Kooperativer Bibliotheksverbund

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Format: Online-Ressource

Note: Gesehen am 15.10.2021

Language: English

Keywords: Zeitschrift ; Zeitschrift

URL: Volltext  (kostenfrei)

URL: Volltext

URL: kostenfrei

URL: http://www.bibliothek.uni-regensburg.de/ezeit/?2254131

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Format: 1 Online-Ressource (267 Seiten)

ISBN: 9789811542916

Series Statement: Disaster Research and Management Series on the Global South Ser

Note: Description based on publisher supplied metadata and other sources , Intro -- Acknowledgements -- Contents -- About the Series Editor -- Notes on Editors -- List of Figures -- List of Tables -- Chapter 1: Introduction: Enhancing Capacity to Manage Disasters -- Introduction -- A Social Scientist's Understanding of AI and BD -- How Would Governance Benefit from AI and Big Data? -- Sophistication in Decision Making Tools -- Diagnostic Capability -- Climate Change Related Early Warning Mechanism Systems -- Indispensability of 'Big Data' for Public Institutions -- Drivers for AI and BD Based Decision Making -- Conclusion -- References -- Part I: New Technologies in Disaster Management -- Chapter 2: Artificial Intelligence and Early Warning Systems -- Introduction -- Disaster Management -- Organization of the Paper -- Early Warning Systems -- Artificial Intelligence -- Machine Learning -- AI and EWS -- Conclusion -- References -- Chapter 3: Artificial Intelligence in Disaster Management: Rescue Robotics, Aerial Mapping and Information Sourcing -- Introduction to AI in Disaster Management -- Land and Water Robots -- Aerial Surveillance and Aid Delivery Robots -- AI as a Software Service During a Disaster -- Adoption of AI in Disaster Management -- Conclusion -- References -- Chapter 4: Optimal Visual Cues for Smartphone Earthquake Alert Systems: Preliminary Data from Lab and Field Experiments -- Experiment 1. Optimal Stimuli for Capturing Peripheral Attention -- Methods -- Discussion Experiment 1 -- Experiment 2. Optimal Stimuli, Personalization and Practice in a Real-Life Field Experiment -- Methods -- Task and Procedure -- Results -- Results -- User Experience Ratings -- In Case of a Real Earthquake, How Likely Would You Be to Trust This App? -- In Case of a Real Earthquake, How Safe Would You Feel with This App? -- To What Degree Did I Feel Safe During Walking During the Alert? -- Overall Mood During the Experiment , Discussion Experiment 2 -- General Discussion -- References -- Chapter 5: Using Artificial Intelligence and Social Media for Disaster Response and Management: An Overview -- Introduction -- Research -- Situational Awareness -- Actionable Insights -- Datasets -- Systems -- Discussion and Conclusion -- References -- Chapter 6: 'Internet of Things' Applications in Disaster Management -- What Is AI and IoT and Why Do They Matter to the Government? -- What Are the Opportunities with IoT for Public Service? -- Why Are Government Agencies Not Able to Leverage IoT? -- What Should Governments Do? -- Risks Related to IoT Expansion -- Summary -- Chapter 7: Samvad: Reaching Out Through Radio and Wireless Network -- Situation in District in Previous Floods -- Response of the District Administration -- Implementation of the Seven-Point Disaster Risk Management Programme -- Impact of Radio Initiatives -- Preparedness Education: Use of Cinema Halls -- Future Plan -- The Final Word -- Chapter 8: Usages of AI Technologies in Nepal's Disaster Management -- Introduction -- Related Issues -- Artificial Intelligence and Disaster Management: Nepal Scenario -- Machine Learning Terminologies -- A Case Study of Flood Damage Prediction -- Koshi River in Nepal -- Data Collection -- Data Visualization -- Flood Map Generation -- Water Level Prediction for Flash Flood -- Rapid Earthquake Assessment from Satellite Imagery -- Unmanned Aerial Vehicle (UAV) Based Emergency Management -- Recommendations from This Study -- Conclusion -- References -- Part II: Government, Governance and Law -- Chapter 9: Enhancing Accountability and Triadic Collaboration in Disaster Governance of Sri Lanka -- The Background -- The Role of Artificial Intelligence in Disaster Risk Reduction and Management -- The Legislative Framework on Disaster Risk Reduction and Management in Sri Lanka , The Role of the Government of Sri Lanka in Disaster Situations -- The Human Right to Be Protected and Relieved from the Disasters -- Sustainable Development and Prevention