Tabla de Contenidos: “…A.3 Sustainability Cultural Indicators Program (SCIP) -- A.4 Army Study to Assess Risk and Resilience in Service members (STARRS) -- A.5 Panel Study of Income Dynamics Childhood Retrospective Circumstances Study (PSID-CRCS) -- Appendix 10.B: Smartphone Prevalence in Web Surveys -- Appendix 10.C: Screen Captures from Peterson et al. (2013) Experiment -- Appendix 10.D: Survey Questions Used in the Analysis of the Peterson et al. (2013) Experiment -- References -- Chapter 11 Survey Research and the Quality of Survey Data Among Ethnic Minorities -- 11.1 Introduction -- 11.2 On the Use of the Terms Ethnicity and Ethnic Minorities -- 11.3 On the Representation of Ethnic Minorities in Surveys -- 11.3.1 Coverage of Ethnic Minorities -- 11.3.2 Factors Affecting Nonresponse Among Ethnic Minorities -- 11.3.3 Postsurvey Adjustment Issues Related to Surveys Among Ethnic Minorities -- 11.4 Measurement Issues -- 11.4.1 The Tradeoff When Using Response-Enhancing Measures -- 11.5 Comparability, Timeliness, and Cost Concerns -- 11.5.1 Comparability -- 11.5.2 Timeliness and Cost Considerations -- 11.6 Conclusion -- References -- Section 3 Data Collection and Data Processing Applications -- Chapter 12 Measurement Error in Survey Operations Management: Detection, Quantification, Visualization, and Reduction -- 12.1 TSE Background on Survey Operations -- 12.2 Better and Better: Using Behavior Coding (CARIcode) and Paradata to Evaluate and Improve Question (Specification) Erro... -- 12.2.1 CARI Coding at Westat -- 12.2.2 CARI Experiments -- 12.3 Field-Centered Design: Mobile App for Rapid Reporting and Management -- 12.3.1 Mobile App Case Study -- 12.3.2 Paradata Quality -- 12.4 Faster and Cheaper: Detecting Falsification With GIS Tools -- 12.5 Putting It All Together: Field Supervisor Dashboards -- 12.5.1 Dashboards in Operations -- 12.5.2 Survey Research Dashboards…”

Tabla de Contenidos: “…-- You put up 100 percent of the capital and you assume 100 percent of the risk. But you earn less than 40 percent of the potential return -- Costs make the difference between investment success and investment failure -- Fund investors deserve a fair shake -- Chapter Five Focus on the Lowest-Cost Funds: The More the Managers Take, the Less the Investors Make -- Fund performance comes and goes. …”

Tabla de Contenidos: “…Intro -- Half Title -- Title Page -- Copyright -- Contents -- Editor Bios -- Contributor Bios -- Acknowledgments -- Introductory Chapter: Asia Finance: The Emergence of Asia Economy and New Development in Finance -- Banking -- Market Developments and Governance Issues -- Sovereign Wealth Funds -- Summary of Individual Chapters in Volume 1 -- Disclaimers -- Part One: Banking -- 1 Risk Rating in Asian Banks -- 1.1 Introduction -- 1.2 PIT Versus Behavior Modeling -- 1.2.1 Transaction Behavior Data -- 1.2.2 Back-Testing -- 1.3 Transaction Behavior Scoring -- 1.3.1 Scoring Process -- 1.3.2 Validation -- 1.4 Simulation and Conclusion -- References -- 2 Private Wealth Management in Asia -- 2.1 Introduction -- 2.2 Global Market Distribution of Private Wealth -- 2.3 Asian Market Distribution of Private Wealth -- 2.4 Asian Private Wealth Management -- 2.5 Asian Centers of Private Wealth Management -- 2.5.1 Hong Kong and Singapore -- 2.5.2 China and India as Emerging Markets -- 2.5.3 Japan's Challenges -- 2.6 Swiss Private Banking Across Asia -- 2.7 Conclusion -- References -- 3 The Banking Networks of Asian Financial Centers -- 3.1 Introduction -- 3.2 The Roots of Asia's Banking Networks -- 3.3 Asia's Leading Financial Centers -- 3.3.1 The View from HSBC -- 3.3.2 The Ranking of Asia's Financial Centers -- 3.4 Asia's Exchanges -- 3.5 China's Financial Centers -- 3.6 Conclusion -- Acknowledgments -- References -- 4 Dynamics of House Prices and Bank Lending in Korea -- 4.1 Introduction -- 4.2 Literature Review -- 4.3 Apartment House Prices and Bank Lending: Stylized Facts in Regional Submarkets of House in Korea -- 4.4 Long-Run Analysis -- 4.5 Short-Run Relationship -- 4.6 Conclusion -- Appendix 1. …”

