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"Academic, industry, and government experts provide a global survey of the financial services sector's transformation by AI, data science, and blockchain"-- Provided by publisher.

• Bayesian Methods for Intelligent Task Assignment in Crowdsourcing Systems.- Designing Societies of Robots.- On the Origins of Imperfection and Apparent Non-Rationality.- Lasso Granger Causal Models: Some Strategies and their Efficiency for Gene Expression Regulatory Networks.- Cooperative Feature Selection in Personalized Medicine.- Imperfect Decision Making and Risk Taking are affected by Personality.
• (source: Nielsen Book Data)

This volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selfish decision makers. The chapters are written by leading experts from different disciplines, all considering the many sources of imperfection in decision making, and always with an eye to decreasing the myriad discrepancies between theory and real world human decision making. Topics addressed include uncertainty, deliberation cost and the complexity arising from the inherent large computational scale of decision making in these systems. In particular, analyses and experiments are presented which concern: * task allocation to maximize "the wisdom of the crowd"; * design of a society of "edutainment" robots who account for one anothers' emotional states; * recognizing and counteracting seemingly non-rational human decision making; * coping with extreme scale when learning causality in networks; * efficiently incorporating expert knowledge in personalized medicine; * the effects of personality on risky decision making. The volume is a valuable source for researchers, graduate students and practitioners in machine learning, stochastic control, robotics, and economics, among other fields.
(source: Nielsen Book Data)

• Intro; Title Page; Contents; Introduction; Motivation; Problem Statement; Challenges and Overview of the State-of-the-Art; Challenges for Efficient SPARQL Query Processing; Challenges for Effective SPARQL Query Processing; Hypotheses and Research Questions; Contributions; Outline; Foundations of Linked Data Management; Linked Data; The Resource Description Framework (RDF); Querying RDF Data: The SPARQL Query Language; Querying RDF Data on the Web; URI Dereferencing and Link Traversal; SPARQL Endpoints; Linked Data Fragments; Foundations of Query Processing; Query Optimization

• Adaptive Query ProcessingAdaptive Query Processing over Linked Data; Introduction; Research Questions; Contributions; Structure of the Chapter; Motivating Example; Related Work; Adaptive Link Traversal Approaches; Adaptive Query Processing Against SPARQL Endpoints; Query Processing Approaches Against TPF Servers; The nLDE Approach; nLDE Query Optimizer; Estimation of Query Plan Cardinalities; Placing Physical Operators; Building Query Tree Plans; nLDE Optimizer: Algorithm Description; Complexity of the nLDE Query Optimizer; nLDE Adaptive Routing Query Engine; Adaptive Operators; Eddies

• Network of Linked Data Eddies (nLDE)Termination of nLDE; Correctness of nLDE; Routing Policies; Routing Policy from Eddies to Adaptive Operators; Routing Policy from Adaptive Operators to Eddies; Experimental Study; Experimental Settings; Efficiency of the nLDE Optimizer; Effectiveness of the nLDE Optimizer; Impact of the nLDE Routing-based Adaptivity on Execution Time in Perfect Networks; Effectiveness of the nLDE Routing-based Adaptivity Under the Presence of Network Delays; Summary and Future Work; Foundations of Crowdsourcing; Overview; Types of Crowdsourcing; Microtasks; Contests

• Crowdsourcing WorkflowsHybrid Crowdsourcing Workflows; Human-based Workflow: Find-Fix-Verify; Crowdsourcing Query Answer Completeness over Linked Data; Introduction; Research Questions; Contributions; Structure of the Chapter; Motivating Example; Related Work; Hybrid Query Processing for Relational Data; Crowd-based Linked Data Management Applications; Web Data Quality Assessment; The HARE Approach; Problem Definition; Proposed Solution; RDF Completeness Model; Representation of the Crowd Knowledge; Crowd Contradiction; Crowd Unknownness; HARE Microtask Manager; User Interface Generator

