.8 Conclusion 293References 29411 CASE STUDIES OF COGNITIVE COMPUTING IN HEALTHCARE SYSTEMS: DISEASE PREDICTION, GENOMICS STUDIES, MEDICAL IMAGE ANALYSIS, PATIENT CARE, MEDICAL DIAGNOSTICS, DRUG DISCOVERY 303V. Sathananthavathi and G. Indumathi11.1 Introduction 30411.1.1 Glaucoma 30411.2 Literature Survey 30611.3 Methodology 30911.3.1 Sclera Segmentation 31011.3.1.1 Fully Convolutional Network 31111.3.2 Pupil/Iris Ratio 31311.3.2.1 Canny Edge Detection 31411.3.2.2 Mean Redness Level (MRL) 31511.3.2.3 Red Area Percentage (RAP) 31611.4 Results and Discussion 31711.4.1 Feature Extraction from Frontal Eye Images 31811.4.1.1 Level of Mean Redness (MRL) 31811.4.1.2 Percentage of Red Area (RAP) 31811.4.2 Images of the Frontal Eye Pupil/Iris Ratio 31811.4.2.1 Histogram Equalization 31911.4.2.2 Morphological Reconstruction 31911.4.2.3 Canny Edge Detection 31911.4.2.4 Adaptive Thresholding 32011.4.2.5 Circular Hough Transform 32111.4.2.6 Classification 32211.5 Conclusion and Future Work 324References 32512 STATE OF MENTAL HEALTH AND SOCIAL MEDIA: ANALYSIS, CHALLENGES, ADVANCEMENTS 327Atul Pankaj Patil, Kusum Lata Jain, Smaranika Mohapatra and Suyesha Singh12.1 Introduction 32812.2 Introduction to Big Data and Data Mining 32812.3 Role of Sentimental Analysis in the Healthcare Sector 33012.4 Case Study: Analyzing Mental Health 33212.4.1 Problem Statement 33212.4.2 Research Objectives 33312.4.3 Methodology and Framework 33312.4.3.1 Big 5 Personality Model 33312.4.3.2 Openness to Explore 33412.4.3.3 Methodology 33512.4.3.4 Detailed Design Methodologies 34012.4.3.5 Work Done Details as Required 34112.5 Results and Discussion 34312.6 Conclusion and Future 345References 34613 APPLICATIONS OF ARTIFICIAL INTELLIGENCE, BLOCKCHAIN, AND INTERNET-OF-THINGS IN MANAGEMENT OF CHRONIC DISEASE 349Geetanjali, Rishabha Malviya, Rajendra Awasthi, Pramod Kumar Sharma, Nidhi Kala, Vinod Kumar and Sanjay Kumar Yadav13.1 Introduction 35013.2 Artificial Intelligence and Management of Chronic Diseases 35113.3 Blockchain and Healthcare 35413.3.1 Blockchain and Healthcare Management of Chronic Disease 35513.4 Internet-of-Things and Healthcare Management of Chronic Disease 35813.5 Conclusions 360References 36014 RESEARCH CHALLENGES AND FUTURE DIRECTIONS IN APPLYING COGNITIVE COMPUTING IN THE HEALTHCARE DOMAIN 367BKSP Kumar Raju Alluri14.1 Introduction 36714.2 Cognitive Computing Framework in Healthcare 37114.3 Benefits of Using Cognitive Computing for Healthcare 37214.4 Applications of Deploying Cognitive Assisted Technology in Healthcare Management 37414.4.1 Using Cognitive Services for a Patient’s Healthcare Management 37514.4.2 Using Cognitive Services for Healthcare Providers 37614.5 Challenges in Using the Cognitive Assistive Technology in Healthcare Management 37714.6 Future Directions for Extending Heathcare Services Using CATs 38014.7 Addressing CAT Challenges in Healthcare as a General Framework 38414.8 Conclusion 384References 385Index 391
Design and Deploy Azure VMware Solutions
Learn the essential design and deployment skills to utilize Azure VMware Solution to seamlessly move your VMware-based workloads from your datacenter to Azure and to integrate your VMware environment with Azure. This book will teach you how to manage your existing environments with the same VMware products you already know while modernizing your applications with Azure native services.Design and Deploy Azure VMware Solutions starts by reviewing Azure VMware essentials, followed by a walkthrough of the methods of preparing and expanding to Azure VMware Solution. Here, you will learn about the layers of Microsoft AVS, including the vSphere, vSAN, NSX-T, and assess and migrate on-prem VMware workloads to Azure VMware Solution using HCX. Further, you will understand how to deploy the desktop and learn Hosting Pool using Horizon on Microsoft Azure, a modern approach to managing and securing Horizon components. VMware Tanzu for modernizing applications in Azure and disaster recovery for VMware workloads in Azure is then discussed in detail. Finally, you will learn monitoring and operations management using the VMware vRealize Suite and see a demonstration of how to plan and deploy Infrastructure as a Service (IaaS) for Azure VMware Solution via vRealize Automation.After reading the book, you will be able to migrate or extend VMware workloads from on-premises to Azure without the need to re-architect applications or retool operations.WHAT WILL YOU LEARN* Get started with Azure VMware Solution* Prepare and plan to utilize Azure VMware Solution* Design and deploy Azure VMware Solution* Manage and secure Azure VMware SolutionWHO IS THIS BOOK FORAzure VMware administrators and Azure Cloud Architects.PUTHIYAVAN UDAYAKUMAR is a cloud infrastructure architect/senior infrastructure consultant with more than 14 years of experience in the information technology industry. He is a Microsoft Certified Azure Solutions Architect Expert, VMware Certified Professional, and VMware vExpert. He has worked as an infrastructure solution architect/senior engineer in designing, deploying, and rolling out complex virtual and cloud infrastructure. He has extensive hands-on experience with products such as Citrix/VMware/Microsoft Virtualization and Cloud technologies. He has a strong knowledge of cloud solution design and deployment, managed cloud services, cloud migration, and multi-cloud infrastructure management services. CHAPTER 1:GETTING STARTED WITH AVSIntroduction to Azure VMware SolutionInner engineering of Azure VMware SolutionIdentify use case for Azure VMware SolutionCHAPTER 2:DATACENTER FOOTPRINT REDUCTION AND EXPANSION WITH AZURE VMWARE SOLUTIONPrepare Azure based VMware vSphere, NSX-T, and vSAN.Design Azure based VMware vSphere, NSX-T, and vSAN.Deploy Azure based VMware vSphere, NSX-T, and vSAN.Monitor and Secure Azure based VMware vSphere, NSX-T, and vSAN.CHAPTER 3:DESKTOP VIRTUALIZATION WITH AZURE VMWARE SOLUTION.Prepare Azure based VMware Horizon and it’ suite.Design Azure based VMware Horizon and it’ suite.Deploy Azure based VMware Horizon and it’ suite.Monitor Azure based VMware Horizon and it’ suite.CHAPTER 4:APPLICATION MODERNIZATION WITH AZURE VMWARE SOLUTION.Prepare Azure based VMware Tanzu Standard.Design Azure based VMware Tanzu Standard.Deploy Azure based VMware Tanzu Standard.Monitor Azure based VMware Tanzu Standard.CHAPTER 5: DISASTER RECOVERY WITH AZURE VMWARE SOLUTION.BC/DR Prepare for VMware workload based out of Azure.BC/DR Design for VMware workload based out of Azure.BC/DR Drill for VMware workload based out of Azure.BC/DR Orchestration for VMware workload based out of Azure.CHAPTER 6:VMWARE VREALIZE SUITE FOR AZURE VMWARE SOLUTION.Plan, optimize, and scale Azure VMware Solution using VMware vRealize Operations Manager.Build an optimized, highly available, and secure network Azure VMware Solution using VMware vRealize Network Insight.Plan and deploy Infrastructure as a Service (IaaS) for Azure VMware Solution via vRealize Automation.CHAPTER 7:ASSESS AND MIGRATE ON-PREM VMWARE WORKLOADS TO AZURE VMWARE SOLUTION.Plan HCX deploymentConfigure Compute and Network profilesMigrate workloads to Azure VMware Solution.