Tabla de Contenidos: “…5 Results and Discussion -- 6 Conclusion -- References -- Approach for Evaluating Changeable Production Systems in a Battery Module Production Use Case -- 1 Introduction -- 2 Industry Survey -- 2.1 Survey Structure -- 2.2 Survey Results -- 3 State of the Art -- 4 Method -- 5 Battery Module Production Use Case -- 6 Summary and Outlook -- References -- Cost-Minimal Selection of Material Supply Strategies in Matrix Production Systems -- 1 Introduction -- 2 State of the Art -- 2.1 Material Supply and the Selection Process of Supply Strategies -- 2.2 Challenges to the Material Supply in Matrix Production -- 2.3 Cost Accounting in the Context of Logistics -- 2.4 Algorithms -- 3 Method for Cost-Minimal Material Supply Strategy Selection -- 3.1 Structure of Method -- 3.2 Cost Side Description of Alternative Supply Strategies -- 3.3 Selection of the Most Cost-Effective Material Supply Strategy in Matrix Production System -- 4 Summary and Outlook -- References -- Assessment of Ergonomics Risk Experienced by Welding Workers in a Rail Component Manufacturing Organization -- 1 Introduction -- 2 Methods and Materials -- 3 Results and Interpretation -- 4 Conclusion -- References -- A Survey of Smart Manufacturing for High-Mix Low-Volume Production in Defense and Aerospace Industries -- 1 Introduction -- 2 Manufacturing Strategies for HMLV in Defense and Aerospace Industries -- 2.1 Major Issues -- 2.2 Advanced Processing Techniques -- 2.3 Make-to-X Strategy -- 2.4 Group Technology -- 2.5 Lean Practices -- 3 A Smart Manufacturing Solution -- 3.1 Automation in Manufacturing -- 3.2 Smart Production Planning and Control -- 4 Concluding Remarks -- References -- Feasibility Analysis of Safety Training in Human-Robot Collaboration Scenario: Virtual Reality Use Case -- 1 Introduction -- 2 Related Work -- 2.1 Virtual Reality Training -- 2.2 Virtual Reality Safety Training…”

Tabla de Contenidos: “…Chapter 10 Cloud Computing Security, Risk, and Challenges: A Detailed Analysis of Preventive Measures and Applications -- 10.1 Introduction -- 10.2 Background -- 10.2.1 History of Cloud Computing -- 10.2.1.1 Software-as-a-Service Model -- 10.2.1.2 Infrastructure-as-a-Service Model -- 10.2.1.3 Platform-as-a-Service Model -- 10.2.2 Types of Cloud Computing -- 10.2.3 Cloud Service Model -- 10.2.4 Characteristics of Cloud Computing -- 10.2.5 Advantages of Cloud Computing -- 10.2.6 Challenges in Cloud Computing -- 10.2.7 Cloud Security -- 10.2.7.1 Foundation Security -- 10.2.7.2 SaaS and PaaS Host Security -- 10.2.7.3 Virtual Server Security -- 10.2.7.4 Foundation Security: The Application Level -- 10.2.7.5 Supplier Data and Its Security -- 10.2.7.6 Need of Security in Cloud -- 10.2.8 Cloud Computing Applications -- 10.3 Literature Review -- 10.4 Cloud Computing Challenges and Its Solution -- 10.4.1 Solution and Practices for Cloud Challenges -- 10.5 Cloud Computing Security Issues and Its Preventive Measures -- 10.5.1 General Security Threats in Cloud -- 10.5.2 Preventive Measures -- 10.6 Cloud Data Protection and Security Using Steganography -- 10.6.1 Types of Steganography -- 10.6.2 Data Steganography in Cloud Environment -- 10.6.3 Pixel Value Differencing Method -- 10.7 Related Study -- 10.8 Conclusion -- References -- Chapter 11 Internet of Drone Things: A New Age Invention -- 11.1 Introduction -- 11.2 Unmanned Aerial Vehicles -- 11.2.1 UAV Features and Working -- 11.2.2 IoDT Architecture -- 11.3 Application Areas -- 11.3.1 Other Application Areas -- 11.4 IoDT Attacks -- 11.4.1 Counter Measures -- 11.5 Fusion of IoDT With Other Technologies -- 11.6 Recent Advancements in IoDT -- 11.7 Conclusion -- References -- Chapter 12 Computer Vision-Oriented Gesture Recognition System for Real-Time ISL Prediction -- 12.1 Introduction -- 12.2 Literature Review…”

