Tabla de Contenidos: “…Administration and management -- 3.1 Backup, recovery, and logging -- 3.1.1 Automatic backup -- 3.1.2 Self-tuning backup and restore -- 3.1.3 Backup compression -- 3.1.4 Logs in backup images -- 3.2 Automated log file management -- 3.3 RECOVER command -- 3.4 Automated table maintenance -- 3.4.1 Automatic statistics collection -- 3.4.2 Automatic statistics profiling -- 3.4.3 Automatic reorganization -- 3.5 Integrated Design Advisor -- Chapter 4. …”

Tabla de Contenidos: “…SQL considerations -- 7.1 SELECT issues -- 7.1.1 Selectivity -- 7.1.2 Statistical sampling -- 7.1.3 SELECT cursors -- 7.1.4 Joins -- 7.2 MATCHES predicate -- 7.3 Comments -- 7.4 SQLCODE and SQLSTATE -- 7.5 Built-in functions -- 7.6 SQL access to system catalogs -- 7.7 Quotations and character strings -- 7.8 Concatenation behavior -- 7.9 Implicit casting -- 7.10 Deferred constraint checking -- 7.11 Set Operators: UNION, INTERSECT, and MINUS -- 7.12 Multi-database access -- 7.13 Temporary tables…”

Tabla de Contenidos: “…6.3 Strong assumptions made by SOTA attacks -- 6.3.1 The state-of-the-art attacks -- 6.3.2 Strong assumptions -- Assumption 1: Knowledge of BatchNorm statistics -- Assumption 2: Knowing or being able to infer private labels -- 6.3.3 Re-evaluation under relaxed assumptions -- Relaxation 1: Not knowing BatchNorm statistics -- Relaxation 2: Not knowing private labels -- 6.4 Defenses against the gradient inversion attack -- 6.4.1 Encrypt gradients -- 6.4.2 Perturbing gradients -- 6.4.3 Weak encryption of inputs (encoding inputs) -- 6.5 Evaluation -- 6.5.1 Experimental setup -- 6.5.2 Performance of defense methods -- 6.5.3 Performance of combined defenses -- 6.5.4 Time estimate for end-to-end recovery of a single image -- 6.6 Conclusion -- 6.7 Future directions -- 6.7.1 Gradient inversion attacks for text data -- 6.7.2 Gradient inversion attacks in variants of federated learning -- 6.7.3 Defenses with provable guarantee -- References -- 2 Emerging topics -- 7 Personalized federated learning: theory and open problems -- 7.1 Introduction -- 7.2 Problem formulation of pFL -- 7.3 Review of personalized FL approaches -- 7.3.1 Mixing models -- 7.3.2 Model-based approaches: meta-learning -- 7.3.3 Multi-task learning -- 7.3.4 Weight sharing -- 7.3.5 Clients clustering -- 7.4 Personalized FL algorithms -- 7.4.1 pFedMe -- 7.4.2 FedU -- 7.5 Experiments -- 7.5.1 Experimental settings -- 7.5.2 Comparison -- 7.6 Open problems -- 7.6.1 Transfer learning -- 7.6.2 Knowledge distillation -- 7.7 Conclusion -- References -- 8 Fairness in federated learning -- 8.1 Introduction -- 8.2 Notions of fairness -- 8.2.1 Equitable fairness -- 8.2.2 Collaborative fairness -- 8.2.3 Algorithmic fairness -- 8.3 Algorithms to achieve fairness in FL -- 8.3.1 Algorithms to achieve equitable fairness -- 8.3.2 Algorithms to achieve collaborative fairness…”

