Computer und IT
Reinforcement Learning
In uralten Spielen wie Schach oder Go können sich die brillantesten Spieler verbessern, indem sie die von einer Maschine produzierten Strategien studieren. Robotische Systeme üben ihre Bewegungen selbst. In Arcade Games erreichen lernfähige Agenten innerhalb weniger Stunden übermenschliches Niveau. Wie funktionieren diese spektakulären Algorithmen des bestärkenden Lernens? Mit gut verständlichen Erklärungen und übersichtlichen Beispielen in Java und Greenfoot können Sie sich die Prinzipien des bestärkenden Lernens aneignen und in eigenen intelligenten Agenten anwenden. Greenfoot (M.Kölling, King’s College London) und das Hamster-Modell (D.Bohles, Universität Oldenburg) sind einfache aber auch mächtige didaktische Werkzeuge, die entwickelt wurden, um Grundkonzepte der Programmierung zu vermitteln. Wir werden Figuren wie den Java-Hamster zu lernfähigen Agenten machen, die eigenständig ihre Umgebung erkunden.Nach seinem Studium der Informatik und Philosophie mit Schwerpunkt künstliche Intelligenz und maschinelles Lernen an der Humboldt-Universität in Berlin und einigen Jahren als Projektingenieur ist Uwe Lorenz derzeit als Gymnasiallehrer für Informatik und Mathematik tätig. Seit seinem Erstkontakt mit Computern Ende der 80er Jahre hat ihn das Thema Künstliche Intelligenz nicht mehr losgelassen.Bestärkendes Lernen als Teilgebiet des Maschinellen Lernens.-Grundbegriffe des Bestärkenden Lernens.-Optimale Entscheidungen in einem bekannten Umweltsystem.-Dynamische Programmierung.- rekursive Tiefensuche.-Entscheiden und Lernen in einem unbekannten Umweltsystem.-Q- und Sarsa Learning, Eignungspfade, Dyna-Q.-Policy Gradient und Actor Critic.- Monte Carlo-Evaluationen und Monte Carlo-Baumsuche (MCTS).-Künstliche neuronalen Netze als Schätzer für Zustandsbewertungen und Handlungspreferenzen.-Werden digitale Agenten bald intelligenter als Menschen sein?.-Leitbilder in der K.I..
Machine Learning and Big Data
THIS BOOK IS INTENDED FOR ACADEMIC AND INDUSTRIAL DEVELOPERS, EXPLORING AND DEVELOPING APPLICATIONS IN THE AREA OF BIG DATA AND MACHINE LEARNING, INCLUDING THOSE THAT ARE SOLVING TECHNOLOGY REQUIREMENTS, EVALUATION OF METHODOLOGY ADVANCES AND ALGORITHM DEMONSTRATIONS.The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems.Subjects covered in detail include:* Mathematical foundations of machine learning with various examples.* An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview.* Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth.* Hands-on machine leaning open source tools viz. Apache Mahout, H2O.* Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning.* Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.UMA N. DULHARE is a Professor in the Department of Computer Science & Eng., MJCET affiliated to Osmania University, Hyderabad, India. She has more than 20 years teaching experience years with many publications in reputed international conferences, journals and online book chapter contributions. She received her PhD from Osmania University, Hyderabad. KHALEEL AHMAD is an Assistant Professor in the Department of Computer Science & Information Technology at Maulana Azad National Urdu University, Hyderabad, India. He holds a PhD in Computer Science & Engineering. He has published more than 25 papers in refereed journals and conferences as well as edited two books. KHAIROL AMALI BIN AHMAD obtained a BSc in Electrical Engineering in 1992 from the United States Military Academy, West Point, MSc in Military Electronic Systems Engineering in 1999 from Cranfield University, England, and PhD from ISAE-SUPAERO, France in 2015. Currently, he is the Dean of the Engineering Faculty at the National Defense University of Malaysia. Preface xixSECTION 1: THEORETICAL FUNDAMENTALS 11 MATHEMATICAL FOUNDATION 3Afroz and Basharat Hussain1.1 Concept of Linear Algebra 31.1.1 Introduction 31.1.2 Vector Spaces 51.1.3 Linear Combination 61.1.4 Linearly Dependent and Independent Vectors 71.1.5 Linear Span, Basis and Subspace 81.1.6 Linear Transformation (or Linear Map) 91.1.7 Matrix Representation of Linear Transformation 101.1.8 Range and Null Space of Linear Transformation 131.1.9 Invertible Linear Transformation 151.2 Eigenvalues, Eigenvectors, and Eigendecomposition of a Matrix 151.2.1 Characteristics Polynomial 161.2.1.1 Some Results on Eigenvalue 161.2.2 Eigendecomposition 181.3 Introduction to Calculus 201.3.1 Function 201.3.2 Limits of Functions 211.3.2.1 Some Properties of Limits 221.3.2.2 1nfinite Limits 251.3.2.3 Limits at Infinity 261.3.3 Continuous Functions and Discontinuous Functions 261.3.3.1 Discontinuous Functions 271.3.3.2 Properties of Continuous Function 271.3.4 Differentiation 28References 292 THEORY OF PROBABILITY 31Parvaze Ahmad Dar and Afroz2.1 Introduction 312.1.1 Definition 312.1.1.1 Statistical Definition of Probability 312.1.1.2 Mathematical Definition of Probability 322.1.2 Some Basic Terms of Probability 322.1.2.1 Trial and Event 322.1.2.2 Exhaustive Events (Exhaustive Cases) 332.1.2.3 Mutually Exclusive Events 332.1.2.4 Equally Likely Events 332.1.2.5 Certain Event or Sure Event 332.1.2.6 Impossible Event or Null Event (ϕ) 332.1.2.7 Sample Space 342.1.2.8 Permutation and Combination 342.1.2.9 Examples 352.2 Independence in Probability 382.2.1 Independent Events 382.2.2 Examples: Solve the Following Problems 382.3 Conditional Probability 412.3.1 Definition 412.3.2 Mutually Independent Events 422.3.3 Examples 422.4 Cumulative Distribution Function 432.4.1 Properties 442.4.2 Example 442.5 Baye’s Theorem 462.5.1 Theorem 462.5.1.1 Examples 472.6 Multivariate Gaussian Function 502.6.1 Definition 502.6.1.1 Univariate Gaussian (i.e., One Variable Gaussian) 502.6.1.2 Degenerate Univariate Gaussian 512.6.1.3 Multivariate Gaussian 51References 513 CORRELATION AND REGRESSION 53Mohd. Abdul Haleem Rizwan3.1 Introduction 533.2 Correlation 543.2.1 Positive Correlation and Negative Correlation 543.2.2 Simple Correlation and Multiple Correlation 543.2.3 Partial Correlation and Total Correlation 543.2.4 Correlation Coefficient 553.3 Regression 573.3.1 Linear Regression 643.3.2 Logistic Regression 643.3.3 Polynomial Regression 653.3.4 Stepwise Regression 663.3.5 Ridge Regression 673.3.6 Lasso Regression 673.3.7 Elastic Net Regression 683.4 Conclusion 68References 69SECTION 2: BIG DATA AND PATTERN RECOGNITION 714 DATA PREPROCESS 73Md. Sharif Hossen4.1 Introduction 734.1.1 Need of Data Preprocessing 744.1.2 Main Tasks in Data Preprocessing 754.2 Data Cleaning 774.2.1 Missing Data 774.2.2 Noisy Data 784.3 Data Integration 804.3.1 χ2 Correlation Test 824.3.2 Correlation Coefficient Test 824.3.3 Covariance Test 834.4 Data Transformation 834.4.1 Normalization 834.4.2 Attribute Selection 854.4.3 Discretization 864.4.4 Concept Hierarchy Generation 864.5 Data Reduction 884.5.1 Data Cube Aggregation 884.5.2 Attribute Subset Selection 904.5.3 Numerosity Reduction 914.5.4 Dimensionality Reduction 954.6 Conclusion 101Acknowledgements 101References 1015 BIG DATA 105R. Chinnaiyan5.1 Introduction 1055.2 Big Data Evaluation With Its Tools 1075.3 Architecture of Big Data 1075.3.1 Big Data Analytics Framework Workflow 1075.4 Issues and Challenges 1095.4.1 Volume 1095.4.2 Variety of Data 1105.4.3 Velocity 1105.5 Big Data Analytics Tools 1105.6 Big Data Use Cases 1145.6.1 Banking and Finance 1145.6.2 Fraud Detection 1145.6.3 Customer Division and Personalized Marketing 1145.6.4 Customer Support 1155.6.5 Risk Management 1165.6.6 Life Time Value Prediction 1165.6.7 Cyber Security Analytics 1175.6.8 Insurance Industry 1185.6.9 Health Care Sector 1185.6.9.1 Big Data Medical Decision Support 1205.6.9.2 Big Data–Based Disorder Management 1205.6.9.3 Big Data–Based Patient Monitoring and Control 1205.6.9.4 Big Data–Based Human Routine Analytics 1205.6.10 Internet of Things 1215.6.11 Weather Forecasting 1215.7 Where IoT Meets Big Data 1225.7.1 IoT Platform 1225.7.2 Sensors or Devices 1235.7.3 Device Aggregators 1235.7.4 IoT Gateway 1235.7.5 Big Data Platform and Tools 1245.8 Role of Machine Learning For Big Data and IoT 1245.8.1 Typical Machine Learning Use Cases 1255.9 Conclusion 126References 1276 PATTERN RECOGNITION CONCEPTS 131Ambeshwar Kumar, R. Manikandan and C. Thaventhiran6.1 Classifier 1326.1.1 Introduction 1326.1.2 Explanation-Based Learning 1336.1.3 Isomorphism and Clique Method 1356.1.4 Context-Dependent Classification 1386.1.5 Summary 1396.2 Feature Processing 1406.2.1 Introduction 1406.2.2 Detection and Extracting Edge With Boundary Line 1416.2.3 Analyzing the Texture 1426.2.4 Feature Mapping in Consecutive Moving Frame 1436.2.5 Summary 1456.3 Clustering 1456.3.1 Introduction 1456.3.2 Types of Clustering Algorithms 1466.3.2.1 Dynamic Clustering Method 1486.3.2.2 Model-Based Clustering 1486.3.3 Application 1496.3.4 Summary 1506.4 Conclusion 151References 151SECTION 3: MACHINE LEARNING: ALGORITHMS & APPLICATIONS 1537 MACHINE LEARNING 155Elham Ghanbari and Sara Najafzadeh7.1 History and Purpose of Machine Learning 1557.1.1 History of Machine Learning 1557.1.1.1 What is Machine Learning? 1567.1.1.2 When the Machine Learning is Needed? 1577.1.2 Goals and Achievements in Machine Learning 1587.1.3 Applications of Machine Learning 1587.1.3.1 Practical Machine Learning Examples 1597.1.4 Relation to Other Fields 1617.1.4.1 Data Mining 1617.1.4.2 Artificial Intelligence 1627.1.4.3 Computational Statistics 1627.1.4.4 Probability 1637.1.5 Limitations of Machine Learning 1637.2 Concept of Well-Defined Learning Problem 1647.2.1 Concept Learning 1647.2.1.1 Concept Representation 1667.2.1.2 Instance Representation 1677.2.1.3 The Inductive Learning Hypothesis 1677.2.2 Concept Learning as Search 1677.2.2.1 Concept Generality 1687.3 General-to-Specific Ordering Over Hypotheses 1697.3.1 Basic Concepts: Hypothesis, Generality 1697.3.2 Structure of the Hypothesis Space 1697.3.2.1 Hypothesis Notations 1697.3.2.2 Hypothesis Evaluations 1707.3.3 Ordering on Hypotheses: General to Specific 1707.3.3.1 Most Specific Generalized 1717.3.3.2 Most General Specialized 1737.3.3.3 Generalization and Specialization Operators 1737.3.4 Hypothesis Space Search by Find-S Algorithm 1747.3.4.1 Properties of the Find-S Algorithm 1767.3.4.2 Limitations of the Find-S Algorithm 1767.4 Version Spaces and Candidate Elimination Algorithm 1777.4.1 Representing Version Spaces 1777.4.1.1 General Boundary 1787.4.1.2 Specific Boundary 1787.4.2 Version Space as Search Strategy 1797.4.3 The List-Eliminate Method 1797.4.4 The Candidate-Elimination Method 1807.4.4.1 Example 1817.4.4.2 Convergence of Candidate-Elimination Method 1837.4.4.3 Inductive Bias for Candidate-Elimination 1847.5 Concepts of Machine Learning Algorithm 1857.5.1 Types of Learning Algorithms 1857.5.1.1 Incremental vs. Batch Learning Algorithms 1867.5.1.2 Offline vs. Online Learning Algorithms 1887.5.1.3 Inductive vs. Deductive Learning Algorithms 1897.5.2 A Framework for Machine Learning Algorithms 1897.5.2.1 Training Data 1907.5.2.2 Target Function 1907.5.2.3 Construction Model 1917.5.2.4 Evaluation 1917.5.3 Types of Machine Learning Algorithms 1947.5.3.1 Supervised Learning 1967.5.3.2 Unsupervised Learning 1987.5.3.3 Semi Supervised Learning 2007.5.3.4 Reinforcement Learning 2007.5.3.5 Deep Learning 2027.5.4 Types of Machine Learning Problems 2037.5.4.1 Classification 2047.5.4.2 Clustering 2047.5.4.3 Optimization 2057.5.4.4 Regression 205Conclusion 205References 2068 PERFORMANCE OF SUPERVISED LEARNING ALGORITHMS ON MULTI-VARIATE DATASETS 209Asif Iqbal Hajamydeen and Rabab Alayham Abbas Helmi8.1 Introduction 2098.2 Supervised Learning Algorithms 2108.2.1 Datasets and Experimental Setup 2118.2.2 Data Treatment/Preprocessing 2128.3 Classification 2128.3.1 Support Vector Machines (SVM) 2138.3.2 Naive Bayes (NB) Algorithm 2148.3.3 Bayesian Network (BN) 2148.3.4 Hidden Markov Model (HMM) 2158.3.5 K-Nearest Neighbour (KNN) 2168.3.6 Training