• Microtask ExecutorHARE Query Optimizer; Complexity of the HARE Query Optimizer; HARE Query Engine; A SPARQL Fuzzy Set Semantics; HARE BGP Executor; Complexity of HARE Query Evaluation; Experimental Study; Experimental Settings; HARE Crowdsourcing Capabilities; Size of Query Answer; Quality of Crowd Answers; Crowd Response Time; Summary and Future Work; Crowdsourcing Linked Data Quality Issues; Introduction; Research Questions; Contributions; Structure of the Chapter; Preliminaries: Linked Data Quality Issues; Related Work; Crowdsourcing in Linked Data Management; Web Data Quality Assessment

• Production and Economic Relations on the Internet: Another Level of Development of Economic Science.- Internet Economy: Existence from the Point of View of Micro-Economic Aspect.- Perspectives of Internet Economy Creation.- Modern Foundations of Internet Economy.
• (source: Nielsen Book Data)

This unique book adopts a problem approach to study the Internet economy, which consists in assessing its contradiction to classic economy, analyzing it, and describing how the Internet economy is developing in modern Russia. The authors study its sense and basic principles, identify the factors that influence its functioning and development, analyze the crisis of the Internet economy, and conduct a comparative analysis of the Internet economy and classic economy. The book is primarily intended for postgraduates, educators and researchers who study the foundations of the modern macro-economy. Based on the conclusions and results presented here, they will be able to create their own scientific studies. Further, problem analysis of the Internet economy and classic economy makes it possible to identify the peculiarities and prospects of development, and to form recommendations for the highly effective management of modern economic systems.
(source: Nielsen Book Data)

• Introduction
• Technological Progress: Logistic Growth or Singularity
• Artificial Intelligence
• Artificial Happiness
• Issues in Artificial Intelligence
• Artificial Intelligence in Economics: ACE
• Economics of Artificial Intelligence
• State of the Art, Challenges for AGI.

The manuscript reviews some key ideas about artificial intelligence, and relates them to economics. These include its relation to robotics, and the concepts of synthetic emotions, consciousness, and life. The economic implications of the advent of artificial intelligence, such as its effect on prices and wages, appropriate patent policy, and the possibility of accelerating productivity, are discussed. The growing field of artificial economics and the use of artificial agents in experimental economics is considered.

• Part I Fundamentals of Cybercrime.- Part II Computer System as Target.- Part III Computer System as Tool.- Part IV Content - Related Offenses.- Part V Privacy, Security and Crime Control.
• (source: Nielsen Book Data)

The purpose of law is to prevent the society from harm by declaring what conduct is criminal, and prescribing the punishment to be imposed for such conduct. The pervasiveness of the internet and its anonymous nature make cyberspace a lawless frontier where anarchy prevails. Historically, economic value has been assigned to visible and tangible assets. With the increasing appreciation that intangible data disseminated through an intangible medium can possess economic value, cybercrime is also being recognized as an economic asset. The Cybercrime, Digital Forensics and Jurisdiction disseminate knowledge for everyone involved with understanding and preventing cybercrime - business entities, private citizens, and government agencies. The book is firmly rooted in the law demonstrating that a viable strategy to confront cybercrime must be international in scope.
(source: Nielsen Book Data)

For any Microsoft Office user familiar with Word, Excel, PowerPoint, Access, or Outlook, this book provides tutorials on the tools and tricks that automate these programs for increased productivity. Each lesson includes instructions and a downloadable file on which to test the VBA macros and procedures provided. Tasks covered include how to run several mail merges at once, removing duplicates from data, and saving email attachments.

• Introduction Example: Bidding on eBay Auction Fundamentals Continuous Double Auctions Interdependent Markets Conclusion.
• (source: Nielsen Book Data)

Automated trading in electronic markets is one of the most common and consequential applications of autonomous software agents. Design of effective trading strategies requires thorough understanding of how market mechanisms operate, and appreciation of strategic issues that commonly manifest in trading scenarios. Drawing on research in auction theory and artificial intelligence, this book presents core principles of strategic reasoning that apply to market situations. The author illustrates trading strategy choices through examples of concrete market environments, such as eBay, as well as abstract market models defined by configurations of auctions and traders. Techniques for addressing these choices constitute essential building blocks for the design of trading strategies for rich market applications. The lecture assumes no prior background in game theory or auction theory, or artificial intelligence.
(source: Nielsen Book Data)

