Computer und IT
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.
Simulation of Power Electronics Circuits with MATLAB®/Simulink®
Design and analyze electronic components and systems with the help of powerful software and effective skillsets. Balancing theory with practical exploration of the relevant software, you'll start solving power electronics problems like a pro. Using MATLAB®/Simulink®, you'll analyze the circuit in a laptop charger; interface with the power electronics converter controlling a washing machine's motor; turn on lamps with an electronic ballast; convert AC into DC power; and more!Power electronics are at the bedrock of all the wonderful devices simplifying our daily life. Designing them isn't just about understanding schematics. It also requires measuring twice and cutting once. In order to save time and money, a power electronics circuit must be simulated before construction. So you'll learn how to work with one of the most powerful simulation tools for this purpose. That way you'll know before you even go to make it whether the circuit works as expected.Learn to work with MATLAB®/Simulink® by directly applying and building the projects in this book. Or use it as a lab manual for power electronics and industrial electronics. Either way, using strong simulations and solid design theory, you'll be able to build power electronics that don't fail.WHAT YOU'LL LEARN* Simulate power electronics effectively before building them* Select suitable semiconductor components for your circuit based on simulation waveforms* Extract dynamic models of converters and design suitable controllers for themWHO THIS BOOK IS FOREngineers and students who want to simulate power electronics circuits in MATLAB®/Simulink®.FARZIN ASADI received his BSc in Electronics Engineering, MSc in Control Engineering and Phd in Mechatronics Engineering.Currently he is with the Department of Electrical and Electronics Engineering at the Maltepe University, Istanbul, Turkey.Dr. Asadi has published more than 40 international papers and 16 books. He is on the editorial board of 7 scientific journals as well. His research interests include switching converters, control theory, robust control of power electronics converters, and robotics.PART 1: INTRODUCTION TO SIMULINK®Chapter 1: Basics of SimulinkIntroductionStep response of a transfer function modelPID controller design in MATLAB environmentFeedback control systemPID controller design in Simulink environmentPlot two or more waveforms in one scope blockChapter 2: Simulation of Dynamical Equations in SimulinkSimulation of differential equationsSimulation of differential equations with only one integrator blockSimulation of differential equations with MATLAB Function blockCopying and taking out a block from the modelState-Space blockTo Workspace blockSimulation of Dynamical equation of Boost converterSimulation of discrete time equations (I)Simulation of discrete time equations (II)ExercisesReferences for further studyPART 2: SIMULATION OF POWER ELECTRONICS CONVERTERS WITH SIMULINK®Chapter 3: Simulation of Uncontrolled Rectifier CircuitsIntroductionSingle phase half wave diode rectifierMeasurement with the oscilloscope blockMeasurement with Multimeter blockMeasurement portMean and RMS blocksInstantaneous power and average powerApparent power and power factorMaking subsystemPower BlockFreewheeling diodeDisabling a blockFourier blockThree phase diode rectifierMeasurement of power factor of three phase uncontrolled rectifierMeasurement of conduction lossChapter 4: Simulation of Controlled Rectifier CircuitsSingle phase half wave thyristor rectifierSingle phase full wave thyristor rectifier ISingle phase full wave thyristor rectifier IIThree phase thyristor rectifierEffect of filter capacitor on the rectifier circuitCoupled inductorsChapter 5: Simulation of DC-DC ConvertersBuck converterOperating mode of the DC-DC converterEffect of input voltage changes on the output voltageEffect of output load change on the output voltageGeneration of PWM signalPWM Generator (DC-DC) blockClosed loop control of buck converterFlyback converterEfficiency of Flyback converterChapter 6: Simulation of InvertersSingle phase PWM inverterTHD blockHarmonic analysis with FFT Analyzer programThree phase PWM inverterConnection port blockChapter 7: Simulation of Motors and GeneratorsSimulation of a DC motorSimulation of a DC generatorInduction motorEffect of harmonics on AC motor speedExercisesReferences for further studyPART 3: DYNAMICS OF DC-DC CONVERTERSChapter 8: State Space AveragingIntroductionState Space AveragingDynamical Equations of Buck ConverterAveraging the Dynamical Equations of Buck ConverterLinearization of Averaged EquationsObtaining the Small Signal Transfer Functions of Buck Converter Using MATLABChapter 9: Input/Output impedance of DC-DC ConvertersInput and Output Impedance of Buck-Boost ConverterInput and Output Impedance of Boost ConverterExercisesReferences for further studyPART 4: IMPORTANT THEORETICAL CONCEPTSChapter 10: Average and RMS ValuesInstantaneous powerAverage powerEffective value of a signalEffective value of sum of two periodic signalsMeasurement of RMS of signalsChapter 11: Power CalculationApparent power and power factorPower computations for linear circuitsChapter 12: Fourier Series and Total Harmonic DistortionFourier seriesFourier series of important wave shapesCalculation of average power using the Fourier seriesTotal Harmonic Distortion (THD)
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
TI-84 Plus CE Graphing Calculator For Dummies