Time 2168.4 Neural Network 2178.4.1 Artificial Neural Networks Architecture 2198.4.2 Application Areas 2228.4.3 Artificial Neural Networks and Time Series 2248.5 Comparisons and Discussions 2258.5.1 Comparison of Classification Accuracy 2258.5.2 Forecasting Efficiency Comparison 2268.5.3 Recurrent Neural Network (RNN) 2268.5.4 Backpropagation Neural Network (BPNN) 2288.5.5 General Regression Neural Network 2298.6 Summary and Conclusion 230References 2319 UNSUPERVISED LEARNING 233M. Kumara Swamy and Tejaswi Puligilla9.1 Introduction 2339.2 Related Work 2349.3 Unsupervised Learning Algorithms 2359.4 Classification of Unsupervised Learning Algorithms 2389.4.1 Hierarchical Methods 2389.4.2 Partitioning Methods 2399.4.3 Density-Based Methods 2429.4.4 Grid-Based Methods 2459.4.5 Constraint-Based Clustering 2459.5 Unsupervised Learning Algorithms in ML 2469.5.1 Parametric Algorithms 2469.5.2 Non-Parametric Algorithms 2469.5.3 Dirichlet Process Mixture Model 2479.5.4 X-Means 2489.6 Summary and Conclusions 248References 24810 SEMI-SUPERVISED LEARNING 251Manish Devgan, Gaurav Malik and Deepak Kumar Sharma10.1 Introduction 25210.1.1 Semi-Supervised Learning 25210.1.2 Comparison With Other Paradigms 25510.2 Training Models 25710.2.1 Self-Training 25710.2.2 Co-Training 25910.3 Generative Models—Introduction 26110.3.1 Image Classification 26410.3.2 Text Categorization 26610.3.3 Speech Recognition 26810.3.4 Baum-Welch Algorithm 26810.4 S3VMs 27010.5 Graph-Based Algorithms 27410.5.1 Mincut 27510.5.2 Harmonic 27610.5.3 Manifold Regularization 27710.6 Multiview Learning 27710.7 Conclusion 278References 27911 REINFORCEMENT LEARNING 281Amandeep Singh Bhatia, Mandeep Kaur Saggi, Amit Sundas and Jatinder Ashta11.1 Introduction: Reinforcement Learning 28111.1.1 Elements of Reinforcement Learning 28311.2 Model-Free RL 28411.2.1 Q-Learning 28511.2.2 R-Learning 28611.3 Model-Based RL 28711.3.1 SARSA Learning 28911.3.2 Dyna-Q Learning 29011.3.3 Temporal Difference 29111.3.3.1 TD(0) Algorithm 29211.3.3.2 TD(1) Algorithm 29311.3.3.3 TD(λ) Algorithm 29411.3.4 Monte Carlo Method 29411.3.4.1 Monte Carlo Reinforcement Learning 29611.3.4.2 Monte Carlo Policy Evaluation 29611.3.4.3 Monte Carlo Policy Improvement 29811.4 Conclusion 298References 29912 APPLICATION OF BIG DATA AND MACHINE LEARNING 305Neha Sharma, Sunil Kumar Gautam, Azriel A. Henry and Abhimanyu Kumar12.1 Introduction 30612.2 Motivation 30712.3 Related Work 30812.4 Application of Big Data and ML 30912.4.1 Healthcare 30912.4.2 Banking and Insurance 31212.4.3 Transportation 31412.4.4 Media and Entertainment 31612.4.5 Education 31712.4.6 Ecosystem Conservation 31912.4.7 Manufacturing 32112.4.8 Agriculture 32212.5 Issues and Challenges 32412.6 Conclusion 326References 326SECTION 4: MACHINE LEARNING’S NEXT FRONTIER 33513 TRANSFER LEARNING 337Riyanshi Gupta, Kartik Krishna Bhardwaj and Deepak Kumar Sharma13.1 Introduction 33813.1.1 Motivation, Definition, and Representation 33813.2 Traditional Learning vs. Transfer Learning 33813.3 Key Takeaways: Functionality 34013.4 Transfer Learning Methodologies 34113.5 Inductive Transfer Learning 34213.6 Unsupervised Transfer Learning 34413.7 Transductive Transfer Learning 34613.8 Categories in Transfer Learning 34713.9 Instance Transfer 34813.10 Feature Representation Transfer 34913.11 Parameter Transfer 34913.12 Relational Knowledge Transfer 35013.13 Relationship With Deep Learning 35113.13.1 Transfer Learning in Deep Learning 35113.13.2 Types of Deep Transfer Learning 35213.13.3 Adaptation of Domain 35213.13.4 Domain Confusion 35313.13.5 Multitask Learning 35413.13.6 One-Shot Learning 35413.13.7 Zero-Shot Learning 35513.14 Applications: Allied Classical Problems 35513.14.1 Transfer Learning for Natural Language Processing 35613.14.2 Transfer Learning for Computer Vision 35613.14.3 Transfer Learning for Audio and Speech 35713.15 Further Advancements and Conclusion 357References 358SECTION 5: HANDS-ON AND CASE STUDY 36114 HANDS ON MAHOUT—MACHINE LEARNING TOOLUma N. Dulhare and Sheikh Gouse14.1 Introduction to Mahout 36314.1.1 Features 36614.1.2 Advantages 36614.1.3 Disadvantages 36614.1.4 Application 36614.2 Installation Steps of Apache Mahout Using Cloudera 36714.2.1 Installation of VMware Workstation 36714.2.2 Installation of Cloudera 36814.2.3 Installation of Mahout 38314.2.4 Installation of Maven 38414.2.5 Testing Mahout 38614.3 Installation Steps of Apache Mahout Using Windows 10 38614.3.1 Installation of Java 38614.3.2 Installation of Hadoop 38714.3.3 Installation of Mahout 38714.3.4 Installation of Maven 38714.3.5 Path Setting 38814.3.6 Hadoop Configuration 39114.4 Installation Steps of Apache Mahout Using Eclipse 39514.4.1 Eclipse Installation 39514.4.2 Installation of Maven Through Eclipse 39614.4.3 Maven Setup for Mahout Configuration 39914.4.4 Building the Path- 40214.4.5 Modifying the pom.xml File 40514.4.6 Creating the Data File 40714.4.7 Adding External Jar Files 40814.4.8 Creating the New Package and Classes 41014.4.9 Result 41114.5 Mahout Algorithms 41214.5.1 Classification 41214.5.2 Clustering 41314.5.3 Recommendation 41514.6 Conclusion 418References 41815 HANDS-ON H2O MACHINE LEARNING TOOL 423Uma N. Dulhare, Azmath Mubeen and Khaleel Ahmed15.1 Introduction 42415.2 Installation 42515.2.1 The Process of Installation 42515.3 Interfaces 43115.4 Programming Fundamentals 43215.4.1 Data Manipulation 43215.4.1.1 Data Types 43215.4.1.2 Data Import 43515.4.2 Models 43615.4.2.1 Model Training 43615.4.3 Discovering Aspects 43715.4.3.1 Converting Data Frames 43715.4.4 H2O Cluster Actions 43815.4.4.1 H2O Key Value Retrieval 43815.4.4.2 H2O Cluster Connection 43815.4.5 Commands 43915.4.5.1 Cluster Information 43915.4.5.2 General Data Operations 44115.4.5.3 String Manipulation Commands 44215.5 Machine Learning in H2O 44215.5.1 Supervised Learning 44215.5.2 Unsupervised Learning 44315.6 Applications of H2O 44315.6.1 Deep Learning 44315.6.2 K-Fold Cross-Authentication or Validation 44815.6.3 Stacked Ensemble and Random Forest Estimator 45015.7 Conclusion 452References 45316 CASE STUDY: INTRUSION DETECTION SYSTEM USING MACHINE LEARNING 455Syeda Hajra Mahin, Fahmina Taranum and Reshma Nikhat16.1 Introduction 45616.1.1 Components Used to Design the Scenario Include 45616.1.1.1 Black Hole 45616.1.1.2 Intrusion Detection System 45716.1.1.3 Components Used From MATLAB Simulator 45816.2 System Design 46516.2.1 Three Sub-Network Architecture 46516.2.2 Using Classifiers of MATLAB 46516.3 Existing Proposals 46716.4 Approaches Used in Designing the Scenario 46916.4.1 Algorithm Used in QualNet 46916.4.2 Algorithm Applied in MATLAB 47116.5 Result Analysis 47116.5.1 Results From QualNet 47116.5.1.1 Deployment 47116.5.1.2 Detection 47216.5.1.3 Avoidance 47316.5.1.4 Validation of Conclusion 47316.5.2 Applying Results to MATLAB 47316.5.2.1 K-Nearest Neighbor 47516.5.2.2 SVM 47716.5.2.3 Decision Tree 47716.5.2.4 Naive Bayes 47916.5.2.5 Neural Network 47916.6 Conclusion 484References 48417 INCLUSION OF SECURITY FEATURES FOR IMPLICATIONS OF ELECTRONIC GOVERNANCE ACTIVITIES 487Prabal Pratap and Nripendra Dwivedi17.1 Introduction 48717.2 Objective of E-Governance 49117.3 Role of Identity in E-Governance 49317.3.1 Identity 49317.3.2 Identity Management and its Buoyancy Against Identity Theft in E-Governance 49417.4 Status of E-Governance in Other Countries 49617.4.1 E-Governance Services in Other Countries Like Australia and South Africa 49617.4.2 Adaptation of Processes and Methodology for Developing Countries 49617.4.3 Different Programs Related to E-Governance 49917.5 Pros and Cons of E-Governance 50117.6 Challenges of E-Governance in Machine Learning 50217.7 Conclusion 503References 503Index 505
Electronics for Beginners
Jump start your journey with electronics! If you’ve thought about getting into electronics, but don’t know where to start, this book gives you the information you need. Starting with the basics of electricity and circuits, you'll be introduced to digital electronics and microcontrollers, capacitors and inductors, and amplification circuits – all while gaining the basic tools and information you need to start working with low-power electronics.Electronics for Beginners walks the fine line of focusing on projects-based learning, while still keeping electronics front and center. You'll learn the mathematics of circuits in an uncomplicated fashion and see how schematics map on to actual breadboards. Written for the absolute beginner, this book steers clear of being too math heavy, giving readers the key information they need to get started on their electronics journey.WHAT YOU’LL LEARN* Review the basic “patterns” of resistor usage—pull up, pull down, voltage divider, and current limiter* Understand the requirements for circuits and how they are put together* Read and differentiate what various parts of the schematics do* Decide what considerations to take when choosing components* Use all battery-powered circuits, so projects are safeWHO THIS BOOK IS FORMakers, students, and beginners of any age interested in getting started with electronics. Jonathan Bartlett is a software developer, researcher, and writer. His first book, Programming from the Ground Up, has been required reading in computer science programs from DeVry to Princeton. He has been the sole or lead author for eight books on topics ranging from computer programming to calculus. He is a technical lead for ITX, where his specialty is getting stuck projects unstuck. Jonathan regularly writes for the blog MindMatters.ai. Chapter 1: IntroductionChapter 2. Before We BeginPart I – Basic ConceptsChapter 3. Dealing with UnitsChapter 4. What is Electricity?Chapter 5. Voltage and ResistanceChapter 6. Your First CircuitChapter 7. Constructing and Testing CircuitsChapter 8. Analyzing Series and Parallel CircuitsChapter 9. Diodes and How to Use ThemChapter 10. Basic Resistor Circuit PatternsChapter 11. Understanding PowerPart II – Digital Electronics and MicrocontrollersChapter 12. Integrated Circuits and Resistive SensorsChapter 13. Using Logic ICsChapter 14. Introduction to MicrocontrollersChapter 15. Building Projects with ArduinoChapter 16. Analog Input and Output on an ArduinoPart III – Capacitors and InductorsChapter 17. Capacitor IntroductionChapter 18. Capacitors as TimersChapter 19. Introduction to Oscillating CircuitsChapter 20. Producing Sound with OscillationChapter 21. InductorsChapter 22. Inductors and Capacitors in CircuitsChapter 23. Reactance and ImpedancePart IV – Amplification CircuitsChapter 24. DC MotorsChapter 25. Amplifying Power with TransistorsChapter 26. Transistor Voltage AmplifiersChapter 27. Examining Partial CircuitsChapter 28. Going FurtherAppendicesA. GlossaryB. Electronics SymbolsC. Integrated Circuit Naming ConventionsD. Electronics Equations and Where They Come FromE. Simplified Datasheets for Common Devices
Neue Möglichkeiten für die Motorsteuergeräte-Software durch Car-to-Cloud-Vernetzung
Lars Hagen zeigt Anwendungsszenarien auf, wie „Connected Car“ und insbesondere Vernetzung durch Car-to-Cloud in der Softwareentwicklung sowie im Serieneinsatz für die Motorsteuerung eingesetzt werden können. Dabei legt der Autor ein Augenmerk auf Themen, die über das reine Datensammeln hinausgehen und sowohl den Up- als auch Download von Daten am Fahrzeug miteinbeziehen. Die externe Rechenleistung auf einer Cloud findet ebenso Berücksichtigung wie die limitierte Datenrate des Fahrzeugbusses und des Mobilfunks. Der Autor: Lars Hagen promovierte am Institut für Fahrzeugtechnik Stuttgart (IFS)der Universität Stuttgart am Lehrstuhl für Fahrzeugantriebe. Außerdem arbeitet er als Software-Funktionsentwickler im Bereich der Motorsteuerung bei einem deutschen Automobilzulieferer. Anwendungsfelder für Car-to-Cloud im Bereich der Motorsteuerung.- Anwendungsbeispiele Applikation und Diagnose für Car-to-Cloud in der Motorsteuergeräte-Software.
