Computer und IT
Advanced R 4 Data Programming and the Cloud
Program for data analysis using R and learn practical skills to make your work more efficient. This revised book explores how to automate running code and the creation of reports to share your results, as well as writing functions and packages. It includes key R 4 features such as a new color palette for charts, an enhanced reference counting system, and normalization of matrix and array types where matrix objects now formally inherit from the array class, eliminating inconsistencies.Advanced R 4 Data Programming and the Cloud is not designed to teach advanced R programming nor to teach the theory behind statistical procedures. Rather, it is designed to be a practical guide moving beyond merely using R; it shows you how to program in R to automate tasks.This book will teach you how to manipulate data in modern R structures and includes connecting R to databases such as PostgreSQL, cloud services such as Amazon Web Services (AWS), and digital dashboards such as Shiny. Each chapter also includes a detailed bibliography with references to research articles and other resources that cover relevant conceptual and theoretical topics.WHAT YOU WILL LEARN* Write and document R functions using R 4* Make an R package and share it via GitHub or privately* Add tests to R code to ensure it works as intended* Use R to talk directly to databases and do complex data management* Run R in the Amazon cloud* Deploy a Shiny digital dashboard* Generate presentation-ready tables and reports using RWHO THIS BOOK IS FORWorking professionals, researchers, and students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to take their R coding and programming to the next level.MATT WILEY leads institutional effectiveness, research, and assessment at Victoria College, facilitating strategic and unit planning, data-informed decision making, and state/regional/federal accountability. As a tenured, associate professor of mathematics, he won awards in both mathematics education (California) and student engagement (Texas). Matt earned degrees in computer science, business, and pure mathematics from the University of California and Texas A&M systems.Outside academia, he co-authors books about the popular R programming language and was managing partner of a statistical consultancy for almost a decade. He has programming experience with R, SQL, C++, Ruby, Fortran, and JavaScript.A programmer, a published author, a mathematician, and a transformational leader, Matt has always melded his passion for writing with his joy of logical problem solving and data science. From the boardroom to the classroom, he enjoys finding dynamic ways to partner with interdisciplinary and diverse teams to make complex ideas and projects understandable and solvable.JOSHUA F. WILEY is a lecturer in the Turner Institute for Brain and Mental Health and School of Psychological Sciences at Monash University. He earned his PhD from the University of California, Los Angeles and completed his post-doctoral training in primary care and prevention. His research uses advanced quantitative methods to understand the dynamics between psychosocial factors, sleep and other health behaviours in relation to psychological and physical health. He develops or co-develops a number of R packages including varian, a package to conduct Bayesian scale-location structural equation models, MplusAutomation, a popular package that links R to the commercial Mplus software, extraoperators for faster logical operations, multilevelTools for diagnostics, effect sizes, and easy display of multilevel / mixed effects models results, and miscellaneous functions to explore data or speed up analysis in JWileymisc.PROGRAMMING1.Programming Basics2.Programming Utilities3.Loops, flow control, and *apply functions4.Writing Functions5.Writing Classes and Methods6.Writing a PackageDATA MANAGEMENT7.Data Management using data.table8.Data Munging With data.table9.Other Tools for Data Management10.Reading Big Data(bases)CLOUD COMPUTING11.Getting a Cloud12.Ubuntu for Windows Users13.Every Cloud has a Shiny lining…14.Shiny Dashboard Sampler15.Dynamic Reports and the CloudReferences (backmatter)
Data Mashup with Microsoft Excel Using Power Query and M
Master the art of loading external data into Excel for use in reporting, charting, dashboarding, and business intelligence. This book provides a complete and thorough explanation of Microsoft Excel’s Get and Transform feature set, showing you how to connect to a range of external databases and other data sources to find data and pull that data into your local spreadsheet for further analysis. Leading databases are covered, including Microsoft Azure data sources and web sources, and you will learn how to access those sources from your Microsoft Excel spreadsheets.Getting data into Excel is a prerequisite for using Excel's analytics capabilities. This book takes you beyond copying and pasting by showing you how to connect to your corporate databases that are hosted in the Azure cloud, and how to pull data from Oracle Database and SQL Server, and other sources.Accessing data is only half the problem, and the other half involves cleansing and rearranging your data to make it useful in spreadsheet form. Author Adam Aspin shows you how to create datasets and transformations. For advanced problems, there is help on the M language that is built into Excel, specifically to support mashing up data in support of business intelligence and analysis. If you are an Excel user, you won't want to be without this book that teaches you to extract and prepare external data ready for use in what is arguably the world’s leading analytics tool.WHAT YOU WILL LEARN* Connect to a range of external data, from databases to Azure sources* Ingest data directly into your spreadsheets, or into PowerPivot data models* Cleanse and prepare external data so it can be used inside Excel* Refresh data quickly and easily to always have the latest information* Transform data into ready-to-use structures that fit the spreadsheet format* Execute M language functions for complex data transformationsWHO THIS BOOK IS FORExcel users who want to access data from external sources—including the Microsoft Azure platform—in order to create business intelligence reporting, dashboards, and visualizations. For Excel users needing to cleanse and rearrange such data to meet their own, specific needs.ADAM ASPIN is an independent business intelligence consultant based in the United Kingdom. He has worked with SQL Server for over 25 years. During this time, he has developed several dozen reporting and analytical systems based on the Microsoft analytics stack.Business intelligence has been Adam’s principal focus for the last 20 years. He has applied his skills for a variety of clients in a range of industry sectors. He is the author of Apress books: SQL Server Data Integration Recipes, Pro Power BI Desktop (now in its third edition), Business Intelligence with SQL Server Reporting Services, and High Impact Data Visualization.A graduate of Oxford University, Adam began his career in publishing before moving into IT. Databases soon became a passion, and his experience in this arena ranges from dBase to Oracle, and Access to MySQL, with occasional sorties into the world of DB2. He is, however, most at home in the Microsoft universe when using SQL Server Analysis Services, SQL Server Reporting Services, and SQL Server Integration Services and Power BI (both on-premise and in Azure).A fluent French speaker, Adam has worked in France and Switzerland for many years.1. Using Power Query to Discover and Load Data into Excel2. Discovering and Loading File-Based Data with Power Query3. Loading Data from Databases and Data Warehouses4. Loading Data from the Web and the Cloud5. Generic Data Sources6. Structuring Imported Data7. Data Transformation8. Restructuring Data9. Complex Data Loads10. Organizing and Managing Queries11. Parameterizing Queries12. The M Language13. Appendix A: Sample Data
Recommender System with Machine Learning and Artificial Intelligence
This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising.This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.SACHI NANDAN MOHANTY received his PhD from IIT Kharagpur, India in 2015 and is now at ICFAI Foundation for Higher Education, Hyderabad, India. JYOTIR MOY CHATTERJEE is working as an Assistant Professor (IT) at Lord Buddha Education Foundation, Kathmandu, Nepal. He has completed M.Tech in Computer Science & Engineering from Kalinga Institute of Industrial Technology, Bhubaneswar, India. SARIKA JAIN obtained her PhD in the field of Knowledge Representation in Artificial Intelligence in 2011. She has served in the field of education for over 18 years and is currently in service at the National Institute of Technology, Kurukshetra. AHMED A. ELNGAR is the Founder and Head of Scientific Innovation Research Group (SIRG) and Assistant Professor of Computer Science at the Faculty of Computers and Information, Beni-Suef University, Egypt. PRIYA GUPTA is working as an Assistant Professor in the Department of Computer Science at Maharaja Agrasen College, University of Delhi. Her Doctoral Degree is from BIT (Mesra), Ranchi. Preface xixAcknowledgment xxiiiPART 1: INTRODUCTION TO RECOMMENDER SYSTEMS 11 AN INTRODUCTION TO BASIC CONCEPTS ON RECOMMENDER SYSTEMS 3Pooja Rana, Nishi Jain and Usha Mittal1.1 Introduction 41.2 Functions of Recommendation Systems 51.3 Data and Knowledge Sources 61.4 Types of Recommendation Systems 81.4.1 Content-Based 81.4.1.1 Advantages of Content-Based Recommendation 111.4.1.2 Disadvantages of Content-Based Recommendation 111.4.2 Collaborative Filtering 121.5 Item-Based Recommendation vs. User-Based Recommendation System 141.5.1 Advantages of Memory-Based Collaborative Filtering 151.5.2 Shortcomings 161.5.3 Advantages of Model-Based Collaborative Filtering 171.5.4 Shortcomings 171.5.5 Hybrid Recommendation System 171.5.6 Advantages of Hybrid Recommendation Systems 181.5.7 Shortcomings 181.5.8 Other Recommendation Systems 181.6 Evaluation Metrics for Recommendation Engines 191.7 Problems with Recommendation Systems and Possible Solutions 201.7.1 Advantages of Recommendation Systems 231.7.2 Disadvantages of Recommendation Systems 241.8 Applications of Recommender Systems 24References 252 A BRIEF MODEL OVERVIEW OF PERSONALIZED RECOMMENDATION TO CITIZENS IN THE HEALTH-CARE INDUSTRY 27Subhasish Mohapatra and Kunal Anand2.1 Introduction 282.2 Methods Used in Recommender System 292.2.1 Content-Based 292.2.2 Collaborative Filtering 322.2.3 Hybrid Filtering 332.3 Related Work 332.4 Types of Explanation 342.5 Explanation Methodology 352.5.1 Collaborative-Based 362.5.2 Content-Based 362.5.3 Knowledge and Utility-Based 372.5.4 Case-Based 372.5.5 Demographic-Based 382.6 Proposed Theoretical Framework for Explanation-Based Recommender System in Health-Care Domain 392.7 Flowchart 392.8 Conclusion 41References 413 2ES OF TIS: A REVIEW OF INFORMATION EXCHANGE AND EXTRACTION IN TOURISM INFORMATION SYSTEMS 45Malik M. Saad Missen, Mickaël Coustaty, Hina Asmat, Amnah Firdous, Nadeem Akhtar, Muhammad Akram and V. B. Surya Prasath3.1 Introduction 463.2 Information Exchange 493.2.1 Exchange of Tourism Objects Data 493.2.1.1 Semantic Clashes 503.2.1.2 Structural Clashes 503.2.2 Schema.org—The Future 513.2.2.1 Schema.org Extension Mechanism 523.2.2.2 Schema.org Tourism Vocabulary 523.2.3 Exchange of Tourism-Related Statistical Data 533.3 Information Extraction 553.3.1 Opinion Extraction 563.3.2 Opinion Mining 573.4 Sentiment Annotation 573.4.1 SentiML 583.4.1.1 SentiML Example 583.4.2 OpinionMiningML 593.4.2.1 OpinionMiningML Example 603.4.3 EmotionML 613.4.3.1 EmotionML Example 613.5 Comparison of Different Annotations Schemes 623.6 Temporal and Event Extraction 643.7 TimeML 653.8 Conclusions 67References 67PART 2: MACHINE LEARNING-BASED RECOMMENDER SYSTEMS 714 CONCEPTS OF RECOMMENDATION SYSTEM FROM THE PERSPECTIVE OF MACHINE LEARNING 73Sumanta Chandra Mishra Sharma, Adway Mitra and Deepayan Chakraborty4.1 Introduction 734.2 Entities of Recommendation System 744.2.1 User 744.2.2 Items 754.2.3 Action 754.3 Techniques of Recommendation 764.3.1 Personalized Recommendation System 774.3.2 Non-Personalized Recommendation System 774.3.3 Content-Based Filtering 774.3.4 Collaborative Filtering 784.3.5 Model-Based Filtering 804.3.6 Memory-Based Filtering 804.3.7 Hybrid Recommendation Technique 814.3.8 Social Media Recommendation Technique 824.4 Performance Evaluation 824.5 Challenges 834.5.1 Sparsity of Data 844.5.2 Scalability 844.5.3 Slow Start 844.5.4 Gray Sheep and Black Sheep 844.5.5 Item Duplication 844.5.6 Privacy Issue 844.5.7 Biasness 854.6 Applications 854.7 Conclusion 85References 855 A MACHINE LEARNING APPROACH TO RECOMMEND SUITABLE CROPS AND FERTILIZERS FOR AGRICULTURE 89Govind Kumar Jha, Preetish Ranjan and Manish Gaur5.1 Introduction 905.2 Literature Review 915.3 Methodology 935.4 Results and Analysis 965.5 Conclusion 97References 986 ACCURACY-ASSURED PRIVACY-PRESERVING RECOMMENDER SYSTEM USING HYBRID-BASED DEEP LEARNING METHOD 101Abhaya Kumar Sahoo and Chittaranjan Pradhan6.1 Introduction 1026.2 Overview of Recommender System 1036.3 Collaborative Filtering-Based Recommender System 1066.4 Machine Learning Methods Used in Recommender System 1076.5 Proposed RBM Model-Based Movie Recommender System 1106.6 Proposed CRBM Model-Based Movie Recommender System 1136.7 Conclusion and Future Work 115References 1187 MACHINE LEARNING-BASED RECOMMENDER SYSTEM FOR BREAST CANCER PROGNOSIS 121G. Kanimozhi, P. Shanmugavadivu and M. Mary Shanthi Rani7.1 Introduction 1227.2 Related Works 1247.3 Methodology 1257.3.1 Experimental Dataset 1257.3.2 Feature Selection 1277.3.3 Functional Phases of MLRS-BC 1287.3.4 Prediction Algorithms 1297.4 Results and Discussion 1317.5 Conclusion 138Acknowledgment 139References 1398 A RECOMMENDED SYSTEM FOR CROP DISEASE DETECTION AND YIELD PREDICTION USING MACHINE LEARNING APPROACH 141Pooja Akulwar8.1 Introduction 1428.2 Machine Learning 1438.2.1 Overview 1438.2.2 Machine Learning Algorithms 1458.2.3 Machine Learning Methods 1468.2.3.1 Artificial Neural Network 1468.2.3.2 Support Vector Machines 1468.2.3.3 K-Nearest Neighbors (K-NN) 1478.2.3.4 Decision Tree Learning 1478.2.3.5 Random Forest 1488.2.3.6 Gradient