Tabla de Contenidos: “…-- 2. Add tests and de-risk change -- 3. Implement change and improve maintainability -- Challenge -- Updating an existing e-commerce site -- Assignment -- Knowledge check -- Summary -- Chapter 11: Data Exploration with ChatGPT -- Introduction -- Business problem -- Problem and data domain -- Dataset overview -- Feature breakdown -- Prompting strategy -- Strategy 1: Task-Actions-Guidelines (TAG) prompt strategy -- Strategy 2: Persona-Instructions-Context (PIC) prompt strategy -- Strategy 3: Learn-Improvise-Feedback-Evaluate (LIFE) prompt strategy -- Data exploration of the Amazon review dataset using the free version of ChatGPT -- Feature 1: Loading the dataset -- Feature 2: Inspecting the data -- Feature 3: Summary statistics -- Feature 4: Exploring categorical variables -- Feature 5: Rating distribution -- Feature 6: Temporal trends -- Feature 7: Review length analysis -- Feature 8: Correlation study -- Data exploration of the Amazon review dataset using ChatGPT-4o -- Assignment -- Challenge -- Summary -- Chapter 12: Building a Classification Model with ChatGPT -- Introduction -- Business problem -- Problem and data domain -- Dataset overview -- Breaking the problem down into features -- Prompting strategy -- Strategy 1: Task-Actions-Guidelines (TAG) prompt strategy -- Strategy 2: Persona-Instructions-Context (PIC) prompt strategy -- Strategy 3: Learn-Improvise-Feedback-Evaluate (LIFE) prompt strategy -- Building a sentiment analysis model to accurately classify Amazon reviews using the free version of ChatGPT -- Feature 1: Data preprocessing and feature engineering -- Feature 2: Model selection and baseline training -- Feature 3: Model evaluation and interpretation -- Feature 4: Handling imbalanced data -- Feature 5: Hyperparameter tuning -- Feature 6: Experimenting with feature representation…”

Tabla de Contenidos: “…Cover -- Half Title -- Series Information -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- I.1 Saudi Sportswashing -- I.2 A System That Is Difficult to Change -- I.3 An Unprecedented Strategy to Disrupt World Sport -- I.4 Regulating International Sport -- I.5 Beyond the Contrast Between the European and American Models of Sport -- I.6 Convergence Between Non-Profit and For-Profit Sport Organisations -- I.7 Competition, But Also Cooperation and Mimicry -- I.8 A Two-Part Presentation -- Notes -- Bibliography -- Part I Three Interconnected Regulatory Systems -- Chapter 1 Olympic Governance Under Pressure -- 1.1 More Assertive Leadership By the IOC -- 1.1.1 The Globalisation of Sport -- 1.1.2 Three Interconnected Forms of Governance -- 1.1.2.1 Systemic Governance -- 1.1.2.2 Political Governance -- 1.1.2.3 ('Good') Organisational Governance -- 1.1.3 Thomas Bach's Realpolitik Approach to Risk Management -- 1.1.3.1 Responsible Autonomy Through Good Governance -- 1.1.3.2 Sport's Societal Benefits Called Into Question -- 1.1.4 An Institutional Balancing Act -- 1.1.4.1 Institutional Work and Legitimacy -- 1.1.4.2 Strategic Responses to Institutional Processes -- 1.2 The IOC's Core Partners -- 1.2.1 International Federations -- 1.2.1.1 Four Categories of IFs -- 1.2.2 National Olympic Committees -- 1.2.2.1 A Wider Range of Services -- 1.2.2.2 The Rise of Asia's Sports Institutions -- 1.2.2.3 Wide Variation in Funding and Professionalisation Strategies -- 1.2.2.4 At the Heart of Controversial International and Domestic (Geo)Political Games -- 1.2.3 Organising Committees of the Olympic Games -- 1.2.3.1 Funding and Delivering Olympic Games -- 1.2.3.2 The Paris 2024 OCOG -- 1.2.3.3 The Controversial Issue of Impacts and Legacy -- 1.2.3.4 A Catalyst for Transforming a Country? …”

Tabla de Contenidos: “…7.5.3 Reflecting automation as a priority with your staff -- 7.5.4 Providing time for training and learning -- 7.6 Filling the skill-set gap -- 7.6.1 But if I build it, I own it -- 7.6.2 Building the new skill set -- 7.7 Approaching automation -- 7.7.1 Safety in tasks -- 7.7.2 Designing for safety -- 7.7.3 Complexity in tasks -- 7.7.4 How to rank tasks -- 7.7.5 Automating simple tasks -- 7.7.6 Automating complicated tasks -- 7.7.7 Automating complex tasks -- Summary -- 8 Off-hour deployments -- 8.1 War story -- 8.2 The layers of a deployment -- 8.3 Making deployments routine affairs -- 8.3.1 Accurate preproduction environments -- 8.3.2 Staging will never be exactly like production -- 8.4 Frequency reduces fear -- 8.5 Reducing fear by reducing risk -- 8.6 Handling failure in the layers of the deployment process -- 8.6.1 Feature flags -- 8.6.2 When to toggle off your feature flag -- 8.6.3 Fleet rollbacks -- 8.6.4 Deployment artifact rollbacks -- 8.6.5 Database-level rollbacks -- 8.7 Creating deployment artifacts -- 8.7.1 Leveraging package management -- 8.7.2 Configuration files in packages -- 8.8 Automating your deployment pipeline -- 8.8.1 Safely installing the new application -- Summary -- 9 Wasting a perfectly good incident -- 9.1 The components of a good postmortem -- 9.1.1 Creating mental models -- 9.1.2 Following the 24-hour rule -- 9.1.3 Setting the rules of the postmortem -- 9.2 The incident -- 9.3 Running the postmortem -- 9.3.1 Choosing whom to invite to the postmortem -- 9.3.2 Running through the timeline -- 9.3.3 Defining action items and following up -- 9.3.4 Documenting your postmortem -- 9.3.5 Sharing the postmortem -- Summary -- 10 Information hoarding: Only Brent knows -- 10.1 Understanding how information hoarding happens -- 10.2 Recognizing unintentional hoarders -- 10.2.1 Documentation isn't valued…”