of Disasters -- Precautionary Principle in Averting Disasters in Advance -- Conclusion -- References -- Chapter 10: Artificial Intelligence and the Legal Response Paradigms in Disaster Management -- Introduction: Disaster Management Challenges and Scope of Employing Artificial Intelligence -- Ethical and Legal Concerns in the Application of Artificial Intelligence -- Regulating Artificial Intelligence -- AI Liability Frameworks -- International Legal Framework on Disaster Management: Environmental and Application of AI -- Conclusion -- References -- Chapter 11: Artificial Intelligence and Disaster Management in Sri Lanka: Problems and Prospects -- Background of the Study and Problem Identification -- Literature Review -- Artificial Intelligence in Disaster Management -- Application of AI in Disaster Management -- Flood Prediction Model in Malaysia -- Machine Learning in the USA to Be Prevented from Earthquakes -- Flood Alert System in India -- High-Performance Computing in Japan to Be Prevented from Earthquakes -- Deep Machine Learning (ML) Algorithm in India as to Filter out Fake News -- Satellite Technology in Disaster Management in Malaysia, Ethiopia and Kenya -- Application of AI in Disaster Risk Management in Sri Lanka: Examples -- Opportunities of Adopting AI for Disaster Management in Sri Lanka -- Problems of Adopting AI for DM in Sri Lanka -- Lack of Knowledge and IT Infrastructure -- AI as Multifaceted Intelligence -- Manageability -- Languages Usage -- Controlling -- Conclusion -- References -- Chapter 12: Applications of Artificial Intelligence in Reconstruction Governance Lessons from Nepal Earthquakes -- Background , Artificial Intelligence and Reconstruction Governance in Nepal -- Research Methodology -- Reconstruction Governance in Nepal -- Response and Relief -- Reconstruction and Rehabilitation Achievements -- Factors Affecting Reconstruction Governance in Nepal -- Conclusions -- References -- Chapter 13: ICT Infrastructure of Disaster Management in India -- Introduction -- Advancements of ICT Applications in Disaster Management -- Can India Manage Disasters without ICT? -- A Spread of ICT Infrastructure in India -- Challenges -- Conclusion -- References -- Part III: Building Community Resilience Through AI -- Chapter 14: Can Community Plans Really Talk? Integrating and Strengthening Communications Through Artificial Intelligence -- Introduction -- Problem Statement -- What Is Hazard Mitigation or Why the Need for it? -- Plan Review and Integration -- Integrating Hazard Mitigation Principles into Other Local Planning Mechanisms (Integrate Through Plans) -- Challenges in Performing Manual Plan Review and Integration -- AI for Plan Integration -- Phase I: Build the Brain (BEB) -- Prepare Ontology, Define Terms, and Develop a Knowledge Base -- Phase II: Process the Plans (PEP) -- Natural Language Processing (NLP) -- Document Tagging Using Ontology -- Recommendation Engine -- Conclusion -- References -- Chapter 15: The Challenge of Resilience in an Age of Artificial Intelligence -- The Coming Age of Artificial Intelligence -- Disaster Preparedness and Resilience -- Prama: The Path of Resilience -- Loss of 'PRAMA' in Society -- Restoring PRAMA -- Survival (Asti): The First Step Toward Resilience -- Allround Development (Bhati) -- Role of Intuitional Practices and the Goal of Anandam -- Conclusion: A Call for Moral Advancement -- References -- Chapter 16: AI in an Urban Village in Delhi -- Introduction -- Vulnerability of Urban Villages: Case Study of Munirka , Vulnerability Mapping through Aerospace Imaging -- Crowd-Sourcing Data from Communities: A Participatory Approach -- Generating Classification Maps: Deep Learning Approach Using Support Vector Machines (SVM) and Feature Extraction -- Limitations in the Use of AI -- Conclusion -- References -- Part IV: Extraneous Influences and Ethics in AI Applications -- Chapter 17: Prevent AI from Influences: A Challenge for Lazy, Profligate Governments -- Disaster Governance: Managing Mechanized Humans or Humanized Robots? -- What Is Likely to Go Wrong? -- Nature of New Regulations Needed -- Conclusion -- References -- Chapter 18: The Final Alert on Ethics in AI Based Technology -- Summing Up Ethical Questions Around AI -- An Example from the Chapters -- Information Is Power! -- Unequal Access to Resources -- Inequality Between Countries -- The Benefits/Dangers of Cell Phone Data -- Consequences and Motivation -- Guideposts as We Move Ahead -- Conclusion -- Reference