YOU AND YOUR TRUSTY TI-84+ CAN ACE MATH, TOGETHERGraphing calculators are an essential tool in many high school and college math courses. TI-84 Plus CE Graphing Calculator For Dummies teaches you how to use these handy little machines with confidence, for basic math and far, far beyond. Packed with insider tips, common mistakes to avoid, and updates on the newest products available, this is the must-have reference to get the most out of your graphing calculator. You’ll learn how to navigate the home screen, menus, and mode settings. And we’ll teach you how to use your calculator to, uh, do math—starting with basic arithmetic functions and stepping up through matrices, complex numbers, and beyond. You can even learn about probability and how to conduct statistical data analysis with your TI-84+. Get graphing!* Grasp the basics of using your TI-84+ graphing calculator* Learn how to use shortcut menus to enter fractions, matrices and logarithms (with a change of base!)* Figure out how to make charts, scatter plots, and more* Get started programming in Python on the new TI-84 Plus CE Python Edition* If you have the older TI-84+, look for tips that point out keystroke and functionality differencesThis edition of TI-84 Plus CE Graphing Calculator For Dummies lets you do everything there is to do with the very latest TI-84+ models. Whatever kind of math you’re doing, you’ll get some quality screen time in, thanks to Dummies. JEFF MCCALLA teaches Algebra II and is the Test Prep Coordinator at St. Mary’s Episcopal School in Memphis, Tennessee. As a regional instructor for Texas Instruments, Jeff has presented sessions at conferences all over the country. In 2009, he received the Presidential Award for Excellence in Science & Mathematics Teaching.Introduction 1PART 1: MAKING FRIENDS WITH THE CALCULATOR 5Chapter 1: Starting with the Basics 7Chapter 2: Doing Basic Arithmetic 25Chapter 3: Dealing with Fractions 35Chapter 4: Solving Equations 41PART 2: TAKING YOUR CALCULATOR RELATIONSHIP TO THE NEXT LEVEL 53Chapter 5: Working with Complex Numbers 55Chapter 6: Understanding the Math Menu and Submenus 61Chapter 7: The Angle and Test Menus 69Chapter 8: Creating and Editing Matrices 79PART 3: GRAPHING AND ANALYZING FUNCTIONS 89Chapter 9: Graphing Functions 91Chapter 10: Exploring Functions 111Chapter 11: Evaluating Functions 127Chapter 12: Graphing Inequalities 143Chapter 13: Graphing Parametric Equations 155Chapter 14: Graphing Polar Equations 163PART 4: WORKING WITH PROBABILITY AND STATISTICS 173Chapter 15: Probability 175Chapter 16: Dealing with Statistical Data 183Chapter 17: Analyzing Statistical Data 193PART 5: DOING MORE WITH YOUR CALCULATOR 209Chapter 18: Communicating with a PC Using TI Connect CE Software 211Chapter 19: Communicating Between Calculators 221Chapter 20: Fun with Images 227Chapter 21: Managing Memory 231PART 6: THE PART OF TENS 237Chapter 22: Ten Essential Skills 239Chapter 23: Ten Common Errors 243Chapter 24: Ten Common Error Messages 249PART 7: APPENDICES 253Appendix A: Creating Calculator Programs 255Appendix B: Controlling Program Input and Output 259Appendix C: Controlling Program Flow 269Appendix D: Introducing Python Programming 281Appendix E: Mastering the Basics of Python Programming 287Index 293
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
Getting Started with Grafana
Begin working with the Grafana data visualization platform. This book is a “how-to manual” for deploying and administering Grafana, creating real-time dashboards and alerts, exploring the data you have, and even synthesizing new data by combining and manipulating data from multiple different sources. You’ll be able to see and manage data on any scale, from your laptop or a Raspberry Pi to a production datacenter or even a multi-region cloud environment!GETTING STARTED WITH GRAFANA takes a hands-on approach. You’ll learn by doing with easy-to-follow examples along with pointers to more resources to help you go deeper. The skills you’ll learn will help you provide business value by monitoring your operations in real time and reacting to changing circumstances as they occur. You’ll be able to derive new insights from your existing data through Grafana’s powerful and beautiful graphing capabilities, and you’ll be able to share your dashboards with colleagues so everyone in your organization can benefit.WHAT YOU WILL LEARN* Connect to data "where it lives” and work with multiple sources of data* Build beautiful and informative dashboards that show real-time status* Deploy Grafana at any scale and manage it efficiently* Integrate with other enterprise systems such as LDAP or Active Directory* Automate creation and deployment of Grafana, dashboards, and alerts* Understand what is available in the Enterprise version of GrafanaWHO THIS BOOK IS FORAnyone who has data that they want to understand visually, IT professionals who work with multiple sources of data on a regular basis and need to make sense of the confusion that this data sprawl causes, and people who learn best by doing and want to get hands-on experience quickly with a project and then grow their knowledgeRONALD MCCOLLAM is a “geek of all trades” with experience ranging from full stack development to IT operations management. He has a strong background in open source software dating back to when a stack of 3.5” Slackware floppies was the easy way to install Linux. When not on the road for work or in his lab building robots that can operate a Ouija board, Ronald resides on his back porch in Somerville, MA with a frosty beverage in hand. IntroductionPART I. GETTING STARTED1. Grafana Cloud2. Working with PanelsPART II. DEPLOYING AND MANAGING GRAFANA3. Deploying Grafana Locally4. Connecting to Data Sources5. User AdministrationPART III. MAKING THINGS USEFUL6. Dashboard Design7. Workflow8. Working with Multiple Data Sources9. Advanced Panels10. Dashboard Variables11. AlertingPART IV. ADVANCED GRAFANA12. Advanced Deployment and Management13. Programmatic Grafana14. Grafana Enterprise
Practical Linux DevOps