Tabla de Contenidos: “…-- The 5-star safety rating -- Exploring the Possibility of a Car Fire (ICE versus EV) -- Car fires by the numbers -- Calculating the risk of an EV catching fire -- One caveat to all this EV fire talk -- The leading cause of vehicle fires -- Okay, but what if I have a pacemaker - you know, electromagnetic motors and such? …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Chapter 1 Introduction -- 1.1 A Basic Paradigm for Marketing Problems -- 1.2 A Simple Example -- 1.3 Benefits and Costs of the Bayesian Approach -- 1.4 An Overview of Methodological Material and Case Studies -- 1.5 Approximate Bayes Methods and This Book -- 1.6 Computing and This Book -- Acknowledgments -- Chapter 2 Bayesian Essentials -- 2.1 Essential Concepts from Distribution Theory -- 2.2 The Goal of Inference and Bayes Theorem -- 2.2.1 Bayes Theorem -- 2.3 Conditioning and the Likelihood Principle -- 2.4 Prediction and Bayes -- 2.5 Summarizing the Posterior -- 2.6 Decision Theory, Risk, and the Sampling Properties of Bayes Estimators -- 2.7 Identification and Bayesian Inference -- 2.8 Conjugacy, Sufficiency, and Exponential Families -- 2.9 Regression and Multivariate Analysis Examples -- 2.9.1 Multiple Regression -- 2.9.2 Assessing Priors for Regression Models -- 2.9.3 Bayesian Inference for Covariance Matrices -- 2.9.4 Priors and the Wishart Distribution -- 2.9.5 Multivariate Regression -- 2.9.6 The Limitations of Conjugate Priors -- 2.10 Integration and Asymptotic Methods -- 2.11 Importance Sampling -- 2.11.1 GHK Method for Evaluation of Certain Integrals of MVN -- 2.12 Simulation Primer for Bayesian Problems -- 2.12.1 Uniform, Normal, and Gamma Generation -- 2.12.2 Truncated Distributions -- 2.12.3 Multivariate Normal and Student t Distributions -- 2.12.4 The Wishart and Inverted Wishart Distributions -- 2.12.5 Multinomial Distributions -- 2.12.6 Dirichlet Distribution -- 2.13 Simulation from Posterior of Multivariate Regression Model -- Chapter 3 MCMC Methods -- 3.1 MCMC Methods -- 3.2 A Simple Example: Bivariate Normal Gibbs Sampler -- 3.3 Some Markov Chain Theory -- 3.4 Gibbs Sampler -- 3.5 Gibbs Sampler for the SUR Regression Model -- 3.6 Conditional Distributions and Directed Graphs…”

Tabla de Contenidos: “…10.3.1 Preprocessing and Data Fusion Techniques -- 10.4 Applications of Machine Learning in Urban Ecosystem Services -- 10.4.1 Urban Green Space Identification and Monitoring -- 10.4.2 Biodiversity Assessment and Conservation -- 10.4.3 Urban Heat Island Detection and Mitigation -- 10.4.4 Air Quality Monitoring and Prediction -- 10.4.5 Flood Risk Assessment and Management -- 10.5 Applications of Machine Learning in Urban Landscape Planning and Design -- 10.5.1 Landscape Connectivity and Fragmentation Analysis -- 10.5.2 Green Infrastructure Planning -- 10.5.3 Urban Greening and Rewilding Strategies -- 10.5.4 Urban Form Optimization for Ecological Resilience -- 10.5.5 Evaluation of Landscape Design Alternatives -- 10.6 Machine Learning for Socio-Ecological Systems in Urban Environments -- 10.6.1 Analyzing Human-Nature Interactions -- 10.6.2 Environmental Justice and Equitable Access to Green Spaces -- 10.6.3 Public Engagement and Decision-Making Support -- 10.6.4 Community-Based Ecological Monitoring and Management -- 10.7 Challenges and Future Directions -- 10.7.1 Data Quality and Availability -- 10.7.2 Interpreting and Validating Machine Learning Models -- 10.7.3 Integrating Cross-Disciplinary Knowledge -- 10.7.4 Ethical Considerations -- 10.7.5 Climate Change and Urban Ecology -- 10.8 Conclusion -- References -- Chapter 11 Application of Machine Learning in Urban Land Use -- 11.1 Introduction -- 11.1.1 Briefly Introduce the Concept of Machine Learning and Urban Land Use -- 11.1.2 Explain the Significance of Integrating Machine Learning in Urban Planning and Management -- 11.2 Background: Understanding Urban Land Use and Machine Learning -- 11.2.1 Discuss the Basics of Urban Land Use, Including its Importance, Challenges, and Traditional Methods Used in Planning…”