• H. Schmitz, S. Niemann: A Bicriteria Traveling Salesman Problem with Sequence Priorities
• P. Vansteenwegen et al.: Metaheuristics for Tourist Trip Planning
• A. Baykasoglu, T. Göçken: Solving Fuzzy Multi-Item Economic Order Quantity Problems via Fuzzy Ranking Functions and Particle Swarm Optimization
• L. Dimitriou, T. Tsekeris: Fixed and Variable Toll Pricing in Road Networks with Direct Search Meta-Heuristics
• M. Ortega-Mier, J. Delgado Hipólito, Á. García-Sánchez: Scatter Search for Locating a Treatment Plant and the Necessary Transfer Centers in a Reverse Network
• W. Souffriau et al.: Variable Neighbourhood Descent for Planning Crane Operations in Train Terminal
• J. Czogalla, A. Fink: Design and Analysis of Evolutionary Algorithms for the No-Wait Flow-Shop Scheduling Problem
• G. di Tollo, D. Maringer: Metaheuristics for the Index Tracking Problem
• J. Rieck, J. Zimmermann: A Hybrid Algorithm for Vehicle Routing of Less-Than-Truckload Carriers.

Most developed economics show the tendency of an increasing importance of modern services such as tourism, logistical services, finance, and others. In many cases, complex optimization problems can be found in this context, and the successful operation of modern services often depends on the ability to solve the obtained optimization models. Metaheuristics on the other hand present an interesting problem-resolution paradigm that has attracted considerable interest in past years. The book combines a set of selected and peer-reviewed articles, presenting novel results of metaheuristics for modern services. In particular, applications in the area of transportation and logistics are considered, while other areas include production and financial services. Novel methodological approaches as well as improved results are obtained, resulting in a considerable contribution to the state-of-the-art of research in metaheuristics.

• Part I. AI and economics
• A Regional Indicator to Describe Potential Artificial Intelligence / Domenico Marino and Domenico Tebala
• Hybrid Fuzzy Differential System and Artificial Neural Networks: some issues in economics / Massimiliano Ferrara, Iside Rita Laganà, and Domenica Stefania Merenda
• Blockchain and Management Accounting Systems Based on Information Sharing / Domenico Nicoló
• Is the Rapid Increase in Maritime Transports a Threat or an Opportunity for Port Cities? A System Dynamics Analysis / Fabio Carlucci, Andrea Cirà, and Francesca Costanza
• Empowering Individuals, Companies and Organizations to Build Trust Through Rated and Digitalized Reputation / Vincenzo Mancuso, Domenico Marino, et. al.
• Part II. AI and law
• Processing of Personal Data and AI: GDPR Guarantees and Limits (Between Individual Data and BIG DATA) / Angela Busacca and Melchiorre A. Monaca
• Algorithmic Bosses and What to Do About Them: Automation, Artificial Intelligence and Labour Protection / Valerio De Stefano
• Roboadvice, Artificial Intelligence and Responsibility: The Regulatory Framework Between Present Scenarios and Future Perspectives / Pasquale Cuzzola
• Part III. AI and smart citizenship
• More Intelligence and Less Clouds in Our Smart Homes / Giancarlo Iannizzotto, Andrea Nucita, et. al.
• Development of a Index for Sustainable Energy Technologies Based on an Intelligent Fuzzy Expert System / Fausto Cavallaro
• Blockchain and Charities: A Systemic Opportunity to Create Social Value / Ardian Foti and Domenico Marino
• AI and Teaching Approach in High School / Marino Tommaso and Pecchio Paola
• Artificial Intelligence and Protection of National Interest. Challenges and Opportunities for Economic Intelligence / Pietro Stilo
• The Future of City/Cities / Domenico Passarelli.