Learn, develop and hone your Linux and DevOps skills by building a lab for learning, testing and exploring the latest Linux and open-source technologies. This book helps Linux users and others to master modern DevOps practices using a wide range of software and tools.Having a home or work-based Linux lab is indispensable to anyone looking to experiment with the ever-evolving landscape of new software and DevOps. With open-source tools and readily available hardware, you will end up with a lab you can use to try virtually any modern software, including Chef, Docker, Kubernetes and stalwarts like DNS, Dovecot, and Postfix for email. You'll set up pipelines for software deployment and focus on discrete projects that help you learn through doing. In the end, you'll acquire the skills needed to become better informed, more marketable engineers and developers, and better able to take on a wide array of software projects with confidence.Practical Linux DevOps is the perfect companion for those who want to learn how to build systems with utility and learn about modern hardware and software practices.WHAT YOU'LL LEARN* Set up a Linux-based virtualization environment and workstation* Create a lab network with a fully qualified domain* Build web-based applications with NGINX and LAMP* Use version-control tools like Git* Automate deployments and configurations* Think like a modern DevOps engineerWHO THIS BOOK IS FORNew and modestly experienced users with basic understanding of a basic understanding of Windows or Linux command line, as well aswould-be and current DevOps engineers, and full-stack and other software developers John S. Tonello writes about technology, software, infrastructure-as-code and DevOps, and has spent more than 20 years working in and around the software industry for companies like Tenable, HashiCorp, SUSE, Chef and Puppet. He’s spent more than 25 years building Linux-based environments, and regularly publishes a wide range of how-to guides and blogs about DevOps, Linux, and software-defined infrastructure.1) INTRODUCTION2) GATHER YOUR HARDWARE2.1 The basics: What you’ll need to build your lab2.2 Using new equipment for your lab2.3 Using old PCs and laptops2.4 Raspberry Pis and IoT devices2.5 Building your network2.6 A few words on networking2.7 Optionally install Webmin3) SETTING UP A VIRTUAL ENVIRONMENT3.1 Why it matters3.2 About ProxMox VE3.3 About plain KVM3.4 Graphical deployments vs. the command line3.5 Set up ProxMox VE3.6 Set up Plain KVM3.7 Command line deployments3.8 Conclusion4) SET UP A DNS SERVER4.1 Why it matters4.2 Sketching out your network4.3 Configure your routers and switches4.4 Deploy a VM to host the DNS server4.5 Install bind4.6 Set up your domain master4.7 Set up an optional slave4.8 Enable and start the named service4.9 Graphical deployments and management4.10 Containerize it5) SET UP AN EMAIL SERVER5.1 Why it matters5.2 Deploy a VM to host your mail server5.3 Install Postfix and Dovecot5.4 Configure Postfix5.5 Configure SMTP with Dovecot5.6 Mail server security considerations5.7 Initial email service tests5.8 Set up Thunderbird, Evolution or other graphical email client5.9 Optional SquirrelMail deployment5.10 Containerize it6) SET UP A LAMP WEB SERVER6.1 Why it matters6.2 Deploy an LXC or VM host6.3 Configure the host network and name6.4 Install the web server6.5 Create your first virtual host6.6 Install PHP6.7 Install MariaDB6.8 Install Adminer or PHPMyAdmin to graphically manager MariaDB6.9 Create a single-file PHP app and connect it to a MariaDB database6.10 Containerize it7) WEBSITE ALTERNATIVES7.1 Why it matters7.2 Deploy a different kind of web server7.3 Try python3 http.server7.4 Deploy Wordpress7.5 Configure Wordpress and login7.6 Containerize it8) SERVER MANAGEMENT AND MAINTENANCE8.1 Why it matters8.2 Install Webmin on a new or existing host8.3 Configure Webmin to manage multiple hosts8.4 Backup your servers9) AUTOMATION WITH CHEF AND ANSIBLE9.1 Why it matters9.2 Install Ansible9.3 Install Chef10) THINK DEVOPS10.1 Why it matters10.2 Why containers and microservices are taking over the world10.3 How automation tools like Ansible and Chef make server configuration easier10.4 Why deploying applications in containers is so popular10.5 Taking advantage of these services in your lab11) EXTEND YOUR LAB11.1 Why it matters11.2 Create a Docker host11.3 Create a GitLab host11.4 Deploy Kubernetes with Git-based scripts11.5 Experiment with OpenStack12) RESOURCES12.1 Github repo12.2 Software links12.3 Helpful sites12.4 References to software used
The OTHER Computer History
The author tells his experiences from the early days of computer science until 2004. At that time when it was not recognized what a gigantic transformation it will bring to the history of humanity.The many detailed technical descriptions are loosed with normal human stories. The inevitable upcoming associations which arose while writing led to parables, illusions, and daring theses. But he soon returns to reality. With a laugh, tears, and a blinking eye, he now says to his 'buddies', the computers: Goodbye. We had an excellent time...Reviewed by Foluso Falaye, Citybboks.The OTHER Computer History is mightily educational, immersing, and inspiring as it made me feel like a part of the events and showed me the harsh realities and tough phases that people with dreams and ideas have to go through to achieve great feats. Some companies succeeded, while others failed embarrassingly; ultimately, a new, phenomenal reality was born out of the collective works of different companies and individuals such as Hans Bodmer, who might not be as glorified as the celebrated names, but who were surely important contributors to the creation of world-changing supercomputersHans Bodmer:Born 1939 in Zurich.Public primary and secondary school in Zurich. Apprenticeship as precision mechanic with Philips:Professional career from 1959-2004 in all kinds of jobs in the IT business: Maintenance Engineer, System Programmer, System Analyst, Telecommunication specialist, Marketing Manager.Worked for:L.M. Ericcson- Burroughs- BULL- Control Data- PR1ME- A mayor Swiss financial InstitutionFreelance Author: Poems, Essays, Compositions.Amateur Actor.