Data Conscience
DATA CONSCIENCE ALGORITHMIC S1EGE ON OUR HUM4N1TYEXPLORE HOW D4TA STRUCTURES C4N HELP OR H1NDER SOC1AL EQU1TY Data has enjoyed ‘bystander’ status as we’ve attempted to digitize responsibility and morality in tech. In fact, data’s importance should earn it a spot at the center of our thinking and strategy around building a better, more ethical world. It’s use—and misuse—lies at the heart of many of the racist, gendered, classist, and otherwise oppressive practices of modern tech. In Data Conscience: Algorithmic Siege on our Humanity, computer science and data inclusivity thought leader Dr. Brandeis Hill Marshall delivers a call to action for rebel tech leaders, who acknowledge and are prepared to address the current limitations of software development. In the book, Dr. Brandeis Hill Marshall discusses how the philosophy of “move fast and break things” is, itself, broken, and requires change. You’ll learn about the ways that discrimination rears its ugly head in the digital data space and how to address them with several known algorithms, including social network analysis, and linear regression A can’t-miss resource for junior-level to senior-level software developers who have gotten their hands dirty with at least a handful of significant software development projects, Data Conscience also provides readers with:* DISCUSSIONS OF THE IMPORTANCE OF TRANSPARENCY##SINGLE_LINE##* EXPLORATIONS OF COMPUTATIONAL THINKING IN PRACTICE##SINGLE_LINE##* STRATEGIES FOR ENCOURAGING ACCOUNTABILITY IN TECH##SINGLE_LINE##* WAYS TO AVOID DOUBLE-EDGED DATA VISUALIZATION##SINGLE_LINE##* SCHEMES FOR GOVERNING DATA STRUCTURES WITH LAW AND ALGORITHMS##SINGLE_LINE##DR. BRANDEIS HILL MARSHALL, PHD, is a computer scientist, tech educator, and data equity consultant. She is a thought leader in broadening participating in data science and puts inclusivity and equity at the center of her work. She obtained her doctorate in Computer Science from Rensselaer Polytechnic Institute.Foreword xixIntroduction xxiPART I TRANSPARENCY 1CHAPTER 1 OPPRESSION BY. . . 3The Law 4Slave Codes 5Black Codes 5The Rise of Jim Crow Laws 8Breaking Open Jim Crow Laws 11Overt Surveillance 12Surveillance at Scale 13The Science 16Numbers 16Anthropometry 18Eugenics 19Summary 23Notes 23Recommended Reading 25CHAPTER 2 MORALITY 27Data Is All Around Us 29Morality and Technology 33Defining Tech Ethics 33Mapping Tech Ethics to Human Ethics 39Squeezing in Data Ethics 45Misconceptions of Data Ethics 49Misconception 1: Goodness of Data, andTech by Proxy, Is Apolitical or Bipartisan 49Misconception 2: Data Ethics Is Focused Solely on Laws Protecting Confidentiality and Privacy 50Misconception 3: Implementing Data Ethics Practices Will Make Data Objective 52Notable Misconception Mentions: Ethics and Diversity, Equity, and Inclusion (DEI) Are Interchangeable 53Another Notable Mention: Software Developers Are Only Responsible for Societal Outcomes Stemming from Their Code 54Limits of Tech and Data Ethics 55Summary 57Notes 57CHAPTER 3 BIAS 61Types of Bias 62Defining Bias 63Concrete Example of Biases 65The Bias Wheel 70Before You Code 73Case Study Scenario: Data Sourcing for an Employee Candidate Résumé Database 77Case Study Scenario: Data Manipulation for an Employee Candidate Résumé Database 78Case Study Scenario: Data Interpretation for an EmployeeCandidate Résumé Database 82Bias Messaging 83Summary 83Notes 84CHAPTER 4 COMPUTATIONAL THINKING IN PRACTICE 87Ready to Code 88The Shampoo Algorithm 89Computational Thinking 91Coding Environments 93Algorithmic Justice Practice 95Code Cloning 97Socio-Techno-Ethical Review: app.py 101Socio-Techno-Ethical Review: screen.py 103Socio-Techno-Ethical Review: search.py 109Summary 114Notes 114PART II ACCOUNTABILITY 117CHAPTER 5 MESSY GATHERING GROVE 119Ask the Why Question 120Collection 124Open Source Dataset Example: Deciding Data Ownership 127Open Source Dataset Example: Considering Data Privacy 129Reformat 133Summary 139Notes 139CHAPTER 6 INCONSISTENT STORAGE SANCTUARY 143Ask the “What” Question 144Files, Sheets, and the Cloud 146Decisions in a Vacuum 149Case Study: Black Twitter 150Modeling Content Associations 153Manipulating with SQL 158Summary 160Notes 161CHAPTER 7 CIRCUS OF MISGUIDED ANALYSIS 163Ask the “How” Question 164Misevaluating the “Cleaned” Dataset 169Overautomating k, K, and Thresholds 177Deepfake Technology 179Not Estimating Algorithmic Risk at Scale 185Summary 187Notes 187CHAPTER 8 DOUBLE-EDGED VISUALIZATION SWORD 191Ask the “When” Question 192Critiquing Visual Construction 197Disabilities in View 201Pretty Picture Mirage 204Case Study: SAT College Board Dataset 207Summary 208Notes 209PART III GOVERNANCE 213CHAPTER 9 BY THE LAW 215Federal and State Legislation 216International and Transatlantic Legislation 219Regulating the Tech Sector 221Summary 228Notes 228CHAPTER 10 BY ALGORITHMIC INFLUENCERS 231Group (Re)Think 232Flyaway Fairness 238Algorithmic Fairness 239Broadening Fairness 241Moderation Modes 245Double Standards 246Calling Out Algorithmic Misogynoir 252Data and Oversight 254Summary 256Notes 256CHAPTER 11 BY THE PUBLIC 263Freeing the Underestimated 264Learning Data Civics 267The State of the Data Industry 271Living in the 21st Century 273Condemning the Original Stain 277Tech Safety in Numbers 279Summary 283Notes 283APPENDIX A CODE FOR APP.PY 287A 287B 288C 288D 289APPENDIX B CODE FOR SCREEN.PY 291A 291B 294C 295APPENDIX C CODE FOR SEARCH.PY 297A 297B 300C 301D 303APPENDIX D PSEUDOCODE FOR FACEIT.PY 305APPENDIX E THE DATA VISUALISATION CATALOGUE’S VISUALIZATION TYPES 309APPENDIX F GLOSSARY 313Index 315
Blockchain Consensus