This book presents original research articles addressing various aspects of artificial intelligence as applied to economics, law, management and optimization. The topics discussed include economics, policies, finance, law, resource allocation strategies and information technology. Combining the input of contributing professors and researchers from Italian and international universities, the book will be of interest to students, researchers and practitioners, as well as members of the general public interested in the economic and policy implications of artificial intelligence. .
(source: Nielsen Book Data)

• A Decision Support System based on Transport Modelling for Events Management in Public Transport Networks.- Existing and Future Investigation of Charging Technology for Electric Bus.- Vehicle Scheduling Model for an Electric Bus Line.- Accessing the Influences of Weather and Environment Factors on Traffic Speed of Freeway.- Multivariate Time Series Analysis Using Recurrent Neural Network to Predict Bike Sharing Demand.- A Leader-based Vehicle Platoon Control Strategy at Signalized Intersections Considering Efficiency.- Real Time Data Processing in Autonomous Vehicles Based on Distributed Architecture: a Case Study.- Modelling the Relationships between Headway and Speed in Saturation Flow of Signalised Intersections.- Emission Evaluation of Marine Traffic.
• (source: Nielsen Book Data)

This book gathers selected papers presented at the KES International Symposium on Smart Transportation Systems (KES STS 2020). Modern transportation systems have undergone a rapid transformation in recent years, producing a range of technological innovations such as connected vehicles, self-driving cars, electric vehicles, Hyperloop, and even flying cars, and with them, fundamental changes in transport systems around the world. The book discusses current challenges, innovations, and breakthroughs in smart transportation systems, as well as transport infrastructure modeling, safety analysis, freeway operations, intersection analysis, and other related cutting-edge topics.
(source: Nielsen Book Data)

• Psychologically Plausible Models in Agent-Based Simulations of Sustainable Behavior.- Modelling Everyday Pro-Environmental Norm Transmission and Diffusion in Workplace Networks.- Empirically-Derived Behavioral Rules in Agent-Based Models Using Decision Trees Learned From Questionnaire Data.- The Implementation of the Theory of Planned Behavior in an Agent-Based Model for Waste Recycling: A Review and a Proposal.- Social Simulations Through an Agent-Based Platform, Location Data and 3D Models.- An Intersection-Centric Auction-Based Traffic Signal Control Framework.- Agentdrive: Agent-Based Simulator for Intelligent Cars and its Application for Development of a Lane-Changing Assistant.- City Parking Allocations as a Bundle of Society-Aware Deals.- Sustainable Farming Behaviours: an Agent Based Modelling and LCA Perspective.- Agent-Based Simulation of Electricity Markets: Risk Management and Contracts for Difference.- Energy Management in the Smart Grids via Intelligent Storage Systems.
• (source: Nielsen Book Data)

Using the O.D.D. (Overview, Design concepts, Detail) protocol, this title explores the role of agent-based modeling in predicting the feasibility of various approaches to sustainability. The chapters incorporated in this volume consist of real case studies to illustrate the utility of agent-based modeling and complexity theory in discovering a path to more efficient and sustainable lifestyles. The topics covered within include: households' attitudes toward recycling, designing decision trees for representing sustainable behaviors, negotiation-based parking allocation, auction-based traffic signal control, and others. This selection of papers will be of interest to social scientists who wish to learn more about agent-based modeling as well as experts in the field of agent-based modeling.
(source: Nielsen Book Data)

• Financial awareness.- Financial education.- Financial forecasting.- Crowdsourcing.- Recommendation and engagement tools.- Development of online platforms.- Air quality monitoring by online platforms.- Measuring and publishing outdoor air pollution levels by online platforms.- Leveraging the power of online social networks, mobile and open hardware technologies, and engagement strategies.
• (source: Nielsen Book Data)

This book contains the papers presented at the two CAPS (Collective Awareness Platforms for Sustainability and Social Innovation) workshops, namely the First International Workshop on the Internet for Financial Collective Awareness and Intelligence, IFIN 2016, and the First International Workshop on Internet and Social Media for Environmental Monitoring, ISEM 2016, held in Florence, Italy in September 2016. The two workshops were collocated with the third International Conference on Internet Science, INSCI 2016. The 8 papers presented have been carefully reviewed and selected from 13 submissions. The papers of the two workshops although targeting different goals aim at developing platforms promoting awareness on different but critical sustainability issues.
(source: Nielsen Book Data)