Project Zero Trust
IMPLEMENT ZERO TRUST INITIATIVES EFFICIENTLY AND EFFECTIVELYIn Project Zero Trust: A Story About a Strategy for Aligning Security and the Business, George Finney, Chief Security Officer at Southern Methodist University, delivers an insightful and practical discussion of Zero Trust implementation. Presented in the form of a fictional narrative involving a breach at a company, the book tracks the actions of the company's new IT Security Director.Readers will learn John Kindervag's 5-Step methodology for implementing Zero Trust, the four Zero Trust design principles, and how to limit the impact of a breach. They'll also find:* Concrete strategies for aligning your security practices with the business* Common myths and pitfalls when implementing Zero Trust and how to implement it in a cloud environment* Strategies for preventing breaches that encourage efficiency and cost reduction in your company's security practicesProject Zero Trust is an ideal resource for aspiring technology professionals, as well as experienced IT leaders, network engineers, system admins, and project managers who are interested in or expected to implement zero trust initiatives.GEORGE FINNEY is the Chief Security Officer at Southern Methodist University. He has taught Cybersecurity at SMU and been recognized as one of the top 100 Chief Information Security Officers in the world by CISOs Connect. He has over 20 years’ experience in the industry with startups, global telecommunication firms, and nonprofits. About the Author xiAcknowledgments xiiiForeword xvIntroduction xxiChapter 1: The Case for Zero Trust 1Key Takeaways 10Chapter 2: Zero Trust Is a Strategy 13Key Takeaways 26The Four Zero Trust Design Principles 27The Five-StepZero Trust Design Methodology 27The Zero Trust Implementation Curve 27Chapter 3: Trust Is a Vulnerability 29Key Takeaways 39Chapter 4: The Crown Jewels 43Key Takeaways 54Chapter 5: The Identity Cornerstone 57Key Takeaways 71Chapter 6: Zero Trust DevOps 73Key Takeaways 83Chapter 7: Zero Trust SOC 87Key Takeaways 100Chapter 8: Cloudy with a Chance of Trust 103Key Takeaways 113Chapter 9: A Sustainable Culture 117Key Takeaways 129Chapter 10: The Tabletop Exercise 133Key Takeaways 147Chapter 11: Every Step Matters 151Key Takeaways 159APPENDIX A: ZERO TRUST DESIGN PRINCIPLES AND METHODOLOGY 165The Four Zero Trust Design Principles 165The Five-Step Zero Trust Design Methodology 166APPENDIX B: ZERO TRUST MATURITY MODEL 167APPENDIX C: SAMPLE ZERO TRUST MASTER SCENARIO EVENTS LIST 171APPENDIX D: FOR FURTHER READING 179Standards, Frameworks, and Other Resources 179Case Studies 180Google BeyondCorp Papers 180Books 181Hardening Guides 181Glossary 183Index 191
Pro Database Migration to Azure
Migrate your existing, on-premises applications into the Microsoft Azure cloud platform. This book covers the best practices to plan, implement, and operationalize the migration of a database application from your organization’s data center to Microsoft’s Azure cloud platform.Data modernization and migration is a technologically complex endeavor that can also be taxing from a leadership and operational standpoint. This book covers not only the technology, but also the most important aspects of organization culture, communication, and politics that so frequently derail such projects. You will learn the most important steps to ensuring a successful migration and see battle-tested wisdom from industry veterans. From executive sponsorship, to executing the migration, to the important steps following migration, you will learn how to effectively conduct future migrations and ensure that your team and your database application delivers on the expected business value of the project.This book is unlike any other currently in the market. It takes you through the most critical business and technical considerations and workflows for moving your data and databases into the cloud, with special attention paid to those who are deploying to the Microsoft Data Platform in Azure, especially SQL Server. Although this book focuses on migrating on-premises SQL Server enterprises to hybrid or fully cloud-based Azure SQL Database and Azure SQL Managed Instances, it also cover topics involving migrating non-SQL Server database platforms such as Oracle, MySQL, and PostgreSQL applications to Microsoft Azure.WHAT YOU WILL LEARN* Plan a database migration that ensures smooth project progress, optimal performance, low operating cost, and minimal downtime* Properly analyze and manage non-technical considerations, such as legal compliance, privacy, and team execution* Perform a thorough architectural analysis to select the best Azure services, performance tiers, and cost-containment features* Avoid pitfalls and common reasons for failure relating to corporate culture, intra-office politics, and poor communications* Secure the proper executive champions who can execute the business planning needed for success* Apply proven criteria to determine your future-state architecture and your migration method* Execute your migration using a process proven by the authors over years of successful projectsWHO THIS BOOK IS FORIT leadership, strategic IT decision makers, project owners and managers, and enterprise and application architects. For anyone looking toward cloud migration projects as the next stage of growth in their careers. Also useful for enterprise DBAs and consultants who might be involved in such projects. Readers should have experience and be competent in designing, coding, implementing, and supporting database applications in an on-premises environment.KEVIN KLINE is a noted database expert and software industry veteran. A long-time Microsoft Data Platform MVP and respected community leader in the database industry, Kevin is a founder and former president of the Professional Association for SQL Server, as well as the author of popular IT books such as SQL in a Nutshell. Kevin is a top-rated speaker at industry trade shows worldwide and has a monthly column at Database Trends & Applications magazine (DBTA.com). He tweets at @kekline and blogs regularly. Kevin is a Head Geek at SolarWinds, a