This book is your comprehensive guide to understanding Blockchain and Blockchain consensus algorithms. It covers distributed systems, distributed consensus, and relevant system models. And you'll explore how classical and modern consensus algorithms work. The book also covers quantum consensus and explains the role that quantum computing plays in distributed systems.Consensus protocols allow participants in distributed systems to agree on a common value, despite faults. It's a fundamentally important construct in distributed systems. As a result of rigorous and ground-breaking research over the last four decades, many consensus mechanisms have been developed and are used in the industry today. However, with the advent of Blockchain technology, a renewed interest has arisen in this area, resulting in more research and innovation.The first Blockchain, Bitcoin, was invented in 2008 and introduced a novel consensus protocol called Nakamoto consensus, a solution to the Byzantine General's problem formulated almost 30 years ago. Since the introduction of Bitcoin, the interest in Blockchain and consensus protocols has risen exponentially. As a result, researchers from academia and industry have proposed many new consensus mechanisms. While fundamental goals and some techniques remain the same as established classical protocols, these modern protocols introduce innovative methods to achieve consensus in Blockchain. Some classical algorithms have been modified to make them suitable for Blockchain and some new protocols have been developed.This book is a detailed account of classical distributed consensus and Blockchain consensus algorithms. It explains why and how cryptocurrencies and Blockchain remain secure and decentralized without depending on a trusted third party. In addition, you'll learn how Blockchain can endure, even with hundreds or thousands of participants, out of which some might be malicious. The book introduces quantum consensus, which deals with the problem of reaching agreement in quantum networks and how to enhance classical results.WHAT YOU WILL LEARN* Understand distributed systems, distributed consensus, and relevant system models and protocols* Understand Blockchain and Blockchain consensus algorithms* Know how classical and modern consensus algorithms work * Know the inner workings of Paxos, RAFT, PBFT, HotStuff, proof of work, proof of stake, GRANDPA, Casper, proof of history, and other consensus protocols* Understand quantum Byzantine agreement and quantum consensusWHO THIS BOOK IS FORDistributed systems and Blockchain students and researchers, Blockchain practitioners, architects, designers, product managers, and developersThis book targets many audiences as well as those with curious minds. It explains the classical consensus mechanisms, Blockchain age consensus protocols, and the latest developments in distributed consensus. The book does not assume any advanced knowledge of Blockchain or distributed systems, but a general understanding of computing and appreciation of Blockchain technology is helpful. Early chapters provide the necessary background to read and understanding consensus-related content quickly.Readers who already understand classical consensus protocols and distributed systems but want to learn about Blockchain consensus will find the book helpful as it covers Blockchain age protocols in detail. Readers who have come to the Blockchain world without any, or with little, background in distributed systems or classical consensus protocols will find this book equally helpful as it provides a solid understanding of classical consensus protocols.If you have no experience in Blockchain or don’t understand distributed computing in general, this book will give you a solid understanding of both subjects and enable you to conduct further research in this exciting area of distributed computing.IMRAN BASHIR has an MSc degree in information security from Royal Holloway, University of London, and a background in software development, solution architecture, infrastructure management, information security, and IT service management. His current focus is on the latest technologies such as Blockchain, IoT, and quantum computing. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and the British Computer Society (BCS). His book on Blockchain technology, Mastering Blockchain, is a widely accepted standard text on the subject. He has worked in various senior technical roles for different organizations around the world. Currently, he is living and working in London, UK.Chapter 1: IntroductionCHAPTER GOAL: TO INTRODUCE DISTRIBUTED COMPUTING CONCEPTS, MODELS AND RELEVANT CONCEPTSNO OF PAGES - 301. Introduction 1.1. What is distributed computing 1.1.1.Characteristics (collection, coherent, same task, consists of nodes etc) 1.2. Distributed algorithms 1.3. Elements of distributed computing 1.4. computational (system) model 1.5. Space / time diagram 1.6. Time, clocks and order 1.7. Broadcast ordering 1.8. types 1.8.1.1.Message passing 1.8.1.2.Shared memory 1.9. Synchrony / timing 1.10. Adversary model 1.11. Faults 1.11.1.Process / program faults 1.11.2.Processor / machine 1.11.3.Communication / link faults 1.11.4.Storage faults 1.11.5.Types (omission, byzantine . . . etc.) 1.11.6.Correct processes 1.11.7.Fault tolerance 1.12. Abstractions 1.12.1.What and why abstractions? 