Boosted Decision Tree (GBDT) 1498.2.3.7 Regularized Greedy Forest (RGF) 1508.3 Recommender System 1518.3.1 Overview 1518.4 Crop Management 1538.4.1 Yield Prediction 1538.4.2 Disease Detection 1548.4.3 Weed Detection 1568.4.4 Crop Quality 1598.5 Application—Crop Disease Detection and Yield Prediction 159References 162PART 3: CONTENT-BASED RECOMMENDER SYSTEMS 1659 CONTENT-BASED RECOMMENDER SYSTEMS 167Poonam Bhatia Anand and Rajender Nath9.1 Introduction 1679.2 Literature Review 1689.3 Recommendation Process 1729.3.1 Architecture of Content-Based Recommender System 1729.3.2 Profile Cleaner Representation 1759.4 Techniques Used for Item Representation and Learning User Profile 1769.4.1 Representation of Content 1769.4.2 Vector Space Model Based on Keywords 1779.4.3 Techniques for Learning Profiles of User 1799.4.3.1 Probabilistic Method 1799.4.3.2 Rocchio’s and Relevance Feedback Method 1809.4.3.3 Other Methods 1819.5 Applicability of Recommender System in Healthcare and Agriculture 1829.5.1 Recommendation System in Healthcare 1829.5.2 Recommender System in Agriculture 1849.6 Pros and Cons of Content-Based Recommender System 1869.7 Conclusion 187References 18810 CONTENT (ITEM)-BASED RECOMMENDATION SYSTEM 197R. Balamurali10.1 Introduction 19810.2 Phases of Content-Based Recommendation Generation 19810.3 Content-Based Recommendation Using Cosine Similarity 19910.4 Content-Based Recommendations Using Optimization Techniques 20410.5 Content-Based Recommendation Using the Tree Induction Algorithm 20810.6 Summary 212References 21311 CONTENT-BASED HEALTH RECOMMENDER SYSTEMS 215Soumya Prakash Rana, Maitreyee Dey, Javier Prieto and Sandra Dudley11.1 Introduction 21611.2 Typical Health Recommender System Framework 21711.3 Components of Content-Based Health Recommender System 21811.4 Unstructured Data Processing 22011.5 Unsupervised Feature Extraction & Weighting 22111.5.1 Bag of Words (BoW) 22111.5.2 Word to Vector (Word2Vec) 22211.5.3 Global Vectors for Word Representations (Glove) 22211.6 Supervised Feature Selection & Weighting 22211.7 Feedback Collection 22511.7.1 Medication & Therapy 22511.7.2 Healthy Diet Plan 22511.7.3 Suggestions 22511.8 Training & Health Recommendation Generation 22611.8.1 Analogy-Based ML in CBHRS 22711.8.2 Specimen-Based ML in CBHRS 22711.9 Evaluation of Content Based Health Recommender System 22811.10 Design Criteria of CBHRS 22911.10.1 Micro-Level & Lucidity 23011.10.2 Interactive Interface 23011.10.3 Data Protection 23011.10.4 Risk & Uncertainty Management 23111.10.5 Doctor-in-Loop (DiL) 23111.11 Conclusions and Future Research Directions 231References 23312 CONTEXT-BASED SOCIAL MEDIA RECOMMENDATION SYSTEM 237R. Sujithra Kanmani and B. Surendiran12.1 Introduction 23712.2 Literature Survey 24012.3 Motivation and Objectives 24112.3.1 Architecture 24112.3.2 Modules 24212.3.3 Implementation Details 24312.4 Performance Measures 24312.5 Precision 24312.6 Recall 24312.7 F- Measure 24412.8 Evaluation Results 24412.9 Conclusion and Future Work 247References 24813 NETFLIX CHALLENGE—IMPROVING MOVIE RECOMMENDATIONS 251Vasu Goel13.1 Introduction 25113.2 Data Preprocessing 25213.3 MovieLens Data 25313.4 Data Exploration 25513.5 Distributions 25613.6 Data Analysis 25713.7 Results 26513.8 Conclusion 266References 26614 PRODUCT OR ITEM-BASED RECOMMENDER SYSTEM 269Jyoti Rani, Usha Mittal and Geetika Gupta14.1 Introduction 27014.2 Various Techniques to Design Food Recommendation System 27114.2.1 Collaborative Filtering Recommender Systems 27114.2.2 Content-Based Recommender Systems (CB) 27214.2.3 Knowledge-Based Recommender Systems 27214.2.4 Hybrid Recommender Systems 27314.2.5 Context Aware Approaches 27314.2.6 Group-Based Methods 27314.2.7 Different Types of Food Recommender Systems 27314.3 Implementation of Food Recommender System Using Content-Based Approach 27614.3.1 Item Profile Representation 27714.3.2 Information Retrieval 27814.3.3 Word2vec 27814.3.4 How are word2vec Embedding’s Obtained? 27814.3.5 Obtaining word2vec Embeddings 27914.3.6 Dataset 28014.3.6.1 Data Preprocessing 28014.3.7 Web Scrapping For Food List 28014.3.7.1 Porter Stemming All Words 28014.3.7.2 Filtering Our Ingredients 28014.3.7.3 Final Data Frame with Dishes and Their Ingredients 28114.3.7.4 Hamming Distance 28114.3.7.5 Jaccard Distance 28214.4 Results 28214.5 Observations 28314.6 Future Perspective of Recommender Systems 28314.6.1 User Information Challenges 28314.6.1.1 User Nutrition Information Uncertainty 28314.6.1.2 User Rating Data Collection 28414.6.2 Recommendation Algorithms Challenges 28414.6.2.1 User Information Such as Likes/ Dislikes Food or Nutritional Needs 28414.6.2.2 Recipe Databases 28414.6.2.3 A Set of Constraints or Rules 28514.6.3 Challenges Concerning Changing Eating Behavior of Consumers 28514.6.4 Challenges Regarding Explanations and Visualizations 28614.7 Conclusion 286Acknowledgements 287References 287PART 4: BLOCKCHAIN & IOT-BASED RECOMMENDER SYSTEMS 29115 A TRUST-BASED RECOMMENDER SYSTEM BUILT ON IOT BLOCKCHAIN NETWORK WITH COGNITIVE FRAMEWORK 293S. Porkodi and D. Kesavaraja15.1 Introduction 29415.1.1 Today and Tomorrow 29415.1.2 Vision 29415.1.3 Internet of Things 29415.1.4 Blockchain 29515.1.5 Cognitive Systems 29615.1.6 Application 29615.2 Technologies and its Combinations 29715.2.1 IoT–Blockchain 29715.2.2 IoT–Cognitive System 29815.2.3 Blockchain–Cognitive System 29815.2.4 IoT–Blockchain–Cognitive System 29815.3 Crypto Currencies With IoT–Case Studies 29915.4 Trust-Based Recommender System 29915.4.1 Requirement 29915.4.2 Things Management 30215.4.3 Cognitive Process 30315.5 Recommender System Platform 30415.6 Conclusion and Future Directions 307References 30716 DEVELOPMENT OF A RECOMMENDER SYSTEM HEALTHMUDRA USING BLOCKCHAIN FOR PREVENTION OF DIABETES 313Rashmi Bhardwaj and Debabrata Datta16.1 Introduction 31416.2 Architecture of Blockchain 31716.2.1 Definition of Blockchain 31816.2.2 Structure of Blockchain 31816.3 Role of HealthMudra in Diabetic 32216.4 Blockchain Technology Solutions 32416.4.1 Predictive Models of Health Data Analysis 32516.5 Conclusions 325References 326PART 5: HEALTHCARE RECOMMENDER SYSTEMS 32917 CASE STUDY 1: HEALTH CARE RECOMMENDER SYSTEMS 331Usha Mittal, Nancy Singla and Geetika Gupta17.1 Introduction 33217.1.1 Health Care Recommender System 33217.1.2 Parkinson’s Disease: Causes and Symptoms 33317.1.3 Parkinson’s Disease: Treatment and Surgical Approaches 33417.2 Review of Literature 33517.2.1 Machine Learning Algorithms for Parkinson’s Data 33717.2.2 Visualization 34017.3 Recommender System for Parkinson’s Disease (PD) 34117.3.1 How Will One Know When Parkinson’s has Progressed? 34217.3.2 Dataset for Parkinson’s Disease (PD) 34217.3.3 Feature Selection 34317.3.4 Classification 34317.3.4.1 Logistic Regression 34317.3.4.2 K Nearest Neighbor (KNN) 34317.3.4.3 Support Vector Machine (SVM) 34417.3.4.4 Decision Tree 34417.3.5 Train and Test Data 34417.3.6 Recommender System 34417.4 Future Perspectives 34517.5 Conclusions 346References 34818 TEMPORAL CHANGE ANALYSIS-BASED RECOMMENDER SYSTEM FOR ALZHEIMER DISEASE CLASSIFICATION 351S. Naganandhini, P. Shanmugavadivu and M. Mary Shanthi Rani18.1 Introduction 35218.2 Related Work 35218.3 Mechanism of TCA-RS-AD 35318.4 Experimental Dataset 35418.5 Neural Network 35718.6 Conclusion 370References 37019 REGULARIZATION OF GRAPHS: SENTIMENT CLASSIFICATION 373R.S.M. Lakshmi Patibandla19.1 Introduction 37319.2 Neural Structured Learning 37419.3 Some Neural Network Models 37519.4 Experimental Results 37719.4.1 Base Model 37919.4.2 Graph Regularization 38219.5 Conclusion 383References 38420 TSARS: A TREE-SIMILARITY ALGORITHM-BASED AGRICULTURAL RECOMMENDER SYSTEM 387Madhusree Kuanr, Puspanjali Mohapatra and Sasmita Subhadarsinee Choudhury20.1 Introduction 38820.2 Literature Survey 39020.3 Research Gap 39320.4 Problem Definitions 39320.5 Methodology 39320.6 Results & Discussion 39420.6.1 Performance Evaluation 39420.6.2 Time Complexity Analysis 39620.7 Conclusion & Future Work 397References 39921 INFLUENCEABLE TARGETS RECOMMENDATION ANALYZING SOCIAL ACTIVITIES IN EGOCENTRIC ONLINE SOCIAL NETWORKS 401Soumyadeep Debnath, Dhrubasish Sarkar and Dipankar Das21.1 Introduction 40221.2 Literature Review 40321.3 Dataset Collection Process with Details 40421.3.1 Main User’s Activities Data 40521.3.2 Network Member’s Activities Data 40521.3.3 Tools and Libraries for Data Collection 40521.3.4 Details of the Datasets 40621.4 Primary Preprocessing of Data 40621.4.1 Language Detection and Translation 40621.4.2 Tagged Tweeters Collection 40721.4.3 Textual Noise Removal 40721.4.4 Textual Spelling and Correction 40721.5 Influence and Social Activities Analysis 40721.5.1 Step 1: Targets Selection From OSMs 40821.5.2 Step 3: Categories Classification of Social Contents 40821.5.3 Step 4: Sentiments Analysis of Social Contents 40821.6 Recommendation System 40921.6.1 Secondary Preprocessing of Data 40921.6.2 Recommendation Analyzing Contents of Social Activities 41121.7 Top Most Influenceable Targets Evaluation 41321.8 Conclusion 41421.9 Future Scope 415References 415Index 417
Building Computer Vision Applications Using Artificial Neural Networks
Apply computer vision and machine learning concepts in developing business and industrial applications using a practical, step-by-step approach.The book comprises four main sections starting with setting up your programming environment and configuring your computer with all the prerequisites to run the code examples. Section 1 covers the basics of image and video processing with code examples of how to manipulate and extract useful information from the images. You will mainly use OpenCV with Python to work with examples in this section.Section 2 describes machine learning and neural network concepts as applied to computer vision. You will learn different algorithms of the neural network, such as convolutional neural network (CNN), region-based convolutional neural network (R-CNN), and YOLO. In this section, you will also learn how to train, tune, and manage neural networks for computer vision. Section 3 provides step-by-step examples of developing business and industrial applications, such as facial recognition in video surveillance and surface defect detection in manufacturing.The final section is about training neural networks involving a large number of images on cloud infrastructure, such as Amazon AWS, Google Cloud Platform, and Microsoft Azure. It walks you through the process of training distributed neural networks for computer vision on GPU-based cloud infrastructure. By the time you finish reading Building Computer Vision Applications Using Artificial Neural Networks and working through the code examples, you will have developed some real-world use cases of computer vision with deep learning.WHAT YOU WILL LEARN· Employ image processing, manipulation, and feature extraction techniques· Work with various deep learning algorithms for computer vision· Train, manage, and tune hyperparameters of CNNs and object detection models, such as R-CNN, SSD, and YOLO· Build neural network models using Keras and TensorFlow· Discover best practices when implementing computer vision applications in business and industry· Train distributed models on GPU-based cloud infrastructureWHO THIS BOOK IS FORData scientists, analysts, and machine learning and software engineering professionals with Python programming knowledge.Shamshad (Sam) Ansari works as President and CEO of Accure Inc, an artificial intelligence automation company that he founded. He has raised Accure from startup to a sustainable business by building a winning team and acquiring customers from across the globe. He has technical expertise in the area of computer vision, machine learning, AI, cognitive science, NLP, and big data. He architected, designed, and developed the Momentum platform that automates AI solution development. He is an inventor and has four US patents in the area of AI and cognitive computing.Shamshad worked as a senior software engineer with IBM, VP of engineering with Orbit Solutions, and as principal architect and director of engineering with Apixio.Section 11. Chapter 1: Prerequisite and Software Installation1.1. Python and PIP1.1.1. Installing Python and PIP on Ubuntu1.1.2. Installing Python and PIP on Mac OS1.1.3. Installing Python and PIP on CentOS 71.1.4. Installing Python and PIP on Windows1.2. Virtualenv1.2.1. Setup and activate virtualenv 1.3. Tensorflow1.3.1. Installing Tensorflow1.4. PyCharm IDE1.4.1. Installing PyCharm1.4.2. Configuring PyCharm to use virtualenv1.5. OpenCV1.5.1. Installing OpenCV1.5.2. Installing OpenCV4 with Python bindings1.6. Additional libraries1.6.1. SciPy1.6.2. MatplotlibChapter 2: Core Concepts of Image and Video Processing1.7. Image processing1.7.1. Image basics 1.7.2. Pixel1.7.3. Pixel color1.7.3.1. Grayscale1.7.3.2. Color1.7.4. Coordinate system 1.7.5. Python and OpenCV code to manipulate images1.7.6. Program: loading, exploring and showing image1.7.7. Program: OpenCV code to access and manipulate pixels1.8. Drawing1.8.1. Drawing a line on an image1.8.2. Drawing a rectangle on an image1.8.3. Drawing a circle on an image1.9. Chapter summary1.10.2. Chapter 3: Techniques of Image Processing2.1. Transformation2.1.1. Resizing2.1.2. Translation2.1.3. Rotation2.1.4. Flipping2.1.5. Cropping2.2. Image arithmetic and bitwise operations2.2.1. Addition2.2.2. Subtraction 2.2.3. Bitwise operations2.2.3.1. OR2.2.3.2. AND2.2.3.3. NOT2.2.3.4. XOR 2.3. Masking2.4. Splitting and merging channels2.5. Smoothing and blurring2.6. Thresholding2.7. Gradient and edge detection2.8. Contours2.9. Chapter summarySection 23. Chapter 4: Building Artificial Intelligence System For Computer Vision3.1. Image processing pipeline3.2. Feature extraction3.2.1. Color histogram3.2.2. GLCM3.2.3. HOG3.2.4. LBP3.3. Feature selection3.3.1. Filter3.3.2. Wrapper3.3.3. Embedded3.3.4. Regularization3.4. Chapter summary4. Chapter 5: Artificial Neural Network for Computer Vision4.1. Introduction to ANN4.1.1. ANN topology4.1.2. Hyperparameters4.1.3. ANN model training using TensorFlow4.1.4. Model evaluation4.1.5. Model deployment4.1.6. Use of trained model4.2. Introduction to Convolution Neural Network (CNN)4.2.1. Core concepts of CNN4.2.2. Creating training set for CNN4.2.3. Training CNN model using TensorFlow4.2.4. Inspecting CNN model and evaluating model fitness4.2.5. Using and deployment of trained model4.3. Introduction to Recurrent Neural Network (RNN) and long short-term Memory (LSTM)4.3.1. Core concepts of RNN and LSTM4.3.2. Creating training set for LSTM4.3.3. LSTM model training using TensorFlow4.3.4. Inspecting LSTM model and assessing fitness4.3.5. Deploying LSTM models in practiceSection 35. Chapter 6: Practical Example 1- Object Detection in Images6. Chapter 7: Practical Example 2- Object Tracking in Videos7. Chapter 8: Practical Example 3- Facial Detection8. Chapter 9: Industrial Application - Realtime Defect Detection in Industrial ManufacturingSection 49. Chapter 10: Training Machine Learning Model on the Cloud9.1. Amazon AWS9.2. Google Cloud Platform (GCP)9.3. Microsoft Azure