Tabla de Contenidos: “…statistical capacity has improved significantly, but the access to administrative data and sound quality-management frameworks lag behind -- Planet conserving nature -- Albania is vulnerable to natural hazards -- Albania faces earthquakes, floods, droughts and extreme temperatures -- Albania has improved disaster-prevention legislation, but implementation remains a challenge -- Preserving the quality of Albania's environmental resources is crucial for development -- Air pollution is a serious concern -- Solid waste management and recycling are lacking -- Managing water services is a considerable challenge and will increase in importance -- Better enforcement and implementation of environmental legislation is key -- Transparent decision-making processes in environmental matters and an open dialogue with civil society and other stakeholders are important -- The high reliance on hydropower gives Albania the most renewable energy profile in the region, but it cannot be expanded without environmental burden -- References -- Notes -- Part III Assessing opportunities and constraints in Bosnia and Herzegovina -- Assessing opportunities and constraints in Bosnia and Herzegovina -- 6 Overview: Identifying strategic opportunities for Bosnia and Herzegovina -- Assessing Bosnia and Herzegovina's development performance -- People towards better lives for all -- Prosperity boosting productivity -- Partnerships and financing financing sustainable development -- Peace and institutions strengthening governance -- Planet conserving nature -- Suggestions for strategic priorities for Bosnia and Herzegovina -- References -- 7 Impact of COVID-19 in Bosnia and Herzegovina -- Evolution of the pandemic -- Policy responses and economic impact -- Dimensions of vulnerability to further socio-economic impact from COVID-19 -- Material well-being -- Health and non-material well-being -- References -- Note -- 8 Multi-dimensional analysis of development in Bosnia and Herzegovina -- People towards better lives for all -- Improving well-being for all -- Strengthening the productive potential and equal participation of all citizens, especially women and youth -- Young people and women find themselves outside the labour market -- Women's participation in society is not yet equal -- Labour market institutions need to be strengthened and working conditions improved -- Boosting education quality -- Improving health and social protection outcomes -- Healthcare governance is fragmented, inefficient and not financially sustainable, leading to subpar and inequitable health outcomes -- The social protection financing model is unsustainable and does not provide equal access to benefits for the most needy -- Prosperity boosting productivity -- Weak investment and productivity have constrained economic growth in the post-crisis period -- A fragmented internal market and high bureaucratic burden create a challenging environment for private-sector development -- The large and inefficient SOE sector creates unfair competition for private enterprises -- Infrastructure gaps impede connectivity with the Western Balkans and beyond -- The lack of a skilled workforce and the lack of technology adoption limit the potential for economic upgrading and expansion of the tradable sector -- Partnerships and financing financing sustainable development -- Low domestic savings and limited external financing have constrained investment -- Strong fiscal performance has created ample fiscal space for combatting the COVID-19 crisis -- Strong revenue performance has not translated into quality public services and infrastructure due to high expenditures on wages and transfers and low investment -- Access to finance is limited for SMEs, particularly start-ups and microenterprises -- Peace and institutions strengthening governance -- The decentralisation framework is asymmetric -- Overly complex institutional design undermines the independence, efficiency and transparency of the judiciary -- Personal connections are inevitable in getting things done but may create social exclusion, distort the labour market and weaken administrative capacity -- Fragmented law enforcement and incoherent legislation frustrate anti-corruption efforts -- Poor governance can explain poor SEO performance -- Bosnia and Herzegovina's statistical capacity requires improvement -- Planet conserving nature -- Bosnia and Herzegovina is vulnerable to natural hazards and climate change -- Bosnia and Herzegovina's rich biodiversity and forest coverage is not sufficiently protected -- High levels of air pollution threaten human health, the economy and the environment -- Inadequate waste management poses a risk to the environment and damages natural resources -- Water management must be improved in Bosnia and Herzegovina to ensure a stable drinking water supply in the long run -- Management of public utility services is too fragmented, and prices are set below operational costs -- Bosnia and Herzegovina's environmental legislation is improving, but enforcement remains a challenge -- Bosnia and Herzegovina's high carbon intensity and low energy efficiency are reflected in high GHG emissions -- Bosnia and Herzegovina is heavily reliant on subsidised coal and does not sufficiently incentivise renewable energies -- Outdated energy infrastructure, SOE monopolies and lengthy procedures are key challenges in the energy sector -- References -- Notes -- Part IV Assessing opportunities and constraints in Kosovo -- Assessing opportunities and constraints in Kosovo -- 9 Overview: Identifying strategic opportunities for Kosovo -- Towards a vision for Kosovo in 2030: strong economic growth and rising living standards contributing to healthier and longer lives built on equal access to quality education, strong human capital, respect for the environment and democratic and effect ... -- Assessing Kosovo's development performance -- Sustainable development: summary of Kosovo's performance and key constraints -- Suggestions for strategic priorities for Kosovo -- References -- 10 Impact of COVID-19 in Kosovo -- Evolution of the pandemic -- Policy responses and economic impact -- Dimensions of vulnerability to further socio-economic impact from COVID-19 -- Material well-being and social protection -- Health and non-material well-being -- References -- 11 Multi-dimensional analysis of development in Kosovo -- People towards better lives for all -- Improving well-being for all -- Regional inequalities are evident in access to basic infrastructure, and minority groups risk being left behind -- Prioritising quality jobs for all -- Young people and women find themselves outside the labour market -- Labour market institutions need to be strengthened and working conditions improved -- Boosting education quality -- Improving health and social protection coverage -- Kosovo's health system fails to deliver quality results and access to care -- The equity and targeting of Kosovo's social protection schemes can be improved -- Ensuring social inclusion of women beyond the labour market -- Achieving social cohesion -- Prosperity boosting productivity -- Kosovo's growth model over the past decade has undermined competitiveness and discouraged productivity-enhancing investment and job creation -- Domestic demand has largely driven economic growth over the past decade -- Investment has supported the growth of non-tradable services and real estate -- Low productivity and high wage growth have limited job creation and weakened competitiveness -- Towards more sustainable growth -- A more reliable electricity supply can improve investment prospects -- Strengthening governance and improving the business environment will reduce the cost of doing business in Kosovo and enhance trust in institutions -- Corruption and weak contract enforcement -- The administrative and bureaucratic burden remains high -- Partnerships and financing financing sustainable development -- Kosovo's economy needs more diversified financing for investment and growth -- Revenue performance has been weak on account of the low tax rates, limited tax base and inefficient tax collection -- Government current expenditures have crowded out investment in priority areas for development -- Access to finance is particularly constrained for SMEs -- Peace and institutions strengthening governance -- An overly complicated structure and…”