CompTIA CySA+ Practice Tests
EFFICIENTLY PREPARE YOURSELF FOR THE DEMANDING COMPTIA CYSA+ EXAMCompTIA CySA+ Practice Tests: Exam CS0-002, 2nd Edition offers readers the fastest and best way to prepare for the CompTIA Cybersecurity Analyst exam. With five unique chapter tests and two additional practice exams for a total of 1000 practice questions, this book covers topics including:* Threat and Vulnerability Management* Software and Systems Security * Security Operations and Monitoring * Incident Response* Compliance and AssessmentThe new edition of CompTIA CySA+ Practice Tests is designed to equip the reader to tackle the qualification test for one of the most sought-after and in-demand certifications in the information technology field today.The authors are seasoned cybersecurity professionals and leaders who guide readers through the broad spectrum of security concepts and technologies they will be required to master before they can achieve success on the CompTIA CySA exam. The book also tests and develops the critical thinking skills and judgment the reader will need to demonstrate on the exam.MIKE CHAPPLE, PHD, CYSA+, CISSP, is Teaching Professor of IT, Analytics, and Operations at the University of Notre Dame. He's a cybersecurity professional and educator with over 20 years of experience. Mike provides cybersecurity certification resources at his website, CertMike.com. DAVID SEIDL, CYSA+, CISSP, PENTEST+, is Vice President for Information Technology and CIO at Miami University. David co-led Notre Dame's move to the cloud, and has written multiple cybersecurity certification books. Introduction xviiChapter 1 Domain 1.0: Threat and Vulnerability Management 1Chapter 2 Domain 2.0: Software and Systems Security 105Chapter 3 Domain 3.0: Security Operations and Monitoring 151Chapter 4 Domain 4.0: Incident Response 207Chapter 5 Domain 5.0: Compliance and Assessment 265Chapter 6 Practice Exam 1 289Chapter 7 Practice Exam 2 315APPENDIX ANSWERS TO REVIEW QUESTIONS 347Answers to Chapter 1: Domain 1.0: Threat and Vulnerability Management 348Answers to Chapter 2: Domain 2.0: Software and Systems Security 381Answers to Chapter 3: Domain 3.0: Security Operations and Monitoring 403Answers to Chapter 4: Domain 4.0: Incident Response 425Answers to Chapter 5: Domain 5.0: Compliance and Assessment 450Answers to Chapter 6: Practice Exam 1 461Answers to Chapter 7: Practice Exam 2 470Index 481
Android Apps Security
Gain the information you need to design secure, useful, high-performing apps that expose end-users to as little risk as possible. This book shows you how to best design and develop Android apps with security in mind: explore concepts that you can use to secure apps and how you can use and incorporate these security features into your apps.WHAT YOU WILL LEARN* Identify data that should be secured* Use the Android APIs to ensure confidentiality and integrity of data* Build secure apps for the enterprise* Implement Public Key Infrastructure and encryption APIs in apps* Master owners, access control lists, and permissions to allow user control over app properties* Manage authentication, transport layer encryption, and server-side securityWHO THIS BOOK IS FORExperienced Android app developers.Sheran Gunasekera is a security researcher and software developer with more than 13 years of information security experience. He is director of research and development for ZenConsult Pte. Ltd., where he oversees security research in both the personal computer and mobile device platforms. Sheran has been very active in BlackBerry and mobile Java security research and was the author of the whitepaper that revealed the inner workings of the first corporate-sanctioned malware application deployed to its subscribers by the UAE telecommunications operator Etisalat. He has spoken at many security conferences in the Middle East, Europe and Asia Pacific regions and also provides training on malware analysis for mobile devices and secure software development for both web and mobile devices. He also writes articles and publishes research on his security-related blog.1. Introduction.- 2. Recap of Secure Development Principles.- 3. Changes in Security Architecture.- 4. Security when Building Apps to Scale.- 5. Testing the Security of Your App (this covers pentesting and bug bounties).- 6. The Toolbag.- 7. Rooting an Android phone. 8. Looking at your App's Data through a Root shell.- Bypassing SSL Pinning (the holy grail of hacking apps).- 10. Reverse Engineering Android Apps.- 11. Incident Response.
Practical Bootstrap
Learn to use one of the most popular CSS frameworks and build mobile-friendly web pages. Used for numerous websites and applications, Bootstrap is a key tool for modern web development.You will explore the grid system and then be introduced to the power of Bootstrap in practical projects. You’ll make navigation bars, use themes and styling, create and manipulate cover pages, admin dashboards, forms, and modal dialogs. You’ll learn to use Scrollspy and create tooltips and popovers.Today's web is responsive and Bootstrap continues to be at the forefront with web professionals. Learn by doing with Practical Bootstrap today.WHAT YOU WILL LEARN* Review how the grid system applies to Bootstrap* Create stunning cover pages that encompass a large background image* Build an admin dashboard page that changes its layout according to a device’s display width* Work with the modal HTML markup and its main parts* Customize modal behavior by setting various options using JavaScript* Integrate Bootstrap JavaScript libraries with your own HTML page* Add scroll spying functionality to your long-content pages* Adjust the offset and activation point of the scroll sectionsWHO THIS BOOK IS FORAnyone who wants to learn how to use Bootstrap. You should have knowledge of HTML, CSS and basic JavaScript.PANOS MATSINOPOULOS loves developing programs, both for web browsers and for mobile apps. He has been doing that for the last 25 years and has developed numerous applications. He also loves writing books, blogging and teaching computer programming. He has organized a lot of programming classes for kids, adults and elderly people.PRACTICAL BOOTSTRAP1. Getting Started2. Advanced Grid Techniques3. Target Project 14. Theme Reference: Part 15. Theme Reference: Part 26. Cover Page Project7. Admin Dashboard8. Forms9. Modal Dialogs10. ScrollSpy11. Tooltips and Popovers
Demystifying Azure AI
Explore artificial intelligence offerings by Microsoft Azure, along with its other services. This book will help you implement AI features in various Azure services to help build your organization and customers.The book starts by introducing you to the Azure Cognitive Search service to create and use an application. You then will learn the built-in automatic tuning intelligence mechanism in Azure SQL Database. This is an important feature you can use to enable Azure SQL Database to optimize the performance of your queries. Next, you will go through AI services with Azure Integration Platform service and Azure Logic Apps to build a modern intelligent workflow in your application. Azure functions are discussed as a part of its server-less feature. The book concludes by teaching you how to work with Power Automate to analyze your business workflow.After reading this book, you will be able to understand and work with different Azure Cognitive Services in AI.WHAT YOU WILL LEARN* Get started with Azure Cognitive Search service* Use AI services with Low Code – Power Automate* Use AI services with Azure Integration services* Use AI services with Azure Server-less offerings* Use automatic tuning in Azure SQL databaseWHO THIS BOOK IS FORAspiring Azure and AI professionalsKASAM SHAIKH, Cloud advocate, is a seasoned professional having 13 years of demonstrated industry experience working as Cloud Architect with one of the leading IT companies in Mumbai, INDIA. He is recognized as MVP by an online Tech community, also a Global AzureAI Speaker, and author of two best-selling books on Microsoft Azure and AI. He is the founder of Azure INDIA (az-INDIA) community, DearAzure, which is an online community for learning AzureAI. He owns a YouTube channel and shares his experience over his website www.kasamshaikh.comCHAPTER 1: AZURE SEARCH WITH AICHAPTER GOALS: In this chapter reader will learn about the only AI powered Cloud search offering by Microsoft, Azure Cognitive Search service. Using Cognitive Services with Azure Search with your web apps. Skip hiring search experts who know what an inverted index is. Don't worry about distributed systems expertise to scale your service to handle large amount of data. And forget about setting up, owning and managing the infrastructure. Let Azure Search do it all for you. When and How to use with step by step Demo creating an application.CHAPTER 2: AI AND BACKEND SERVICE OFFERINGCHAPTER GOALS: In this Chapter reader will learn the built-in automatic tuning intelligence mechanism in Azure SQL Database. Automatic tuning is a fully managed intelligent performance service that uses built-in intelligence to continuously monitor queries executed on a database, and it automatically improves their performance. Automatic tuning in Azure SQL Database might be one of the most important features that one can enable on Azure SQL Database to optimize the performance of your queries.CHAPTER 3: AI SERVICES WITH AZURE IPAASCHAPTER GOALS: In this Chapter, readers will explore the Azure Cognitive services, that can be leverage with Azure Integration Platform service, Azure Logic Apps. This gives developer a power to infuse an intelligent workflow in application. Will have a step by step demo to create a workflow with business use-case.CHAPTER 4: AI SERVICES WITH SERVERLESS OFFERINGCHAPTER GOALS: In this Chapter, readers will explore the Azure Cognitive services offerings, that can be leverage with Azure Serverless Offerings, Azure Functions. This gives developer a power to extend the scalable Functions with a smart and intelligent functional implementation in application. Will have a step by step demo on how to work with Cognitive extensions.CHAPTER 5: AI WITH NO CODECHAPTER GOALS: In this Chapter, readers will explore the Azure Cognitive services offerings, that can be leverage with No Code – PowerAutomate. This gives business an easy hand to present with a smart analysis in business workflow. Will have a step by step demo to create a workflow.
Understanding Hybrid Environments in SharePoint 2019
Design and develop hybrid scenarios with SharePoint Online and SharePoint Server 2019. This book will help you build hybrid environments from scratch, and covers basic to advanced scenarios.The book takes you through the setup and configuration of SharePoint 2019 on virtual machines in Microsoft Azure. It gives you an overview of the features of the SharePoint Server 2019 User Experience. Integration of SP 2019 with PowerApps and Microsoft Flow is covered, along with Power BI reporting. You will learn to manage the data gateway and understand how to publish the Power BI Report. Configuration of the SP 2019 home page is explained and you learn how to enable a hybrid experience via the SP Office 365 link setting. You will know how to register a public domain in Office 365 and configure it for SP hybrid. Cloud hybrid search with the PowerShell script approach is discussed as well as SP 2019 hybrid Self-Service Site Creation. Configuration of the hybrid taxonomy, content types, and SharePoint framework development with SP 2019 are covered. And the book ends on using the office online server for SharePoint Server 2019 along with SP Server 2019 upgrade scenarios.After reading this book, you will be able to build and manage different hybrid environments with SharePoint Online and SharePoint Server 2019.WHAT WILL YOU LEARN* Enlarge your hybrid terminology* Get familiar with the new and improved features of SharePoint Server 2019* Develop a hybrid experience with SharePoint Server 2019* Enable hybrid search* Understand the on-premises data gateway* Configure and integrate SharePoint Server 2019 with Power Platform, including Power BI, Power Apps, and Power AutomateWHO IS THIS BOOK FORSharePoint professionals who want to configure hybrid solutions in SharePointNANDDEEP SADANAND NACHAN is a Microsoft MVP (Office Apps and Services) and technology architect with experience in Microsoft technologies, including SharePoint, MS Azure, and .NET. He has been working with SharePoint for the last 15+ years and has worked with SharePoint versions starting with SharePoint 2007 (MOSS). He wrote the book Mastering SharePoint Framework. He organizes and speaks at industry seminars, conferences, and community events, including SPS, Global Microsoft 365 Developer Bootcamp, and Global Power Platform Bootcamp. He is an active contributor to Office 365 Dev Patterns and Practices on GitHub and writes articles on his blog. He is also a creative and technically sound photographer with experience in custom and specialized photography.SMITA SADANAND NACHAN is a SharePoint professional with 12+ years of experience in design, implementation, configuration, and maintenance of large-scale projects. She focuses on architectural design and implementation, website design and development, and complete application development cycles, with an intense focus on SharePoint and Office 365. She is a frequent speaker at various community events, including SPS, Global Microsoft 365 Developer Bootcamp, and Global Power Platform Bootcamp. She is a travel, fashion, and food blogger.CHAPTER 1, SETUP SHAREPOINT 2019 DEVELOPER VM IN MS AZUREChapter Goal:· Hardware and Software Requirements· Microsoft Azure VM Setup Options· Setup Server 2019 Trial VM in MS AzureCHAPTER 2, CONFIGURE SHAREPOINT SERVER 2019Chapter Goal:· Setup Active Directory· Setup AD Users· Configure SharePoint 2019 with AutoSPInstaller· Convert SharePoint Trial to RTM LicenseCHAPTER 3, SHAREPOINT SERVER 2019 USER EXPERIENCEChapter Goal:· SharePoint 2019 User Experience· Modern User ExperienceCHAPTER 4, SHAREPOINT SERVER 2019 FEATURES OVERVIEWChapter Goal:· SharePoint 2019 Focus Areas· Improved Features· Features Leaving Behind / Does Not make to On-PremiseCHAPTER 5, SHAREPOINT 2019 POWERAPPS INTEGRATIONChapter Goal:· PowerApps Overview· Install Gateway· Build PowerApps Canvas App· Gateway Windows Service· Publish the PowerApps AppCHAPTER 6, SHAREPOINT 2019 MS FLOW INTEGRATIONChapter Goal:· MS Flow Overview· On-premises Data Gateway· Configure MS Flow Connection· Create Microsoft