leading vendor of management and observability tools for networks, systems, databases, applications, IT team services, and IT security.DENIS MCDOWELL has been designing and implementing technology solutions with Microsoft Data Platform technologies for over 25 years. Denis’ 10 years leading the Application Management practice for a managed services provider and subsequent experience consulting in financial technologies led him to develop broad and deep expertise architecting requirements-driven cloud solutions to meet the business objectives of his customers. Denis is a certified Microsoft Azure Data Platform Engineer and speaks regularly at industry events and conferences around the world. Denis is a consultant at QBE, LLC, a leading management and technology consulting organization for the federal government and defense and intelligence communities and is currently the Principal Cloud Architect for the US.Army’s Enterprise Cloud Management Agency (ECMA).DUSTIN DORSEY has been architecting and managing SQL Server solutions for healthcare companies for well over a decade. While he has built his career in database administration, he has also spent significant time working in development and business intelligence. During this time, Dustin has gained a keen interest and specialization in cost management around the data platform both on-premise and in the cloud that he has used to save organizations millions of dollars. Dustin is an international speaker and can be seen writing articles on popular SQL websites as well as on his own blog at DustinDorsey.com. He is also active in the community both as a local user group leader and event organizer..MATT GORDON is a Microsoft Data Platform MVP and has worked with SQL Server since 2000. He is the leader of the Lexington, KY Data Technology Group and a frequent domestic and international community speaker. He's an IDERA ACE alumnus and 2021 Friend of Redgate. His original data professional role was in database development, which quickly evolved into query tuning work that further evolved into being a DBA in the healthcare realm. He has supported several critical systems utilizing SQL Server and managed dozens of 24/7/365 SQL Server implementations. Following several years as a consultant, he is now the Director of Data and Infrastructure for rev.io, where he is implementing data governance, DevOps, and performance improvements enterprisewide.1. The Azure SQL Data Platform2. Planning Considerations and Analysis3. Budgeting for an Azure Migration4. Azure Cost Management5. Service and Systems Monitoring6. Migrating Data and Code7. Team Success Factors8. Security, Privacy, and Compliance with the Law9. Documenting Data Sources and Metadata in a Data Dictionary10. Moving Your Data to the Cloud11. Data Validation Testing12. Post-Migration Tasks13. Post Mortem
Kanban - mehr als Zettel
- Was hat es mit der Kanban-Methode auf sich? - Worin unterscheiden sich Board, System und Methode? - Wie geht man mit Widerstand bei der Einführung um? - Was passiert, wenn wir die Menge paralleler Arbeit limitieren? - Wer übernimmt die Verantwortung für Dienstleistung und ihre Verbesserung? - Neu in der 2. Auflage: das Thema „Elektronische Werkzeuge“ sowie vertiefende Inhalte - Ihr exklusiver Vorteil: E-Book inside beim Kauf des gedruckten Buches Jan hat ein Problem, denn er steht kurz davor, rauszufliegen. Dabei hat er gerade erst angefangen! Sein Unternehmen ist in einer Krise und schuld daran ist natürlich Jans Abteilung. Keine seiner althergebrachten Techniken funktioniert und die Mitarbeitenden sind an ihrer Belastungsgrenze. Seine Partnerin rät ihm zur Kanban-Methode: Sie soll die Grundlage sein, die Leistung der Abteilung zu verbessern. Eigentlich wäre ihm jeder Strohhalm recht, gleichzeitig fürchtet er den Widerstand seiner Mitarbeitenden und seines Chefs gegen ein evolutionäres Vorgehen. Aber Anja zeigt ihm, wie er diese Widerstände mit der Kanban-Methode bewältigen kann. Er beginnt gemeinsam mit seinen Mitarbeitenden die Methode auszuprobieren und macht nach und nach aus Gegnern Komplizen. Aber können sie damit auch das Unternehmen retten? Florian Eisenberg beschreibt in diesem Business-Roman den Weg eines Abteilungsleiters im Spannungsfeld zwischen Kunden, Mitarbeitenden und Management. Dabei wird deutlich, dass der Einsatz der Kanban-Methode die bestehenden Denkprozesse in Bezug auf das Management immer wieder hinterfragt. WEITERE INHALTE // - Kanban als Management-Methode verstehen - Mit Widerstand umgehen - Kundenbeziehungen gestalten - Evolutionäre Verbesserung implementieren - Kanban-Kadenzen
Impact of Class Assignment on Multinomial Classification Using Multi-Valued Neurons
Multilayer neural networks based on multi-valued neurons (MLMVNs) have been proposed to combine the advantages of complex-valued neural networks with a plain derivative-free learning algorithm. In addition, multi-valued neurons (MVNs) offer a multi-valued threshold logic resulting in the ability to replace multiple conventional output neurons in classification tasks. Therefore, several classes can be assigned to one output neuron. This book introduces a novel approach to assign multiple classes to numerous MVNs in the output layer. It was found that classes that possess similarities should be allocated to the same neuron and arranged adjacent to each other on the unit circle. Since MLMVNs require input data located on the unit circle, two employed transformations are reevaluated. The min-max scaler utilizing the exponential function, and the 2D discrete Fourier transform restricting to the phase information for image recognition. The evaluation was performed on the Sensorless Drive Diagnosis dataset and the Fashion MNIST dataset. JULIAN KNAUP received his B. Sc. in Electrical Engineering and his M. Sc. in Information Technology from the University of Applied Sciences and Arts Ostwestfalen-Lippe. He is currently working on machine learning algorithms at the Institute Industrial IT and researching AI potentials in product creation. 1 Introduction.- 2 Preliminaries.- 3 Scientific State of the Art.- 4 Approach.- 5 Evaluation.- 6 Conclusion and Outlook.