1.12.2.to understand / build distributed computing – helps to state and reason about distributed computing 1.12.3.System model 1.12.4.Processes and links 1.12.5.Communication and networks 1.12.5.1.Latency and bandwidth 1.12.6.Agreement 1.12.7.Cryptography abstractions 1.13. Role of cryptography in distributed systems and consensus 1.14. CAP theorem 1.15. Summary Chapter 2: CryptographyCHAPTER GOAL: TO UNDERSTAND CRYPTOGRAPHY AND BUILD GROUND FOR USE OF CRYPTOGRAPHY IN CONSENSUSNO OF PAGES - 302. Cryptography 2.1.1.What is cryptography 2.1.2.CIA triad 2.1.3.Symmetric cryptography 2.1.4.Asymmetric cryptography 2.1.5.Hash functions 2.1.6.MACs 2.1.7.Digital signatures 2.1.8.Privacy 2.1.9.Zero knowledge 2.1.10.Summary Chapter 3: Distributed consensusCHAPTER GOAL: TO INTRODUCED THE SUBJECT OF CONSENSUS, WHY ITS IMPORTANT, HISTORY, HOW IT DEVELOPED, WHAT ARE THE REQUIREMENTS AND RELEVANT RESULTS AND ABSTRACTIONS.NO OF PAGES - 303. Distributed consensus 3.1. What is distributed consensus 3.1.1.Agreement abstraction 3.2. Goal of distributed consensus 3.2.1.Safety 3.2.2.liveness 3.2.3.Other properties and goals 3.3. Consensus system model 3.4. History 3.5. Types 3.6. Building blocks 3.7. Other abstractions 3.8. Two generals problem - network model 3.9. Byzantine generals problem - node behaviour model 3.10. Byzantine agreement 3.11. Replication 3.12. Primary - backup model 3.13. State machine replication 3.14. Fundamental results, lower bounds / upper bounds 3.15. FLP impossibility 3.16. How to circumvent FLP impossibility 3.16.1.Synchrony models , assumptions, eventual, partial synchrony etc. 3.16.2.Other methods 3.17. Cryptography in distributed consensus 3.17.1.Privacy in consensus algorithms 3.18. Where are we now 3.19. Summary Chapter 4: BlockchainCHAPTER GOAL: TO INTRODUCE BLOCKCHAIN, ITS STRUCTURE, USE CASES AND TECHNICAL UNDERPINNINGS.NO OF PAGES - 204. Blockchain 4.1. What is blockchain 4.2. Background 4.2.1.The first blockchain 4.2.2.Bitcoin 4.2.3.Smart contracts 4.3. Blockchain is a distributed system 4.4. Basic structure 4.5. Benefits 4.6. Types 4.7. Available platforms 4.7.1.Ethereum 4.8. Use cases 4.9. Summary Chapter 5: blockchain consensusCHAPTER GOAL: INTRODUCE BLOCKCHAIN CONSENSUS, RELATIONSHIP WITH CLASSICAL CONSENSUS, NEED OF CONSENSUS IN BLOCKCHAIN, HOW GENERALLY IT WORKS AND BITCOIN PROTOCOL.NO OF PAGES - 205. Blockchain consensus 5.1. Relationship with distributed systems 5.2. Why consensus is needed 5.3. First appearance – with Bitcoin to circumvent double spending and Sybil attack 5.4. A confusion – a consensus mechanism, consensus facilitation mechanism or a Sybil attack defence mechanism 5.5. First blockchain consensus – in Bitcoin – PoW 5.6. Summary Chapter 6: early protocolsCHAPTER GOAL: TO INTRODUCE WHAT DISTRIBUTED CONSENSUS PROTOCOLS WERE DEVELOPED EARLIER , SOME FUNDAMENTAL RELEVANT RESULTS AND HOW THESE PROTOCOLS WORK. THIS WILL DEVELOP INTUITION AND BASIS FOR MORE ADVANCED TOPICS IN THE NEXT CHAPTER.NO OF PAGES - 206. Early protocols 6.1. Byzantine agreement 6.2. Ben-Or 6.3. DLS 6.4. View stamped replication 6.5. Summary Chapter 7: Classical consensus algorithmsCHAPTER GOAL: INTRODUCED CLASSICAL CONSENSUS PROTOCOLS , INLUDING PAXOS, RAFT, PBFT AND UNDERSTAND HOW THEY WORK IN DETAILNO OF PAGES - 307. Classical protocols / algorithms This chapter covers classical protocols in detail, their design, how they work and where they are implemented. How and if they can be used in blockchain 7.1. Paxos 7.2. RAFT 7.3. PBFT 7.4. How to adapt these classical protocols for blockchain. 7.5. Summary Chapter 8: blockchain age protocolsCHAPTER GOAL: INTRODUCTION TO MODERN BLOCKCHAIN PROTOCOLS AND HOW THEY WORK.NO OF PAGES - 408. Modern - blockchain age protocols 8.1. PoW 8.2. PoS 8.3. Tendermint 8.4. Hotstuff 8.5. SBFT 8.6. Summary Chapter 9: newer protocolsCHAPTER GOAL: TO INTRODUCE NEWER CLASS OF PROTOCOLS , SPECIFICALLY DEVELOPED FOR BLOCKCHAINS AND CRYPTOCURRENCIESNO OF PAGES - 109. Other newer protocols 9.1. Snow flake family 9.2. Other exotic ideas and protocols 9.3. Summary Chapter 10: Design and implementationCHAPTER GOAL: TO INTRODUCE HOW TO MODEL, DESIGN AND VERIFY CONSENSUS PROTOCOLSNO OF PAGES : 3010. Design and implementation 10.1. Design 10.2. Formal methods in computer science 10.3. Correctness proofs 10.4. Formal spec and verification of consensus algorithms 10.5. Using TLA+ 10.6. Other Correctness proofs 10.7. Performance 10.7.1.Scalability and efficiency 10.8. Security 10.9. Implementation 10.10. Selection / evaluation criteria / Evaluation 10.10.1.Complexity concepts 10.10.2.Summary Chapter 11: current landscape and futureCHAPTER GOAL: GENERAL OVERVIEW OF LATEST STATE OF THE ART AND CURRENT CHALLENGES ALONG WITH RESEARCH DIRECTIONS.NO OF PAGES : 511. Current landscape, research directions and future 11.1. State of the art 11.2. Challenges 11.3. Research directions 11.4. Future 11.5. Exotic ideas 11.6. Conclusion
Computer Science Security
This book serves as a guide to help the reader develop an awareness of security vulnerabilities and attacks, and encourages them to be circumspect when using the various computer resources and tools available today. For experienced users, Computer Science Security presents a wide range of tools to secure legacy software and hardware.Computing has infiltrated all fields nowadays. No one can escape this wave and be immune to security attacks, which continue to evolve, gradually reducing the level of expertise needed by hackers.It is high time for each and every user to acquire basic knowledge of computer security, which would enable them to mitigate the threats they may face both personally and professionally. It is this combined expertise of individuals and organizations that will guarantee a minimum level of security for families, schools, the workplace and society in general.Ameur Salem Zaidoun received a National Diploma in Computer Engineering from ENSI, Tunisia, and