Flow· Test the MS FlowCHAPTER 7, SHAREPOINT 2019 - POWER BI REPORTINGChapter Goal:· Power BI Overview· On-premises Data Gateway· Manage Gateway· Create Data Source· Install Personal Gateway· Power BI Reports· Publish the Power BI Report· Scheduled Refresh of DatasetsCHAPTER 8, SHAREPOINT 2019 - CONFIGURE HOME PAGEChapter Goal:· SharePoint Home· Features of Home Page· Supporting ServicesCHAPTER 9, SHAREPOINT 2019 - ENABLE HYBRID EXPERIENCEChapter Goal:· SharePoint Hybrid· Enable Hybrid Experience· SPO365 Link Settings Overview· Re-run SharePoint Hybrid Configuration Wizard· Configuration SummaryCHAPTER 10. REGISTER PUBLIC DOMAIN WITH OFFICE 365Chapter Goal:· Need for Public Domain· Buy a new domain· Use an existing domain· Set Default Office 365 Domain· Edit Active UsersCHAPTER 11, CONFIGURE OFFICE 365 FOR SHAREPOINT HYBRID· Prerequisites· Add UPN suffix to the Local AD· Manage Office 365 Directory from Azure Active Directory· Verify User Sync· Assign Licenses to UsersCHAPTER 12, SHAREPOINT 2019 - CLOUD HYBRID SEARCHChapter Goal:· Cloud Hybrid Search Overview· Enable Hybrid Search Experience· PowerShell Script Approach· On-Premises Cloud Search Service Application Configuration· Verify Hybrid SearchCHAPTER 13, SHAREPOINT 2019 HYBRID SELF-SERVICE SITE CREATIONChapter Goal:· Hybrid Self-Service Site Creation· Enable Hybrid Experience· Create Site Collection Page· Enable Hybrid Self-Service Site Creation from SharePoint Hybrid Configuration Wizard· Manage hybrid self-service site creation· Test Hybrid Self-Service Site CreationCHAPTER 14, SHAREPOINT 2019 CONFIGURE HYBRID TAXONOMY· SharePoint Hybrid Taxonomy· Copy SharePoint Server Taxonomy to SharePoint Online· Configure hybrid SharePoint taxonomy· Timer Job· Verify Taxonomy Groups ReplicationCHAPTER 15, SHAREPOINT 2019 CONFIGURE HYBRID CONTENT TYPESChapter Goal:· SharePoint Hybrid Content Types· Content Type Hub in SharePoint Online· Copy SharePoint Server Content Types to SharePoint Online· Configure hybrid SharePoint Content Types· Timer Job· Verify Content Types ReplicationCHAPTER 16, SHAREPOINT FRAMEWORK DEVELOPMENT WITH SHAREPOINT 2019· Decide Upon SharePoint Framework Version· Get SharePoint Server 2019 Ready for SPFx· Develop SharePoint Framework Web Part· Run the SPFx WebPart· TroubleshootingCHAPTER 17, OFFICE ONLINE SERVER FOR SHAREPOINT SERVER 2019Chapter Goal:· Introduction to Office Online Server· Download and Install Office Online Server· Install Office Online Server· Setup Office Online Farm· Verify the Office Online Server working· Connecting to SharePoint 2019 FarmCHAPTER 18, SHAREPOINT SERVER 2019 UPGRADE SCENARIOSChapter Goal:· Upgrade Scenarios· High Level Upgrade Scenarios
Design Thinking in Software and AI Projects
Learn the fundamentals of Design Thinking and how to apply Design Thinking techniques in defining software development and AI solutions. Design Thinking is an approach to innovation which identifies problems and generates solution ideas that can be rapidly proven through prototyping.This book provides a brief history of Design Thinking and an overview of the process. It then drills down into more detail regarding methods and tools used in a Design Thinking workshops leading to useful prototypes. Guidance is provided on:* Preparing for a Design Thinking Workshop * Uncovering potential business problems that might be solved* Prioritizing potential solutions* Identifying and characterizing stakeholders* Choosing the right prototypes for development* Limiting scope and best practices in prototype buildingThe book concludes with a discussion of best practices in operationalizing successful prototypes, and describes change management techniques critical for successful adoption. You can use the knowledge gained from reading this book to incorporate Design Thinking techniques in your software development and AI projects, and assure timely and successful delivery of solutions.WHAT YOU WILL LEARN* Gain foundational knowledge of what Design Thinking is and when to apply the technique* Discover preparation and facilitation techniques used in workshops* Know how ideas are generated and then validated through prototyping* Understand implementation best practices, including change management considerationsWHO THIS BOOK IS FORBusiness decision makers and project stakeholders as well as IT project owners who seek a method leading to fast development of successful software and AI prototypes demonstrating real business value. Also for data scientists, developers, and systems integrators who are interested in facilitating or utilizing Design Thinking workshops to drive momentum behind potential software development and AI projects.ROBERT STACKOWIAK works as an independent consultant, advisor, and author. He is a former data & artificial intelligence architect and technology business strategist at the Microsoft Technology Center in Chicago, and previously worked in similar roles at Oracle and IBM. He has conducted business discovery workshops, ideation workshops, and technology architecture sessions with many of North America’s leading-edge companies across a variety of industries and with government agencies. Bob has also spoken at numerous industry conferences internationally, served as a guest instructor at various universities, and is an author of several books. You can follow him on Twitter @rstackow and read his articles and posts on LinkedIn.TRACEY KELLY is Envisioning Lead with the Catalyst team at Microsoft. She has been leading the design thinking training through North America and Europe to help Microsoft technology-focused architects and business leadership transition and transform to customer-centric and business outcome solutions. Tracey is also on the board of the Women’s Technology Coalition and a former Women in Technology Director in Dallas. She leads design workshops and customer strategy sessions and has a long 20-year history of technology and design leadership at Fortune 500 companies to drive innovation.Chapter 1: Design Thinking Overview and History.- Chapter 2: Preparing for a Workshop.- Chapter 3: Problem Definition.- Chapter 4: Solution Definition.- Chapter 5: Prototype Creation.- Chapter 6: Production Development.- Chapter 7: Production Rollout.- Chapter 8. Appendix A: Sources.
Exploring C++20
Discover everything you need to know about C++ in a logical progression of small lessons that you can work through as quickly or as slowly as you need. This book divides C++ up into bite-sized chunks that will help you learn the language one step at a time. Fully updated to include C++20, it assumes no familiarity with C++ or any other C-based language.Exploring C++20 acknowledges that C++ can be a complicated language, so rather than baffle you with complex chapters explaining functions, classes, and statements in isolation you’ll focus on how to achieve results. By learning a little bit of this and a little of that you’ll soon have amassed enough knowledge to be writing non-trivial programs and will have built a solid foundation of experience that puts those previously baffling concepts into context.In this fully-revised third edition of Exploring C++, you’ll learn how to use the standard library early in the book. Next, you’ll work with operators, objects, and data-sources in increasingly realistic situations. Finally, you’ll start putting the pieces together to create sophisticated programs of your own design confident that you’ve built a firm base of experience from which to grow.WHAT YOU WILL LEARN* Grasp the basics, including compound statements, modules, and moreWork with custom types and see how to use them * Write useful algorithms, functions, and more* Discover the latest C++ 20 features, including concepts, modules, and ranges* Apply your skills to projects that include a fixed-point numbers and body-mass index applicationsCarry out generic programming and apply it in a practical project * Exploit multiple inheritance, traits/policies, overloaded functions, and metaprogrammingWHO THIS BOOK IS FORExperienced programmers who may have little or no experience with C++ who want an accelerated learning guide to C++20 so they can hit the ground running.Ray Lischner has a bachelor's degree in computer science from Caltech and a master's in computer science from Oregon State University. He worked as a software developer for a dozen years, at big and small companies across the US, using PL/I, C, C++, Delphi, Smalltalk, and various assembly languages on both large and small systems. He has been self-employed as a consultant, trainer, and author for the last ten years. Ray taught computer science at Oregon State University for several years and specialized in teaching introductory computer programming. He taught courses in C and C++ and software engineering.Part I: The Basics.-1. Honing your tools.-2. Reading C++ Code.-3. Integer Expressions.-4. Strings.-5. Simple Input.-6. Error Messages.-7. For Loops.-8. Formatted Output.-9. Arrays and Vectors.-10. Algorithms and Ranges.-11. Increment and Decrement.-12. Conditions and Logic.-13. Compound Statements.-14. Introduction to File I/O.-15. The Map Data Structure.-16. Type Synonyms.-17. Characters.-18. Character Categories.-19. Case-Folding.-20. Writing Functions.-21. Function Arguments.-22. Using Ranges.-23. Using Iterators.-24. Unnamed Functioins.-25. Overloading Function Names.-26. Big and Little Numbers.-27. Very Big and Very Little Numbers.-28. Documentation.- 29. Project 1: Body-Mass IndexPart II: Custom Types.-30. Custom Types.-31. Overloading Operators.-32. Custom I/O Operators.-33. Assignment and Initialization.-34. Writing Classes.- 35. More About Member Functions.-36. Access Levels.-37. Understanding Object-Oriented Programming.-38. Inheritance.-39. Virtual Functions.-40. Classes and Types.-41. Declarations and Definitions.- 42. Modules.-43. Old-Fashioned "Modules".-44. Function Objects.-45. Useful Algorithms.-46. More About Iterators.-47. Ranges, Views and Adaptors.-48. Exceptions.-49. More Operators.-50. Project 2: Fixed-Point Numbers.-Part III: Generic Programming.-51. Function Templates.-52. Class Templates.-53. Template Specialization.-54. Partial Template Specialization.-55. Template Constraints.-56. Names and Namespaces.-57. Containers.-58. Locales and Facets.-59. International Characters.-60. TextI/O.-61. Project3: Currency Type.-Part IV: Real Programming.-62. Pointers.-63. Regular Expressions.-64. Moving Data with Rvalue References.-65. Smart Pointers.-66. Files and File Names.-67. Working with Bits.-68. Enumerations.-69. Multiple Inheritance.-70. Concepts, Traits and Policies.-71. Names, Namespaces, and Templates.-72. Overloaded Functions and Operators.-73. Programming at Compile Time.-74. Project 4: Calculator.
Practical Numerical C Programming
Master the C code appropriate for numerical methods and computational modeling, including syntax, loops, subroutines, and files. Then, this hands-on book dives into financial applications using regression models, product moment correlation coefficients, and asset pricing.Next, Practical Numerical C Programming covers applications for engineering/business such as supermarket stock reordering simulation as well as flight information boards at airports and controlling a power plant. Finally, the book concludes with some physics including building simulation models for energy and pendulum motion. Along the way, you’ll learn center-of-mass calculations, Brownian motion, and more.After reading and using this book, you'll come away with pragmatic case studies of actual applications using C code at work. Source code is freely available and includes the latest C20 standard release.WHAT YOU WILL LEARN* Apply regression techniques to find the pattern for depreciation of the value of cars over a period of years* Work with the product moment correlation coefficient technique to illustrate the accuracy (or otherwise) of regression techniques* Use the past stock values of an asset to predict what its future values may be using Monte Carlo methods* Simulate the buying of supermarket stock by shoppers and check the remaining stock: if it is too low print a message to reorder the stock* Create a file of arrivals for an airport and send data to the airport’s display boards to show the current situation for the incoming flights* Simulate the patterns of particles moving in gases or solids WHO THIS BOOK IS FORProgrammers and computational modelers with at least some prior experience with programming in C as well as programming in general.Philip Joyce has 28 years experience as a software engineer – working on control of steel production, control of oil refineries, communications software (pre-Internet), office products (server software), and computer control of airports. Programming in Assembler, COBOL, Coral66, C, and C++. Mentor to new graduates in the company. He also has a MSc in computational physics (including augmented matrix techniques and Monte Carlo techniques using Fortran) - Salford University 1996. Chartered scientist, chartered physicist, member of the Institute of Physics (member of the higher education group).Chapter 1 Review of CReview of C and SDK with reference to the topics in this book.Reminds the reader of C syntax.Use loops, subroutines, file access.Create typical programs in C using SDK ExercisesPART 1 – FINANCIAL APPLICATIONSChapter 2 Regression:Use regression techniques to find the pattern for depreciation of the value of cars over a period of years.Program written will create graphical displays to illustrate the topic.ExercisesChapter 3 Product Moment Correlation Coefficient (PMCC):Use this technique to illustrate the accuracy (or otherwise) of regression techniques.ExercisesChapter 4 : Asset PricingUse the past stock values of an Asset to predict what its future values may be using Monte Carlo methods.Graphics displays to illustrate the topic.ExercisesPART 2 – ENGINEERING/INDUSTRIAL/COMMERCIAL APPLICATIONSChapter 5: Supermarket Stock Reordering SimulationCreate a file of stock for a supermarket. Simulate the buying of stock by shoppers. Check the remaining stock. If it is too low print a message to reorder the stock.ExercisesChapter 6: Flight Information Boards at AirportsCreate a file of arrivals for an airport. Send data to the airport’s display boards to show the current situation for the incoming flights. Update a flight and refresh the information to the display boards.ExercisesChapter 6 : Power Plant ControlProgram receives messages about pressures, temperatures, flow rates etc for a power plant. The program checks for values outside safety ranges and acts upon any problem values by sending messages to both the gauges and the managers responsible for them.ExercisesPART 3 – PHYSICS APPLICATIONSChapter 8 Potential Energy and Kinetic Energy SimulationUse formulas for Potential Energy and Kinetic Energy to show how one falls at the same rate as the other rises.ExercisesChapter 9 Pendulum Simulation Use formulas for the motion of a pendulum to create a graph to illustrate the mathematical relationship on a graph .ExercisesChapter 10: Centre of Mass CalculationCalculate the centre of mass of unusually-shaped objects.ExercisesChapter 11 : Brownian MotionSimulate the patterns of particles moving in gases or solids.Graphical displays to illustrate the topic.ExercisesChapter 12 Vacancy Model of Atoms Moving in SolidsDemonstrate the Vacancy Model of atoms moving in solids where they can move into empty sites within the solid. Graphical displays will show the movement of the atoms within a 2D site.ExercisesAPPENDICESA. C Programming Code GuideB. Answers to exercisesThese could be contained in an included CD which could also contain some data files the students could use in their examples