Tabla de Contenidos: “…6.2 The Effect of Offering a Choice of Modes -- 6.3 Getting People to Respond Online -- 6.4 Sequencing Different Modes of Data Collection -- 6.5 Separating the Effects of Mode on Selection and Reporting -- 6.5.1 Conceptualizing Mode Effects -- 6.5.2 Separating Observation from Nonobservation Error -- 6.5.2.1 Direct Assessment of Measurement Errors -- 6.5.2.2 Statistical Adjustments -- 6.5.2.3 Modeling Measurement Error -- 6.6 Maximizing Comparability Versus Minimizing Error -- 6.7 Conclusions -- References -- Chapter 7 Mobile Web Surveys: A Total Survey Error Perspective -- 7.1 Introduction -- 7.2 Coverage -- 7.3 Nonresponse -- 7.3.1 Unit Nonresponse -- 7.3.2 Breakoffs -- 7.3.3 Completion Times -- 7.3.4 Compliance with Special Requests -- 7.4 Measurement Error -- 7.4.1 Grouping of Questions -- 7.4.1.1 Question-Order Effects -- 7.4.1.2 Number of Items on a Page -- 7.4.1.3 Grids versus Item-By-Item -- 7.4.2 Effects of Question Type -- 7.4.2.1 Socially Undesirable Questions -- 7.4.2.2 Open-Ended Questions -- 7.4.3 Response and Scale Effects -- 7.4.3.1 Primacy Effects -- 7.4.3.2 Slider Bars and Drop-Down Questions -- 7.4.3.3 Scale Orientation -- 7.4.4 Item Missing Data -- 7.5 Links Between Different Error Sources -- 7.6 The Future of Mobile web Surveys -- References -- Chapter 8 The Effects of a Mid-Data Collection Change in Financial Incentives on Total Survey Error in the National Survey of Famil... -- 8.1 Introduction -- 8.2 Literature Review: Incentives in Face-to-Face Surveys -- 8.2.1 Nonresponse Rates -- 8.2.2 Nonresponse Bias -- 8.2.3 Measurement Error -- 8.2.4 Survey Costs -- 8.2.5 Summary -- 8.3 Data and Methods -- 8.3.1 NSFG Design: Overview -- 8.3.2 Design of Incentive Experiment -- 8.3.3 Variables -- 8.3.4 Statistical Analysis -- 8.4 Results -- 8.4.1 Nonresponse Error -- 8.4.2 Sampling Error and Costs -- 8.4.3 Measurement Error…”

Tabla de Contenidos: “…9.3 Artificial Intelligence: Transport System and Healthcare -- 9.4 Artificial Intelligence Algorithms -- 9.5 AI Workflow -- 9.6 AI for ITS and e-Healthcare Tasks -- 9.7 Intelligent Transportation, Healthcare, and IoT -- 9.8 AI Techniques Used in ITS and e-Healthcare -- 9.9 Challenges of AI and ML in ITS and e-Healthcare -- 9.10 Conclusions -- References -- Chapter 10 Classification of Dementia Using Statistical First-Order and Second-Order Features -- 10.1 Introduction -- 10.2 Materials and Methods -- 10.2.1 Dataset -- 10.2.2 Image Pre-Processing -- 10.3 Proposed Framework -- 10.3.1 Discrete Wavelet Transform -- 10.3.1.1 Statistical Features -- 10.3.2 Classification -- 10.3.2.1 K-Nearest Neighbor -- 10.3.2.2 Linear Discriminant Analysis -- 10.3.2.3 Support Vector Machine -- 10.3.3 Performance Measure -- 10.4 Experimental Results and Discussion -- 10.5 Conclusion -- References -- Chapter 11 Pulmonary Embolism Detection Using Machine and Deep Learning Techniques -- 11.1 Introduction -- 11.2 The State-of-the-Art of PE Detection Models -- 11.3 Literature Survey -- 11.4 Publications Analysis -- 11.5 Conclusion -- References -- Chapter 12 Computer Vision Techniques for Smart Healthcare Infrastructure -- 12.1 Introduction -- 12.2 Literature Survey -- 12.2.1 Computer Vision -- 12.2.1.1 Computer Vision Techniques for Safety and Driver Assistance -- 12.2.1.2 Types of Optical Character Recognition Systems -- 12.2.1.3 Phases of Optical Character Recognition -- 12.2.1.4 Threshold Segmentation -- 12.2.1.5 Edge Detection Operator -- 12.2.1.6 Use Cases of OCR -- 12.2.1.7 List of Research Papers -- 12.2.2 How is IoT Changing the Face of Information Science? …”

