Tabla de Contenidos: “…Cover -- Copyright page -- Dedication -- Contents at a Glance -- Table of Contents -- Foreword -- Acknowledgments -- Lynn Langit -- Davide Mauri -- Sahil Malik -- Introduction -- Who This Book Is For -- What This Book Is About -- Part I, "Business Intelligence for Business Decision Makers and Architects" -- Part II, "Microsoft SQL Server 2008 Analysis Services for Developers" -- Part III, "Microsoft SQL Server 2008 Integration Services for Developers" -- Part IV, "Microsoft SQL Server Reporting Services and Other Client Interfaces for Business Intelligence" -- Prerelease Software -- Hardware and Software Requirements -- Find Additional Content Online -- Support for This Book -- Questions and Comments -- Part I: Business Intelligence for Business Decision Makers and Architects -- Chapter 1: Business Intelligence Basics -- Business Intelligence and Data Modeling -- OLTP and OLAP -- Online Transactional Processing -- Online Analytical Processing -- Common BI Terminology -- Data Warehouses -- Data Marts -- Cubes -- Decision Support Systems -- Data Mining Systems -- Extract, Transform, and Load Systems -- Report Processing Systems -- Key Performance Indicators -- Core Components of a Microsoft BI Solution -- SQL Server 2008 Analysis Services -- SQL Server 2008 Reporting Services -- SQL Server 2008 -- SQL Server 2008 Integration Services -- Optional Components of a Microsoft BI Solution -- Query Languages used in BI Solutions -- MDX -- DMX -- XMLA -- RDL -- Summary -- Chapter 2: Visualizing Business Intelligence Results -- Matching Business Cases to BI Solutions -- Top 10 BI Scoping Questions -- Components of BI Solutions -- Understanding Business Intelligence from a User's Perspective -- Demonstrating the Power of BI Using Excel 2007 -- Understanding Data Mining via the Excel Add-ins -- Viewing Data Mining Structures Using Excel 2007.…”

Tabla de Contenidos: “…. -- See also -- Data onboarding - defining field extractions -- Getting ready -- How to do it... -- How it works... -- See also -- Data onboarding - defining event types and tags -- Getting ready -- How to do it... -- How it works... -- There's more... -- Adding event types and tags using eventtypes.conf and tags.conf -- See also -- Installing the Machine Learning Toolkit -- Getting ready -- How to do it... -- How it works... -- Chapter 2: Diving into Data - Search and Report -- Introduction…”

Tabla de Contenidos: “…How to trigger the extraction of a body image from a body schema: The MNS scenario -- 7. …”

Tabla de Contenidos: “…Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- Chapter 1 Cybersecurity Risk Assessment in Advanced Metering Infrastructure -- 1.1 Introduction -- 1.2 Preliminaries -- 1.2.1 Advanced Metering Infrastructure -- 1.2.2 AMI Components -- 1.2.3 AMI Tiers -- 1.2.4 Information Security Risk Assessment -- 1.3 Implementation of the AMI System's Risk Assessment -- 1.3.1 Risk Identification Phase for the AMI System -- 1.3.2 AMI Vulnerabilities -- 1.3.3 Risk Profiling Phase for the AMI System -- 1.3.4 Risk Treatment Phase for the AMI System -- 1.4 Discussion and Recommendations -- 1.4.1 Recommendations -- 1.5 Conclusion -- Acknowledgment -- References -- Chapter 2 A Generative Neural Network for Improving Metamorphic Malware Detection in IoT Mobile Devices -- 2.1 Introduction -- 2.2 Background -- 2.2.1 Machine Learning -- 2.2.2 Deep Learning Malware Detection -- 2.2.3 Adversarial Machine Learning -- 2.2.4 Generative Adversarial Networks -- 2.2.5 Related Work -- 2.3 Methodology -- 2.3.1 Dataset -- 2.3.2 Dynamic Analysis -- 2.3.3 Data Preparation -- 2.3.4 Image Generation -- 2.3.5 Adversarial Samples -- 2.3.6 Convolutional Neural Network (CNN) -- 2.4 Experimental Design -- 2.4.1 Experimental Setup -- 2.4.2 Behavior Feature Extraction -- 2.4.3 Words to Images -- 2.4.4 Synthetic Images -- 2.4.5 Image Classification -- 2.5 Results and Discussion -- 2.5.1 Assessing the Evasive Effectiveness of the Generated Samples Using a CNN Classifier -- 2.5.2 Assessing the Effectiveness of the CNN Classifier with a Novel Dataset Including a Newly Generated Batch of Malicious Samples for Each Family Produced by the DCGAN -- 2.5.3 Evaluation -- 2.6 Conclusion -- Notes -- References -- Chapter 3 A Physical-Layer Approach for IoT Information Security During Interference Attacks -- 3.1 Introduction…”