The Art of Immutable Architecture
This book teaches you how to evaluate a distributed system from the perspective of immutable objects. You will understand the problems in existing designs, know how to make small modifications to correct those problems, and learn to apply the principles of immutable architecture to your tools.Most software components focus on the state of objects. They store the current state of a row in a relational database. They track changes to state over time, making several basic assumptions: there is a single latest version of each object, the state of an object changes sequentially, and a system of record exists.This is a challenge when it comes to building distributed systems. Whether dealing with autonomous microservices or disconnected mobile apps, many of the problems we try to solve come down to synchronizing an ever-changing state between isolated components. Distributed systems would be a lot easier to build if objects could not change.After reading THE ART OF IMMUTABLE ARCHITECTURE, you will come away with an understanding of the benefits of using immutable objects in your own distributed systems. You will learn a set of rules for identifying and exchanging immutable objects, and see a collection of useful theorems that emerges and ensures that the distributed systems we build are eventually consistent. Using patterns, you will find where the truth converges, see how changes are associative, rather than sequential, and come to feel comfortable understanding that there is no longer a single source of truth. Practical hands-on examples reinforce how to build software using the described patterns, techniques, and tools. By the end, you will possess the language and resources needed to analyze and construct distributed systems with confidence.The assumptions of the past were sufficient for building single-user, single-computer systems. But as we expand to multiple devices, shared experiences, and cloud computing, they work against us. It is time for a new set of assumptions. Start with immutable objects, and build better distributed systems.WHAT YOU WILL LEARN* Evaluate a distributed system from the perspective of immutable objects * Recognize the problems in existing designs, and make small modifications to correct them* Start a new system from scratch, applying patternsApply the principles of immutable architecture to your tools, including SQL databases, message queues, and the network protocols that you already use * Discover new tools that natively apply these principles WHO THIS BOOK IS FORSoftware architects and senior developers. It contains examples in SQL and languages such as JavaScript and C#. Past experience with distributed computing, data modeling, or business analysis is helpful.MICHAEL L. PERRY has built upon the works of mathematicians such as Bertrand Meyer, Leslie Lamport, and Donald Knuth to develop a mathematical system for software development. He has captured this system in a set of open source projects. Michael often presents on math and software at events and online. You can find out more at qedcode.com.Part I: DefinitionChapter 1. Why Immutable ArchitectureChapter 2. Forms of Immutable ArchitectureChapter 3. How to Read a Historical ModelPart II: ApplicationChapter 4. Location IndependenceChapter 5. AnalysisChapter 6. State TransitionsChapter 7. SecurityChapter 8. PatternsPart III: ImplementationChapter 9. Query InversesChapter 10. SQL DatabasesChapter 11. CommunicationChapter 12. Generated Behaviors
People-Centric Skills
USE YOUR INTERPERSONAL AND COMMUNICATION SKILLS AS A FINANCIAL PROFESSIONAL TO WORK SUCCESSFULLY WITH CLIENTSEmbark on a journey to further develop your career when you read People-Centric Skills: Interpersonal and Communication Skills for Financial Professionals, 2nd Edition. Business leaders consider employee communication skills and critical thinking abilities as essential elements for success. In their work, all professionals must communicate clearly and rely on their interpersonal skills to be successful.This second edition of People-Centric Skills shares the fictional story of Dalton Zimmer, executive coach and public speaker. Dalton, all the while juggling his business, kids and social life, provides coaching and communication strategies for handling challenging situations faced by his clients.This insightful narrative will help you expand communication and soft skills as a CPA, auditor, financial planner or other financial professional.As Generation Z is entering the work force, the communication gap between Z and Boomers or Generation X is widening significantly. New to the second edition, you’ll find a discussion of communication between generations and how to bridge them as a financial professional.You can be a more people-centric leader as you engage with a wide range of clients and associates. This book can be a first step to improving interpersonal and communication skills as you continue to develop in your career.DANNY M. GOLDBERG, CPA, CIA, CISA, is the founder of GoldSRD, a leading staffing, recruiting, and professional development firm. He has over 22 years of experience and is an IIA - Dallas Chapter board member. He is well-published, from numerous articles to three other books, and is a highly-regarded speaker on all facets of audit and people-centric skills. Foreword ixPreface xiAcknowledgments xvChapter 1 The People-Centric Journey Begins Anew 1Chapter 2 Reading Through People 5Chapter 3 Emotional Intelligence 29Chapter 4 Different Points of View: Using Self-Awareness and Empathy Effectively 47Chapter 5 Wrong Mode = Wrong Mood: Determining the Optimal Mode of Communication 51Chapter 6 Influencing Change Throughout Any Business 59Chapter 7 Projecting the Real You: Public Speaking 65Chapter 8 Coaching and Mentoring 93Chapter 9 Presentation Skills and Body Language 99Chapter 10 Thinking Quickly on Your Feet 125Chapter 11 Coaching and Mentoring, Revisited 131Chapter 12 Crisis Management 139Epilogue 143Appendix 145About the Author 179Index 181
iPhone Photography For Dummies
CREATE BEAUTIFUL IPHONE PHOTOS WITH THE TECHNIQUES FOUND IN THIS BOOKiPhone Photography For Dummies, shares the expertise of photography workshop instructor Mark Hemmings as he shows readers how to get stunning images using their favorite iPhone. By implementing Hemmings' simple techniques, you'll get professional-looking results in a fraction of the time you’d expect. You’ll learn to:* Adjust camera settings* Create majestic landscape images* Capture exciting action shots* Shoot beautiful portraits* Select an editing app* Share and organize images* Shoot photos comfortably while on the goPerfect for those who want to take breathtaking photos without investing in a top-of-the-line camera, iPhone Photography For Dummies takes the guesswork and luck out of creating beautiful imagery. It shows people without formal training in photography how to make meaningful and noticeable improvements in their shooting technique using either the latest iPhone model or older versions of the device.MARK HEMMINGS can be found traveling the world conducting photography workshops with a big emphasis on iPhone photography. He has a great passion for teaching iPhone camera best practices, which shows in his daily Instagram photo lessons. Mark has been a professional photographer since 1997 and an iPhone travel photographer since 2012. INTRODUCTION 1About This Book 1Foolish Assumptions 2Icons Used in This Book 3Beyond the Book 3Where to Go from Here 3PART 1: FAST-TRACKING YOUR PHOTOGRAPHY SKILLS 5CHAPTER 1: INTRODUCING IPHONE PHOTOGRAPHY 7Getting to Know the Camera in Your iPhone 7Models with one lens 8Models with two lenses 8Models with three lenses 9Future models with more than three lenses 11Taking a Quick Tour of the iPhone Camera App 11Taking a Photo 12Viewing Your iPhone Photos 14Editing Your iPhone Photo 16Sharing Your Photos 17CHAPTER 2: TAKING THE COMPLETE CAMERA WALK-AROUND 19Discovering the Different Ways to Open Your Camera 20Properly Holding Your iPhone for Steady Photographs 21Taking a Photo Without Using the Normal Shutter Button 23Side shutter using the volume buttons 23Apple EarPods volume controller shutter release 23Zooming in to Your Subject 25Zooming best practices 26Cropping instead of digital zooming 28Using the Selfie Camera with and without Background Blur 29Knowing When to Use (and not Use) the Camera Flash 32Getting to Know Live Photos 36Using Live Photo 37Making sure Live Photo isn’t on by default 38Using the Camera Timer for Stable and Sharp Photos 39Family portraits 41Selfies with a tripod or stand 43Landscape photography using your timer 43Getting Creative with Filters 44Choosing the best camera filter for your photograph 45Reverting to the original 48CHAPTER 3: SETTING UP YOUR CAMERA FOR PHOTOGRAPHIC GREATNESS 49Turning on iCloud Photos 50Optimizing iPhone Storage 52Downloading the Original Photos 53Uploading to My Photo Stream 54Sharing Albums 55Watching Cellular Data 56Auto-Playing Videos and Live Photos 57Viewing Full HDR 58Transferring to Mac or PC 58Customizing Your iPhone’s Camera 59CHAPTER 4: APPLYING IPHONE AUTO-MODE SETTINGS 63Zooming with Various Camera Modes 63Preparing to Take Selfies 64Lighting and background 64Light case options 66Selfie sticks and stabilizers 66Taking Selfies 69Choose the Portrait selfie mode 70Adjust depth control 70Choose your favorite type of selfie light 72Fine-tune the light intensity and your effect intensity 75Add photo filters 79Add a flash 80Use your self-timer 80Adjust selfie zoom to add your friends into the picture 81Considering Square Photos 82Accessing your square camera 84Following square photography best practices 84Creating Panoramic Photos 85Panoramic best practices for frame-worthy photographs 85Vertical panoramic photos! 87PART 2: DISCOVERING THE FUNDAMENTALS OF PHOTOGRAPHIC GENRES 89CHAPTER 5: CREATING PERFECT LANDSCAPE PHOTOGRAPHS 91Camera Considerations 92Long-pressing the screen where your main subject is located 93Controlling focus and exposure with the AE/AF Lock 95Lighting Considerations 96Taking advantage of the magic hours 96Timing magic hour 98Gear Considerations 100Steadying your iPhone camera with a tripod 100Choosing which lens to use (for multilens iPhones) 101Photography Tips for Your Next Outing 103Applying the Rule of Thirds for better compositions 104Using the Self Timer option for shake-free photos 105CHAPTER 6: SHOOTING SPORTS PHOTOGRAPHY 107Camera Considerations 108Gear Considerations 109A tough iPhone protective case 110An iPhone screen protector 110A monopod for steadier sports photos 110A foldable camping seat 111Battery packs or battery cases 111Touch-sensitive gloves in cold weather 111A telephoto lens attachment for distant athletes 112Lighting Considerations 112Photographing into the setting sun 113Using your Portrait mode 113Using shadows for a fine art sports photography look 114Creating silhouette sports photos 114Photographing during golden hour 116Trusting your iPhone to produce accurate colors 116Photography Tips for Dynamic Sports Photos 117Using the Rule of Thirds 117Panning your camera to create motion blur 118Choosing the best-looking stride 119Photographing from a lower position 121Creating contrast between athlete and background 121Photographing athlete group photos 122CHAPTER 7: SAVING MEMORIES THROUGH FAMILY AND INDIVIDUAL PORTRAITURE 125Camera Considerations 126Portrait mode pros and cons 126Burst mode 127Self-timer 129Gear Considerations 130Lighting Considerations 132Underexposing your portraits for extra drama 132Looking out the window 134Photographing travel photos during blue hour 135Using shadows to create pictograms 136Making use of silhouettes for dramatic portraits 136Placing family members in the shade for even light 137Photographing from behind with low sunlight 139Photography Tips for Your Next Portrait Session 140Photographing from a lower position 140Choosing black and white for fine art portraits 141Trying forced perspective techniques for fun family pics 141Using the Thirds grid for environmental portraits 142Creating humorous photos to keep the mood light 143Avoiding objects sticking out of people’s heads 145Choosing to include mirrors in your compositions 145Including family member’s interests 146Avoiding overcast skies 146Combining family photos using the Layout app 149Photographing from an aerial perspective 149Screenshot photos for family who are away 150CHAPTER 8: DOCUMENTING YOUR TRAVEL AND VACATION IN PICTURES 153Camera Considerations 154Using Burst mode for moving subjects 154Capturing portraits with both Portrait and Photo modes 154Photographing nonportraits in Portrait mode 157Choosing black and white for iconic locations 158Photographing cityscapes through windows 160Photographing travel scenes with the ultra wide lens 160Getting detail in night scenes using Night mode 161Choosing all three lenses for iconic scenes 163Gear Considerations 163Extending your photography with battery packs 164Packing a tabletop-sized tripod 165Photographing or filming yourself with a selfie stick 165Purchasing the best protective case for your needs 165Choosing a tough mobile device bag 168Keeping your gear safe while traveling 168Lighting Considerations 169Backlight 169Side light 170Raking light 170Reflective light 172Diagonal light 172Silhouette light 173Shadow light 174Magic hour light 174Blue and yellow light 176Photography Tips for Your Next Trip 177Practicing design-based photography 177Placing S-curves in your compositions 178Framing your primary subject 178Photographing exterior architecture twice 179Including