OCP Oracle Certified Professional Java SE 17 Developer Practice Tests
EFFICIENTLY AND EFFECTIVELY PREPARE FOR THE OCP JAVA SE 17 CERTIFICATION EXAMIn OCP Oracle Certified Professional Java SE 17 Developer Practice Tests: Exam 1Z0-829, a team of veteran Java developers helps you gain the confidence and knowledge you need to take the Oracle Certified Professional Java SE 17 Developer exam. Hundreds of domain-by-domain questions accompany complimentary online access to bonus questions, providing you with over 1000 practice questions and answers.You’ll also get:* Access to Sybex's proven online interactive learning environment and test bank* Comprehensive coverage of every domain included on the OCP Java SE 17 Developer exam* Three complete bonus practice exams to help you gain confidence and develop proficiency in Java developmentThis resource is perfect for anyone preparing to take Exam 1Z0-829. It also belongs on the bookshelves of novice and veteran Java programmers hoping to improve their on-the-job coding skills.ABOUT THE AUTHORSJEANNE BOYARSKY is a Java Champion and has worked as a Java developer for a major bank for more than 20 years. She is a senior moderator at CodeRanch, and trains and mentors students of all levels, including the programming department of a FIRST robotics team. SCOTT SELIKOFF has been a professional Java Enterprise architect for over 20 years. He currently works as a Staff Software Engineer at Google, specializing in Architecture and Cloud Services. He is a Leader of the Garden State Java User Group, helping to facilitate discussions and exchange of ideas within the community. Introduction xviiChapter 1 Handling Date, Time, Text, Numeric and Boolean Values 1Chapter 2 Controlling Program Flow 25Chapter 3 Utilizing Java Object-Oriented Approach 45Chapter 4 Handling Exceptions 149Chapter 5 Working with Arrays and Collections 181Chapter 6 Working with Streams and Lambda Expressions 211Chapter 7 Packaging and Deploying Java Code and Use the Java Platform Module System 267Chapter 8 Managing Concurrent Code Execution 295Chapter 9 Using Java I/O API 319Chapter 10 Accessing Databases Using JDBC 339Chapter 11 Implementing Localization 353Chapter 12 Practice Exam 1 365Chapter 13 Practice Exam 2 391Chapter 14 Practice Exam 3 417APPENDIX ANSWERS TO REVIEW QUESTIONS 443Chapter 1: Handling Date, Time, Text, Numeric and Boolean Values 444Chapter 2: Controlling Program Flow 450Chapter 3: Utilizing Java Object-Oriented Approach 455Chapter 4: Handling Exceptions 482Chapter 5: Working with Arrays and Collections 489Chapter 6: Working with Streams and Lambda Expressions 498Chapter 7: Packaging and Deploying Java Code and Use the Java Platform Module System 516Chapter 8: Managing Concurrent Code Execution 524Chapter 9: Using Java I/O API 530Chapter 10: Accessing Databases Using JDBC 535Chapter 11: Implementing Localization 538Chapter 12: Practice Exam 1 541Chapter 13: Practice Exam 2 548Chapter 14: Practice Exam 3 554Index 561
Raspberry Pi Image Processing Programming
Understand the concepts of image processing with Python 3 and create applications using Raspberry Pi 4. This book covers image processing with the latest release of Python 3, using Raspberry Pi OS and Raspberry Pi 4B with the 8 GB RAM model as the preferred computing platform.This second edition begins with the installation of Raspberry Pi OS on the latest model of Raspberry Pi and then introduces Python programming language, IDEs for Python, and digital image processing. It also illustrates the theoretical foundations of Image processing followed by advanced operations in image processing. You'll then review image processing with NumPy, and Matplotlib followed by transformations, interpolation, and measurements of images.Different types of filters such as Kernels convolution filters, low pass filters, high pass filters, and Fourier filters are discussed in a clear, methodical manner. Additionally, the book examines various image processing techniques such as Morphology, Thresholding, and Segmentation, followed by a chapter on live webcam input with OpenCV, an image processing library with Python. The book concludes with an appendix covering a new library for image processing with Python, pgmagik, followed by a few important tips and tricks relevant to RPi.WHAT YOU'LL LEARN* Get started with Raspberry Pi and Python* Understand Image Processing with Pillow* See how image processing is processed using Numpy and Matplotlib* Use Pi camera and webcamWHO THIS BOOK IS FORRaspberry Pi and IoT enthusiasts, and Python and Open Source professionalsASHWIN PAJANKAR earned a Master of Technology in Computer Science Engineering from IIIT Hyderabad and has over 25 years of programming experience. He started his journey in programming and electronics at the tender age of 7 with a MS-DOS computer and BASIC programming language. He is now proficient in Assembly programming, C, C++, Java, Shell Scripting, JavaScript, Go Programming, HTML, and Python. His other technical expertise includes single board computers such as Raspberry Pi and Banana Pro, and microcontroller boards (Arduino), and embedded boards (BBC Micro Bit). He has worked extensively on domains such as Software/Product