Tabla de Contenidos: “…Benchmarking with timeit -- Working with the memory profiler -- Running in Parallel on Multiple Cores -- Performing multicore parallelism -- Demonstrating multiprocessing -- Chapter 13 Exploring Data Analysis -- The EDA Approach -- Defining Descriptive Statistics for Numeric Data -- Measuring central tendency -- Measuring variance and range -- Working with percentiles -- Defining measures of normality -- Counting for Categorical Data -- Understanding frequencies -- Creating contingency tables -- Creating Applied Visualization for EDA -- Inspecting boxplots -- Performing t-tests after boxplots -- Observing parallel coordinates -- Graphing distributions -- Plotting scatterplots -- Understanding Correlation -- Using covariance and correlation -- Using nonparametric correlation -- Considering chi-square for tables -- Working with Cramér's V -- Modifying Data Distributions -- Using different statistical distributions -- Creating a Z-score standardization -- Transforming other notable distributions -- Chapter 14 Reducing Dimensionality -- Understanding SVD -- Looking for dimensionality reduction -- Using SVD to measure the invisible -- Performing Factor Analysis and PCA -- Considering the psychometric model -- Looking for hidden factors -- Using components, not factors -- Achieving dimensionality reduction -- Squeezing information with t-SNE -- Understanding Some Applications -- Recognizing faces with PCA -- Extracting topics with NMF -- Recommending movies -- Chapter 15 Clustering -- Clustering with K-means -- Understanding centroid-based algorithms -- Creating an example with image data -- Looking for optimal solutions -- Clustering big data -- Performing Hierarchical Clustering -- Using a hierarchical cluster solution -- Visualizing aggregative clustering solutions -- Discovering New Groups with DBScan -- Chapter 16 Detecting Outliers in Data…”

Tabla de Contenidos: “…-- Securing access - unveiling API keys and authentication -- Installing the Arduino Cloud CLI -- Interacting with devices -- Creating a device -- Listing devices -- Deleting a device -- Tagging and untagging a device -- Extracting a template from a Thing -- Creating a Thing…”

Tabla de Contenidos: “…-- Quality of output -- Plagiarism and copyright infringement -- Doesn't evoke human emotions -- Errors -- Ethical considerations -- Hallucinations -- Chapter 2 Embracing AI as Your Business Writing Assistant -- Introducing a Buffet of AI Lingo -- Targeting the Right Audience -- Cutting Through Writer's Block -- Generating Content -- Creating Visuals -- Enhancing the Tone -- Researching and Fact-Checking -- Enhancing SEO -- Creating Language Bridges -- Preparing Abstracts and Executive Summaries -- Extracting TOCs, Glossaries, and Indexes -- Helping with Proofreading and Editing -- Determining Readability -- Collaborating in Real Time -- Detecting Plagiarism -- Chapter 3 Navigating the World of AI at Your Own Pace -- Absorbing Wisdom from the Backseat -- Dipping One Toe in the Chatbot Water -- Discovering varied information at your fingertips -- Embracing the bot revolution -- Composing text -- Finding chatbot faves -- Playing with the Pros -- Choosing an AI Writing Tool -- Summarizing the Synergy between WI and AI -- Considering Copyright Laws…”