pattern photos of unusual scenes 181Scheduling famous landmarks as early as possible 182Waiting for animals to move into the picture space 182Choosing your background first 184Matching color when possible 184Including national text and fonts in your photos 185Composing with equidistance 186Tightening the view of iconic buildings 186Composing family members looking into the frame 188Reviewing your favorite establishments 188CHAPTER 9: CREATING STILL LIFE AND PRODUCT PHOTOGRAPHY ON THE CHEAP 191Camera Considerations 192Gear Considerations 192Purchasing backgrounds for flat-lay still life photos 193Choosing the appropriate background for your product 194Using clear glass as a background 195Floating flowers in water 195Purchasing Bristol board for interior product photography 196Photographing still life photos in a greenhouse 196Adding opaque paper to windows for unique backgrounds 198Lighting Considerations 199Photographing your product indoors 199Using side light 201Using backlight 201Using harsh overhead light 203Using doorway light 204Photographing with mixed lighting 205Adding shadows to your still life photos 206Photographing throughout the day 206Creating Beautiful Still Life Photos 208Composing symmetrical photos properly 208Arranging foliage to catch the setting sun 209Following the equidistance principle 209Creating both color and black-and-white versions 210Adding negative space to be used for text 211CHAPTER 10: TAKING IT TO THE STREETS: PHOTOGRAPHING STRANGERS213Camera Considerations 213Choosing black and white for most photos 214Cropping best practices 215Placing more importance on drama instead of sharpness 217Using background blur to maintain privacy 217Photographing with Burst mode for perfect timing 218Gear Considerations 219Using waterproof cases for rainy days 219Choosing other stability options besides tripods 220Lighting Considerations 221Waiting for people to walk into a ray of light 222Allowing shadows to work as metaphors 222Blurring people by photographing at dusk 223Maintaining anonymity by using backlight 224Capturing mannequins with window reflections 225Photographing only a person’s shadow for extra mystery 225Raking light for textured backgrounds 227Photography Tips for Your Next Day (or Night) on the Town 228Finding your background first 228Choosing an aerial perspective 229Being culturally sensitive 229Maintaining a sense of lightness and humor 231Choosing the best stride 232Showing the urban environment using a wide lens 232Composing with a sense of direction 233Avoiding faces to maintain anonymity 235PART 3: EDITING, ORGANIZING, AND SHARING YOUR PHOTOS 237CHAPTER 11: EDITING WITH THE IOS PHOTOS APP 239Opening Your Photos App 239Exploring Your Editing Options 241Starting with Auto adjustments 241Getting to know the editing tools 243Applying Filters 247Vivid 247Vivid Warm 249Vivid Cool 249Dramatic 250Dramatic Cool 250Mono 250Silvertone 250Noir 250Cropping an Image 251Using (and disabling) the Auto Crop tool 252Flipping your image horizontal 252Rotating your photo 90 degrees 253Adjusting Aspect Ratio 253Editing Your Portrait Photography 255CHAPTER 12: ORGANIZING AND SHARING YOUR PHOTOS LIKE A PRO 259Thinking about Post-Production Workflow 259Deleting Unwanted Photos 260Deleting a photo 261Recovering a deleted photo 262Favoriting Photos with the Heart Icon 263Diving into Album Organization 265Selecting photos to create a new album 266Removing a photo from an album 267Using albums wisely 268Finding photos of a single person 268Sorting your files by media types 269Using the Other Albums section 270Knowing When to Use the Photos Section 270Making the For You Section Work, Well, for You 271Using the Search Tool within the Photos App 272Sharing Your Photos 273Using shared albums 273Sharing to any location 275Sharing to your social media channels 276PART 4: THE PART OF TENS 277CHAPTER 13: TEN IOS APPS THAT WILL ENHANCE YOUR PHOTOGRAPHY 279Mark’s Suggested Free Photography Apps 279Adobe Photoshop Express 280Adobe Photoshop Fix 280Adobe Lightroom CC 280Instagram 280Facetune2 281Mark’s Suggested Paid Photography Apps 281TouchRetouch 281Slow Shutter Cam 281Brushstroke 282SKRWT 282LensFlare 282CHAPTER 14: TEN TIPS FOR SHOOTING AND SHARING VIDEO WITH YOUR IPHONE 283Accessing the Video Camera within the Photos App 283Holding Your iPhone Properly for Smooth Video Recording 284Trimming the Length of Any Video 284Adjusting the Exposure and Filter Settings 285Cropping Your Video 285Choosing a Video Aspect Ratio 285Choosing Vertical or Horizontal Orientation 286Creating Time-Lapse Photography Video Clips 286Capturing Dramatic Video Clips with Slo-Mo 287Exporting Your Finished Videos 288CHAPTER 15: TEN EXTRA EDITING FEATURES TO JAZZ UP YOUR IMAGES 289Adding Notes and Text Using Markup 289Adding Your Signature to Your Photos 290Adding Extra Markup Options to Your Photo 291Deleting Your Markups to Return to Your Original Photo 291Creating and Editing a Live Photo 291Creating a Loop Photo 292Creating a Bounce Photo 292Exporting Your Live, Loop, and Bounce Photos 293Creating a Long Exposure Photo 293Creating Abstract Photos Using Long Exposure 294Index 295
Understanding Microsoft Teams Administration
Explore solutions, best practices, tips, and workarounds to plan, design, customize, implement, and manage Microsoft Teams in any environment.The book starts with an overview of Microsoft Teams where you will go through the teams architecture, teams/channels, audio/video meetings, and the phone system. It further dives into deployment and management of teams, clients, guests and external access, and live events, followed by network assessment and bandwidth planning for Teams. Here, you will learn about deployment of quality of service and how to configure your phone systems using direct routing and calling plans. Moving forward, you will learn Microsoft Teams administration and policy management along with the migration process of Skype for Business on-prem to Microsoft Teams. Towards the end, you will learn troubleshooting techniques in Teams for call quality issues and connectivity challenges.After reading Understanding Microsoft Teams Administration, you will be able to effectively configure, customize, and manage the Teams experience using the Teams admin portal and other tools and techniques.WHAT YOU WILL LEARN* Understand the Microsoft Teams architecture including the different components involved* Enable and manage external and guest access for Teams users* Manage Teams and channels with a private channel* Implement quality of service for audio/video calls and meetings* Establish Office 365 data classifications, loss prevention plans, and governance* Manage resource types, licensing, service health reporting, and support* Work with Microsoft Teams room and live event management* Implement and manage messaging, calling policies, and settingsWHO THIS BOOK IS FORAdministrators and technical consultants working on Teams.BALU ILAG is a Microsoft Certified Trainer (MCT), Microsoft 365 Certified Teams Administrator Associate, and Microsoft Certified Solutions Expert (MCSE) for communication and productivity. He has written several blog posts on unified communication and collaboration technologies including subjects ranging from a how-to guide to best practices and troubleshooting.He is currently working as an Office 365 and collaboration specialist at Juniper networks. Balu has over 13 years' experience in messaging, telecom and unified communications and collaboration and is focused on Microsoft Teams and Microsoft Office 365 collaboration. His role is a combination of product administration, product development, and strategic guidance for enterprise customers.CHAPTER 1: MICROSOFT TEAMS OVERVIEWa. What is Microsoft Teams?b. Teams Architecturec. Teams Team and channelsd. Meeting, Tab, Files and Wikie. Teams Audio/video call and meetingf. Teams phone system overviewCHAPTER 2: TEAMS CLIENT DEPLOYMENT AND USER PROVISIONINGa. Deploy and Manage Teams clientb. Manage Teams storagec. Manage External and Guest accessCHAPTER 3: ORGANIZATION READINESS FOR MICROSOFT TEAMSa. Network assessment and bandwidth planning for Teamsb. Deploy Quality of Servicec. Configure phone systema. Configure Teams Direct Routingb. Configure Microsoft Calling pland. Customizes and manage Live eventCHAPTER 4: MICROSOFT TEAMS ADMINISTRATION AND POLICY MANAGEMENTa. Enable and users for Microsoft Teamsb. Organization wide setting for Microsoft Teamsc. Meeting, Live event and messaging policyd. Manage Phone number and Voice routing policyCHAPTER 5: MIGRATION FROM SKYPE FOR BUSINESS (LYNC) ON-PREM AND ONLINE TO MICROSOFT TEAMSa. Get ready for Microsoft Teamsb. Plan user migration wiselyc. Migrate user from Skype for Business Online to Microsoft Teamsd. Migrate user from Skype for Business On-prem to Microsoft Teamse. Maintain momentum after migrationCHAPTER 6: MICROSOFT TEAMS TROUBLESHOOTING APPROACHES1. Solve Teams sign-in issues2. Analyze call quality and Troubleshoot call quality issues3. Troubleshoot Live event issues4. Solve connectivity challenges
Learn Java for Android Development
Gain the essential Java language skills necessary for using the Android SDK platform to build Java-based Android apps. This book includes the latest Java SE releases that Android supports, and is geared towards the Android SDK version 10. It includes new content including JSON documents, functional programming, and lambdas as well as other language features important for migrating Java skills to Android development.Android is still the world's most popular mobile platform and because this technology is still mostly based on Java, you should first obtain a solid grasp of the Java language and its APIs in order to improve your chances of succeeding as an effective Android apps developer. Learn Java for Android Development, 4th Editionhelps you do that.Each of the book’s chapters provides an exercise section that gives you the opportunity to reinforce your understanding of the chapter’s material. Answers to the book’s more than 500 exercises are provided in an appendix. Once you finish, you will be ready to begin your Android app development journey using Java.WHAT YOU WILL LEARN* Discover the latest Java programming language features relevant to Android SDK development* Apply inheritance, polymorphism, and interfaces to Android development* Use Java collections, concurrency, I/O, networks, persistence, functional programming, and data access in Android apps* Parse, create, and transform XML and JSON documents* Migrate your Java skills for mobile development using the Android platformWHO THIS BOOK IS FORProgrammers with at least some prior Java programming experience looking to get into mobile Java development with the Android platform.PETER SPÄTH consults, trains/teaches, and writes books on various subjects, with a primary focus on software development. With a wealth of experience in Java-related languages, the release of Kotlin for building Android apps made him enthusiastic about writing books for Kotlin development in the Android environment. He also graduated in 2002 as a physicist and soon afterward became an IT consultant, mainly for Java-related projects.JEFF FRIESEN is a freelance tutor and software developer with an emphasis on Java (and now Android). In addition to authoring Learn Java for Android Development and co-authoring Android Recipes, Jeff has written numerous articles on Java and other technologies for JavaWorld, informIT, Java.net, and DevSource.1: Getting Started with JavaTalking about ART and licensing here2: Learning Language Fundamentals3: Discovering Classes and Objects4: Discovering Inheritance, Polymorphism, and Interfaces5: Mastering Advanced Language Features, Part 16: Mastering Advanced Language Features, Part 27: Exploring the Basic APIs, Part18: Exploring the Basic APIs, Part29: Functional Programming and Lambdas10: Exploring the Collections Framework11: Exploring the Concurrency Utilities12: Performing Classic I/O13: Accessing Networks14: Migrating to New I/O15: Accessing Databases16: Parsing, Creating, and Transforming XML Documents17: Working With JSON DocumentsA. Solutions to Exercises
Computational and Analytic Methods in Science and Engineering
This contributed volume collects papers presented at a special session of the conference Computational and Mathematical Methods in Science and Engineering (CMMSE) held in Cadiz, Spain from June 30 - July 6, 2019. Covering the applications of integral methods to scientific developments in a variety of fields, ranging from pure analysis to petroleum engineering, the chapters in this volume present new results in both pure and applied mathematics. Written by well-known researchers in their respective disciplines, each chapter shares a common methodology based on a combination of analytic and computational tools. This approach makes the collection a valuable, multidisciplinary reference on how mathematics can be applied to various real-world processes and phenomena. Computational and Analytic Methods in Science and Engineering will be ideal for applied mathematicians, physicists, and research engineers. CHRISTIAN CONSTANDA, holder of the C.W. Oliphant Endowed Chair in Mathematics at the University of Tulsa, is the chairman of the International Consortium for Integral Methods in Science and Engineering (IMSE). He organizes IMSE conferences all over the world, and is the author and editor of 32 books and over 150 journal articles. 1 New Numerical Results for the Optimization of Neumann Eigenvalues2 Transient Convection-Diffusion-Reaction Problems with Variable Velocity Field by Means of DRBEM with Different Radial Basis Functions3 On a Parametric Representation of the Angular Neutron Flux in the Energy Range from 1 eV to 10MeV4 A Boundary Integral Equation Formulation for Advection–Diffusion–Reaction Problems with Point Sources5 Displacement Boundary Value Problem for a Thin Plate in an Unbounded Domain6 A Dirichlet Spectral Problem in Domains Surrounded by Thin Stiff and Heavy Bands7 Spectral Homogenization Problems in Linear Elasticity with Large Reaction Terms Concentrated in Small Regions of the Boundary8 The Mathematical Modelling of the Motion of Biological Cells in Response to Chemical Signals9 Numerical Calculation of Interior Transmission Eigenvalues with Mixed Boundary Conditions10 An Inequality for H¨older Continuous Functions Generalizing a Result of CarloMiranda11 Two-Phase Three-Component Flow in PorousMedia:Mathematical Modeling of Dispersion-Free Pressure Behavior12 Error Analysis and the Role of Permutation in Dynamic Iteration SchemesIndex
Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks
Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.KRISHNA KANT SINGH is an Associate Professor in Electronics and Communications Engineering in KIET Group of Institutions, Ghaziabad, India. Dr. Singh has acquired BTech, MTech, and PhD (IIT Roorkee) in the area of machine learning and remote sensing. He has authored more than 50 technical books and research papers in international conferences and SCIE journals. AKANSHA SINGH is an Associate Professor in Department of Computer Science Engineering in Amity University, Noida, India. Dr. Singh has acquired BTech, MTech, and PhD (IIT Roorkee) in the area of neural network and remote sensing. She has authored more than 40 technical books and research papers in international conferences and SCIE journals. Her area of interest includes Mobile Computing, Artificial Intelligence, Machine Learning, Digital Image Processing. KORHAN CENGIZ received his PhD in Electronics Engineering from Kadir Has University, Istanbul, Turkey, in 2016. He has served as keynote speakers at many conferences. His research interests include wireless sensor networks, routing protocols, wireless communications, 5G systems, statistical signal processing, and spatial modulation. DAC-NHUONG LE has a MSc and PhD in computer science from Vietnam National University, Vietnam in 2009 and 2015, respectively. He is Associate Professor in Computer Science, Deputy-Head of Faculty of Information Technology, Haiphong University, Vietnam. He has a total academic teaching experience of 12+ years with many publications in reputed international conferences, journals and online book chapters. His area of research includes: evaluation computing and approximate algorithms, network communication, security and vulnerability, network performance analysis and simulation, cloud computing, IoT and image processing in biomedical. Preface xiii1 MACHINE LEARNING ARCHITECTURE AND FRAMEWORK 1Nilanjana Pradhan and Ajay Shankar Singh1.1 Introduction 21.2 Machine Learning Algorithms 31.2.1 Regression 31.2.2 Linear Regression 41.2.3 Support Vector Machine 41.2.4 Linear Classifiers 51.2.5 SVM Applications 81.2.6 Naïve Bayes Classification 81.2.7 Random Forest 91.2.8 K-Nearest Neighbor (KNN) 91.2.9 Principal Component Analysis (PCA) 91.2.10 K-Means Clustering 101.3 Business Use Cases 101.4 ML Architecture Data Acquisition 141.5 Latest Application of Machine Learning 151.5.1 Image Identification 161.5.2 Sentiment Analysis 161.5.3 News Classification 161.5.4 Spam Filtering and Email Classification 171.5.5 Speech Recognition 171.5.6 Detection of Cyber Crime 171.5.7 Classification 171.5.8 Author Identification and Prediction 181.5.9 Services of Social Media 181.5.10 Medical Services 181.5.11 Recommendation for Products and Services 181.5.11.1 Machine Learning in Education 191.5.11.2 Machine Learning in Search Engine 191.5.11.3 Machine Learning in Digital Marketing 191.5.11.4 Machine Learning in Healthcare 191.6 Future of Machine Learning 201.7 Conclusion 22References 232 COGNITIVE COMPUTING: ARCHITECTURE, TECHNOLOGIES AND INTELLIGENT APPLICATIONS 25Nilanjana Pradhan, Ajay Shankar Singh and Akansha Singh2.1 Introduction 262.1 The Components of a Cognitive Computing System 272.3 Subjective Computing Versus Computerized Reasoning 282.4 Cognitive Architectures 292.5 Subjective Architectures and HCI 312.6 Cognitive Design and Evaluation 322.6.1 Architectures Conceived in the 1940s Can’t Handle the Data of 2020 412.7 Cognitive Technology Mines Wealth in Masses of Information 412.7.1 Technology is Only as Strong as Its Flexible, Secure Foundation 422.8 Cognitive Computing: Overview 432.9 The Future of Cognitive Computing 47References 493 DEEP REINFORCEMENT LEARNING FOR WIRELESS NETWORK 51Bharti Sharma, R.K Saini, Akansha Singh and Krishna Kant Singh3.1 Introduction 513.2 Related Work 543.3 Machine Learning to Deep Learning 553.3.1 Advance Machine Learning Techniques 563.3.1.1 Deep Learning 563.3.2 Deep Reinforcement Learning (DRL) 573.3.2.1 Q-Learning 583.3.2.2 Multi-Armed Bandit Learning (MABL) 583.3.2.3 Actor–Critic Learning (ACL) 583.3.2.4 Joint Utility and Strategy Estimation Based Learning 593.4 Applications of Machine Learning Models in Wireless Communication 593.4.1 Regression, KNN and SVM Models for Wireless 603.4.2 Bayesian Learning for Cognitive Radio 603.4.3 Deep Learning in Wireless Network 613.4.4 Deep Reinforcement Learning in Wireless Network 623.4.5 Traffic Engineering and Routing 633.4.6 Resource Sharing and Scheduling 643.4.7 Power Control and Data Collection 643.5 Conclusion 65References 664 COGNITIVE COMPUTING FOR SMART COMMUNICATION 73Poonam Sharma, Akansha Singh and Aman Jatain4.1 Introduction 744.2 Cognitive Computing Evolution 754.3 Characteristics of Cognitive Computing 764.4 Basic Architecture 774.4.1 Cognitive Computing and Communication 774.5 Resource Management Based on Cognitive Radios 784.6 Designing 5G Smart Communication with Cognitive Computing and AI 804.6.1 Physical Layer Design Based on Reinforcement Learning 824.7 Advanced Wireless Signal Processing Based on Deep Learning 844.7.1 Modulation 854.7.2 Deep Learning for Channel Decoding 864.7.3 Detection Using Deep Learning 874.8 Applications of Cognition-Based Wireless Communication 874.8.1 Smart Surveillance Networks for Public Safety 884.8.2 Cognitive Health Care Systems 884.9 Conclusion 89References 895 SPECTRUM SENSING AND ALLOCATION SCHEMES FOR COGNITIVE RADIO 91Amrita Rai, Amit Sehgal, T.L. Singal and Rajeev Agrawal5.1 Foundation and Principle of Cognitive Radio 925.2 Spectrum Sensing for Cognitive Radio Networks 945.3 Classification of Spectrum Sensing Techniques 955.4 Energy Detection 975.5 Matched Filter Detection 1005.6 Cyclo-Stationary Detection 1035.7 Euclidean Distance-Based Detection 1075.8 Spectrum Allocation for Cognitive Radio Networks 1085.9 Challenges in Spectrum Allocation 1185.9.1 Spectrum and Network Heterogeneity 1195.9.2 Issues and Challenges 1205.10 Future Scope in Spectrum Allocation 122References 1236 SIGNIFICANCE OF WIRELESS TECHNOLOGY IN INTERNET OF THINGS (IOT) 131Ashish Tripathi, Arun Kumar Singh, Pushpa Choudhary, Prem Chand Vashist and K. K. Mishra6.1 Introduction 1326.1.1 Internet of Things: A Historical Background 1336.1.2 Internet of Things: Overview, Definition, and Understanding 1336.1.3 Internet of Things: Existing and Future Scopes 1356.2 Overview of the Hardware Components of IoT 1366.2.1 IoT Hardware Components: Development Boards/Platforms 1366.2.1.1 Arduino 1366.2.1.2 Raspberry Pi 1376.2.1.3 BeagleBone 1376.2.2 IoT Hardware Components: Transducer 1386.2.2.1 Sensors 1386.2.2.2 Actuators 1386.3 Wireless Technology in IoT 1396.3.1 Topology 1396.3.1.1 Mesh Topology 1406.3.1.2 Star Topology 1416.3.1.3 Point-to-Point Topology 1416.3.2 IoT Networks 1426.3.2.1 Nano Network 1426.3.2.2 Near-Field Communication (NFC) Network 1436.3.2.3 Body Area Network (BAN) 1436.3.2.4 Personal Area Network (PAN) 1436.3.2.5 Local Area Network (LAN) 1436.3.2.6 Campus/Corporate Area Network (CAN) 1436.3.2.7 Metropolitan Area Network (MAN) 1446.3.2.8 Wide Area Network (WAN) 1446.3.3 IoT Connections 1446.3.3.1 Device-to-Device (D2D)/Machine-to-Machine (M2M) 1446.3.3.2 Machine-to-Gateway/Router (M2G/R) 1456.3.3.3 Gateway/Router-to-Data System (G/R2DS) 1456.3.3.4 Data System to Data System (DS2DS) 1456.3.4 IoT Protocols/Standards 1456.3.4.1 Network Protocols for IoT 1466.3.4.2 Data Protocols for IoT 1486.4 Conclusion 150References 1507 ARCHITECTURES AND PROTOCOLS FOR NEXT-GENERATION COGNITIVE NETWORKING 155R. Ganesh Babu, V. Amudha and P. Karthika7.1 Introduction 1567.1.1 Primary Network (Licensed Network) 1567.1.2 CR Network (Unlicensed Network) 1577.2 Cognitive Radio Network Technologies and Applications 1597.2.1 Classes of CR 1597.2.2 Next Generation (xG) of CR Applications 1627.3 Cognitive Computing: Architecture, Technologies, and Intelligent Applications 1637.3.1 CR Physical Architecture 1637.4 Functionalities of CR in NeXt Generation (xG) Networks 1647.5 Spectrum Sensing 1657.5.1 Spectrum Decision 1657.5.2 Spectrum Mobility 1657.5.3 CR Network Functions 1667.6 Cognitive Computing for Smart Communications 1677.6.1 CR Technologies 1677.7 Spectrum Allocation in Cognitive Radio 1697.8 Cooperative and Cognitive Network 1737.8.1 Cooperative Centralized Coordinated 1737.8.2 Cooperative Decentralized (Distributed) Coordinated and Uncoordinated 176References 1768 ANALYSIS OF PEAK-TO-AVERAGE POWER RATIO IN OFDM SYSTEMS USING COGNITIVE RADIO TECHNOLOGY 179Udayakumar Easwaran, Poongodi Palaniswamy and Vetrivelan Ponnusamy8.1 Introduction 1808.2 OFDM Systems 1818.3 Peak-to-Average Power Ratio 1838.4 Cognitive Radio (CR) 1848.5 Related Works 1868.6 Neural Network System Model 1938.7 Complexity Examination 1948.8 PAPR and BER Examination 1958.9 Performance Evaluation 1968.10 Results and Discussions 1968.11 Conclusion 200References 2009 A THRESHOLD-BASED OPTIMIZATION ENERGY-EFFICIENT ROUTING TECHNIQUE IN HETEROGENEOUS WIRELESS SENSOR NETWORKS 203Samayveer Singh9.1 Introduction 2049.2 Literature Review 2059.3 System Model 2079.3.1 Four-Level Heterogeneous Network Model 2089.3.2 Energy Dissipation Radio Model 2109.4 Proposed Work 2119.4.1 Optimum Cluster Head Election of the Proposed Protocol 2119.4.2 Information Congregation and Communication Process Based on Chaining System for Intra and Inter‑Cluster Communication 2149.4.3 The Complete Working Process of the Proposed Method 2149.5 Simulation Results and Discussions 2169.5.1 Network Lifetime and Stability Period 2179.5.2 Network Outstanding Energy 2199.5.3 Throughput 2199.5.4 Comparative Analysis of the Level-4 Network Protocols 2229.6 Conclusion 222References 22310 EFFICACY OF BIG DATA APPLICATION IN SMART CITIES 225Sudipta Sahana, Dharmpal Singh and Pranati Rakshit10.1 Introduction 22610.1.1 Characteristics of Big Data 22710.1.1.1 Velocity 22710.1.1.2 Volume 22710.1.1.3 Value 22810.1.1.4 Variety 22810.1.1.5 Veracity 22810.1.2 Definition of Smart Cities 22810.2 Types of Data in Big Data 22910.2.1 Structured Data 22910.2.2 Unstructured Data 23010.2.3 Semi-Structured Data 23010.3 Big Data Technologies 23110.3.1 Apache Hadoop 23110.3.2 HDFS 23110.3.3 Spark 23210.3.4 Microsoft HDInsight 23210.3.5 NoSQL 23310.3.6 Hive 23310.3.7 Sqoop 23410.3.8 R 23510.3.9 Data Lakes 23510.4 Data Source for Big Data 23510.4.1 Media 23610.4.2 Cloud 23610.4.3 The Web 23610.4.4 IOT 23610.4.5 Databases as a Big Data Source 23710.4.6 Hidden Big Data Sources 23710.4.6.1 Email 23710.4.6.2 Social Media 23810.4.6.3 Open Data 23810.4.6.4 Sensor Data 23810.4.7 Application-Oriented Big Data Source for a Smart City 23810.4.7.1 Healthcare 23810.4.7.2 Transportation 23910.4.7.3 Education 24010.5 Components of a Smart City 24110.5.1 Smart Infrastructure 24110.5.1.1 Intelligent Lighting 24110.5.1.2 Modern Parking Systems 24110.5.1.3 Associated Charging Points 24210.5.2 Smart Buildings and Belongings 24210.5.2.1 Safety and Security Systems 24210.5.2.2 Smart Sprinkler Systems for Gardens 24210.5.2.3 Smart Heating and Ventilation 24210.5.3 Smart Industrial Environment 24310.5.4 Smart City Services 24310.5.4.1 Smart Stalls 24310.5.4.2 Monitoring of Risky Areas 24410.5.4.3 Public Safety 24410.5.4.4 Fire/Explosion Management 24410.5.4.5 Automatic Health-Care Delivery 24410.5.5 Smart Energy Management 24410.5.5.1 Smart Grid 24510.5.5.2 Intelligent Meters 24510.5.6 Smart Water Management 24510.5.7 Smart Waste Management 24510.6 Challenge and Solution of Big Data for Smart City 24610.6.1 Challenge in Big Data for Smart City 24610.6.1.1 Data Integration 24610.6.1.2 Security and Privacy 24610.6.1.3 Data Analytics 24710.6.2 Solution of Challenge Smart City 24710.6.2.1 Conquering Difficulties with Enactment 24710.6.2.2 Making People Smarter—Education for Everyone 24810.7 Conclusion 248References 249Index 251
Parallel Algorithms in Computational Science and Engineering
This contributed volume highlights two areas of fundamental interest in high-performance computing: core algorithms for important kernels and computationally demanding applications. The first few chapters explore algorithms, numerical techniques, and their parallel formulations for a variety of kernels that arise in applications. The rest of the volume focuses on state-of-the-art applications from diverse domains. By structuring the volume around these two areas, it presents a comprehensive view of the application landscape for high-performance computing, while also enabling readers to develop new applications using the kernels. Readers will learn how to choose the most suitable parallel algorithms for any given application, ensuring that theory and practicality are clearly connected. Applications using these techniques are illustrated in detail, including:* Computational materials science and engineering* Computational cardiovascular analysis* Multiscale analysis of wind turbines and turbomachinery* Weather forecasting* Machine learning techniquesParallel Algorithms in Computational Science and Engineering will be an ideal reference for applied mathematicians, engineers, computer scientists, and other researchers who utilize high-performance computing in their work.State-of-the-Art Sparse Direct Solvers.- The Effect of Various Sparsity Structures on Parallelism and Algorithms to Reveal Those Structures.- Structure-Exploiting Interior Point Methods.- Parallel Hybrid Sparse Linear System Solvers.- Computational Material Science and Engineering.- Computational Cardiovascular Analysis with the Variational Multiscale Methods and Isogeometric Discretization.- ALE and Space-Time Variational Multiscale Isogeometric Analysis of Wind Turbines and Turbomachinery.- Variational Multiscale Flow Analysis in Aerospace, Energy, and Transportation Technologies.- Multiscale Crowd Dynamics Modeling and Safety Problems Towards Parallel Computing.- HPC for Weather Forecasting.- A Simple Study of Pleasing Parallelism on Multicore Computers.- Parallel Fast Time-Domain Integral-Equation Methods for Transient Electromagnetism Analysis.- Parallel Optimization Techniques for Machine Learning.