Testing, Software Automation, Databases, Data Analytics, Computer Vision, and Web Development.Ashwin is currently a freelance online instructor teaching programming and electronics to more than 8200 professionals. He also regularly conducts live programming boot camps for software professionals. His growing Youtube channel has an audience of more than 11000 subscribers. He has published more than 20 books on programming and electronics with many international publications, including Apress and regularly reviews books on programming written by other authors.Chapter 1: Introduction to Single Board Computers and RPiChapter Goal: Brief intro into SBCs and RPiNo of pagesSub -Topics1. SBCs2. Raspberry Pi3. Raspberry Pi Imager and setup4. Configuring the PiChapter 2: Introduction to Python and Digital Image ProcessingChapter Goal: Brief acquaintance with the subject of the bookNo of pages:Sub - Topics:1. History of Python2. Features3. Installation of Python on Raspberry Pi4. IDEs for Python5. Digital Image ProcessingChapter 3: Getting Started with Image ProcessingChapter Goal: Getting to understand the basicsNo of pages:Sub - Topics:1. Image Sources (Standard Image Datasets)2. Various Cameras for RPi3. Pillow Basics4. Tk Basics5. Reading and displaying images with Pillow and Tk6. Image PropertiesChapter 4: Basic Operations on ImagesChapter Goal: Getting to know PillowNo of pages:Sub - Topics:1. Image modulea) Image channelsb) Mode Conversionc) Blendingd) Resizinge) Rotationf) Crop and pasteg) Alpha compositionh) Mandelbrot seti) Noise and gradient2. ImageChops module3. ImageOps moduleChapter 5: Advanced Operations on ImagesChapter Goal: Filtering and Enhancements1. Image filter (will cover more filters in the second edition)2. Image enhancements (will cover additional effects)3. Color quantization4. Histogram and equalizationChapter 6: Scientific PythonChapter Goal: Introduction to the Scientific Python1. The SciPy stack2. NumPy, SciPy, and Matplotlib3. Image Processing with NumPy and MatplotlibChapter 7: Transformations, Interpolation, and MeasurementsChapter Goal: Transformations and Measurements1. Transformations and Interpolationsa) Affine_transformb) Geometric_transformc) Map_coordinatesd) Rotatee) Shiftf) Spline_filterg) Spline_filter1dh) Zoom2. Measurementsa) Center_of_massb) Extremac) Find_objectsd) Histograme) Labelf) Labeled_comprehensiong) Maximumh) Maximum_positioni) Meanj) Mediank) Minimuml) Minimum_positionm) Standard_deviationn) Sum_labelso) Variancep) Watershed_iftChapter 8: Filters and their ApplicationChapter Goal: Study Various types of filters1. Kernels, Convolution, Filters2. Correlation3. Low Pass Filtersa) Blurring Filter (Gaussian, Gaussian 1D, uniform, uniform 1D, percentile, rank)b) Noise Removal (Gaussian, Median, Maximum, Minimum, rank)4. High Pass filtersa) Prewittb) Sobelc) Laplaciand) Gaussian Gradient Magnitudee) Gaussian Laplace5. Fourier FiltersChapter 9: Morphology, Thresholding, and SegmentationChapter Goal: Study operations1. Morphologya) Distance transformb) Structuring Element (generate_binary_structure)c) Binary Morphological Operationsd) Greyscale Morphological Operationse) More Morphological Operations2. Thresholding and SegmentationChapter 10: pgmagikChapter Goal: Learn pgmagic library in detail1. Installation2. Creating images3. Draw text4. Image filter and transformation5. Bezier curve6. Blob7. Circle8. Animation
Pro C# 10 with .NET 6
Welcome to the most comprehensive foundational guide available on the topic of C# coding and .NET. This book goes beyond “do this, to achieve this” to drill down into the core stuff that makes a good developer, great. This expanded 11th edition delivers loads of new content on Entity Framework, Razor Pages, Web APIs and more. You will find the latest C# 10 and .NET 6 features served up with plenty of “behind the curtain” discussion designed to expand developers’ critical thinking skills when it comes to their craft. Coverage of ASP.NET Core, Entity Framework Core, and more sits alongside the latest updates to the new unified .NET platform, from performance improvements to Windows Desktop apps on .NET 6, updates in XAML tooling, and expanded coverage of data files and data handling. Going beyond the latest features in C# 10, all code samples are rewritten for this latest release.Dive in and discover why this essential classic is a favorite of C# developers worldwide. Gain a solid foundation in object-oriented development techniques, attributes and reflection, generics and collections, and numerous advanced topics not found in other texts (such as CIL opcodes and emitting dynamic assemblies). PRO C# 10 WITH .NET 6 will build your coding confidence putting C# into practice, and exploring the .NET universe and its vast potential on your own terms.WHAT YOU WILL LEARN* Explore C# 10 features and updates in records and record structs, global and implicit using directives, file level namespaces, extended property patterns, and more* Develop applications with C# and modern frameworks for services, web, and smart client applications* Hit the ground running with ASP.NET Core web applications using MVC and Razor Pages, including view components, custom tag helpers, custom validation, GDPR support, and areas* Build ASP.NET RESTful services complete with versioning, enhanced swagger, and basic authentication* Embrace Entity Framework Core for building real-world, data-centric applications, with deeply expanded coverage new to this edition including SQL Server temporal table support* Dive into Windows Desktop Apps on .NET 6 using Windows Presentation Foundation* Understand the philosophy behind .NET* Discover the new features in .NET 6, including single file applications, smaller container images, and moreWHO THIS BOOK IS FORDevelopers of any level who want to either learn C# and .NET or want to take their skills to the next level.