Tabla de Contenidos: “…Chapter 8 Code Refactoring with Copilot -- Introducing Code Refactoring with Copilot -- Establishing the Example Project -- Prerequisites -- Refactoring Duplicate Code -- Adding Unit Tests -- Refactoring Duplicate Error Handling Code -- Refactoring Validators -- Adding Unit Tests -- Extracting Validation Code to Functions -- Refactoring Bad Variable Names -- Documenting and Commenting Code -- Method Documentation -- Project Documentation -- Conclusion -- Chapter 9 Enhancing Code Security -- Detailing Code Security -- Establishing the Example Project -- Prerequisites -- Exploring Code Security -- Using HTTPS -- Implementing Validation -- Conclusion -- Finding and Fixing Security Issues -- Fixing Weak Password Hashing -- Fixing SQL Injection -- Conclusion -- Chapter 10 Accelerating DevSecOps Practices -- Detailing DevSecOps -- Simplifying Containers -- Creating a Container -- Deploying a Container -- Applying Security Controls -- Automating Infrastructure as Code -- Creating IaC -- Deploying Code Using Terraform -- Applying Security Controls -- Streamlining CI/CD Pipelines -- Creating CI Pipeline -- Adding Security Scanning -- Creating CD Pipeline -- Conclusion -- Chapter 11 Enhancing Development Environments with Copilot -- Amplifying Visual Studio with Copilot -- Prerequisites -- Installing the GitHub Copilot Extension -- Exploring Code Completions -- Chatting with Copilot -- Elevating Azure Data Studio with Copilot -- Prerequisites -- Installing the GitHub Copilot Extension -- Constructing Database Schemas with Copilot -- Inserting Test Data with Copilot -- Querying with Copilot -- Boosting JetBrains IntelliJ IDEA with Copilot -- Prerequisites -- Installing the GitHub Copilot Extension -- Exploring Code Completions -- Chatting with Copilot -- Enhancing Neovim with Copilot -- Prerequisites -- Installing the GitHub Copilot Extension…”

Tabla de Contenidos: “…Best practice 1 - Completely understanding the project goal -- Best practice 2 - Collecting all fields that are relevant -- Best practice 3 - Maintaining the consistency and normalization of field values -- Best practice 4 - Dealing with missing data -- Best practice 5 - Storing large-scale data -- Best practices in the training set generation stage -- Best practice 6 - Identifying categorical features with numerical values -- Best practice 7 - Deciding whether to encode categorical features -- Best practice 8 - Deciding whether to select features and, if so, how to do so -- Best practice 9 - Deciding whether to reduce dimensionality and, if so, how to do so -- Best practice 10 - Deciding whether to rescale features -- Best practice 11 - Performing feature engineering with domain expertise -- Best practice 12 - Performing feature engineering without domain expertise -- Binarization and discretization -- Interaction -- Polynomial transformation -- Best practice 13 - Documenting how each feature is generated -- Best practice 14 - Extracting features from text data -- tf and tf-idf -- Word embedding -- Word2Vec embedding -- Best practices in the model training, evaluation, and selection stage -- Best practice 15 - Choosing the right algorithm(s) to start with -- Naïve Bayes -- Logistic regression -- SVM -- Random forest (or decision tree) -- Neural networks -- Best practice 16 - Reducing overfitting -- Best practice 17 - Diagnosing overfitting and underfitting -- Best practice 18 - Modeling on large-scale datasets -- Best practices in the deployment and monitoring stage -- Best practice 19 - Saving, loading, and reusing models -- Saving and restoring models using pickle -- Saving and restoring models in TensorFlow -- Saving and restoring models in PyTorch -- Best practice 20 - Monitoring model performance -- Best practice 21 - Updating models regularly…”