Alexa Tipps und Tricks für Dummies
Erfahren Sie, was Sie mit Alexa alles anstellen können - von der Soundanpassung des Lautsprechers mit der Equalizer-Funktion über das Freisprechen mit Drop In bis hin zum Automatisieren von Abläufen mit Routinen. So bringen Sie mit Alexa mehr Freude und mehr Intelligenz in Ihre Wohnung und Ihren Alltag. Dieses Buch zeigt Ihnen neben vielen Tipps und Tricks auch versteckte Funktionen und Top-Secrets, die nicht jeder kennt. Die Zahl der Anwendungen, auf die man mit Alexa zugreifen kann, steigt ständig: Ja, Sie können auch Ihr Smart Home über Alexa steuern. Das Buch enthält auch Hinweise zum Datenschutz. Benjy Thömmes ist Schüler und lebt in Gerolstein in der Eifel. Seit 2017 betreibt er einen eigenen Blog www.blog.yourecho.de, auf dem er regelmäßig über Alexa, den Amazon Echo und Smart-Home-Technologien schreibt.Über den Autor 9EINFÜHRUNG 19Über dieses Buch 19Törichte Annahmen über den Leser 20Was Sie nicht lesen müssen 20Wie dieses Buch aufgebaut ist 20Teil I: Im Grunde soll sie helfen 21Teil II: Mit Automatisierungen und Skills das Leben erleichtern 21Teil III: Mehr Funktionen für Suchtis 21Teil IV: Der Teil der Zehnen 21Symbole, die in diesem Buch verwendet werden 22TEIL I: IM GRUNDE SOLL SIE HELFEN 23KAPITEL 1 ALEXA, IHRE EIGENSCHAFTEN UND GRUNDEINSTELLUNGEN 25Die Bedeutung der verschiedenfarbigen Lichtringe 25Der blaue Lichtring 25Der rote Lichtring 26Der grüne Lichtring 26Der gelbe Lichtring 26Der lilafarbene Lichtring 26Die Grundeinstellungen von Alexa-Geräten 27Standort ändern 27Zeitzone ändern 28Maßeinheiten umstellen 28Sprache einstellen 28Aktivierungswort auswählen 29WLAN-Verbindung von Alexa ändern 29Gerät von Amazon-Konto abmelden 31KAPITEL 2 DER NEUE DJ: MUSIK HÖREN MIT ALEXA 33Alexa mit einem Musikdienst verbinden 33Standardmusikdienst festlegen 35Den Sound von Alexa mithilfe des Equalizers anpassen 36Konkurrenz für das Küchenradio 36Multiroom-Audio: Musik synchronauf mehreren Geräten abspielen 36Amazon Music: Musik über das Smartphone an Alexa senden 37KAPITEL 3 KALENDER: ALEXA ORGANISIERT IHREN TAG 39Kalender mit Alexa verbinden 39Den Draht zwischen Kalender und Alexa wieder trennen 41KAPITEL 4 SPRACHANTWORTEN: ALEXA ANTWORTET MAL ANDERS 43Der Alexa-Kurzmodus 43Der Alexa-Flüstermodus 44Die Geschwindigkeit ändern, in der Alexa spricht 45KAPITEL 5 IHR NEUES TELEFON HEIẞT ALEXA 47Anrufe und Nachrichten mit Alexa 47Skypen über Alexa 49Drop In 50Ankündigungen 51KAPITEL 6 ALEXA, NUN SEI DOCH MAL STILL 53Benachrichtigungen verwalten 53Der Bitte-nicht-stören-Modus 55Manuell: Selbst ein- und ausschalten 56Planmäßig: Alexa, du weißt, wann ich keine Zeit habe 56Den Benachrichtigungston deaktivieren 57KAPITEL 7 DAS SMART HOME ÜBER ALEXA STEUERN 59Alexa, schalte mein neues Gerät an! 59Geräte in Gruppen ordnen 61Smart-Home-Geräte aus der Alexa-App löschen 62Fire TV und den Fire-TV-Stick mit Alexa verbinden 63KAPITEL 8 WISSEN, WAS IN DER WELT PASSIERT 65Nachrichten über Alexa hören 65Alexa weiß, was Sie interessiert 66Neue Anbieter zur täglichen Zusammenfassung hinzufügen 66Die Reihenfolge der täglichen Zusammenfassung ändern 67Einen Nachrichtenanbieter wieder entfernen 67Kein Tor mehr verpassen 67Neue Mannschaften zum Update hinzufügen 68Neues Team und altes weg 69KAPITEL 9 ALLES ÜBER WECKER, TIMER UND ERINNERUNGEN 71Der Wecker kann mehr, als nur gestellt zu werden 71Wenn der Standard-Weckerton nervt 72Starten Sie mit Musik in den Tag 73Weg mit der Eieruhr, her mit Alexa! 73Erinnerungen 74KAPITEL 10 EINKAUFSLISTEN UND TO-DO-LISTEN 75Ihre Standardlisten über Alexa verwalten 75Listen über das Smartphone aufrufen und verwalten 76Eine neue Liste erstellen 77Ihre Alexa-Listen mit Drittanbieter-Apps synchronisieren 77KAPITEL 11 ALEXA, DIE KÜCHEN- UND EINKAUFSHILFE 81Alexa nach Rezepten suchen lassen 81Von zu Hause aus einkaufen 82Alexa, ich brauche Nudeln! 82Produkt leer, aber Verpackung noch vorhanden? 82Alexa bestellt? – Niemals! 83TEIL II: ALEXA AUTOMATISIEREN UND IHR WISSEN ERWEITERN 85KAPITEL 12 ALLES ÜBER SKILLS 87Wo gibt es diese Skills? 87Skills wieder deaktivieren 88Skills für die Kleinen 88In-Skill-Käufe in Skills für Kinder deaktivieren 89KAPITEL 13 ALEXA BLUEPRINTS: EIGENE SKILLS ERSTELLEN 91Die Grundlagen 91Wer ist dran? Alexa lässt den Zufall entscheiden! 92Ein Quiz erstellen 94Eigene Fragen und Antworten definieren 96KAPITEL 14 AUTOMATISIERUNGEN ÜBER ALEXA-ROUTINEN 99Die Grundlagen 99Eine Alexa-Routine erstellen 100Der Auslöser 100Die Aktionen 101KAPITEL 15 ZUSAMMEN GEHT (FAST) ALLES: IFTTT UND ALEXA 105TEIL III: NOCH MEHR FUNKTIONEN FÜR SUCHTIS 109KAPITEL 16 STIMMPROFILE: ALEXA ERKENNT, WER GERADE SPRICHT 111Alexa verraten, wer Sie sind 111Wenn Alexa Sie oft nicht erkennt 112KAPITEL 17 ALEXA IST AUF VIELEN GERÄTEN ZU HAUSE 115Alexa auch auf dem Handy nutzen 115Alexa als Standard-Sprachassistentin einstellen (nur Android!) 116Alexa ist überall, auch auf Ihrem Windows-10-Gerät 117Alexa auf Windows-10-Geräten installieren 117Die Alexa-App kann noch mehr! 118Fire TV und Alexa gehören zusammen 119Alexa geht fremd 120KAPITEL 18 DATENSCHUTZ UND ALEXA 121Alexa, vergiss, was ich gesagt habe! 121Verlauf von Smart-Home-Geräten löschen 122Sparsam mit Daten für Skills umgehen 124Es geht noch mehr 124KAPITEL 19 WAS KANN EINE ALEXA MIT DISPLAY MEHR? 127Videos, Filme und mehr – Alexa wird zum Fernseher 127Prime-Serienjunkies haben nun noch einfacher Zugriff auf Serien und Filme 128Musikvideos kostenlos über den Echo Show schauen 128Filmtrailer über den Echo Show schauen 128Durchs Web surfen 128Das Smart Home vom Display aus verwalten 129Smart-Home-Kameras und -Türklingeln immer im Blick 129Alexa wird zur Steuerzentrale 129Fotos anschauen 130Display-Hintergrund wählen 130Das war noch nicht alles 131TEIL IV: DER TOP-TEN-TEIL 133KAPITEL 20 DIE 10 BESTEN PRODUKTIVITÄTS-SKILLS FÜR DEN ALLTAG 135Abfallkalender 135TV Digital Fernsehprogramm 135wikiHow 136Deutsche Bahn 136Chefkoch 136Spritpreise 137Stundenplan 137Stoppuhr Deluxe 137Landkarte 138Wiki Deutschland 138KAPITEL 21 DIE BESTEN SPIELCHEN FÜR ALEXA 139Wahrheit oder Lüge 139Quizduell 139Burger Imperium 140Was singt Dave? Das Musikquiz 140Akinator 140Tag X 140Schätze den Preis 141Würdest du eher? 141Stadt, Land, Fluss 141Nervensäge 141KAPITEL 22 10 IDEEN FÜR ROUTINEN 143Mit Routine in den Tag starten 143Die Morgen-Routine als Wecker-Ersatz 144Nach dem Wecker noch mal ans Aufstehen erinnert werden 144Schlafenszeit, auch für dich, Alexa! 145Sonnenaufgang mit den Lampen simulieren 146Einen Befehl blockieren 147Natürlicher mit Alexa sprechen 148Hau drauf, Licht aus 148Sturzalarm, wenn keine Bewegung mehr erkannt wird 149Keiner mehr zu Hause 149KAPITEL 23 10 LÖSUNGEN FÜR 6 HÄUFIGE STÖRUNGEN 151Alexa fühlt sich immer angesprochen 151Alexa ist schwerhörig 152Die Smart-Home-Geräte funktionieren nicht 153Alexa, starte mal neu! 153Gerät umbenennen 154Router 24/7 online lassen 154Das 2,4-GHz-Band nicht deaktivieren 154Gerät neu erkennen lassen 155Alexa spielt keine Musik mehr! 155Der Bildschirm zeigt nichts mehr an 156Der Lichtring von Alexa leuchtet blau und dreht sich die ganze Zeit! 156Stichwortverzeichnis 159
Applied Machine Learning for Health and Fitness
Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine learning practitioner or anyone interested in AI applications. These ML models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in motion capture, gaming, cinema production and more.Packed with fun, practical applications for sports, machine learning models used in the book include supervised, unsupervised and cutting-edge reinforcement learning methods and models with popular tools like PyTorch, Tensorflow, Keras, OpenAI Gym and OpenCV. Author Kevin Ashley—who happens to be both a machine learning expert and a professional ski instructor—has written an insightful book that takes you on a journey of modern sport science and AI.Filled with thorough, engaging illustrations and dozens of real-life examples, this book is your next step to understanding the implementation of AI within the sports world and beyond. Whether you are a data scientist, a coach, an athlete, or simply a personal fitness enthusiast excited about connecting your findings with AI methods, the author’s practical expertise in both tech and sports is an undeniable asset for your learning process. Today’s data scientists are the future of athletics, and Applied Machine Learning for Health and Fitness hands you the knowledge you need to stay relevant in this rapidly growing space.WHAT YOU'LL LEARN* Use multiple data science tools and frameworks* Apply deep computer vision and other machine learning methods for classification, semantic segmentation, and action recognition* Build and train neural networks, reinforcement learning models and more* Analyze multiple sporting activities with deep learning* Use datasets available today for model trainingUse machine learning in the cloud to train and deploy models* Apply best practices in machine learning and data scienceWHO THIS BOOK IS FORPrimarily aimed at data scientists, coaches, sports enthusiasts and athletes interested in connecting sports with technology and AI methods.Kevin Ashley is a Microsoft architect, IoT expert, and professional ski instructor. He is an author and developer of top sports and fitness apps and platforms such as Active Fitness and Winter Sports with a multi-million user audience. Kevin often works with sports scientists, Olympic athletes, coaches and teams to advance technology use in sports.IntroductionMachine Learning is fun with sensors and sports. Today’s data scientist is out there, on the ski slopes, or surfing the waves, and best way to apply machine learning is real life scenarios of sports. What can we do if we had the best, the ultimate model of our body and health monitoring us constantly? So, when we wanted to start a new sport, for example skiing or surfing, our personal body assistant could give us suggestions, like a personal coach. With machine learning and AI methods, imagine having a coach next to you 24/7.Part I: SensorsChapter 1: Getting StartedWhy are sensors important for health and fitness? For coaches, athletes and health professionals, they provide and objective picture of your activity. It’s often impossible to capture micro-movements and forces of a downhill racer, moving at 100 mph down a winding ski trail, but when equipped with sensors, every aspect of that movement can be captured, analyzed and studied. In this book we’ll use various IoT devices that can be used for sports data collection: inertial measurement units (IMUs), attitude and heading reference systems (AHRS), inertial navigation systems (INS/GPS), pressure sensors and others.1. Types of sensors and what they measurea. IMUs, AHRSb. INS/GPSc. Pressure sensorsd. Heart ratee. Vision and camera2. Sport science and dataa. Why is data frequency so important? A typical GPS device in your mobile phone works at 1Hz, that is one reading per second. Why isn’t this enough for most sports applications?b. Machine Learning really cares about data frequencies, as a rule of thumb we will use 100 Hz for most sensor data we collect3. How can Machine Learning help?a. Problems solved by machine learning for human movement, health and fitness applications4. Visualizing sports from sensor dataProject: First look at athlete movement analysis with a sample sensor data setChapter 2: Sensor HardwareIt turns out they don’t sell sensors with built in machine learning at convenience stores just yet! So, we made some. We go over some sport specific requirements for sensors, where and how sensors are placed on the body and equipment. In this chapter we will cover choices for sensor hardware, communication from sensors for data collection and data choices for IoT devices.1) Sensor IoT devices: IMU, AHRS, INS/GPS, Pressure, Proximity2) Sensor communication3) Data choices for IoT devicesProject: Learning to work with a sample SensorKit datasetChapter 3: Sensor SoftwareOur sensor is operating at a relatively high frequency of 100 samples per second (100 Hz). We need a special software to connect our sensor to the app. In this chapter we include a practical project on how to connect our sensor via a protocol like Bluetooth Low Energy to a mobile device and transfer data to the cloud.1) Sensor firmware2) Algorithms for sensor data processing3) Connecting with the app and the SDKProject: Writing the code to connect from sensor to the cloudChapter 4: 3D Printing SensorsProject: 3D printing is a fantastic technology for custom applications like sports! In this chapter I included a fun project on designing the case for our sensor, using 3D design software like Fusion 360 and 3D printing our sensor.1) Designing sensor casing model for sports2) Printing the sensor3) Every sport is different!Project: Designing a case and 3D printing our sensorPart II: Sensor DataSensors generate an enormous amount of data! In this part we learn about different types of sensor data, how to parse it, store it, transfer between IoT devices and the cloud.Chapter 5: Collecting sensor dataThis is where we sports scientists have most fun: data science on the ski slopes and surfing the waves! In this chapter I included a project.1) Sports and sensor placement2) Designing sports experiments3) Software and mobile devices for sports4) Sensor data for MLProject: Collecting dribble data from a basketball sensorChapter 6: Storing and