Von Data Mining bis Big Data
Von Datensalat zu DatenschatzEine wichtige Säule von Industrie 4.0 ist Big Data. Hierbei geht es um die intelligente Verwertung riesiger Datenmengen mit dem Ziel Prozesse besser zu beherrschen oder neue Geschäftsfelder zu finden. Big Data für sich zu erschließen bedeutet nichts anderes als einen Schatz zu heben, der in der Fülle von Informationen, die Sie in Ihrem Unternehmen anhäufen, verborgen liegt. Dieses Buch enthält die Schatzkarte. Hier erfahren Sie- wie mit Hilfe von Data Mining-Techniken unbekannte Zusammenhänge und Strukturen über den datenliefernden Prozess entdeckt werden können- wie mit den gewonnenen Erkenntnissen detaillierte Vorhersagen über das zukünftige Prozessverhalten und Strategien zur Optimierung ganzer Fabriken abgeleitet werden- welche Tools und Plattformen es gibt, um Big Data wirtschaftlich sinnvoll in Ihr Unternehmen einzuführen- wie andere Firmen aus verschiedensten Branchen mit Big Data erfolgreiche Effizienzsteigerungen erreicht habenWenn Sie sich im Rahmen der aktuell laufenden Digitalisierungswelle fragen, welche der modernen Techniken wofür genutzt werden können oder müssen, um den Anschluss nicht zu verpassen, dann ist dieses Buch genau das richtige für Sie. Der Autor hat über 20 JahreErfahrung bei der Entwicklung von Data Mining-Technologien und bei ihrem Einsatz in der Industrie. Prof. Dr. Otte leitet die Lehre im Bereich Künstliche Intelligenz im Masterstudium an der Hochschule Ulm
Komplexität von Algorithmen
Dieses Lehrbuch, entstanden aus einer Anfängervorlesung aus dem Informatik-Studiengang an der Leibniz Universität Hannover, bietet einen ersten Einstieg in den Bereich der Komplexitätstheorie.Der Leser wird mit den wichtigsten Begriffen und Resultaten aus diesem Bereich vertraut gemacht: Komplexitätsklassen, vollständige („schwierigste“) Probleme in einer Komplexitätsklasse – detailliert am Begriff der NP-Vollständigkeit und an vielen Beispielen ausgeführt – sowie Approximationsalgorithmen als Lösungsmöglichkeit für viele NP-vollständige Probleme.Außerdem enthält das Buch eine große Anzahl an Übungsaufgaben (mit vielen Lösungen) wie auch abschließend die Möglichkeit, sein erarbeitetes Wissen in zwei exemplarischen Klausuren zu prüfen.
Modulare Softwarearchitektur
MODULARE SOFTWAREARCHITEKTUR //- Lesen Sie Grundlegendes über die neue Schule der Softwarearchitektur- Nutzen Sie die konkreten Empfehlungen zum Bau langlebiger, weil modularer Enterprise- bzw. Makro-Architektur- Erfahren Sie, wie Sie Schritt für Schritt Ihr System zu einer modularen Architektur hin umbauen können- Lernen Sie die Prinzipien nachhaltiger und modularer Architektur anhand eines 16-teiligen Code-Tutorials kennen- Das 5C-Modell stellt eine Alternative zum Microservice-Hype dar und ist auch bei komplexen Architekturen anwendbarDie Softwarekrise hält die IT-Branche seit den 1960er-Jahren in Atem. Damals wurde heftig darüber debattiert, ob man nicht lieber auf die Verwendung des GOTO-Schlüsselworts und x-beliebiger Sprünge im Code verzichten sollte. Mit der zunehmenden Leistungsfähigkeit der Hardware wurde auch die darauf laufende Software immer komplexer, und man suchte nach Rezepten, um deren Wartbarkeit zu verbessern.Diese Entwicklung ist bis heute nicht abgeschlossen. Die digitale Transformation der Gesellschaft bringt für bestehende IT-Landschaften neue Herausforderungen mit sich. Enterprise-Architekturen, die sich oft durch ungeplant entstandene Komplexität auszeichnen, sollen plötzlich weiterwachsen. Da die klassischen Muster der Enterprise-Architektur dabei oft versagen, werden zurzeit neue Prinzipien und Muster entwickelt, welche genau diese Komplexität in geordnete Bahnen lenken sollen.Dieses Buch behandelt diese Themen und stellt den Bezug zu den guten alten Mustern und Prinzipien des Softwaredesigns her.AUS DEM INHALT //Über Softwarearchitektur/Migration von Legacy-Systemen/Domain Driven Design/Muster modularer Mikro-Architektur/Muster modularer Makro-Architektur bzw. langlebige Enterprise-Architektur/Monolithen, Microservices und Alternativen/Antipattern und Pitfalls modularer Architektur und wie man diese vermeidet/Umsetzung modularer Architektur in komplexen Organisationen Herbert Dowalil ist seit vielen Jahren als Softwareentwickler, -architekt, Trainer und inzwischen auch als Autor tätig. Dabei beschäftigt er sich mit der Frage, wie man durch den Entwurf effizienter Strukturen langfristig Produktivität und Flexibilität sicherstellt. Sein Hauptaugenmerk gilt dabei einer der Schwachstellen der IT-Branche, nämlich der modularen Enterprise- bzw. Makro-Architektur.
Visual Studio Extensibility Development
Learn the extensibility model of Visual Studio to enhance the Visual Studio integrated development environment (IDE). This book will cover every aspect, starting from developing an extension to publishing it and making it available to the end user.The book begins with an introduction to the basic concepts of Visual Studio including data structures and design patterns and moves forward with the fundamentals of the VS extensibility model. Here you will learn how to work on Roslyn - the .NET compiler platform - and load extensions in VS. Next, you will go through the extensibility model and see how various extensions, such as menus, commands, and tool windows, can be plugged into VS. Moving forward, you’ll cover developing VS extensions and configuring them, along with demonstrations on customizing extension by developing option pages. Further, you will learn to create custom code snippets and use a debugger visualizer. Next, you will go through creation of project and item templates including deployment of VS extensions using continuous integration (CI). Finally, you will learn tips and tricks for Visual Studio and its extensibility and integration with Azure DevOps.After reading Visual Studio Extensibility Development you will be able to develop, deploy, and customize extensions in Visual Studio IDE.WHAT YOU WILL LEARN* Discover the Visual Studio extensibility and automation model* Code Visual Studio extensions from scratch* Customize extensions by developing a tools option page for them* Create project templates, item templates, and code snippets.* Work with code generation using T4 templates* Code analysis and refactoring using Roslyn analyzers* Create and deploy a private extension gallery and upload the extensions* Upload a VS extension using CI* Ship your extension to Visual Studio MarketplaceWHO THIS BOOK IS FORDevelopers in Visual Studio IDE covering C#, Visual Basic (VB), JavaScript, and CSS.RISHABH VERMA is a Microsoft certified professional and works at Microsoft as a senior development consultant, helping the customers to design, develop, and deploy enterprise-level applications. An electronic engineer by education, he has 12+ years of hardcore development experience on the .NET technology stack. He is passionate about creating tools, Visual Studio extensions, and utilities to increase developer productivity. His interests are .NET Compiler Platform (Roslyn), Visual Studio extensibility, code generation and .NET Core. He is a member of .NET foundation (https://www.dotnetfoundation.org). He occasionally blogs at https://rishabhverma.net/. He has authored books on .NET Core 2.0 and .NET Core 3.1 prior to this title.His twitter id is @VermaRishabh and his linkedIn page is https://www.linkedin.com/in/rishabhverma/CHAPTER 1: BASICS PRIMERCHAPTER GOAL: The objective of this chapter is to introduce the basic concepts to the reader that would be required through-out this book, so that he gets comfortable in this learning journey.NO OF PAGES :50-60SUB -TOPICS1. What is a compiler?2. What is an SDK (Software Development Kit)?3. Recap of Tree Data structure1. Tree traversal,2. Abstract Syntax trees4. MEF (Managed Extensibility Framework) Basics.5. Visual Studio & its history6. XML & JSON7. Serialization & Deserialization.8. Revisiting Visitor, Abstract Factory and Factory design patterns.9. MSBuild basics10. Async-await.CHAPTER 2: GETTING STARTEDCHAPTER GOAL: With the fundamentals strongly in place, we are now good to get started with Visual Studio (VS) Extensibility model. We will do our setup in this chapter. This chapter would introduce the VS Extensibility, Roslyn to the reader. The reader would also learn to write and debug a VS extension.NO OF PAGES: 40SUB - TOPICS1. Prerequisites and installation of VS2. Anatomy of a VSIX3. How Visual Studio discovers and loads extensions.4. VSPackage5. Async Loading6. Writing your first simple templatized Visual Studio Extension.7. Roslyn - .NET Compiler platform fundamentalsCHAPTER 3: EXTENDING VISUAL STUDIOCHAPTER GOAL: This chapter would introduce the extensibility model and how various extensions can be plugged in VSas menus, commands, tool window, code window, solution explorer etcNO OF PAGES: 40-50SUB - TOPICS:1. The Visual Studio Extensibility model2. Tool Window extension3. Menus & commands,4. Code Window extension5. Solution explorer item extensionCHAPTER 4: DEVELOPING REAL WORLD EXTENSIONS - ICHAPTER GOAL: This chapter dives into developing useful real-world VS Extensions and shows how they can be made configurable by customizing UI and options page.We would also learn how to write to output window and manipulate documents and projects in this chapter.NO OF PAGES: 40-50SUB - TOPICS:1. VS Extension to search on MSDN/Bing/Google.2. VS Extension to generate HTTP Client proxy class for HTTP Web API using T4 templates.3. VS Extension to generate test data.4. Customizing extension by developing Tools option page.5. Customizing UI of extension.CHAPTER 5: DEVELOPING REAL WORLD EXTENSIONS - IICHAPTER GOAL: This chapter is the continuation of last chapter and continues the development of useful real-world VS extensions but this time using the .NET Compiler platform – Roslyn.NO OF PAGES: 40-50SUB - TOPICS:1. Rewrite VS Extension to generate HTTP Client proxy class for HTTP Web API using Roslyn.2. Developing a custom code analysis Visual Studio Extension.3. Developing a light bulb style code refactoring.4. Developing Roslyn based extension to generate unit tests using T4 template.CHAPTER 6: DO MORE WITH VS SDKCHAPTER GOAL: This chapter introduces the reader with famous Visual Studio Isolated and integrated Shell to develop applications that looks like Visual Studio and also develops handy productivity boosters like custom code snippets, debugger visualizers, modifying intellisense, debugging experience for developersNO OF PAGES: 40-50SUB - TOPICS:1. VS Isolated and Integrated Shell2. Developing applications that look like Visual Studio.3. Extending the debugger.4. Create custom code snippets.5. Create Debugger Visualizer for view data while debugging.6. Modifying intellisense.CHAPTER 7: TEMPLATES, DEBUGGING VS EXTENSIONSCHAPTER GOAL: This chapter explains how to create project and item templates. The chapter also shows a sample code lens extension then dives into debugging the extensionNO OF PAGES: 40-50SUB - TOPICS:6. Code lens sample extension.7. Creating Project and Item template.8. Debugging VS Extensions.CHAPTER 8: DEPLOYING VS EXTENSIONSCHAPTER GOAL: This chapter explains how to deploy VS extensions using continuous integration (CI). The chapter also explains how the extension can be made available to the world by uploading in marketplace. We also discuss how to make a private extension gallery and host your extension there.NO OF PAGES: 40-50SUB - TOPICS:9. Deploying a VS Extension using CI.10. Creating a private extension gallery/ Atom feed11. Hosting extension in private gallery.12. Sharing extension with the world using marketplace.CHAPTER 9: TIPS, TRICKS, EXTENSIONS AND WORDSCHAPTER GOAL: This chapter discusses few of the coolest tips and tricks for Visual Studio and its extensibility and shares few highly useful extensions. The chapter and book conclude with closing remarks on extensibility of Visual Studio Code and integration with Visual Studio Team Services (VSTS) or Azure DevOps.NO OF PAGES: 30-40SUB - TOPICS:1. Cool Tips and tricks2. Useful Extensions for C#, VB, JS, TS and CSS developers.3. A word on Visual Studio Code Extensibility4. Integration with VSTS or Azure DevOpsUseful Resources – 1 pageMore Reading – 1 pageCode Samples – Link to code samples from GitHub.