• Smart City Concepts: Chances and Risks of Energy Efficient Urban Development
• Smart Cities Concept and Challenges: Bases for the Assessment of Smart City Projects Constructing Human Smart Cities
• An Open Platform for Children's Independent Mobility
• Architectures and Requirements for the Development of Smart Cities: A Literature Study
• People in Smart Buildings: Daily Practices in Automated Areas
• Introducing Flexibility into Data Centers for Smart Cities
• Automatic Validation for Crowd Simulation: Test Suite for a Pedestrian Simulator based on Different Scenarios
• A 2DOFvibrational Energy Harvester Exploiting Velocity Amplification: Modeling and Testing
• Voltage Control in Low Voltage Grids with Distributed Energy Resources: A Droop-based Approach
• A Sensitivity based Approach for Efficient PMU Deployment on Smart Grid
• Using Flexibility Information for Planning the Energy Demand of Households
• Laser Scanner and Camera Fusion for Automatic Obstacle Classification in ADAS Application
• Vehicle Routing to Minimizing Hybrid Fleet Fuel Consumption
• Service-Oriented Architecture for Cloud Application Development
• An Integrated Architecture for Simulation and Modeling of Small- and Medium-sized Transportation and Communication Networks
• Privacy Endangerment from Protocol Data Sets in VANETs and Countermeasures.

This book constitutes the thoroughly refereed proceedings of the 4th International Conference on Smart Cities and Green ICT Systems, SMARTGREENS 2015, and the 1st International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2015, held in Lisbon, Portugal, in May 2015. The 15 full papers of SMARTGREENS 2015 presented were carefully reviewed and selected from 73 submissions. VEHITS 2015 received 27 paper submissions from which 3 papers were selected and published in this book. The papers reflect topics such as smart cities, energy-aware systems and technologies, sustainable computing and communications, sustainable transportation and smart mobility.

• Issues in the Use of the Recovery Watcher for Situation Awareness in Disaster and Inclusive Communications.- Information Technology Reengineering of the Electricity Generation System in Post-Disaster Recovery.- Information Technologies for Assessing the Impact of Climate Change and Natural Disasters in Socio-Economic Systems.- Problems of Cultural Heritage Preservation in the Context of the Armed Conflict Growth.- Proposal of Evacuation Support System and Evaluation by Multi-agent Simulation in a Regional Disaster.- Environmental Monitoring and Population Protection from Environmental Factors.- Ecological Internet Resource as a tool for Information Control of the Harmful Effects of Environmental Pollution.- Building Resilient Community by Public Private Partnership - From Science to Action in Developing Countries.- Building Resilient Community Through Plant Back Better Initiative - Guiding Principles and Best Practices.- An Earth Coordinate System for Earthquake Forecasting using SLHGN.- ImpactMap: A Collaborative Environment to Support Impact Projection of Complex Decision.- Ad-Hoc Architecture of Systems for Disaster Risk Management.- Conceptual Model of Debris Flow Information System.- Towards an Indoor Navigation Application for Emergency Evacuations and Persons with Visual Impairments - Experiences from First Responders and End Users.- Cyber Crisis Management Roles - a Municipality Responsibility Case Study.- Mathematical Model of Management of the Integral Risk of Emergency Situation on the Example of Fires.- 3D Simulation to Validate Autonomous Intervention Systems Architecture for Disaster Management.- Classification of Social Media Messages Posted at the Time of Disaster.- Workshop Program on Disaster Prevention and Mitigation for Young Generation Utilizing Disaster Information Tweeting and Mapping System.
• (source: Nielsen Book Data)

This volume constitutes the refereed and revised post-conference proceedings of the 4th IFIP TC 5 DCITDRR International Conference on Information Technology in Disaster Risk Reduction, ITDRR 2019, in Kyiv, Ukraine, in October 2019. The 17 full papers and 2 short papers presented were carefully reviewed and selected from 53 submissions. The papers focus on various aspects and challenges of coping with disaster risk reduction. The main topics include areas such as natural disasters, big data, cloud computing, Internet of Things, mobile computing, emergency management, disaster information processing, and disaster risk assessment and management.
(source: Nielsen Book Data)