is a university teacher at ISET of Siliana at the level of Lecturer Technologist. An ex-developer and security consultant, he is a CCNA R&S-, DevNet- and CCNA-Security-certified and a Huawei HCNA-R&S-certified Cisco Instructor.List of Acronyms xiIntroduction xiiiCHAPTER 1 GENERAL CONCEPTS IN SECURITY 11.1 Introduction 11.2 Reasons for security 21.2.1 Technical issues 21.2.2 Social factors 41.3 Security attacks 51.3.1 Passive/active classification of attacks 51.3.2 Direct/indirect classification of attacks 81.3.3 Examples of attacks 101.3.4 Some statistics 121.4 Security objectives 131.4.1 Establishing a culture 131.4.2 Establishing technical solutions 131.5 Security fields 141.5.1 Energy security 141.5.2 Organizational and physical security 151.5.3 Software security 161.6 Normalization of security 181.6.1 Fundamental issues and general presentation 181.6.2 ISO 7498-2 norm 191.7 Security services 241.7.1 Authentication 251.7.2 Confidentiality 271.7.3 Integrity 271.7.4 Non-repudiation 271.7.5 Traceability and access control 271.7.6 Service availability 271.8 Security mechanisms 281.8.1 Encryption 281.8.2 Integrity check 291.8.3 Access check 291.8.4 Electronic signature 301.8.5 Notarization 301.9 Good practices 311.10 Conclusion 31CHAPTER 2 SECURITY WEAKNESSES 332.1 Introduction 332.2 Weakness in the TCP/IP 342.2.1 ARPANet, the ancestor of the Internet 342.2.2 The Internet and security problems 342.2.3 The Internet and the ability to analyze 352.3 Weaknesses due to malware and intrusion tools 362.3.1 Viruses 372.3.2 Worms 402.3.3 Spam 412.3.4 Software bomb 422.3.5 Trojan horse 422.3.6 Spyware 432.3.7 Keylogger 442.3.8 Adware 442.3.9 Other malware 452.3.10 Comparison of intrusion tools 462.4 Conclusion 46CHAPTER 3 AUTHENTICATION TECHNIQUES AND TOOLS 493.1 Introduction 493.2 Theoretical concepts of authentication 503.2.1 Identification 503.2.2 Authentication 513.3 Different types of authentications 513.3.1 Local service authentication 513.3.2 Network authentication 523.4 AAA service 563.4.1 Local AAA 573.4.2 Server AAA 593.5 Conclusion 63CHAPTER 4 TECHNIQUES AND TOOLS FOR CONTROLLING ACCESS, ACL AND FIREWALLS 654.1 Introduction 654.2 Access control list 664.2.1 ACL classification 664.2.2 ACL configuration in Cisco 684.2.3 ACL configuration for Huawei 744.3 Firewall 784.3.1 Filtering function 794.3.2 Functionalities of tracing and NAT 814.3.3 Firewall architecture 824.3.4 How a firewall works 844.3.5 Firewall classifications 844.3.6 Stateful firewall 864.3.7 Zone-based firewall 874.3.8 Firewall examples 904.4 The concept of a DMZ 924.4.1 Implementation of topologies 924.5 Conclusion 95CHAPTER 5 TECHNIQUES AND TOOLS FOR DETECTING INTRUSIONS 975.1 Introduction 975.2 Antivirus 975.2.1 Functions of an antivirus 975.2.2 Methods for detecting a virus 985.2.3 Actions taken by an antivirus 985.2.4 Antivirus components 995.2.5 Antivirus and firewall comparison 995.3 Intrusion detection systems 1005.3.1 IDS purposes 1005.3.2 IDS components and functions 1005.3.3 IDS classification 1025.3.4 Examples of IDS/IPS 1055.4 Conclusion 107CHAPTER 6 TECHNIQUES AND TOOLS FOR ENCRYPTION, IPSEC AND VPN 1096.1 Introduction 1096.2 Encryption techniques 1106.2.1 Basic principles of encryption 1116.2.2 Cryptoanalysis 1126.2.3 Evolution of cryptography 1136.2.4 The concept of certificates 1176.2.5 Comparison of encryption techniques 1186.3 IPSec 1196.3.1 Ah 1206.3.2 Esp 1206.3.3 Different IPSec modes 1216.3.4 Different IPSec implementations 1226.3.5 Different IPSec encapsulations 1226.3.6 IKE protocol 1256.4 VPNs 1266.4.1 Issues and justifications 1266.4.2 VPN principles 1276.4.3 Different types of VPNs 1276.4.4 Different tunneling protocols 1286.4.5 Site-to-site IPSec VPN configuration 1296.5 Conclusion 131CHAPTER 7 NEW CHALLENGES AND TRENDS IN SECURITY, SDN AND IOT 1337.1 Introduction 1337.2 SDN security 1347.2.1 General description of an SDN 1347.2.2 SDN architecture 1357.2.3 SDN components 1367.2.4 Security issues in SDNs 1387.2.5 Security solutions for SDNs 1397.3 IoT/IoE security 1417.3.1 Sensor networks 1417.3.2 Security issues in the IoT 1437.3.3 Blockchain: an IoT security solution 1457.4 Conclusion 146CHAPTER 8 SECURITY MANAGEMENT 1478.1 Introduction 1478.2 Security audits 1488.2.1 Objectives 1488.2.2 Audit action diagram 1498.2.3 Organizational and physical audit 1508.2.4 Technical audit 1518.2.5 Intrusive test 1528.2.6 Audit methodologies 1528.3 Security policy demonstration 1558.3.1 Security test and evaluation 1558.3.2 Security policy development 1598.3.3 Elements of a security policy 1618.4 Norms, directives and procedures 1628.4.1 ISO 27000 norm 1638.4.2 ISO/FDIS 31000 norm 1638.4.3 ISO/IEC 38500 norm 1648.5 Conclusion 164References 165Index 167
AI and the Future of the Public Sector
DISCOVER HOW DATA, ANALYTICS, AND AI WILL TRANSFORM PUBLIC SERVICES FOR THE BETTERIn AI and the Future of the Public Sector: The Creation of Public Sector 4.0, renowned executive and consultant Tony Boobier delivers a comprehensive reference of the most relevant and central issues regarding the adoption and implementation of AI in the public sector. In the book, you'll find out why data and analytics are the solution to significant and ongoing problems in the public service relating to its ability to effectively provide services in an environment of reduced funding. You'll also discover the likely impact of future technological developments, like 5G and quantum computing, as well as explore the future of healthcare and the effective digitalization of the healthcare industry.The book also offers:* Discussions of policing 4.0 and how data and analytics will transform public