Computational Models for Cognitive Vision
LEARN HOW TO APPLY COGNITIVE PRINCIPLES TO THE PROBLEMS OF COMPUTER VISIONComputational Models for Cognitive Vision formulates the computational models for the cognitive principles found in biological vision, and applies those models to computer vision tasks. Such principles include perceptual grouping, attention, visual quality and aesthetics, knowledge-based interpretation and learning, to name a few. The author’s ultimate goal is to provide a framework for creation of a machine vision system with the capability and versatility of the human vision.Written by Dr. Hiranmay Ghosh, the book takes readers through the basic principles and the computational models for cognitive vision, Bayesian reasoning for perception and cognition, and other related topics, before establishing the relationship of cognitive vision with the multi-disciplinary field broadly referred to as “artificial intelligence”. The principles are illustrated with diverse application examples in computer vision, such as computational photography, digital heritage and social robots. The author concludes with suggestions for future research and salient observations about the state of the field of cognitive vision.Other topics covered in the book include:· knowledge representation techniques· evolution of cognitive architectures· deep learning approaches for visual cognitionUndergraduate students, graduate students, engineers, and researchers interested in cognitive vision will consider this an indispensable and practical resource in the development and study of computer vision.HIRANMAY GHOSH, PHD, was a Research Advisor to TATA Consultancy Services and an Adjunct Faculty Member with the National Institute of Technology Karnataka. During his long professional career, he has served several reputed organizations, including CMC, ECIL and C-DOT and TCS. He was an Adjunct Faculty Member with IIT Delhi, and with the National Institute of Technology Karnataka. He is a Senior Member of IEEE, Life Member of IUPRAI, and a Member of ACM. About the Author ixAcknowledgments xiPreface xiiiAcronyms xv1 INTRODUCTION 11.1 What Is Cognitive Vision 21.2 Computational Approaches for Cognitive Vision 31.3 A Brief Review of Human Vision System 41.4 Perception and Cognition 61.5 Organization of the Book 72 EARLY VISION92.1 Feature Integration Theory 92.2 Structure of Human Eye 102.3 Lateral Inhibition 132.4 Convolution: Detection of Edges and Orientations 142.5 Color and Texture Perception 172.6 Motion Perception 192.6.1 Intensity-Based Approach 192.6.2 Token-Based Approach 202.7 Peripheral Vision 212.8 Conclusion 243 BAYESIAN REASONING FOR PERCEPTION AND COGNITION 253.1 Reasoning Paradigms 263.2 Natural Scene Statistics 273.3 Bayesian Framework of Reasoning 283.4 Bayesian Networks 323.5 Dynamic Bayesian Networks 343.6 Parameter Estimation 363.7 On Complexity of Models and Bayesian Inference 383.8 Hierarchical Bayesian Models 393.9 Inductive Reasoning with Bayesian Framework 413.9.1 Inductive Generalization 413.9.2 Taxonomy Learning 453.9.3 Feature Selection 463.10 Conclusion 474 LATE VISION 514.1 Stereopsis and Depth Perception 514.2 Perception of Visual Quality 534.3 Perceptual Grouping 554.4 Foreground–Background Separation 594.5 Multi-stability 604.6 Object Recognition 614.6.1 In-Context Object Recognition 624.6.2 Synthesis of Bottom-Up and Top-Down Knowledge 644.6.3 Hierarchical Modeling 654.6.4 One-Shot Learning 664.7 Visual Aesthetics 674.8 Conclusion 695 VISUAL ATTENTION 715.1 Modeling of Visual Attention 725.2 Models for Visual Attention 755.2.1 Cognitive Models 755.2.2 Information-Theoretic Models 775.2.3 Bayesian Models 785.2.4 Context-Based Models 795.2.5 Object-Based Models 815.3 Evaluation 825.4 Conclusion 846 Cognitive Architectures 876.1 Cognitive Modeling 886.1.1 Paradigms for Modeling Cognition 886.1.2 Levels of Abstraction 916.2 Desiderata for Cognitive Architectures 926.3 Memory Architecture 946.4 Taxonomies of Cognitive Architectures 976.5 Review of Cognitive Architectures 996.5.1 STAR: Selective Tuning Attentive Reference 1006.5.2 LIDA: Learning Intelligent Distribution Agent 1026.6 Biologically Inspired Cognitive Architectures 1056.7 Conclusions 1067 KNOWLEDGE REPRESENTATION FOR COGNITIVE VISION 1097.1 Classicist Approach to Knowledge Representation 1097.1.1 First Order Logic 1117.1.2 Semantic Networks 1137.1.3 Frame-Based Representation 1147.2 Symbol Grounding Problem 1177.3 Perceptual Knowledge 1187.3.1 Representing Perceptual Knowledge 1197.3.2 Structural Description of Scenes 1207.3.3 Qualitative Spatial and Temporal Relations 1227.3.4 Inexact Spatiotemporal Relations 1247.4 Unifying Conceptual and Perceptual Knowledge 1277.5 Knowledge-Based Visual Data Processing 1287.6 Conclusion 1298 DEEP LEARNING FOR VISUAL COGNITION 1318.1 A Brief Introduction to Deep Neural Networks 1328.1.1 Fully Connected Networks 1328.1.2 Convolutional Neural Networks 1348.1.3 Recurrent Neural Networks 1378.1.4 Siamese Networks 1408.1.5 Graph Neural Networks 1408.2 Modes of Learning with DNN 1428.2.1 Supervised Learning 1428.2.1.1 Image Segmentation 1428.2.1.2 Object Detection 1448.2.2 Unsupervised Learning with Generative Networks 1448.2.3 Meta-Learning: Learning to Learn 1468.2.3.1 Reinforcement Learning 1488.2.3.2 One-Shot and Few-Shot Learning 1488.2.3.3 Zero-Shot Learning 1508.2.3.4 Incremental Learning 1508.2.4 Multi-task Learning 1528.3 Visual Attention 1548.3.1 Recurrent Attention Models 1558.3.2 Recurrent Attention Model for Video 1588.4 Bayesian Inferencing with Neural Networks 1598.5 Conclusion 1609 APPLICATIONS OF VISUAL COGNITION 1639.1 Computational Photography 1639.1.1 Color Enhancement 1649.1.2 Intelligent Cropping 1669.1.3 Face Beautification 1679.2 Digital Heritage 1689.2.1 Digital Restoration of Images 1689.2.2 Curating Dance Archives 1709.3 Social Robots 1729.3.1 Dynamic and Shared Spaces 1739.3.2 Recognition of Visual Cues 1749.3.3 Attention to Socially Relevant Signals 1759.4 Content Re-purposing 1779.5 Conclusion 17910 CONCLUSION 18110.1 “What Is Cognitive Vision” Revisited 18110.2 Divergence of Approaches 18310.3 Convergence on the Anvil? 185References 187Index 215
Das 3D-Scanner-Praxisbuch
3D-Scannen verständlich erklärt und zum Eigen-Nachbau.Mario Lukas beleuchtet in seinem Buch »Das 3D-Scanner-Praxisbuch« das gesamte Wissens- und Erfahrungsspektrum zum Thema »3D-Scanner«. Er beschreibt dabei alle wichtigen Aspekte und Schritte: Aufbau und Einrichtung der Scanner, die richtige Vorbereitung der Vorlagen, den Scan, die Aufbereitung von Punktwolke und Gittermodell und schließlich den 3D-Ausdruck.Lernen Sie dabei vier verschiedene Arten von Scannern kennen:Laserscanner (FabScanPi)Fotogrammetrie-Scanner (OpenScan-Pi-3D-Scanner)Tiefensensoren-basierte ScannerPersonenscans (Kinect) und Streifenlicht-ScannerIm Praxisteil des Buches beschreibt der Autor ausführlich in Schritt-für-Schritt-Anleitungen den Bau eines Laser-Scanners aus einem Raspberry Pi und einer Raspberry-Pi-Camera sowie den Bau eines Scanners für große Objekte und Personen mit einer Kinect-Videospielkonsole.Die Software-Bearbeitungskette im Post-Scanning-Prozess zur Erzielung hochwertiger Scan-Ergebnisse machen das Buch zu einem Standardwerk des 3D-Scannings. Beispiele aus dem Praxiseinsatz in der Maker-Werkstatt und drei vollständige Beispielprojekte bieten Ihnen viel Inspiration für Ihre eigenen Projekte. Lukas gelingt es mit diesem Buch, die spannende Entwicklung im 3D-Scanning-Bereich umfassend darzustellen und für die Maker-Welt zu öffnen.Über den Autor:Mario Lukas hat Informatik an der RWTH Aachen studiert und arbeitet als Software-Entwickler. Er publizierte Artikel zu seiner Maker-Tätigkeit in diversen Fachmagazinen und ist Co-Autor der bei dpunkt erschienenen Bücher „Licht und Spaß“ und „Das Calliope-Buch“.Hauptsächlich beschäftigt er sich mit den Themen „3D-Scannen“ und „3D-Druck“. Mehrfach konnte er bei nationalen und internationalen Wettbewerben mit seinen Kreationen gute Platzierungen belegen. Mario betreut seit Jahren federführend das FabScanPi-3D-Scanner-Projekt. Er ist einer der Gründungsväter des Vereins Freie Maker e.V.
Practical R 4
Get started with an accelerated introduction to the R ecosystem, programming language, and tools including R script and RStudio. Utilizing many examples and projects, this book teaches you how to get data into R and how to work with that data using R. Once grounded in the fundamentals, the rest of Practical R 4 dives into specific projects and examples starting with running and analyzing a survey using R and LimeSurvey. Next, you'll carry out advanced statistical analysis using R and MouselabWeb. Then, you’ll see how R can work for you without statistics, including how R can be used to automate data formatting, manipulation, reporting, and custom functions.The final part of this book discusses using R on a server; you’ll build a script with R that can run an RStudio Server and monitor a report source for changes to alert the user when something has changed. This project includes both regular email alerting and push notification. And, finally, you’ll use R to create a customized daily rundown report of a person's most important information such as a weather report, daily calendar, to-do's and more. This demonstrates how to automate such a process so that every morning, the user navigates to the same web page and gets the updated report.WHAT YOU WILL LEARN* Set up and run an R script, including installation on a new machine and downloading and configuring R* Turn any machine into a powerful data analytics platform accessible from anywhere with RStudio Server* Write basic R scripts and modify existing scripts to suit your own needs* Create basic HTML reports in R, inserting information as needed* Build a basic R package and distribute itWHO THIS BOOK IS FORSome prior exposure to statistics, programming, and maybe SAS is recommended but not required.JON WESTFALL is an award-winning professor, published author, and practicing cognitive scientist. He teaches a variety of courses in psychology, from introduction to psychology to upper-level seminars. His current research focuses on the variables that influence economic and consumer finance decisions, and the retention of college students. With applications to both psychology and marketing, his work finds an intersection between basic and applied science. His current appointment is as an assistant professor of psychology, coordinator of the first year seminar program, and coordinator of the Okra Scholars program at Delta State University. Previously he was a visiting assistant professor at Centenary College of Louisiana, and the associate director for research and technology at the Center for Decision Sciences, a center within Columbia Business School at Columbia University in New York City. He now maintains a role with Columbia as a research affiliate and technology consultant.In addition to his research, Dr. Westfall also has career ties in information technology, where he has worked as a consultant since 1997, founding his own firm, Bug Jr. Systems. As a consultant he has developed custom software solutions (including native Windows 32 applications, Windows .NET applications, Windows Phone 7 and Android mobile applications, as well as ASP, ASP.NET, and PHP web applications). He has also served as a senior network and systems architect and administrator (on both Windows and Unix networks, and hybrids) and has also been recognized as a Microsoft Most Valuable Professional (MVP) 2008 – 2012. He has authored several books, and presented at academic as well as technology conferences and gatherings.Chapter 1: Getting Up and Running with RChapter 2: Getting Data into RChapter 3: Project 1: Launching, Analyzing, and Reporting a Survey using R and LimeSurveyChapter 4: Project 2: Advanced Statistical Analysis using R and Mouselab WebChapter 5: R in Everyday LifeChapter 6: Project 3: The R Form MailerChapter 7: Project 4: The R Powered PresentationChapter 8: R AnywhereChapter 9: Project 5: The Change Alert!Chapter 10: Project 6: The R Personal AssistantDETAILED VIEW BELOWChapter 1: Getting Up and Running with RChapter Goal:• Explain what R is, and what R isn’t• Explain the R landscape – it’s open source nature and the various ways people use it.• Explain how R is installed, what types of systems it runs on, and how the user interacts with it.• Explain the basic R script, running basic commands in R (e.g., a “Hello World”) and basic computations.Chapter 2: Feed the Beast: Getting Data into R• Explain the different types of data that R can work with, and how that data is stored.• Explain the basics of connecting R to flat files, database files, database servers, and published data on the internet.• Give examples for downloading data directly from Google Sheets, websites, and more directly from R.• Give examples of basic data scraping with R.• Explain writing of data objects to native RData format as well as other formats for interchangeable use.Chapter 3: Recipe 1: Launching, Analyzing, and Reporting a Survey using R and LimeSurvey• Explain a real-world scenario: A survey project applicable to market research.• Discuss an open-source tool, LimeSurvey, that can be used to create a survey, collect responses, and download those responses into R.• Bring the data into R and run basic summary statistics on the data.• Take those analyses farther into inferential statistics (Linear Regression).Chapter 4: Recipe 2: Advanced Statistical Analysis using R and Mouselab Web• A deeper data scenario than Chapter 3 discussing how Mouselab Web (an open source tool) can be used to track how people view products and services and make decisions.• Introduces advanced statistical design using Linear Mixed-methods regressions.• Also introduces the idea of R packages, and the perils of using packages (e.g., concerns over future-proofing). This chapter is a very deep concept that will be presented accessibly, so that readers learn the takeaways regarding how R works and how to futureproof your R projects, but also get a bit of a unique project applicable to psychology and market research.Chapter 5: R in Everyday Life• Perhaps you’re not a statistician, you just want R to be useful to you in your job. This chapter discusses how R can be used to automate…o Data formattingo Data manipulationo Data reporting• This chapter also talks about how users can write custom functions in R to speed up their workflows.• Finally this chapter talks about how to export results from R into common desktop software such as Microsoft Office.Chapter 6: Recipe 3: The R Form Mailer• Mail Merge is a great tool in Microsoft Office, but it’s entirely graphically driven – point and click, drag and drop. What if you could script it?• This recipe discusses scripting a Mail Merge type activity – sending custom emails with report information directly from R through an email server.• Along the way we learn a bit more about data manipulation by taking long format data (sales figures) and calculating salesmen commissions, then providing a report to each salesperson in their email.Chapter 7: Recipe 4: The R Powered Presentation• Discusses a real-world scenario where a presentation must be given that includes real-time data collection.• Participants during the presentation can take a quick survey (Using Google Forms), which will then be analyzed during the presentation and reported by the speaker.• Discusses how R can create and export results nearly instantly, right on a speaker’s laptop during the presentation.Chapter 8: R Anywhere• Final part of the book discusses using R on a server for always-on analytics, using open source software (RStudio Server).• The computing requirements for such a system, and how one sets it up either on a spare machine or on a dedicated Virtual Private Server.• Potential uses for such a setup, from analysis from devices that don’t support R (e.g., an iPad), or analysis for long-running tasks.Chapter 9: Recipe 5: The Change Alert!• Often work life requires one to check reports or other items to see if something has changed – perhaps a new person has been added to a team, or a new student added to a class.• This recipe demonstrates how to build a script with R that can run on an RStudio Server and monitor a report source for changes, and alert the user when something has changed.• This recipe demonstrates not only regular email alerting, but also push notification alerting through the service Pushover, an ultra low-cost ($4.99, one time) option for customized push notifications.Chapter 10: Recipe 6: The R Personal Assistant• Demonstrates how to use R to create a customized daily rundown report of a person’s most important information, such as the weather report, daily calendar, to-dos, and more.• Demonstrates how to automate such a process so that every morning the user navigates to the same webpage and gets the updated report.• Demonstrates how to build a simple skill in Amazon Alexa that will read the report daily as the user’s command.
Automation and Collaborative Robotics
Understand the current and future research into technologies that underpin the increasing capabilities of automation technologies and their impact on the working world of the future.Rapid advances in automation and robotics technologies are often reported in the trade and general media, often relying on scary headlines such as “Jobs Lost to Robots.” It is certainly true that work will change with the advent of smarter and faster automated workers; however, the scope and scale of the changes is still unknown. Automation may seem to be here already, but we are only at the early stages.AUTOMATION AND COLLABORATIVE ROBOTICS explores the output of current research projects that are improving the building blocks of an automated world. Research into collaborative robotics (cobotics) is merging digital, audio, and visual data to generate a commonly held view between cobots and their human collaborators. Low-power machine learning at the edge of the network can deliver decision making on cobots or to their manipulations. Topics covered in this book include:* Robotic process automation, chatbots, and their impact in the near future* The hype of automation and headlines leading to concerns over the future of work* Component technologies that are still in the research labs* Foundational technologies and collaboration that will enable many tasks to be automated with human workers being re-skilled and displaced rather than replacedWHAT YOU WILL LEARN* Be aware of the technologies currently being researched to improve or deliver automation* Understand the impact of robotics, other automation technologies, and the impact of AI on automation* Get an idea of how far we are from implementation of an automated future* Know what work will look like in the future with the deployment of these technologiesWHO THIS BOOK IS FORTechnical and business managers interested in the future of automation and robotics, and the impact it will have on their organizations, customers, and the business world in generalPETER MATTHEWS, based in the UK, is a writer and research scientist. Peter has more than 40 years of IT experience ranging from mainframe/Unix programming, development and relational databases to secure cloud computing, DevOps and cobotics. Peter’s research work has been concerned with leading edge technology for a major proportion of that time. Projects have included machine learning algorithms for soccer clubs, multi-level secure database systems and object-oriented application infrastructures. He has also led groups investigating the influence of macro social, political and economic trends on future technology.Peter’s current research is focused on automation and robotics. Past research has covered Internet of Things, cloud computing, data curation, smart buildings and cobotics. Peter has been the CA project lead on cobotics under the auspices of the Centre for Visual and Decision Informatics, a USA National Science Foundation initiative.Peter has authored and co-authored academic papers including “Data Is the New Currency” in the proceedings of New Security Paradigms Workshop. Other writing includes books on “Ingres Visual Programming”, co-authoring of “The Innovative CIO: How IT Leaders Can Drive Business Innovation” and co-editor/contributor to “MOdel-Driven Approach for design and execution of applications on multiple Clouds”STEVEN GREENSPAN, PhD is an innovator of information and communications technology (ICT), with over 50 publications in peer-reviewed journals and 79 US patents, including the first patent to describe two-factor and two-device authentication and authorization. This invention is widely used throughout the world to ensure secure access to web services and it showcased the value of keeping the human-in-the-loop. His current research interests include user experience and collaboration in complex systems, differential privacy, ethical decision-making, and innovative approaches for integrating scientific research into socially responsible applications and services. Outside of his professional work, he devotes much of his time to community groups that focus on environmental, economic and social justice. He also serves on the advisory boards of the AABGU/ Philadelphia Academic Bridge, and several startups.Steve has a PhD in Cognitive Psychology from the State University of New York at Buffalo and conducted postdoctoral research at the University of California at San Diego and Indiana University. During the writing of this book, he was a Visiting Scholar at the University of Virginia. Previously, he was a Research Scientist and Vice President of Strategic Research at CA Technologies managing an international team of information technology scientists. He has also consulted to Avaya on the UX design of mobile phones, and was a Distinguished Member of the Technical Staff at AT&T Bell Laboratories.