Tabla de Contenidos: “…Moderation effect of Gender on Knowledge Acquisition to Employee Satisfaction -- Moderation effect of Gender on Knowledge Sharing to Employee Satisfaction -- Moderation effect of Gender on Knowledge Creation to Employee Satisfaction -- Moderation effect of Gender on Knowledge Storage to Employee Satisfaction -- Moderation effect of Gender on Knowledge Retention to Employee Satisfaction -- Discussion -- Conclusion -- Implications -- Managerial Implications -- Research Implications -- Future Research Implications -- References -- 9 Strengthening Employer Branding with Corporate : Social Responsibility -- Introduction -- Employer Branding -- Meaning and Definition -- Internal Employer Branding -- External Employer Branding -- Employer Branding and Employee Gratification -- Importance of EB -- Strategic Activities of EB -- Employer Value Propositions (EPV) -- Linking with Campus -- Motivational Factors -- Creative Marketing Team -- Feedback and Research -- Statistics -- Corporate Social Responsibility -- Meaning and Definition -- Company Act 2013 -- CSR Pillars -- Corporate Social Responsibilities toward Various Stakeholders -- Corporate Social Responsibility and Branding -- Employer Branding and Corporate Social Responsibility -- Concept -- Relationship between EB and CSR -- Significance of EB and CSR -- Increase Organizational Social Behavior and Strengthen the Employer-Employee Relation -- Boost Employee Identity with the Organization -- Enhance Employee Retention and Organizational Assurance -- Develop Employee Engagement and Performance -- More Appealing Company Culture to Prospective Employees -- Facts and Figures -- Signaling Theory -- Social Identity Theory -- Conclusion -- Notes -- References -- 10 Enhancing Employee Happiness: Branding as an Employer of Choice -- Introduction -- Theoretical Background and Literature Review…”

“…Using the household survey statistics of consumption expenditure, an analysis of utilization or benefit incidence of public spending on social sectors in India is achieved, covering education and health sectors…”

Tabla de Contenidos: “…-- Digitalisation, including ICT and other intangibles -- The growing digital economy needs to be accurately reflected in economic statistics -- ICT has a positive impact on productivity, but how large? …”

“…It also includes shorter reviews of policy developments in Australia, Belgium, New Zealand, Norway, Spain and Turkey; energy balances and key statistical data of all Member countries; and key energy statistics over a 20-year period…”

“…About This Book Set up and manage your very first online store with a friendly and engaging approach using Magento 2 Create your own shipping rates matrix and connect to shippers such as UPS, FedEx, and USPS Create reports to track store sales, customer activity, and statistics Who This Book Is For Have you been trying to create a website without luck using different platforms, but have never tried Magento before? …”

“…What You Will Learn Read a csv file into python and R, and print out some statistics on the data Gain knowledge of the data formats and programming structures involved in retrieving API data Make effective use of regular expressions in the data wrangling process Explore the tools and packages available to prepare numerical data for analysis Find out how to have better control over manipulating the structure of the data Create a dexterity to programmatically read, audit, correct, and shape data Write and complete programs to take in, format, and output data sets In Detail Around 80% of time in data analysis is spent on cleaning and preparing data for analysis. …”

Tabla de Contenidos: “…The coordinate system -- Faceting -- Theme -- Installing ggplot2 -- Scatter plots -- Histogram plots -- Density plots -- Probability plots -- dnorm() -- pnorm() -- rnorm() -- Box plots -- Residual plots -- Summary -- Chapter 5: Creating Aesthetically Pleasing Reports with knitr and R Markdown -- Technical requirements -- Installing R Markdown -- Working with R Markdown -- Reproducible data analysis reports with knitr -- Exporting and customizing reports -- Summary -- Section 2: Univariate, Time Series, and Multivariate Data -- Chapter 6: Univariate and Control Datasets -- Technical requirements -- Reading the dataset -- Cleaning and tidying up the data -- Understanding the structure of the data -- Hypothesis tests -- Statistical hypothesis in R -- The t-test in R -- Directional hypothesis in R -- Correlation in R -- Tietjen-Moore test -- Parsimonious models -- Probability plots -- The Shapiro-Wilk test -- Summary -- Chapter 7: Time Series Datasets -- Technical requirements -- Introducing and reading the dataset -- Cleaning the dataset -- Mapping and understanding structure -- Hypothesis test -- t-test in R -- Directional hypothesis in R -- Grubbs' test and checking outliers -- Parsimonious models -- Bartlett's test -- Data visualization -- Autocorrelation plots -- Spectrum plots -- Phase plots -- Summary -- Chapter 8: Multivariate Datasets -- Technical requirements -- Introducing and reading a dataset -- Cleaning the data -- Mapping and understanding the structure -- Hypothesis test -- t-test in R -- Directional hypothesis in R -- Parsimonious model -- Levene's test -- Data visualization -- Principal Component Regression -- Partial Least Squares Regression -- Summary -- Section 3: Multifactor, Optimization, and Regression Data Problems -- Chapter 9: Multi-Factor Datasets -- Technical requirements -- Introducing and reading the dataset…”