parsing dataStoring sensor data is an interesting subject: at 100 Hz we have a lot of data from sports!1) Data frequency and aggregation decisions2) What to calculate on the sensors3) Sending data to the cloudProject: Writing code to parse and store sensor dataChapter 7: Managing and streaming IoT data in the cloudAn overview of modern IoT data technologies for the cloud, this chapter is about managing and streaming IoT data in the cloud.1) Non-relational databases for sensor data2) Streaming IoT data: (Spark, Kafka, Azure Stream Analytics)3) Data pipelines for IoTProject: Storing and streaming IoT data in the cloudPart III: Machine Learning for Health, Fitness and SportsFrom sensor data to physics of sports, movement analysis and machine learning models.Chapter 8: Physics of sportsSports scientists believe that each sport can be described mathematically with physics, let’s dive into sport science! In this chapter we’ll have a physics project to help us better understand the models.1) Physics of movement2) Sensors and physicsProject: Calculating forces for an athlete, using physicsChapter 9: Machine Learning modelsMachine Learning models for sports. This chapter defines reasoning behind various algorithms for machine learning in sports, as applied to sensor data.1) Raw sensor data2) Clean and transform the data3) Engineering features4) Supervised Learning5) Unsupervised Learning6) Reinforcement LearningProject: Creating a machine learning model from our experimentsChapter 10: Applying Machine Learning for various activitiesIn this chapter we look at some applications of sensors for sports, fitness and health.1) Skiing and snowboarding2) Basketball3) Tennis4) Diving5) Javelin6) SurfingPart III: Visualizing SensorsUsing computer vision and visualizing sports data in 3D and VR.Chapter 11: Computer visionComputer vision is an important way of tracking athletes in real time.1) Computer vision for sports overview2) 3D body rendering3) Problems with computer vision vs sensors (occlusion)4) Winning scenario: combining sensors with computer vision5) Project: using computer vision for athletic performanceProject: using computer vision for athletic performanceChapter 12: Visualizing athlete in 3D, Holograms and VRIn this chapter we’ll touch the holy grail of sports science: visualizing athlete in full 3D, as a holographic avatar.1) Methods and requirements for 3D visualization2) Using Unity to visualize dataChapter 13: Vision and SensorsThis chapter is about combining vision and sensors. Imagine, if we had to bring visual and sensor data together, then we have a tool that can provide both a near-real time visual feedback and video analysis.1) Combining sensor and video dataProject: Combining sensor and video data for analysisPart V: What the Coach needsFrom individual athletes to the team: this chapter would make the coach happy! Often, tracking an individual athlete with sensors is not enough: coaches or health professionals deal with teams they need to analyze.Chapter 14: Coach and team view on the dataWorking with coaches on US Olympic Team, WTA, WNBA, professional ski and snowboard instructors, I learned a lot about requirements that coaches have on the sensors, data, analytics and presentation of the data.1) Coaches and teams view2) Looking across the entire team3) Coach dashboard (PowerBI)Project: Creating a coach dashboard with PowerBIChapter 15: Connected sensors and sports teamsFrom individual athletes and sports, to connected experiences.1) Sensor data from the team prospective2) Connected teamConclusion: What’s nextThis book provides a toolkit, a foundation for a sports scientist or a data professional to use sensors and machine learning for insights about athlete performance and injury prevention.PROJECTS1) First look at athlete movement analysis with a sample sensor data set2) Learning to work with a sample sport dataset3) Writing the code to connect from sensor to the cloud4) Writing code to parse and store sensor data5) Storing and streaming IoT data in the cloud6) Designing a case and 3D printing our sensor7) Collecting dribble data from a basketball sensor8) Calculating forces for an athlete, using physics9) Creating a machine learning model from our experiments10) Using computer vision for athletic performance11) Combining sensor and video data for analysis12) Creating a coach dashboard with PowerBI for the team
Hands-on Time Series Analysis with Python
Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks.You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima.The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands -On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more.WHAT YOU'LL LEARN:· Explains basics to advanced concepts of time series· How to design, develop, train, and validate time-series methodologies· What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results· Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series.· Univariate and multivariate problem solving using fbprophet.WHO THIS BOOK IS FORData scientists, data analysts, financial analysts, and stock market researchersVISHWAS B V is a Data Scientist, AI researcher and Sr. AI Consultant, Currently living in Bengaluru(INDIA). His highest qualification is Master of Technology in Software Engineering from Birla Institute of Technology & Science, Pilani, and his primary focus and inspiration is Data Warehousing, Big Data, Data Science (Machine Learning, Deep Learning, Timeseries, Natural Language Processing, Reinforcement Learning, and Operation Research). He has over seven years of IT experience currently working at Infosys as Data Scientist & Sr. AI Consultant. He has also worked on Data Migration, Data Profiling, ETL & ELT, OWB, Python, PL/SQL, Unix Shell Scripting, Azure ML Studio, Azure Cognitive Services, and AWS.ASHISH PATEL is a Senior Data Scientist, AI researcher, and AI Consultant with over seven years of experience in the field of AI, Currently living in Ahmedabad(INDIA). He has a Master of Engineering Degree from Gujarat Technological University and his keen interest and ambition to research in the following domains such as (Machine Learning, Deep Learning, Time series, Natural Language Processing, Reinforcement Learning, Audio Analytics, Signal Processing, Sensor Technology, IoT, Computer Vision). He is currently working as Senior Data Scientist for Cynet infotech Pvt Ltd. He has published more than 15 + Research papers in the field of Data Science with Reputed Publications such as IEEE. He holds Rank 3 as a kernel master in Kaggle. Ashish has immense experience working on cross-domain projects involving a wide variety of data, platforms, and technologiesChapter 1: Time Series and its CharacteristicsChapter 2: Data Wrangling and Preparation for Time SeriesChapter 3: Smoothing MethodsChapter 4: Regression Extension Techniques for Time SeriesChapter 5: Bleeding Edge TechniquesChapter 6: Bleeding Edge Techniques for Univariate Time SeriesChapter 7: Bleeding Edge Techniques for Multivariate Time SeriesChapter 8: Prophet
Practical Test Automation
Learn the principles behind test-driven development (TDD) and behavior-driven development (BDD) and see how Jasmine, RSpec and Cucumber can be used to your advantage. This book examines some of the leading technologies used for testing.You'll see how to use Jasmine’s features to work with a JavaScript application. You will learn how to use Mini Test and RSpec with Ruby and Rubymine. Finally, you’ll use Cucumber to develop your software using a BDD approach.Understanding test automation is a vital skill for any web developer. Practical Test Automation breaks down for you some of the important TDD and BDD technologies on the modern web.WHAT YOU'LL LEARN* Test an example JavaScript application with Jasmine* Use Jasmine with JS Bin* Work with Minitest for test-driven development* Test an example Ruby project with RSpec* Use Cucumber and Gherkin for behavior-driven development* Integrate Cucumber with RSpec WHO THIS BOOK IS FORThis book is for anyone who wants to learn test automation and more about test-driven development and behavior-driven development.PANOS MATSINOPOULOS loves developing programs, both for web browsers and for mobile apps. He has been doing that for the past 25 years and has developed numerous applications. He also loves writing books, blogging and teaching computer programming. He has organized a lot of programming classes for kids, adults and elderly people. You can read find him on Twitter @pmatsino.PRACTICAL TEST AUTOMATIONChapter 1. Introduction to JasmineChapter 2. Advanced JasmineChapter 3. Using MinitestChapter 4. Introduction to RSpecChapter 5. Useful RSpec ToolsChapter 6. Introduction to CucumberChapter 7. Advanced Cucumber
Hacking of Computer Networks
The objective of the book is to summarize to the user with main topics in computer networking hacking.The book consists of the following parts:Part 1: Lab SetupPart2: Foot printing and ReconnaissancePart 3: Scanning MethodologyPart 4: EnumerationPart 5:System HackingPart 6: Trojans and Backdoors and VirusesPart 7: Sniffer and Phishing HackingPart 8: Hacking Web ServersPart 9:Hacking Windows and Linux SystemsPart 10: Wireless HackingPart 11: Hacking Mobile ApplicationsI am Dr. Hidaia Mahmoud Mohamed Alassouli. I completed my PhD degree in Electrical Engineering from Czech Technical University by February 2003, and my M. Sc. degree in Electrical Engineering from Bahrain University by June 1995. I completed also one study year of most important courses in telecommunication and computer engineering courses in Islamic university in Gaza. So, I covered most important subjects in Electrical Engineering, Computer Engineering and Telecommunications Engineering during my study. My nationality is Palestinian from gaza strip.I obtained a lot of certified courses in MCSE, SPSS, Cisco (CCNA), A+, Linux.I worked as Electrical, Telecommunicating and Computer Engineer in a lot of institutions. I worked also as a computer networking administrator. I had considerable undergraduate teaching experience in several types of courses in many universities. I handled teaching the most important subjects in Electrical and Telecommunication and Computer Engineering. I could publish a lot of papers a top-tier journals and conference proceedings, besides I published a lot of books in Publishing and Distribution houses.I wrote a lot of important Arabic articles on online news websites. I also have my own magazine website that I publish on it all my articles: http:// www.anticorruption.000space.comMy personal website: www.hidaia-alassouli.000space.comEmail: hidaia_alassouli@hotmail.com
Digitale Transformation und neue Führungspositionen. Wie Chief Digital Officers die digitale Transformation von Unternehmen erfolgreich steuern
Die fortschreitende Digitalisierung verändert Wirtschaft und Unternehmen enorm. Durch technologische Innovationen und den damit verbundenen Konkurrenzdruck müssen Unternehmen neue Führungsmodelle und Geschäftsstrukturen entwickeln. Eine Möglichkeit, die digitale Transformation von Unternehmen zu steuern, ist der Einsatz eines Chief Digital Officers. Was sind die Aufgaben eines Chief Digital Officers? Über welche Kompetenzen sollte dieser verfügen? Und welche Rolle spielt ein Chief Digital Officer bei der digitalen Transformation eines Unternehmens? Die Autorin Vivien Wika klärt die wichtigsten Fragen zur Führungsposition des Chief Digital Officers und hebt dessen Bedeutung für die digitale Transformation eines Unternehmens hervor. Wika erläutert die weltweite Verteilung von Chief Digital Officers und wirft einen Blick in die Zukunft dieser Führungskräfte. Aus dem Inhalt: - Big Data; - Künstliche Intelligenz; - Design-Thinking; - Digitalisierungsstrategie; - Change-Management
Tribe of Hackers Blue Team
BLUE TEAM DEFENSIVE ADVICE FROM THE BIGGEST NAMES IN CYBERSECURITYThe Tribe of Hackers team is back. This new guide is packed with insights on blue team issues from the biggest names in cybersecurity. Inside, dozens of the world’s leading Blue Team security specialists show you how to harden systems against real and simulated breaches and attacks. You’ll discover the latest strategies for blocking even the most advanced red-team attacks and preventing costly losses. The experts share their hard-earned wisdom, revealing what works and what doesn’t in the real world of cybersecurity.Tribe of Hackers Blue Team goes beyond the bestselling, original Tribe of Hackers book and delves into detail on defensive and preventative techniques. Learn how to grapple with the issues that hands-on security experts and security managers are sure to build into their blue team exercises.* Discover what it takes to get started building blue team skills* Learn how you can defend against physical and technical penetration testing* Understand the techniques that advanced red teamers use against high-value targets* Identify the most important tools to master as a blue teamer* Explore ways to harden systems against red team attacks* Stand out from the competition as you work to advance your cybersecurity careerAuthored by leaders in cybersecurity attack and breach simulations, the Tribe of Hackers series is perfect for those new to blue team security, experienced practitioners, and cybersecurity team leaders. Tribe of Hackers Blue Team has the real-world advice and practical guidance you need to advance your information security career and ready yourself for the blue team defense.MARCUS J. CAREY is a cybersecurity researcher and inventor with patents in cryptography and cybersecurity and more than 25 years of experience in protecting the public, private, and government sectors. He was the Founder and CEO of Threatcare, which was acquired by ReliaQuest. Follow Marcus on Twitter at @marcusjcarey. JENNIFER JIN graduated from the University of Texas at Austin in 2017 and started her first job at Threatcare soon after. She became Threatcare's Head of Communications and Marketing before Threatcare was acquired by ReliaQuest. Jennifer helped host the very first Tribe of Hackers Summit and is the co-author of Tribe of Hackers. Follow Jennifer on Twitter at @jen_jin. Acknowledgments viiForeword ixIntroduction xi01 Marcus J. Carey 102 Danny Akacki 603 Ricky Banda 904 William Bengtson 1405 Amanda Berlin 2006 O’Shea Bowens 2707 John Breth 3108 Lee Brotherston 3809 Ronald Bushar 4710 Christopher Caruso 5611 Eddie Clark 6612 Mark Clayton 7413 Ayman Elsawah 8014 Sahan Fernando 9115 Stephen Hilt 9616 Bea Hughes 10117 Terence Jackson 10918 Tanya Janca 11319 Ruth Juma 11920 Brendon Kelley 12321 Shawn Kirkland 12922 Sami Laiho 13923 Kat Maddox 14324 Jeffrey Man 14725 April Mardock 15426 Bright Gameli Mawudor 15927 Duncan McAlynn 16428 Frank McGovern 17029 Donald McFarlane 17230 Nathan McNulty 18031 James Medlock 18732 Daniel Miessler 19233 Alyssa Miller 19634 Maggie Morganti 20535 Justin Moss 21136 Mark Orlando 21837 Mitch Parker 22438 Stuart Peck 23139 Carlos Perez 23640 Quiessence Phillips 24241 Lauren Proehl 24842 Josh Rickard 25543 Megan Roddie 26644 Jason Schorr 27045 Chris Sistrunk 27446 Jayson E. Street 28047 Michael Tanji 28648 Ronnie Tokazowski 29449 Ashley Tolbert 29850 Ismael Valenzuela 30451 Dave Venable 32152 Robert "TProphet" Walker 32653 Jake Williams 33454 Robert Willis 340