• Group Problem Structuring and Negotiation
• Effects of Small Group Discussion: Case Study of Community Disaster Risk Management in Japan
• Understanding PSM Interventions Through Sense-Making and the Mangle of Practice Lens
• Negotiation and Group Processes
• Social Consciousness in Post-conflict Reconstruction
• How to Order the Alternatives, Rules, and the Rules to Choose Rules: When the Endogenous Procedural Choice Regresses
• The Hidden Costs of the Door-in-the-Face Tactic in Negotiations
• Preference Analysis and Decision Support
• Understanding and Using the Group Decision Analysis Model
• Distributive Justice, Legitimizing Collective Choice Procedures, and the Production of Normative Equilibria in Social Groups: Towards a Theory of Social Order
• A Multiple Criteria Model for Comparison of Subjective-Objective Evaluations and Its Application
• Using Surrogate Weights for Handling Preference Strength in Multi-criteria Decisions
• Veto Values Within MAUT for Group Decision Making on the Basis of Dominance Measuring Methods with Fuzzy Weights
• Inaccuracy in Defining Preferences by the Electronic Negotiation System Users
• A Multi-criteria Group Decision-Making Approach for Facility Location Selection Using PROMETHEE Under a Fuzzy Environment
• An Interval-Valued Hesitant Fuzzy TOPSIS Method to Determine the Criteria Weights
• Multiple Attribute Group Decision Making Under Hesitant Fuzzy Environment
• Formal Models
• Using Ordinal Regression for Interactive Evolutionary Multiple Objective Optimization with Multiple Decision Makers
• Fiscal-Monetary Game Analyzed with Use of a Dynamic Macroeconomic Model
• Voting and Collective Decision-Making
• A Framework for Aiding the Choice of a Voting Procedure in a Business Decision Context
• Vote Swapping in Representative Democracy
• The Choice of Voting Rules Based on Preferences over Criteria
• Conflict Resolution in Energy and Environmental Management
• Controversy Over the International Upper Great Lakes Study Recommendations: Pathways Towards Cooperation
• Option Prioritization for Three-Level Preference in the Graph Model for Conflict Resolution
• Negotiation Support Systems and Studies
• The Role of Communication Support for Electronic Negotiations
• More Than Words: The Effect of Emoticons in Electronic Negotiations
• Online Collaboration and Competition
• A Longitudinal Case Study on Risk Factor in Trust Development of Facilitated Collaboration
• Intention to Repurchase Group Coupon Service: The Intertwined Effect of Service Quality of Vendor and Service Provider
• Defining Human-Machine Micro-Task Workflows for Constitution Making
• Creating Value Through Crowdsourcing: The Antecedent Conditions
• On Integrating an IS Success Model and Multicriteria Preference Analysis into a System for Cloud-Computing Investment Decisions
• Demand Management with Energy Generation and Storage in Collectives
• Market Mechanisms and Their Users
• Back-End Bidding for Front-End Negotiation: A Model
• Lot-Rolling
• Supply Chain Negotiation in a Two-Stage Multi-echelon System
• Procurement Auctions: Improving Efficient Winning Bids Through Multi-bilateral Negotiations.

This book constitutes the proceedings of the 15th International Conference on Group Decision and Negotiation, GDN 2015, held in Warsaw, Poland, in June 2015. The GDN meetings aim to bring together researchers and practitioners from a wide spectrum of fields, including economics, management, computer science, engineering, and decision science. From a total of 119 submissions, 32 papers were accepted for publication in this volume. The papers are organized into topical sections on group problem structuring and negotiation, negotiation and group processes, preference analysis and decision support, formal models, voting and collective decision making, conflict resolution in energy and environmental management, negotiation support systems and studies, online collaboration and competition, and market mechanisms and their users.

• Introduction
• The challenges of automated item picking: the last mile of logistics for e-commerce
• Robotic Sensing for Item Picking
• Gripper Design and Grasping Strategies
• Machine Learning for Item Identification and Pose Estimation
• Machine Learning for Motion Planning
• Efficient Task Planning Strategies.