“…It covers a range of topics, from statistical data prediction to Kalman filtering, from black-box model identification to parameter estimation, from spectral analysis to predictive control. …”

Tabla de Contenidos: “…Intro -- Table of contents -- Basic statistics of Iceland, 2014 -- Executive summary -- Main findings -- Iceland's economic prospects are good, but capital controls and wage increases are key challenges -- Output has recovered -- Fiscal policy has become more sustainable, but contingent liabilities remain a risk -- The budget deficit has been eliminated -- Barriers to entrepreneurship, lack of competition and weaknesses in education undermine productivity -- Barriers to entrepreneurship are high -- Key recommendations -- Lifting capital controls while preserving stability -- Securing fiscal sustainability -- Setting the course for productivity growth -- Assessment and recommendations -- Figure 1. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Part 1: Introduction to Data Observability -- Chapter 1: Fundamentals of Data Quality Monitoring -- Learning about the maturity path of data in companies -- Identifying information bias in data -- Data producers -- Data consumers -- The relationship between producers and consumers -- Asymmetric information among stakeholders -- Exploring the seven dimensions of data quality -- Accuracy -- Completeness -- Consistency -- Conformity -- Integrity -- Timeliness -- Uniqueness -- Consequences of data quality issues -- Turning data quality into SLAs -- An agreement as a starting point -- The incumbent responsibilities of producers -- Considerations for SLOs and SLAs -- Indicators of data quality -- Data source metadata -- Schema -- Lineage -- Application -- Statistics and KPIs -- Examples of SLAs, SLOs, and SLIs -- Alerting on data quality issues -- Using indicators to create rules -- The data scorecard -- Summary -- Chapter 2: Fundamentals of Data Observability -- Technical requirements -- From data quality monitoring to data observability -- Three principles of data observability -- Data observability in IT observability -- Key components of data observability -- The contract between the application owner and the marketing team -- Observing a timeliness issue -- Observing a completeness issue -- Observing a change in data distribution -- Data observability in the enterprise ecosystem -- Measuring the return on investment - defining the goals -- Summary -- Part 2: Implementing Data Observability -- Chapter 3: Data Observability Techniques -- Analyzing the data -- Monitoring data asynchronously -- Monitoring data synchronously -- Analyzing the application -- The anatomy of an external analyzer -- Pros and cons of the application analyzer method…”

“…A mathematical background with a conceptual understanding of calculus and statistics is also desired. What You Will Learn Get a practical deep dive into deep learning algorithms Explore deep learning further with Theano, Caffe, Keras, and TensorFlow Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines Dive into Deep Belief Nets and Deep Neural Networks Discover more deep learning algorithms with Dropout and Convolutional Neural Networks Get to know device strategies so you can use deep learning algorithms and libraries in the real world In Detail With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. …”

“…Easy-to-follow guidelines from a pro for simplifying your investments, protecting yourself from the investment sharks and achieving financial freedom Drawing on his years as an investor for leading banks in the U.S. and Asia, Philip Cheng delivers down-to-earth strategies guaranteed to make you ""shark-proof"" while you optimize investment returns. Statistics show that only 20% of small investors ever come close to achieving their investment goals. …”

“…"Business ratios, formulas, and statistics are used by controllers, business managers, and analyst to evaluate company operations and performance. …”

“…In this non-technical book for executives, business owners, and marketers, search engine strategy guru Vanessa Fox--who created Google's portal for site owners, Google Webmaster Central--explains what every marketer or business owner needs to understand about search rankings, search data, comprehensive search strategies, and integrating your strategy into the businesses processes. Updated statistics, tools, and recommendations Details about the latest changes from Google, Bing, and the overall search landscape Explanation and recommendations related to Google's substantial new search algorithm, know as "Panda" Discussion of the changing landscape of the integration of search and social media, including the addition of Google+ to the mix Traditional marketing isn't enough anymore. …”

“…"Many DEI interventions lack rigor and measurable value beyond staff composition, statistics, and surveys. Data-Driven DEI presents readers with science-based, technology-enabled assessments and tools that will help individuals and organizations achieve measurable lasting impact. …”