Practical Entity Framework
Determine your object relational mapper (ORM) of choice for enterprise applications using .NET Framework, and especially .NET Framework Core 3.1 and higher. Real-world examples and considerations are presented in this book to help you create robust and efficient database solutions. Throughout the text, actual problems, questions, and common pitfalls are provided to help you recognize optimal solutions for maximum success in the different application scenarios you might encounter.PRACTICAL ENTITY FRAMEWORK begins with a simple overview of the two most common approaches to working with databases—database first and code first—and then focuses on working in a code first manner. Taking the code first approach allows the entire database to be built and maintained in code so there is never a situation in which you cannot restore the database schema. Additionally, the code first approach creates an entirely transparent record of changes to the database that is easily tracked in source control. Emphasis throughout the book is on leaving you well positioned to architect and lead data development efforts for your organization.WHAT YOU WILL LEARN* Build robust and maintainable databases using a code first approach* Create and execute stored procedures, triggers, and functions* Analyze and optimize performance of database queries* Ensure data integrity through keys, constraints, and relationshipsWHO THIS BOOK IS FOR.NET developers who work with enterprise-level applications and need to interact with data structures and data within the back end data store, developers who want to take a code first approach to building database applications to prevent conflicts and optimize efficiency, and those who are moving into full-stack roles, or into senior and architectural roles, and will be responsible for database design and implementationBRIAN GORMAN is a developer, computer science instructor, and trainer, and has been working in .NET technologies as long as they have existed. He was originally MCSD certified in .NET 1 and has recently re-certified with MCSA: Web Apps and MCSD: App Builder certifications. Additionally, he became an MCT as of April 2019, and is focusing in on developing and training developers with full-stack web solutions with .NET Core and Azure. In addition to working with .NET technologies, Brian also teaches computer science for Franklin University, where his courses taught have included data structures, algorithms, design patterns, and, more recently, full-stack solutions in the capstone practicum course. IntroductionPART I. GETTING STARTED1. Introduction to Entity Framework2. Working with an Existing Database3. Entity Framework: Code FirstPART II. BUILDING THE DATA SOLUTION4. Models and the Data Context5. Constraints, Keys, and Relationships6. Data Access (Create, Read, Update, Delete)7. Stored Procedures, Views, and Functions8. Sorting, Filtering, and PagingPART III. ENHANCING THE DATA SOLUTION9. LINQ for Queries and Projections10. Encryption of Data11. Repository and Unit of Work Patterns12. Unit Testing, Integration Testing, and Mocking13. Alternatives to Entity Framework: Dapper14. Asynchronous Data Operations and Multiple Database ContextsPART IV. RECIPES FOR SUCCESS15. .NET 5 and Entity Framework16. Appendix A: Troubleshooting
Decoding Blockchain for Business
Business professionals looking to understand the impact, future, and limitations of blockchain need look no further. This revolutionary technology has impacted business and the economy in unprecedented ways within the past decade, and it is only continuing to grow. As a leader in your organization, it is vital that you decode blockchain and optimize all the ways in which it can improve your business.Author of Decoding Blockchain for Business, Stijn Van Hijfte, expertly emphasizes the imperative of professionals in any sector of industry to understand the core concepts and implications of blockchain technology. Cryptocurrencies, cryptotrading, and constantly-changing tax structures for financial systems using blockchain technologies are covered in detail. The lasting effects of blockchain across specific industries such as media, real estate, finance, and regulatory bodies are addressed with an insightful eye from Van Hijfte.If not properly implemented with care and a foundation of knowledge, blockchain brings risks and uncertainties to a company. Know your technology to be ready for the present and the future, and stay ahead of the curve. Blockchain is here to stay, and Decoding Blockchain for Business is your professional roadmap.WHAT YOU WILL LEARN* Discover the risks associated with blockchain if not properly implemented* Gain insights on how blockchain technology affects other booming topics such as AI, IoT, and RPA* Look at the regulations surrounding Blockchain in different countriesWHO THIS BOOK IS FORBusiness professionals looking to understand the impact, future, and limitations of Blockchain and how individuals and companies should prepare for this technology.Stijn Van Hijfte has experience as a consultant, lecturer, and an innovation officer and has worked over the years with cloud, AI, automation, and blockchain technology. Since 2015 he has been experimenting and exploring the blockchain space, gaining deeper insight into the entire ecosystem. This insight ranges from setting up nodes and writing smart contracts, to the legal implications of GDPR, ICOs, and cryptocurrencies. Among others, he holds degrees in economics, IT, and data science. He currently works at Deloitte as a senior consultant.
Ganzheitliche Businessmodell-Transformation
In diesem Buch wird der regelkreisorientierte Changemanagementprozess zur Implementierung eines digitalen Businessmodells über das ganzheitliche Organisation 4.0-MITO-Konfigurationsmanagement beschrieben. Die inhaltliche Kapitelstruktur dieses MITO-Buches mit der Beschreibung der unterschiedlichen Transformations-Gestaltungssichten orientiert sich an dem übergeordneten betrieblichen Regelkreisprinzip innerhalb der in Abbildung 1 gezeigten MITO-Modellsegmente. Wobei das Managementsegment (M) noch vorgabeseitig in das prozessbezogene Führungssegment und rückmeldeseitig in das sachbezogene Leitungssegment unterteilt ist. Das darauf aufsetzende MITO-Businessmodell ergänzt die 5 Modellsegmente um das nachgelagerte Kunden- und vorgelagerte Lieferantensegment und integriert in die MITO-Modelldarstellung das hierarchische Prozessebenenmodell für die Konzeption des prozessorientierten Ziel-, Führungs- und Leitungssystems.Hartmut F. Binner war von 1978 - 2009 hauptamtlicher Professor an der Hochschule Hannover im Fachbereich Maschinenbau. Im Rahmen seiner Doktorarbeit entwickelte er die Swimlane-Darstellung, heute wesentliches BPMN 2.0-Strukturelement.Von 1999 - 2003 war er Präsident des REFA e.V., von 2007-2017 Vorstandsvorsitzender der Gesellschaft für Organisation.Innerhalb der letzten 20 Jahre schrieb er Beiträge in über 500 Zeitschriften und mehr als 18 Grundlagenwerke zum Thema Organisations- und Prozessgestaltung. Seit Dezember 2015 ist er der Vice Chairman der iTA (IT Automotive Service Partner e.V.).
Enterprise AI For Dummies
MASTER THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN YOUR ENTERPRISE WITH THE BOOK SERIES TRUSTED BY MILLIONSIn Enterprise AI For Dummies, author Zachary Jarvinen simplifies and explains to readers the complicated world of artificial intelligence for business. Using practical examples, concrete applications, and straightforward prose, the author breaks down the fundamental and advanced topics that form the core of business AI.Written for executives, managers, employees, consultants, and students with an interest in the business applications of artificial intelligence, Enterprise AI For Dummies demystifies the sometimes confusing topic of artificial intelligence. No longer will you lag behind your colleagues and friends when discussing the benefits of AI and business.The book includes discussions of AI applications, including:* Streamlining business operations* Improving decision making* Increasing automation* Maximizing revenueThe For Dummies series makes topics understandable, and as such, this book is written in an easily understood style that's perfect for anyone who seeks an introduction to a usually unforgiving topic.ZACHARY JARVINEN, MBA/MSC is a product & marketing executive and sought-after author and speaker in the Enterprise AI space. Over the course of his career, he's headed up Technology Strategy for Artificial Intelligence and Analytics at OpenText, expanded markets for Epson, worked at the U.S. State Department, and was a member of the 2008 Obama Campaign Digital Team. Presently, Zachary is focused on helping organizations get tangible benefits from AI. INTRODUCTION 1About This Book 2Strong, Weak, General, and Narrow 2Foolish Assumptions 3Icons Used in This Book 4Beyond the Book 4Where to Go from Here 5PART 1: EXPLORING PRACTICAL AI AND HOW IT WORKS 7CHAPTER 1: DEMYSTIFYING ARTIFICIAL INTELLIGENCE 9Understanding the Demand for AI 11Converting big data into actionable information 11Relieving global cost pressure 13Accelerating product development and delivery 14Facilitating mass customization 14Identifying the Enabling Technology 14Processing 15Algorithms 15Data 16Storage18Discovering How It Works 18Semantic networks and symbolic reasoning 19Text and data mining 20Machine learning 22Auto-classification 24Predictive analysis 25Deep learning 26Sentiment analysis 27CHAPTER 2: LOOKING AT USES FOR PRACTICAL AI 29Recognizing AI When You See It 30ELIZA 30Grammar check 30Virtual assistants 30Chatbots 31Recommendations 31Medical diagnosis 32Network intrusion detection and prevention 33Fraud protection and prevention 34Benefits of AI for Your Enterprise 34Healthcare 35Manufacturing 36Energy 36Banking and investments 37Insurance 37Retail 38Legal 39Human resources 39Supply chain 40Transportation and travel 40Telecom 41Public sector 41Professional services 42Marketing 43Media and entertainment 43CHAPTER 3: PREPARING FOR PRACTICAL AI 45Democratizing AI 46Visualizing Results 46Comparison 46Composition 47Distribution 48Relationship 48Digesting Data 50Identifying data sources 52Cleaning the data 52Defining Use Cases 54A → B 55Good use cases 55Bad use cases 56Reducing bias 58Choosing a Model 59Unsupervised learning 59Supervised learning 60Deep learning 60Reinforcement learning 61CHAPTER 4: IMPLEMENTING PRACTICAL AI 63The AI Competency Hierarchy 63Data collection 63Data flow 64Explore and transform 64Business intelligence and analytics 64Machine learning and benchmarking 65Artificial intelligence 65Scoping, Setting Up, and Running an Enterprise AI Project 65Define the task 67Collect the data 68Prepare the data 69Build the model 70Test and evaluate the model 72Deploy and integrate the model 72Maintain the model 72Creating a High-Performing Data Science Team 73The Critical Role of Internal and External Partnerships 74Internal partnerships 74External partnerships 75The importance of executive buy-in 75Weighing Your Options: Build versus Buy 75When you should do it yourself 75When you should partner with a provider 77Hosting in the Cloud versus On Premises 77What the cloud providers say 78What the hardware vendors say 78The truth in the middle 78PART 2: EXPLORING VERTICAL MARKET APPLICATIONS 81CHAPTER 5: HEALTHCARE/HMOS: STREAMLINING OPERATIONS 83Surfing the Data Tsunami 84Breaking the Iron Triangle with Data 84Matching Algorithms to Benefits 86Examining the Use Cases 87Delivering lab documents electronically 87Taming fax 88Automating redaction 88Improving patient outcomes 89Optimizing for a consumer mindset 89CHAPTER 6: BIOTECH/PHARMA: TAMING THE COMPLEXITY 91Navigating the Compliance Minefield 92Weaponizing the Medical, Legal, and Regulatory Review 93MLR review for product development 93MLR review for sales and marketing 94Enlisting Algorithms for the Cause 95Examining the Use Cases 96Product discovery 96Clinical trials 96Product development 96Quality control 97Predictive maintenance 97Manufacturing logistics 97Regulatory compliance 98Product commercialization 98Accounting and finance 98CHAPTER 7: MANUFACTURING: MAXIMIZING VISIBILITY 99Peering through the Data Fog 100Finding ways to reduce costs 100Handling zettabytes of data 101Clearing the Fog 101Connected supply chain 102Proactive replenishment103Predictive maintenance 104Pervasive visibility 104Clarifying the Connection to the Code 106Optimize inventory 106Optimize maintenance 106Optimize supply chain106Improve quality 106Automate repetitive tasks 107Examining the Use Cases 107Minimize risk 107Maintain product quality107Streamline database queries 108Outsource predictive maintenance 108Customize products 109Expand revenue streams 109Save the planet 109Delegate design 110CHAPTER 8: OIL AND GAS: FINDING OPPORTUNITY IN CHAOS 111Wrestling with Volatility 111Pouring Data on Troubled Waters 112Deriving meaningful insights 113Regaining control over your data 113Wrangling Algorithms for Fun and Profit 114Examining the Use Cases 115Achieving predictive maintenance 115Enhancing maintenance instructions 115Optimizing asset performance 116Exploring new projects 116CHAPTER 9: GOVERNMENT AND NONPROFITS: DOING WELL BY DOING GOOD 119Battling the Budget 120Government 120Nonprofit 122Fraud 122Optimizing Past the Obstacles 123Digital transformation 123The future of work 124Data security 125Operational costs 125Fraud 125Engagement 126Connecting the Tools to the Job 128Examining the Use Cases 129Enhance citizen services 129Provide a global voice of the citizen 130Make your city smarter 130Boost employee productivity and engagement 131Find the right employees (and volunteers) 131Improve cybersecurity 132CHAPTER 10: UTILITIES: RENEWING THE BUSINESS 133Coping with the Consumer Mindset 134Utilizing