safety* Explorations of the future of education and how ai can dramatically enhance educational standards while reducing costs* Treatments of the internationalization of public services and its impact on agencies and departments everywhereA can't-miss resource for public sector employees at the managerial and professional levels, AI and the Future of the Public Sector is an insightful and timely blueprint to the effective use of artificial intelligence that belongs in the bookshelves of policy makers, academics, and public servants around the world.Acknowledgments xvAbout the Author xviiIntroduction xixCHAPTER 1Understanding the Key Building Blocks of Progress 11.1 Introduction 11.2 Key Building Blocks of Data Science and AI 21.2.1 Data Acquisition 21.2.2 Data Maintenance 21.2.3 Analysis 31.2.4 Communication 31.2.5 Machine Learning 41.2.6 Artificial Intelligence 41.2.7 Advantages and Disadvantages 41.2.8 Four Key Focuses for Future AI 51.3 Quantum Computing 71.3.1 What Is Quantum Computing? 71.3.2 Impact on Cybersecurity 91.4 Proliferation of Devices 91.5 5G and the Impact of Advanced Communications 111.5.1 Global Transformation 121.6 Public Sectors 4.0 121.7 Conclusion 141.8 Notes 15CHAPTER 2Office of Finance 172.1 Introduction 172.2 Forecasting and Public Finance Management 182.3 Forecasting 192.3.1 Qualitative Forecasting 192.3.2 Quantitative Forecasting 202.3.3 Forecasting for Public Sector Transformation 212.3.4 Managing Risk and Uncertainty 222.3.5 Forecasting in IT Projects 232.3.6 The Move Toward Activity-Based Costing 242.3.7 Hard Benefits and Soft Benefits 242.3.8 Enterprise Resource Planning 262.3.9 AI and Governmental Administration 282.3.10 Global Partnership on AI 292.4 Conclusion 302.5 Notes 30CHAPTER 3Public Order and Safety 333.1 Introduction 333.2 The Future of Policing in an AI Era 333.2.1 Transformation of Police Work 343.2.2 Criminal Use of AI 363.2.3 Police Use of New Technologies 363.2.4 Case Studies in Policing 373.2.5 Policing in China 383.2.6 Forward-Looking Policing 393.3 AI in Policing 413.3.1 Impact on Police Behavior 423.4 The Citizen as a Key Component of Future Policing 423.5 Police and Location Analytics 433.6 Policing Summary 443.7 Border Security and AI 453.8 Customs Reform 463.8.1 The Citizen and Taxation 473.9 Fire Safety and AI 483.9.1 Natural Fire Prevention 493.9.2 Prevention of Urban Fires 493.9.3 Smart Homes and Fire Detection 493.9.4 Commercial Fire Prevention 503.9.5 Firefighting Using AI 503.9.6 Fire Station Locations 513.10 Conclusion 513.11 Notes 52CHAPTER 4Personal Social Services 554.1 Introduction 554.2 Care Homes 564.2.1 The UK Model 574.2.2 Care Homes in Japan 594.2.3 The Canadian Picture 604.2.4 The Emergence of AgeTech 604.2.5 Going Forward 614.2.6 Conclusion 614.3 Impact on Children 624.4 Mental Health 644.5 Social Protection 664.5.1 Social Risk Framework 674.6 Employment and Benefit Management 704.7 Conclusion 724.8 Notes 73CHAPTER 5Health 775.1 Introduction 775.2 Digitalization and Its Importance in Healthcare 775.2.1 Different Categories of Data Sources in Healthcare 785.3 Medical Monitoring and Biosensors 795.3.1 Use of Biosensors in Mental Health 815.4 Innovating to Zero in Healthcare 825.4.1 Zero Invasive Surgery 825.4.2 Zero Waste Management 835.4.3 Zero Surgical Errors 845.5 Tissue Engineering 845.6 Cybernetics 855.7 Advancements in Drug Creation and Treatment 865.8 Case Studies in Healthcare 875.8.1 Ping An Good Doctor 875.8.2 Cancer Screening Case Study 875.9 Paramedics and AI 885.10 Cybersecurity in Healthcare 895.11 Conclusion 905.12 Notes 91CHAPTER 6Education 936.1 Introduction 936.2 Learning for the Future 946.3 Teaching in the Future 966.3.1 The Use of AI for Predicting Exam Success 976.4 AI and Language in the Classroom 986.4.1 Automated Essay Scoring 986.4.2 Removing Communication Barriers 996.5 Robots in the Classroom 996.6 The Shortage of Tech Talent 1006.7 Case Studies in Education 1016.8 Conclusion 1016.9 Notes 102CHAPTER 7Defense 1057.1 Introduction 1057.2 Use Cases of AI in Defense 1067.2.1 Intelligence, Surveillance, and Reconnaissance 1077.2.2 Logistics 1087.2.3 Cyberspace Operations 1087.2.4 Information Operations and “Deep Fakes” 1087.2.5 Command and Control 1087.2.6 AI and Augmented Reality Soldiers 1097.2.7 Semi-Autonomous and Autonomous Vehicles 1097.3 Ethical Issues 1107.4 Drones 1117.5 Conclusion 1137.6 Notes 114CHAPTER 8Smarter Cities and Transportation 1158.1 Introduction 1158.2 Smarter Cities 1158.2.1 Smart Infrastructure 1168.2.2 Smart Transportation 1168.2.3 Street Lighting 1168.2.4 Water Utilities 1178.2.5 Emergency Services 1178.2.6 Waste Collection and Disposal 1188.2.7 Maintenance of Public Places 1188.2.8 Humans as Devices 1188.2.9 Data Challenges for Smart Cities 1198.3 Transportation 1198.3.1 Traffic Management 1208.3.2 Road Safety 1208.3.3 Highway Maintenance 1218.3.4 Autonomous Trams 1218.3.5 Autonomous Taxis 1238.4 Railways and the Future of Rail 1238.4.1 Net Zero in Rail 1248.4.2 AI and Effective Rail Timetabling 1258.5 Air Travel 1268.6 Conclusion 1288.7 Notes 128CHAPTER 9Housing and the Environment 1319.1 Introduction 1319.2 AI in Social Housing 1319.2.1 Risk Management in Social Housing 1339.2.2 Transforming the Tenant Experience 1339.2.3 Case Study – Housemark Pilot 1349.2.4 Social Housing Fraud 1359.2.5 Tenant Viewpoint 1369.2.6 AI as a Virtual Housing Assistant 1379.2.7 Chatbots in Social Housing 1379.3 AI and the Environment 1389.4 Management of Natural Disasters 1399.4.1 Flooding and Flood Management 1399.4.2 Flood Defense 1409.4.3 Earthquakes, Windstorms, and Forest Fires 1419.5 Conclusion 1419.6 Notes 142CHAPTER 10Employment, Industry, and Agriculture 14510.1 Introduction 14510.2 Employment 14510.2.1 Unemployment 14610.3 AI and Industry 14810.3.1 State-Owned Enterprises 14910.3.2 China Model 15010.3.3 South African Model 15010.3.4 UK Model 15010.3.5 SOEs in the United States 15110.4 Agriculture 15110.4.1 The Role of AI in Agricultural Policy 15210.4.2 The Role of AI in Environmental Issues 15310.5 Conclusion 15310.6 Notes 154CHAPTER 11The Role of the State 15711.1 Introduction 15711.2 What Is the Role of the State? 