Tabla de Contenidos: “…9 1.1.4 FTC June 2015 Guidance on Data Security 11 1.1.5 FTC Protecting Personal Information Guide 14 1.1.6 Lessons from FTC Cybersecurity Complaints 15 1.1.6.1 Failure to Secure Highly Sensitive Information 16 1.1.6.1.1 Use Industry-Standard Encryption for Sensitive Data 16 1.1.6.1.2 Routine Audits and Penetration Testing are Expected 17 1.1.6.1.3 Health-Related Data Requires Especially Strong Safeguards 18 1.1.6.1.4 Data Security Protection Extends to Paper Documents 19 1.1.6.1.5 Business-to-Business Providers also are Accountable to the FTC For Security of Sensitive Data 20 1.1.6.1.6 Companies are Responsible for the Data Security Practices of Their Contractors 22 1.1.6.1.7 Make Sure that Every Employee Receives Regular Data Security Training for Processing Sensitive Data 23 1.1.6.1.8 Privacy Matters, Even in Data Security 23 1.1.6.1.9 Limit the Sensitive Information Provided to Third Parties 24 1.1.6.2 Failure to Secure Payment Card Information 24 1.1.6.2.1 Adhere to Security Claims about Payment Card Data 24 1.1.6.2.2 Always Encrypt Payment Card Data 25 1.1.6.2.3 Payment Card Data Should be Encrypted Both in Storage and at Rest 26 1.1.6.2.4 In-Store Purchases Pose Significant Cybersecurity Risks 26 1.1.6.2.5 Minimize Duration of Storage of Payment Card Data 28 1.1.6.2.6 Monitor Systems and Networks for Unauthorized Software 29 1.1.6.2.7 Apps Should Never Override Default App Store Security Settings 29 1.1.6.3 Failure to Adhere to Security Claims 30 1.1.6.3.1 Companies Must Address Commonly Known Security Vulnerabilities 30 1.1.6.3.2 Ensure that Security Controls are Sufficient to Abide by Promises about Security and Privacy 31 1.1.6.3.3 Omissions about Key Security Flaws can also be Misleading 33 1.1.6.3.4 Companies Must Abide by Promises for Security-Related Consent Choices 33 1.1.6.3.5 Companies that Promise Security Must Ensure Adequate Authentication Procedures 34 1.1.6.3.6 Adhere to Promises about Encryption 35 1.2 State Data Breach Notification Laws 36 1.2.1 When Consumer Notifications are Required 37 1.2.1.1 Definition of Personal Information 37 1.2.1.2 Encrypted Data 38 1.2.1.3 Risk of Harm 39 1.2.1.4 Safe Harbors and Exceptions to Notice Requirement 39 1.2.2 Notice to Individuals 40 1.2.2.1 Timing of Notice 40 1.2.2.2 Form of Notice 40 1.2.2.3 Content of Notice 41 1.2.3 Notice to Regulators and Consumer Reporting Agencies 41 1.2.4 Penalties for Violating State Breach Notification Laws 42 1.3 State Data Security Laws 42 1.3.1 Oregon 43 1.3.2 Rhode Island 45 1.3.3 Nevada 45 1.3.4 Massachusetts 46 1.4 State Data Disposal Laws 49 2 Cybersecurity Litigation 51 2.1 Article III Standing 52 2.1.1 Applicable Supreme Court Rulings on Standing 53 2.1.2 Lower Court Rulings on Standing in Data Breach Cases 57 2.1.2.1 Injury-in-Fact 57 2.1.2.1.1 Broad View of Injury-in-Fact 57 2.1.2.1.2 Narrow View of Injury-in-Fact 60 2.1.2.2 Fairly Traceable 62 2.1.2.3 Redressability 63 2.2 Common Causes of Action Arising from Data Breaches 64 2.2.1 Negligence 64 2.2.1.1 Legal Duty and Breach of Duty 65 2.2.1.2 Cognizable Injury 67 2.2.1.3 Causation 69 2.2.2 Negligent Misrepresentation or Omission 70 2.2.3 Breach of Contract 72 2.2.4 Breach of Implied Warranty 76 2.2.5 Invasion of Privacy by Publication of Private Facts 80 2.2.6 Unjust Enrichment 81 2.2.7 State Consumer Protection Laws 82 2.3 Class Action Certification in Data Breach Litigation 84 2.4 Insurance Coverage for Cybersecurity Incidents 90 2.5 Protecting Cybersecurity Work Product and Communications from Discovery 94 2.5.1 Attorney-Client Privilege 96 2.5.2 Work Product Doctrine 98 2.5.3 Non-Testifying Expert Privilege 101 2.5.4 Applying the Three Privileges to Cybersecurity: Genesco v. …”