Beginning Unity Android Game Development
Master the art of programming games for Android using the Unity3D game engine. This book will help you understand basic concepts of game development in Unity. By the end of Beginning Unity Android Game Development, you will have the knowledge to confidently build an Android game.The book starts by explaining simple programming concepts to make beginners comfortable with the jargon. You will then learn to navigate around the Unity interface and use basic tools (hand, move, rotate, scale, and rect). You will also be acquainted with the creation of basic 3D objects in the game while understanding the purpose of several of Unity’s windows.In the last chapters, you will learn to create a simple game for Android using the concepts studied in the previous chapters. Scripts will be written to handle the behaviors of the player and enemies as well as to handle other aspects of the game. The author shares tips along the way to help improve in-game performance, such as switching to the universal rendering pipeline when targeting mobile platforms.At the end of the book, you will have a solid knowledge in making basic Android games that can be upgraded later to make more complex games.WHAT YOU WILL LEARN* Explore basic Unity and C# programming concepts and scripting for Android games* Navigate around the Unity interface and use its basic tools* Make the most of popular components and features of Unity* Write an Android game with optimizationsWHO THIS BOOK IS FORAbsolute beginners learning to program games for the Android platform using Unity3D. Basic knowledge of programming would be beneficial for the reader but is not required.Kishan started out by learning programming at a young age with Python. Finding a bigger interest in game development, he has been developing games using the Unity game engine for over four years now. He is also a Linux lover and has worked on his own distribution. Currently, he resides in his home country, Mauritius, where he often participates in major technical events and hackathons with Cyberstorm.mu while developing quality games and improving his portfolio with new skills.CHAPTER 1: PROGRAMMING CONCEPTSChapter Goal: This chapter is intended to make the reader feel comfortable with basic programming concepts and operations. It will make further topics about game dev scripting more accessible to those with no past programming experience.Sub -Topics:1. Fundamentals of programming2. Variables, constants, and types3. Arithmetic operations4. Boolean expressions5. Selection6. Iteration7. FunctionsCHAPTER 2: INTRODUCTION TO UNITYChapter Goal: This chapter provides an introduction to the Unity game engine and IDE. It shows how to navigate around, create basic objects and using transform tools to move, scale and rotate. The purpose of the Scene, Game, Hierarchy, Inspector, Project and Asset Store windows are also discussed.Sub -Topics:1. Creating a Unity account2. Downloading Unity and required add-ons3. Scene view4. Game view5. Hierarchy window6. Inspector window7. Using the transform tools8. Project window9. Asset store windowCHAPTER 3: GAMEOBJECTS, PREFABS, MATERIALS, AND COMPONENTSChapter Goal: We learn more about GameObjects, the benefits of making prefabs, and the use of several components. A small overview of the need to use materials is also provided.Sub -Topics:1. What are GameObjects and Prefabs2. Transform component3. Camera component4. Lighting component5. Renderer component6. Collider component7. Rigidbody component8. Audio source component9. Particle emitter component10. Trail renderer component11. MaterialsCHAPTER 4: USER INTERFACEChapter Goal: The Canvas component is introduced and the reader will learn about making a game more interactive using touch input.Sub -Topics:1. The Canvas component2. Text3. Image/RawImage4. Slider5. Input field6. Button7. Introduction to input axesCHAPTER 5: BUILDING OUR FIRST ANDROID GAME - SPHERE SHOOTERChapter Goal: After creating a new project, we learn about switching to a more lightweight rendering pipeline. The reader will learn how to create the game environment, first enemy, player tank and bullets. Scripts will also need to be written to handle player movement, shooting, enemy instantiation and behavior.Sub -Topics:1. The lightweight rendering pipeline2. Creating game terrain and adjusting lighting3. Making prefabs for the player, first enemy, and bullets4. Player movement5. Player shooting6. Spawning enemies7. Enemy movement8. Enemy destruction9. Game overCHAPTER 6: IMPROVING THE GAME - SPHERE SHOOTERChapter Goal: We will learn how to make the game more interesting by creating simple but elegant canvas elements, introduce concepts such as health and score, make two more types of enemies, introduce pickups, add more sound effects to the game along with particle systems, implement mobile controls and exporting a build ready to be played.Sub -Topics1. Fancy Menu when starting the game and dying2. Adding the concept of score3. Adding the concept of health4. Implementing particle systems5. Making a new faster enemy6. Making a new bigger enemy7. Creating a health pickup8. Adding sound effects9. Mobile joysticks10. Editing player settings and exporting11. What next?
Beginning Sensor Networks with XBee, Raspberry Pi, and Arduino
Build sensor networks with Python and MicroPython using XBee radio modules, Raspberry Pi, and Arduino boards. This revised and updated edition will put all of these together to form a sensor network, and show you how to turn your Raspberry Pi into a MySQL database server to store your sensor data!You'll review the different types of sensors and sensor networks, along with new technology, including how to build a simple XBee network. You'll then walk through building an sensor nodes on the XBee, Raspberry Pi, and Arduino, and also learn how to collect data from multiple sensor nodes. The book also explores different ways to store sensor data, including writing to an SD card, sending data to the cloud, and setting up a Raspberry Pi MySQL server to host your data. You'll even learn how to connect to and interact with a MySQL database server directly from an Arduino! Finally you'll see how to put it all together by connecting your sensor nodes to your new Raspberry Pi database server.If you want to see how well XBee, Raspberry Pi, and Arduino can get along, especially to create a sensor network, then Beginning Sensor Networks with XBee, Raspberry Pi, and Arduino is just the book you need.WHAT YOU'LL LEARN* Code your sensor nodes with Python and MicroPython* Work with new XBee 3 modules* Host your data on Raspberry Pi* Get started with MySQL* Create sophisticated sensor networksWHO THIS BOOK IS FORThose interested in building or experimenting with sensor networks and IoT solutions, including those with little or no programming experience. A secondary target includes readers interested in using XBee modules with Raspberry Pi and Arduino, those interested in controlling XBee modules with MicroPython.Charles Bell conducts research in emerging technologies. He is a member of the Oracle MySQL Development team and is the team lead for the MySQL Utilities team. He lives in a small town in rural Virginia with his loving wife. He received his Doctor of Philosophy in Engineering from Virginia Commonwealth University in 2005. Dr. Bell is an expert in the database field and has extensive knowledge and experience in software development and systems engineering. His research interests include 3D printers, microcontrollers, three-dimensional printing, database systems, software engineering, and sensor networks. He spends his limited free time as a practicing Maker focusing on microcontroller projects and refinement of three-dimensional printers. Dr. Bell maintains a blog on his research projects and many other interests.Chapter 1: Introduction to Sensor NetworksChapter Goal: Provide the reader with the basics of sensor network terminology.• Anatomy of a Sensor Network• Communication Media• Types of Sensor Nodes• SensorsChapter 2: Brief Introduction to XBeeChapter Goal: Explain the XBee modules, their protocols, and demonstrate basic usage.• What is an XBee?• XBee Primer• Introducing MicroPython• An XBee Wireless Chat Room• Building an XBee-ZB Mesh Network• Component Shopping List• Troubleshooting Tips and Common IssuesChapter 3: How to Program in MicroPythonChapter Goal: Teach readers how to program in MicroPython• Basic Concepts• Variables and Statements• Loops• Methods and Classes• MicroPython Libraries• Built-In and Standard LibrariesChapter 4: XBee-based Sensor NodesChapter Goal: Demonstrate how to create sensor nodes using XBee modules.• How to Host Sensors with XBee• Building an XBee Environment Sensor• Example: Using XBee Modules to Gather DataChapter 5: Raspberry Pi-based Sensor NodesChapter Goal: Demonstrate how to create sensor nodes using Raspberry Pi including introducing the Raspberry Pi.• What is a Raspberry Pi?• Raspberry Pi Tutorial• Hosting Sensors with Raspberry Pi• Project: Building a Raspberry Sensor Node• Project: Building a Raspberry Barometric Pressure Sensor Node• Project: Creating a Raspberry Pi Data Collector for XBee Sensor Nodes• Component Shopping ListChapter 6: Arduino-based Sensor NodesChapter Goal: Demonstrate how to create sensor nodes using an Arduino including introducing the Arduino platform.• What is an Arduino?• Arduino Tutorial• Hosting Sensors with Arduino• Project: Building an Arduino Temperature Sensor• Project: Using an Arduino as A Data Collector for XBee Sensor Nodes• Component Shopping ListChapter 7: Methods for Storing Sensor DataChapter Goal: Explain how to store sensor data on the sensor or data nodes.• Storage Methods• Local Storage Options for the Arduino• Local Storage Options for the Raspberry Pi• Remove Storage Options• Component Shopping ListChapter 8: Turning Your Raspberry Pi into a Database ServerChapter Goal: Introduce MySQL and demonstrate how to setup a Raspberry Pi as a MySQL Database server.• What is MySQL?• Getting Started with MySQL• Building a Raspberry Pi MySQL Server• Component Shopping ListChapter 9: MySQL and Arduino: United at Last!Chapter Goal: Introduce MySQL Connector/Arduino and demonstrate how to connect Arduino directly to a MySQL server to store data via several example projects.• Introducing Connector/Arduino• Building Connector/Arduino-Enabled Sketches• Troubleshooting Connector/Arduino• A Tour of the MySQL Connector/Arduino Code• Project: Building an Arduino MySQL Client• Project: Inserting Data from Variables• Project: How to Perform SELECT Queries• Component Shopping ListChapter 10: Building Your Network: Arduino Wireless Aggregator + Wireless Sensor Node + Raspberry Pi ServerChapter Goal: Provide an overview of how sensor networks are constructed and provide a foundation for further exploration including a simple, complete project.• Data Aggregator Nodes• Component Shopping ListChapter 11: Putting It All Together• Sensor Networks Best Practices• Choosing Sensor Nodes• Project: Home Temperature Monitoring• Optional Component Shopping List
Practical hapi
Understand the core concepts of hapi and learn to build RESTful APIs that are quick, useful, and productive. Created by the mobile team at Walmart Labs, hapi is a light Node.js framework that is perfect for building API servers, websites, and HTTP proxy applications.With this quick guide, you'll learn the basics of hapi and use those skills to build an application and a REST API with MySQL. You'll then wrap up with a Capstone project of industry relevance, understanding solution design, and how hapi fits into industry relevant projects for data driven apps.Used by companies such as PayPal and Mozilla, hapi is a key framework for anyone serious about enterprise web development. Practical hapi will ensure you focus your time on critical project tasks instead of building infrastructure.WHAT YOU'LL LEARN* Utilize the power of RESTful APIs and Node.js* Build your first hapi application based on its core concepts* Work with promises and asynchronous programming effectively* Use Sequelize for database connectivityWHO THIS BOOK IS FORAnyone with basic knowledge of JavaScript or Node.js who wants to learn to work with hapi. A primer for the relevant Node.js and JavaScript is provided so those with general programming experience can also use this book.KANIKA SUD has been working on the web for over 10 years now. Her work spans enterprise CMSes in JAVA, backend technologies in the LAMP stack and MEAN stack. She has also worked on open source e-commerce CMSes and UX strategy.1. Understanding RESTful APIs2. Beginning Node.js3. Asynchronous JavaScript4. Your First hapi Application5. Building on the Basics: Validation, Authentication, and Plugins6. Database Connectivity7. Capstone Project- REST API for Polling App8. Appendix
Webpack for Beginners
Learn how to use Webpack from installation to configuration without the hassle of complex examples. Webpack has become one of the most popular module bundlers in recent years; it’s widely used by developers, companies, and organizations of all sizes, and many web frameworks use it for the management of their assets. If you are serious about web development these days then you must learn and understand Webpack.You will begin by installing and configuring Webpack, and learn how to write modular code. You’ll then move onto understanding the usage of loaders and plugins with practical use cases, how to make aliases and resolve folders, cache busting, and installing third-party libraries such as jQuery, Bootstrap, QuillJS, and more. By the end of this book you will feel confident and ready to start using Webpack in your projects.Free from complex examples and intended to be as easy-to-follow as possible, this book is ideal for anyone who knows basic HTML, JavaScript, and how to work on the command line. Upgrade your developer skillset using Webpack for Beginners today.WHAT YOU WILL LEARN* Install and configure Webpack beyond the default settings* Efficiently work with plugins and loaders* Optimize Webpack for production* Use instant refreshing with the Webpack dev server and hot module replacement* Explore how to install some common JavaScript librariesWHO THIS BOOK IS FORThis book is conceived for beginners and newcomers to Webpack, and assumes you have some very basic knowledge in JavaScript, HTML and working on the command line. This step-by-step guide will help you understand and clarify everything you need to know to bundle your JavaScript hassle-free.Mohamed Bouzid has over 11 years' experience in technology and web development. From humble beginnings as a global freelancer, he has transitioned to the entrepreneurial world making products that people love and use every day. When not coding he can be found at the gym or at the coffee shop where he talks with friends about life, startups, and tech. 1. Webpack: First Steps2. Write Modular Code3. Loaders and Plugins4. Cache5. Resolving Folders6. Webpack DevServer7. Installing Third Party Libraries8. Conclusion