“Amazing! Provides easy-to-follow explanations and examples. I remember reading the first version of this book; this is a ‘must-have’ for your collection if you are learning .NET!”– Rick McGuire, Senior Application Development Manager, Microsoft“Phil is a journeyman programmer who brings years of experience and a passion for teaching to make this fully revised and modernized ‘classic’ a ‘must-have’. Any developer who wants full-spectrum, up-to-date coverage of both the C# language and how to use it with .NET and ASP.NET Core should get this book.”– Brian A. Randell, Partner, MCW Technologies and Microsoft MVPANDREW TROELSEN has more than 20 years of experience in the software industry. Over this time he has worked as a developer, educator, author, public speaker, and now team lead and lead engineer. He is the author of numerous books in the Microsoft universe. He holds a master of science degree in software engineering (MSSE) from the University of St. Thomas and another in computational linguistics (CLMS) from the University of Washington.PHIL JAPIKSE is an international speaker, Microsoft MVP, ASPInsider, Professional Scrum Trainer, and a passionate member of the developer community. He is the lead director of the Cincinnati .NET User Group and the Cincinnati Software Architect Roundtable, and he founded the CincyDeliver conference, Currently, he works as a Director of Consulting and Enterprise Architect. Follow him on his blog (skimedic.com) or on Twitter @skimedic.Part 1: Introducing C# and .NET 61 Introducing C# and .NET (Core) 62 Building C# ApplicationsPart 2: Core C# Programming3 Core C# Programming Constructs, Part 14 Core C# Programming Constructs, Part 2Part 3: Object Oriented Programming with C#5 Understanding Encapsulation6 Understanding Inheritance and Polymorphism7 Understanding Structured Exception Handling8 Working with Interfaces9 Understanding Object LifetimePart 4: Advanced C# Programming10 Collections and Generics11 Advanced C# Language Features12 Delegates, Events, and Lambda Expressions13 LINQ To Objects14 Processes, AppDomains, and Load Contexts15 Multithreaded, Parallel, and Async ProgrammingPart 5: Programming with .NET Core Assemblies16 Building and Configuring Class Libraries17 Type Reflection, Late Binding, Attributes, and Dynamic Types18 Understanding CIL and the Role of Dynamic AssembliesPart 6: File Handling, Object Serialization, and Data Access19 File I/O and Object Serialization20 Data Access with ADO.NETPart 7: Entity Framework Core21 Introducing Entity Framework Core22 Exploring EF Core23 Build a Data Access Layer with Entity Framework Core24 Test Driving the Autolot Data Access LayerPart 8: Windows Client Development25 Introducing Windows Presentation Foundation and XAML26 WPF Controls, Layouts, Events, and Data Binding27 WPF Graphics Rendering Services28 WPF Resources, Animations, Styles, and Templates29 WPF Notifications, Validations, Commands, and MVVMPart 9: ASP.NET Core30 Introducing ASP.NET Core31 Diving into ASP.NET Core32 RESTful Services with ASP.NET Core33 Web Applications using MVC34 Web Applications using Razor Pages
A Corporate Librarian's Guide to Information Governance and Data Privacy
WITH THE EXPANSION OF TECHNOLOGY AND GOVERNANCE, THE INFORMATION GOVERNANCE INDUSTRY HAS EXPERIENCED DRAMATIC AND OFTEN, SUDDEN CHANGES. Among the most important shifts are the proliferation of data privacy rules and regulations, the exponential growth of data and the need for removing redundant, obsolete, and trivial information and the growing threat of litigation and regulatory fines based on a failure to properly keep records and manage data. At the same time, longstanding information governance standards and best practices exist, which transcend the sudden vicissitudes of the day.This volume focuses on these core IG principles, with an emphasis on how they apply to our target audience, which includes law librarians, legal and research staff and other individuals and departments in both the public and private sectors who engage deeply with regulatory compliance matters.Core topics that will be addressed include:* the importance of implementing and maintaining cohesive records management workflows that implement the classic principles of capturing, checking, recording, consolidation, and review;* the classic records management principles of Accountability, Transparency, Integrity, Protection, Compliance, Accessibility, Retention and Disposition; and* archives Management and the two principles of Providence and Original Order.