Tabla de Contenidos: “…References -- 12 Laboratories for Research on Freight Systems and Planning -- 12.1 Introduction -- 12.2 Future Mobility Sensing, a Behavioral Laboratory -- 12.2.1 Background -- 12.2.2 FMS Architecture -- 12.2.3 Applications -- 12.3 SimMobility, a Simulation Laboratory -- 12.3.1 Background -- 12.3.2 SimMobility Architecture -- 12.3.3 Applications -- 12.4 Demonstrations -- 12.4.1 Freight-Vehicle Route-Choice Model -- 12.4.2 Quantification of Model Performance -- 12.4.3 Replication of Specific Freight and Non-Freight-Vehicle Tours -- 12.5 Concluding Remarks -- References -- 13 Urban Risks and Resilience -- 13.1 Introduction -- 13.2 Risks, Exposure, and Vulnerability -- 13.3 Urban Resilience and Capacities -- 13.3.1 The Definitional Quagmire -- 13.3.2 Objects of Analysis -- 13.4 Measurement and Assessment Informatics -- 13.5 Science Informs Practice and Practice Informs Science -- 13.6 Moving Forward -- References -- 14 Urban Crime and Security -- 14.1 Introduction -- 14.2 Urban Crime -- 14.2.1 Historical Roots in Understanding Urban Crime: An Environmental Perspective -- 14.2.2 Theoretical Concepts in Environmental Criminology -- 14.3 Urban Security -- 14.3.1 Fear of Crime in Urban Areas -- 14.3.2 Implementation of Crime Prevention -- 14.4 Latest Tools in Urban Crime Analysis and Security -- 14.4.1 Crime Hotspot Mapping: From Retrospective Analysis to Prediction -- 14.4.2 Advanced Police Patrolling Strategies -- 14.5 Intelligent Data-Driven Policing -- 14.6 Summary -- References -- 15 Urban Governance -- 15.1 Transparency and City Open Data -- 15.1.1 Open Data Platforms -- 15.1.2 Open Data and Accountability -- 15.1.3 Why Are Goals Important? …”

Tabla de Contenidos: “…Noteworthy Research Challenges and Risks -- 6.5.4.1. Extraneous and confounding variables -- 6.5.4.2. …”

Tabla de Contenidos: “…Tracking test projects -- 1.3. Risks and systems-of-systems -- 1.4. Particularities related to SoS -- 1.5. …”

Tabla de Contenidos: “…1.15.5 Superconductors -- 1.15.6 3D printing -- 1.15.7 Autonomous vehicle -- 1.16 Conclusion -- References -- Chapter 2: Advances of deep learning and related applications -- 2.1 Introduction -- 2.2 Deep learning techniques -- 2.3 Multilayer perceptron -- 2.4 Convolutional neural network -- 2.5 Recurrent neural network -- 2.6 Long-term short-term memory -- 2.7 GRU -- 2.8 Autoencoders -- 2.9 Attention mechanism -- 2.10 Deep generative models -- 2.11 Restricted Boltzmann machine -- 2.12 Deep belief network -- 2.13 Modern deep learning platforms -- 2.13.1 PyTorch -- 2.13.2 TensorFlow -- 2.13.3 Keras -- 2.13.4 Caffe (Convolutional architecture for fast feature embedding) and Caffe2 -- 2.13.5 Deeplearning4j -- 2.13.6 Theano -- 2.13.7 Microsoft cognitive toolkit (CNTK) -- 2.14 Challenges of deep learning -- 2.15 Applications of deep learning -- 2.16 Conclusion -- References -- Chapter 3: Machine learning for big data and neural networks -- 3.1 Introduction -- 3.2 Machine learning and fundamentals -- 3.2.1 Supervised learning -- 3.2.2 Decision trees -- 3.2.3 Linear regression -- 3.2.4 Naive Bayes -- 3.2.5 Logistic regression -- 3.3 Unsupervised learning -- 3.3.1 K-Means algorithm -- 3.3.2 Principal component analysis -- 3.4 Reinforcement learning -- 3.5 Machine learning in large-scale data -- 3.6 Data analysis in big data -- 3.7 Predictive modelling -- 3.7.1 Understanding customer behavior and preferences -- 3.7.2 The role of supply chain and performance management in organizational success -- 3.7.3 Management of quality and enhancement -- 3.7.4 Risk mitigation and detection of fraud -- 3.8 Neural networks -- 3.8.1 Artificial neural network -- 3.8.2 RNN -- 3.8.3 CNN -- 3.8.4 Deep learning using convolutional neural networks to find building defects -- 3.9 Data generation and manipulation -- 3.9.1 Generative Adversarial Networks…”