15711.3 What Is Surveillance? 15911.4 Reasons for Surveillance 16011.5 Surveillance Capitalism 16111.6 Surveillance in Covid “Track and Trace” 16311.7 Data Justice and Independent Oversight 16411.8 A Contrary View 16611.9 The Ethics of Surveillance 16711.10 Nudging the Citizen 16811.11 Conclusion 17011.12 Notes 171CHAPTER 12Risk and Cybercrime 17312.1 Introduction 17312.2 The Nature of Risk 17312.2.1 Management of Risk 17412.2.2 Three Lines of Risk Defense 17612.3 Roles and Responsibilities in the Public Sector 17612.4 Examples of Risk 17612.4.1 Technology and System Failure 17712.4.2 Data Security and Privacy 17812.4.3 Employee Error 17912.4.4 Failure of Processes, Systems, and Policies 18012.4.5 Reputational Risk 18112.4.6 External Risk 18312.5 Cybercrime in the Public Sector 18312.6 Prevention of Cybercrime and Protection from It 18612.6.1 Air Gapping 18612.6.2 Supply Chain Vulnerability 18612.6.3 Impact on Insurance Coverage 18712.7 The Use of AI in Managing Risk 18712.8 Conclusion 18812.9 Notes 189CHAPTER 13Implementation – Leadership and Management 19113.1 Introduction 19113.2 Leadership 19213.2.1 Transfer of Private Sector Leaders to the Public Sector 19513.3 Leaders or Managers? 19613.4 Managing the Mission 19713.4.1 Creating the Mission 19713.4.2 Prioritization: Where to Start? 19813.4.3 Communicating the Mission Statement 19913.5 Management of Resources 20113.5.1 Technical versus Traditional 20113.5.2 Specialist versus Generalist 20113.5.3 Training and Education 20213.6 Management of Key Stakeholders 20413.6.1 Worker Representation and Trade Unions 20513.6.2 US Policy Recommendations 20713.6.3 German Policy Recommendations 20813.6.4 “Dignity at Work” and Working from Home 20913.7 Conclusion 21113.8 Notes 211CHAPTER 14Further Implementation Issues 21314.1 Introduction 21314.2 A Theoretical Approach to Change 21314.3 Managing the Problem of Bias 21714.3.1 Data Exclusion from Marginalized Communities 21914.3.2 Locational Data Issues 22014.4 Operational Considerations 22014.4.1 Piloting and Test Running the System 22014.4.2 Measuring Benefit 22114.4.3 Independent Review 22214.5 Outsourcing, Partnering, and Supply Chain Management 22214.6 The Concept of “Nudge” 22614.7 Global Considerations 22814.8 Conclusion 23114.9 Notes 232CHAPTER 15Conclusion 23315.1 Reflections 23315.2 AI and the Real Pace of Change 23415.3 Measuring ROI – More Art Than Science? 23515.4 AI and Stimulation of Wider Reforms 23615.5 The Role of Government in Public Sector Transformation 23715.6 Moving the Goalposts 23815.7 Notes 239Appendix A: The Seven Principles of Public Life 241Appendix B: Transformation Roadmap for Public Services 243Appendix C: List of Tables 245Appendix D: List of Figures 247Index 249
Designing Human-Centric AI Experiences
User experience (UX) design practices have seen a fundamental shift as more and more software products incorporate machine learning (ML) components and artificial intelligence (AI) algorithms at their core. This book will probe into UX design’s role in making technologies inclusive and enabling user collaboration with AI.AI/ML-based systems have changed the way of traditional UX design. Instead of programming a method to do a specific action, creators of these systems provide data and nurture them to curate outcomes based on inputs. These systems are dynamic and while AI systems change over time, their user experience, in many cases, does not adapt to this dynamic nature.Applied UX Design for Artificial Intelligence will explore this problem, addressing the challenges and opportunities in UX design for AI/ML systems, look at best practices for designers, managers, and product creators and showcase how individuals from a non-technical background can collaborate effectively with AI and Machine learning teams.You Will Learn:* Best practices in UX design when building human-centric AI products or features* Ability to spot opportunities for applying AI in their organizations* Advantages and limitations of AI when building software products* Ability to collaborate and communicate effectively with AI/ML tech teams • UX design for different modalities (voice, speech, text, etc.)* Designing ethical AI systemAkshay Kore is a product designer and has led the design of AI products at Fortune 500 companies and high-growth startups. He studied Interaction design at IIT Bombay.Akshay has written articles for various publications like Hackernoon, The Startup, UX Planet, UX collective, etc., on user experience design, artificial intelligence, and voice interfaces. He frequently talks about designing AI products at conferences, design schools, organizations, and podcasts. Currently, he lives in Bangalore with his wife and two cats.Part 1: Intelligence.- Chapter 1: On intelligence.- Chapter 2: Intelligent Agents.- Chapter 3: Incorporating Artificial Intelligence.- Part 2: Decisions.- Chapter 4: Building Trust.- Chapter 5: Designing Feedback.- Chapter 6: Handling Errors.- Part 3: Design.- Chapter 7: IE Ethics.- Chapter 8: Prototyping AI Products.- Part 4: Teamwork .- Chapter 9: Understanding AI Terminology.- Chapter 10: Working Effectively with AI Tech Teams.- Epilogue.