Big Data 135The smart grid 135Empowering the organization 136Connecting Algorithms to Goals 136Examining the Use Cases 137Optimizing equipment performance and maintenance 137Enhancing the customer experience 137Providing better support 138Streamlining back-office operations 138Managing demand 139CHAPTER 11: BANKING AND FINANCIAL SERVICES: MAKING IT PERSONAL 141Finding the Bottom Line in the Data 142Moving to “open banking” 142Dealing with regulation and privacy 143Offering speedier service 144Leveraging Big Data 144Restructuring with Algorithms 145Examining the Use Cases 146Improving personalization 146Enhancing customer service 146Strengthening compliance and security 147CHAPTER 12: RETAIL: READING THE CUSTOMER’S MIND 149Looking for a Crystal Ball 150Omnichanneling 150Personalizing 151Reading the Customer’s Mail 152A fluid omnichannel experience 153Enhanced personalization 153Accurate forecasting 153Looking Behind the Curtain 154Examining the Use Cases 155Voice of the customer 155Personalized recommendations 155AI-powered inventory 156CHAPTER 13: TRANSPORTATION AND TRAVEL: TUNING UP YOUR RIDE 157Avoiding the Bumps in the Road 158Planning the Route 159Checking Your Tools 161Examining the Use Cases 162Autonomous vehicles 162Predictive maintenance 162Asset performance optimization 163Enhanced driver and passenger experiences 164CHAPTER 14: TELECOMMUNICATIONS: CONNECTING WITH YOUR CUSTOMERS 167Listening Past the Static 168Finding the Signal in the Noise 168Looking Inside the Box 169Examining the Use Cases 170Achieve predictive maintenance and network optimization 170Enhance customer service with chatbots 170Improve business decisions 171CHAPTER 15: LEGAL SERVICES: CUTTING THROUGH THE RED TAPE 173Climbing the Paper Mountain 173Reading and writing 174And arithmetic 175Foot in mouth disease 175Planting Your Flag at the Summit 175Linking Algorithms with Results 177Examining the Use Cases 178Discovery and review 178Predicting cost and fit 179Analyzing data to support litigation 180Automating patent and trademark searches 180Analyzing costs for competitive billing 180CHAPTER 16: PROFESSIONAL SERVICES: INCREASING VALUE TO THE CUSTOMER 181Exploring the AI Pyramid 182Climbing the AI Pyramid 183Unearthing the Algorithmic Treasures 184Healthcare 184Content management 184Compliance 185Law 185Manufacturing 186Oil and gas 186Utilities 186Examining the Use Cases 187Document intake, acceptance, digitization, maintenance, and management 187Auditing, fraud detection, and prevention187Risk analysis and mitigation 187Regulatory compliance management 188Claims processing 188Inventory management 188Resume processing and candidate evaluation 188CHAPTER 17: MEDIA AND ENTERTAINMENT: BEATING THE GOLD RUSH 189Mining for Content 190Asset management 190Metadata 191Distribution 191Silos 192Content compliance 192Striking It Rich 193Metadata 193Digital distribution 193Digital asset management 194Assaying the Algorithms 194Examining the Use Cases 195Search optimization 195Workflow optimization 196Globalization 196PART 3: EXPLORING HORIZONTAL MARKET APPLICATIONS 197CHAPTER 18: VOICE OF THE CUSTOMER/CITIZEN: FINDING COHERENCE IN THE CACOPHONY 199Hearing the Message in the Media 200Delivering What They Really Want 201Answering the Right Questions 203Examining Key Industries 204Consumer packaged goods 205Public and nonprofit organizations 205CHAPTER 19: ASSET PERFORMANCE OPTIMIZATION: INCREASING VALUE BY EXTENDING LIFESPANS 207Spying on Your Machines 208Fixing It Before It Breaks 209Learning from the Future 210Data collection 210Analysis 211Putting insights to use 212Examining the Use Cases 212Production automation and quality control 213Preventive maintenance 213Process optimization 215CHAPTER 20: INTELLIGENT RECOMMENDATIONS: GETTING PERSONAL 217Making Friends by the Millions 218Listening to social media 218Mining data exhaust 219Reading Minds 219Knowing Which Buttons to Push 219Popular product recommendation 220Market-basket analysis 220Propensity modelling 220Data and text mining 222Collaborative filtering (CF) 223Content-based filtering (CBF) 224Cross-validation 224Data visualization 225Examining Key Industries 226Finance 226Credit card offers 227Retail 228CHAPTER 21: CONTENT MANAGEMENT: FINDING WHAT YOU WANT, WHEN YOU WANT IT 231Introducing the Square Peg to the Round Hole 232Categorizing and organizing content 232Automating with AI 233Finding Content at the Speed of AI 233Expanding Your Toolbox 235Access the content 235Extract concepts and entities 235Categorize and classify content 236Automate or recommend next best actions 236Examining the Use Cases 236Legal discovery process 237Content migration 237PII detection 237CHAPTER 22: AI-ENHANCED CONTENT CAPTURE: GATHERING ALL YOUR EGGS INTO THE SAME BASKET 239Counting All the Chickens, Hatched and Otherwise 240Tracing the history of capture technology 240Moving capture technology forward 241Monetizing All the Piggies, Little and Otherwise 241Streamline back-office operations 242Improve compliance 242Reduce risk of human error 243Support business transformation 243Improve operational knowledge 243Getting All Your Ducks in a Row 244Capture 244Digitize where needed 244Process, classify, and extract 244Validate edge cases 245Manage 246Visualize 246Examining Key Industries 246Financial services 246State government 247Healthcare 247CHAPTER 23: REGULATORY COMPLIANCE AND LEGAL RISK REDUCTION: HITTING THE BULLSEYE ON A MOVING TARGET 249Dodging Bullets 250Fines 250Increasing regulation 252Data privacy 254Strategy 254Shooting Back 255Make better decisions 255Increase customer confidence 256Win more business 257Boost the bottom line 257Building an Arsenal 258Examining the Use Cases 259Manage third-party risk 259Manage operational risk 259Monitor compliance risk 260Monitor changes in regulations 261Maintain data privacy 261Maintain data security 262Detect fraud and money laundering 262Optimize workflow 263CHAPTER 24: KNOWLEDGE ASSISTANTS AND CHATBOTS: MONETIZING THE NEEDLE IN THE HAYSTACK 265Missing the Trees for the Forest 266Recognizing the problem 266Defining terms 267Hearing the Tree Fall 268Making Trees from Acorns 269Examining the Use Cases 270Customer support 270Legal practice 271Enterprise search 272Compliance management 272Academic research 272Fact checking 273CHAPTER 25: AI-ENHANCED SECURITY: STAYING AHEAD BY WATCHING YOUR BACK 275Closing the Barn Door 276The story in the statistics 276The state of current solutions 278Locking the Barn Door 279Knowing Which Key to Use 281Examining the Use Cases 283Detecting threats by matching a known threat marker 284Detecting breaches by identifying suspicious behaviour 284Remediating attacks 286PART 4: THE PART OF TENS 287CHAPTER 26: TEN WAYS AI WILL INFLUENCE THE NEXT DECADE 289Proliferation of AI in the Enterprise 290AI Will Reach Across Functions 291AI R&D Will Span the Globe 291The Data Privacy Iceberg Will Emerge 292More Transparency in AI Applications 292Augmented Analytics Will Make It Easier 293Rise of Intelligent Text Mining 293Chatbots for Everyone 294Ethics Will Emerge for the AI Generation 294Rise of Smart Cities through AI 294CHAPTER 27: TEN REASONS WHY AI IS NOT A PANACEA 297AI is Not Human 298Pattern Recognition is Not the Same As Understanding 299AI Cannot Anticipate Black Swan Events 300AI Might Be Democratized, but Data is Not 302AI is Susceptible to Inherent Bias in the Data 302#RacialBias 303#GenderBias 303#EthnicBias 303Collection bias 304Proxy bias 304AI is Susceptible to Poor Problem Framing 305AI is Blind to Data Ambiguity 306AI Will Not, or Cannot, Explain Its Own Results 307AI sends you to jail 307AI cuts your medical benefits 308AI and the black box 308AI diagnoses your latent schizophrenia309AI can be fooled 310AI is Not Immune to the Law of Unintended Consequences 311Index
Rational Cybersecurity for Business
Use the guidance in this comprehensive field guide to gain the support of your top executives for aligning a rational cybersecurity plan with your business. You will learn how to improve working relationships with stakeholders in complex digital businesses, IT, and development environments. You will know how to prioritize your security program, and motivate and retain your team.Misalignment between security and your business can start at the top at the C-suite or happen at the line of business, IT, development, or user level. It has a corrosive effect on any security project it touches. But it does not have to be like this.Author Dan Blum presents valuable lessons learned from interviews with over 70 security and business leaders. You will discover how to successfully solve issues related to: risk management, operational security, privacy protection, hybrid cloud management, security culture and user awareness, and communication challenges.This open access book presents six priority areas to focus on to maximize the effectiveness of your cybersecurity program: risk management, control baseline, security culture, IT rationalization, access control, and cyber-resilience. Common challenges and good practices are provided for businesses of different types and sizes. And more than 50 specific keys to alignment are included.WHAT YOU WILL LEARN* Improve your security culture: clarify security-related roles, communicate effectively to businesspeople, and hire, motivate, or retain outstanding security staff by creating a sense of efficacy* Develop a consistent accountability model, information risk taxonomy, and risk management framework* Adopt a security and risk governance model consistent with your business structure or culture, manage policy, and optimize security budgeting within the larger business unit and CIO organization IT spend* Tailor a control baseline to your organization’s maturity level, regulatory requirements, scale, circumstances, and critical assets* Help CIOs, Chief Digital Officers, and other executives to develop an IT strategy for curating cloud solutions and reducing shadow IT, building up DevSecOps and Disciplined Agile, and more* Balance access control and accountability approaches, leverage modern digital identity standards to improve digital relationships, and provide data governance and privacy-enhancing capabilities* Plan for cyber-resilience: work with the SOC, IT, business groups, and external sources to coordinate incident response and to recover from outages and come back stronger* Integrate your learnings from this book into a quick-hitting rational cybersecurity success planWHO THIS BOOK IS FORChief Information Security Officers (CISOs) and other heads of security, security directors and managers, security architects and project leads, and other team members providing security leadership to your businessDan Blum is an internationally recognized cybersecurity and risk management strategist. He is a former Golden Quill Award-winning VP, Distinguished Analyst at Gartner, Inc., and has served as the de facto head of security for startups and consulting companies. He's advised hundreds of corporations, universities, and government organizations, and currently partners with top media, analyst firms, and clients to produce cybersecurity thought leadership research and to deliver cybersecurity workshops and coaching for security leaders.INTRODUCTIONExplain the book’s focus, audience, organization, and contents.CHAPTER 1: RATIONALIZE CYBERSECURITY FOR YOUR BUSINESS LANDSCAPEDescribes the six cybersecurity priority focus areas.CHAPTER 2: IDENTIFY AND EMPOWER SECURITY-RELATED ROLESExplains how the people in the business each contribute to the secure operation of the business and its digital systems.CHAPTER 3: ESTABLISH A CONTROL BASELINECombs through control frameworks such as ISO 27001 and the NIST Cybersecurity Framework to select controls providing a minimum viable program (MVP) for many businesses. It also details how to align people, process, and technology for these controls; how to scale the implementation for different types of businesses; and how to sure share responsibility for delivering the controls with third parties.CHAPTER 4: SIMPLIFY AND RATIONALIZE IT AND SECURITYArgues that security leaders have a stake in developing an effective IT strategy, what that strategy might look like, and how security leaders – who don’t own IT - can still engage IT functions to help develop and deliver on the strategy.CHAPTER 5: MANAGE RISK IN THE LANGUAGE OF BUSINESSClarifies why risk management literally must be the brains of the security program. It must analyze, monitor, and communicate what potential losses or circumstances constitute the business’s top risk scenarios. An effective tiered risk analysis process can efficiently address the myriad secondary risk issues that arise through processes and prioritize controls or other risk treatments.CHAPTER 6: CREATE A STRONG SECURITY CULTUREBrings the cultural subtext that can make or break a cybersecurity environment into the foreground. It analyzes the components of security culture and provides guidance on how to devise a security culture improvement process and measure its effectiveness. User awareness, training, and appropriate day to day engagement with the business can all play a part in forging a constructive security culture.CHAPTER 7: PUT THE RIGHT GOVERNANCE MODEL IN PLACEContrasts basic security governance structures that businesses can use, and provides guidance on how to select one and make it work. It describes core elements of the security program such as steering committees and security policy life cycle management. It also offers guidance on where the CISO should report in an organization.CHAPTER 8: CONTROL ACCESS WITH MINIMAL DRAG ON THE BUSINESSExplains why access is the critical balance beam for the business, compliance mandates, and the security program. It addresses the need for information classification, data protection, and identity and access management (IAM) controls to implement access restrictions as required to reduce risk or attain regulatory compliance but do so in a way that enables appropriate digital relationships and data sharing with internal and external users.CHAPTER 9: INSTITUTE RESILIENCE, DETECTION, AND RESPONSEGuides readers on how to formulate contingency plans and strategies for detection, response, and recovery which together comprise cyber-resilience.CHAPTER 10: PUTTING THE PIECES TOGETHERSummarizes guidance given throughout the book in the “keys” for aligning with the business. It reiterates guidance on how to scale security programs and the way they align to the business based on business size, complexity, and other factors.