Tabla de Contenidos: “…7.6 Conclusion -- References -- 8 Performance Isolation in Cloud-Based Big Data Architectures -- 8.1 Introduction -- 8.2 Background -- 8.2.1 Cloud Computing -- 8.2.2 Big Data Architecture -- 8.3 Case Study and Problem Statement -- 8.3.1 Case Study -- 8.3.2 Problem Statement -- 8.4 Performance Monitoring in Cloud-Based Systems -- 8.5 Application Framework for Performance Isolation -- 8.6 Evaluation of the Framework -- 8.6.1 Evaluation Results -- 8.7 Discussion -- 8.8 Related Work -- 8.9 Conclusion -- References -- 9 From Legacy to Cloud: Risks and Bene ts in Software Cloud Migration -- 9.1 Introduction -- 9.2 Research Method -- 9.2.1 Pilot Study -- 9.2.2 Search Strategy -- 9.2.2.1 Research question -- 9.2.2.2 Data sources -- 9.2.2.3 Search query -- 9.2.2.4 Search process -- 9.2.2.5 Selection of primary studies -- 9.2.2.6 Included and excluded studies -- 9.2.2.7 Search result management -- 9.2.2.8 Quality assessment of primary studies -- 9.2.3 Data Extraction -- 9.2.4 Data Analysis Method -- 9.3 Results -- 9.3.1 Overview of Primary Studies and Quality Evaluation -- 9.3.2 Bene ts and Risks -- 9.3.3 General Measures -- 9.3.4 Models and Frameworks for Cloud Migration -- 9.4 Discussion -- 9.4.1 Findings and Lessons Learned -- 9.4.2 Threats to Validity -- 9.5 Conclusion -- References -- 10 Big Data: A Practitioners Perspective -- 10.1 Big Data Is a New Paradigm - Differences With Traditional Data Warehouse, Pitfalls and Consideration -- 10.1.1 Differences With Traditional Data Warehouse -- 10.1.2 Pitfalls -- 10.1.2.1 Insuf cient volume of data -- 10.1.2.2 Not having a business challenge -- 10.1.2.3 Ignoring the data quality -- 10.1.2.4 Big data can predict the future -- 10.1.3 Considerations -- 10.2 Product Considerations for Big Data - Use of Open Source Products for Big Data, Pitfalls and Considerations…”

Tabla de Contenidos: “…The Distal Foundation -- 5.2.4.3. The Risk Assessment Overlay -- 5.2.4.4. Combining the Distal and Proximate Risk Drivers -- 5.2.4.5. …”

Tabla de Contenidos: “…-- 1.5.2 Types of Resources -- 1.6 Entrepreneurial Traits -- 1.6.1 Need for Achievement -- 1.6.2 Locus of Control -- 1.6.3 Risk-taking -- 1.6.4 Visionary and Dreamer -- 1.6.5 Innovative-Being Interested in Something 'New' -- 1.6.6 Dynamic Leadership -- 1.6.7 Resourcefulness -- 1.6.8 Excellent Networker -- 1.7 Difference between Inventors and Entrepreneurs -- 1.8 Inspiration from Role Models and Social Support -- 1.8.1 Social and Psychological Support -- 1.8.2 Moral Support -- 1.8.3 Professional Support System -- 1.9 Business Model -- 1.10 Entrepreneurship-Mindset -- 1.11 Big Companies vs Start-Ups -- 1.12 Misconceptions and Myths about Entrepreneurship -- 1.12.1 Entrepreneurs are Risk-takers -- 1.12.2 Entrepreneurship is All About Money and Getting Rich Quickly -- 1.12.3 Starting a Business Requires a Lot of Money -- 1.12.4 Entrepreneurs are Born -- 1.12.5 Entrepreneurship Requires High-tech Invention -- 1.12.6 Ignorance is Bliss for Entrepreneurs -- 1.12.7 Entrepreneurs Require Expertise and Experience in Their Line of Business -- 1.12.8 Venture Capital Funding is a Must -- 1.12.9 Banks Do Not Lend Money to Start-ups -- 1.12.10 Starting a Business is Easy -- Key Concepts -- Endnotes -- References -- Conceptual Questions -- Critical Thinking Questions -- Case 1.1: How I Made my first Million -- Case 1.2: from Candle Seller to CEO -- Chapter 2: Entrepreneurship Development in Emerging Markets -- Learning Objectives -- 2.1 Types of Start-Ups -- 2.2 Intrapreneurship -- 2.3 Why Does One become an Entrepreneur?…”