Create an Enterprise-Level Test Automation Framework with Appium
Think from a framework design perspective and move beyond straightforward coding skills. You’ll design an enterprise level test framework that is capable of supporting both TDD and BDD at the same time, using the latest open source tools and coding best practices. Taking a less-is-more approach, superfluous information is excised in favor of sleek and direct instruction and focused coding practices.Your framework will be built with Spring-Boot, Gradle, and Junit. And it will support HP QC integration, Allure (TDD, BDD), Extent (BDD), and customized Pdf reporting (TDD, BDD). Extensive utilities are provided, such as HP ALM integration, device management utilities, email reporting, pdf reporting, OCR utility, Log utility, and more! There’s also a special chapter on internationalization/localization testing in multiple languages. After reading this book, you’ll have full confidence in your ability to build new test automation frameworks for yourself.Though primarily written for software professionals who are in test automation, recent engineering graduates who have programming knowledge and want to prepare for a role in mobile test automation will also find what’s taught here helpful. Test engineers and computer science graduates alike can use what they learn here to become absolute pros in test automation.WHAT YOU'LL LEARN* Design an enterprise level mobile test automation framework capable of supporting both TDD and BDD* Work with the latest open source tools and coding best practices* Build with Spring-Boot, Gradle, and Junit while supporting HP QC integration, Allure, Extent, and customized PDF reportingWHO THIS BOOK IS FORSoftware professionals working in test automation. Recent engineering graduates who have programming knowledge and want to prepare for a role in mobile test automation should also find it helpful.KOUSHIK DAS is an Automation Architect with over 18 years of experience in software development, manual testing, and test automation. He has built automation frameworks for mobile, web, and desktop applications using a variety of tools. Koushik believes in leveraging the power of test automation whenever possible and has recently published a book to help mobile test automation engineers graduate to an architect role. When not testing software, Koushik likes reading, traveling, and playing with his baby daughter.IntroductionChapter 1: Automation Framework OverviewFramework Technology StackFramework Key FeaturesScripting Strategy FollowedAutomation Coding Standards FollowedChapter 2: Creating the Wireframes with Spring-BootBootstrapping with Spring-BootOpening Your Project in IntelliJIntelliJ PluginsDeciding on your Folder StructureChapter 3: Configuring GradlePreparing build.gradlePreparing gradle.propertiesCreating Annotations for Gradle TasksPreparing settings.gradleChapter 4: Creating the Properties FilesCreating Your Properties FilesReading from Properties File with Spring-Boot LibraryReading from Properties File Traditional WayChapter 5: Creating Android, iOS and Web Drivers On DemandCreating Driver with Standard Desired CapabilitiesCreating Driver with Default ServiceCreating Drivers for Grid or Cloud ExecutionQuitting Driver and TeardownChapter 6: Enhancing Framework – Common Mobile ActionsCreating VariablesCoding for Common Screen ActionsChapter 7: Creating Page ObjectsInitializing Page Objects and Workflow ClassDeciding on Locator StrategyWriting Page Object MethodsChapter 8: Writing Your First Test SuiteUsing Various AnnotationsWriting Soft AssertionsPlugging in the Reporting ModuleRunning Test Suite in GradleChapter 9: Importing Test Data From Excel, XML or Other FormatsImporting Test Data from ExcelImporting Test Data From XML and Other FormatsChapter 10: Adding BDD Capabilities with CucumberUsing Spring Runner with CucumberGenerating ExtentReport in Runner ClassWriting Step DefinitionsRunning Test Suite in GradleChapter 11: Adding Allure Reporting for TDD and BDDGenerating Allure ReportViewing Allure ReportChapter 12: Making Extent Report Better and Workable with JunitMaking Extent Report Work with JUnitImproving Extent Report to Print Data-TablesCreating Separate Extent Report for each Test-SuiteChapter 13: Creating a PDF Report with ScreenshotsCreating a PDF Util to Generate reports for each Test SuitePassing Parameters to PDF Util from Test SuiteMerging Multiple PDFsChapter 14: Enhancing Framework – ScreenshotsCreating Screenshot and Saving at Default LocationCreating Screenshot and Saving at Variable LocationCreating Screenshot with Page Object NameChapter 15: Testing Multiple Apps and Versions in Same Test SuiteTesting Multiple Versions of App in Same Test SuiteTesting Multiple Apps in Same Test SuiteBest Practices To FollowChapter 16: Running Scripts or Batch Files From Test SuiteScenarios Where Running Script or Batch Files Are RequiredRunning Script or Batch Files from Test SuiteSome ExamplesChapter 17: API TestingTesting REST API with Web ClientExampleChapter 18: Advanced Topic I – Adding Device Management FunctionsOverviewUnlocking DeviceToggling Wi-FiSetting LanguageSetting Device Date, Time, Timezone and Time FormatReading Device PropertiesEnabling and Disabling App NotificationsChapter 19: Advanced Topic 2 – Integrating with HP ALMUsing ALM 15.x APILogin and AuthenticationCRUD Operations in AboutAppTestSuiteChapter 20: Advanced Topic 3 – Adding Localization Testing CapabilitiesDeciding on Approach Based on RequirementsLocalization Testing in AndroidLocalization Testing in iOSChapter 21: Advanced Topic 4 – Implementing Parallel Test ExecutionManaging multiple SessionsUpdating BaseTest ClassUpdating Test Suites and Step DefinitionsChapter 22: Other UtilitiesOverviewOCR UtilImage Comparison UTILEmail UtilAppendixAudience: Intermediate