This book is a compilation of advanced research and applications on robotic item picking and warehouse automation for e-commerce applications. The works in this book are based on results that came out of the Amazon Robotics Challenge from 2015-2017, which focused on fully automated item picking in warehouse setting, a topic that has been assumed too complicated to solve or has been reduced to a more tractable form of bin picking or single-item table top picking. The books contributions reveal some of the top solutions presented from the 50 participant teams. Each solution works to address the time-constraint, accuracy, complexity, and other difficulties that come with warehouse item picking. The book covers topics such as grasping and gripper design, vision and other forms of sensing, actuation and robot design, motion planning, optimization, machine learning and artificial intelligence, software engineering, and system integration, among others. Through this book, the authors describe how robot systems are built from the ground up to do a specific task, in this case, item picking in a warehouse setting. The compiled works come from the best robotics research institutions and companies globally. Presents an inside look at the various solutions for automated warehouse item picking based on the Amazon Robotics Challenge (ARC) Contains details of the challenges and solutions involved in automating item picking Provides details and insights on the solutions of the winning teams Includes chapters written by scientists and engineers at the forefront of robotics research.

• 1. Introduction to Emergency Management.-
• 2. Big Data.-
• 3. Learning Algorithms for Emergency Management.-
• 4. Knowledge Graphs and Natural-Language Processing.-
• 5. Social Media Mining for Disaster Management and Community Resilience.-
• 6. Big Data-Driven Citywide Human Mobility Modeling for Emergency Management.-
• 7. Smartphone based Emergency Communication.-
• 8. Emergency Information Visualisation. .
• (source: Nielsen Book Data)

This contributed volume discusses essential topics and the fundamentals for Big Data Emergency Management and primarily focusses on the application of Big Data for Emergency Management. It walks the reader through the state of the art, in different facets of the big disaster data field. This includes many elements that are important for these technologies to have real-world impact. This book brings together different computational techniques from: machine learning, communication network analysis, natural language processing, knowledge graphs, data mining, and information visualization, aiming at methods that are typically used for processing big emergency data. This book also provides authoritative insights and highlights valuable lessons by distinguished authors, who are leaders in this field. Emergencies are severe, large-scale, non-routine events that disrupt the normal functioning of a community or a society, causing widespread and overwhelming losses and impacts. Emergency Management is the process of planning and taking actions to minimize the social and physical impact of emergencies and reduces the community's vulnerability to the consequences of emergencies. Information exchange before, during and after the disaster periods can greatly reduce the losses caused by the emergency. This allows people to make better use of the available resources, such as relief materials and medical supplies. It also provides a channel through which reports on casualties and losses in each affected area, can be delivered expeditiously. Big Data-Driven Emergency Management refers to applying advanced data collection and analysis technologies to achieve more effective and responsive decision-making during emergencies. Researchers, engineers and computer scientists working in Big Data Emergency Management, who need to deal with large and complex sets of data will want to purchase this book. Advanced-level students interested in data-driven emergency/crisis/disaster management will also want to purchase this book as a study guide.
(source: Nielsen Book Data)

• Table of Contents Machine Learning for Trading - From Idea to Execution Market and Fundamental Data - Sources and Techniques Alternative Data for Finance - Categories and Use Cases Financial Feature Engineering - How to Research Alpha Factors Portfolio Optimization and Performance Evaluation The Machine Learning Process Linear Models - From Risk Factors to Return Forecasts The ML4T Workflow - From Model to Strategy Backtesting (N.B. Please use the Look Inside option to see further chapters).
• (source: Nielsen Book Data)

Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes data Who this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
(source: Nielsen Book Data)

• Biometrics
• Document image inspection
• Applications.

This book constitutes the refereed post-conference proceedings of the 5th and 6th International Workshops on Computational Forensics, IWCF 2012 and IWCF 2014, held in Tsukuba, Japan, in November 2010 and August 2014. The 16 revised full papers and 1 short paper were carefully selected from 34 submissions during a thorough review process. The papers are divided into three broad areas namely biometrics; document image inspection; and applications.