Additional Edition: Erscheint auch als Druck-Ausgabe Kumar, T. V. Vijay AI and Robotics in Disaster Studies Singapore : Springer Singapore Pte. Limited,c2020 ISBN 9789811542909

Language: English

Subjects: Computer Science , Economics , General works

Keywords: Aufsatzsammlung

URL: Volltext  (URL des Erstveröffentlichers)

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Format: XI, 398 S.

Series Statement: University of California engineering and sciences extension series

Language: Undetermined

Subjects: Economics , Comparative Studies. Non-European Languages/Literatures

Keywords: Sprache ; Computer ; Computerlinguistik ; Mensch-Maschine-Kommunikation ; Natürliche Sprache ; Aufsatzsammlung ; Aufsatzsammlung ; Aufsatzsammlung ; Aufsatzsammlung

URL: Inhaltsverzeichnis

URL: Inhaltsverzeichnis

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Format: XXII, 310 S. , graph. Darst.

Edition: 1. publ.

ISBN: 9780521194822 , 9780521123365

Note: "The practical benefits of computational logic need not be limited to mathematics and computing. As this book shows, ordinary people in their everyday lives can profit from the recent advances that have been developed for artificial intelligence. The book draws upon related developments in various fields from philosophy to psychology and law. It pays special attention to the integration of logic with decision theory, and the use of logic to improve the clarity and coherence of communication in natural languages such as English. This book is essential reading for teachers and researchers who may be out of touch with the latest developments in computational logic. It will also be useful in any undergraduate course that teaches practical thinking, problem solving or communication skills. Its informal presentation makes the book accessible to readers from any background, but optional, more formal, chapters are also included for those who are more technically oriented"-- Provided by publisher. -- "The practical benefits of computational logic need not be limited to mathematics and computing. As this book shows, ordinary people in their everyday lives can profit from the recent advances that have been developed for artificial intelligence. The book draws upon related developments in various fields from philosophy to psychology and law. It pays special attention to the integration of logic with decision theory, and the use of logic to improve the clarity and coherence of communication in natural languages such as English"-- Provided by publisher.

Language: English

Subjects: Computer Science , Philosophy

Keywords: Computational logic ; Logisches Denken ; Künstliche Intelligenz

URL: Cover image

URL: Inhaltsverzeichnis

URL: Cover

URL: Inhaltstext

URL: http://assets.cambridge.org/97805211/94822/cover/9780521194822.jpg

URL: Inhaltsverzeichnis

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Format: 1 Online-Ressource (176 S.) , graph. Darst.

ISBN: 3540500111 , 0387500111

Series Statement: Lecture notes in computer science 320

Language: English

Subjects: Computer Science

Keywords: Computerlinguistik ; Datenverarbeitung ; Natürliche Sprache ; Festschrift ; Konferenzschrift

DOI: 10.1007/3-540-50011-1

URL: Volltext

Author information: Blaser, Albrecht 1933-

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Format: 1 Online-Ressource (511 Seiten)

ISBN: 9783030363758

Note: Description based on publisher supplied metadata and other sources , Intro -- Preface -- Motivation -- Purpose of the Book -- Who Is This Book for? -- How This Book Is Structured -- What This Book Is NOT About -- Features of the Book -- Acknowledgments -- Contents -- Part I: From Business Problems to Data Science -- Chapter 1: Data Science Based on Artificial Intelligence -- 1.1 Big Data, Big Mess, Big Opportunity -- 1.1.1 From Hype to Competitive Advantage -- 1.1.2 Key Buzzwords Explained -- 1.1.3 Why Now? -- 1.2 What Is AI-Based Data Science? -- 1.2.1 Definition of AI-Based Data Science -- 1.2.2 Features of AI-Based Data Science -- 1.3 Competitive Advantages of AI-Based Data Science -- 1.3.1 Creating ''Objective Intelligence'' -- 1.3.2 Dealing with Uncertainty -- 1.3.3 Dealing with Complexity -- 1.3.4 Generating Novelty -- 1.3.5 Low-Cost Modeling and Optimization -- 1.4 Key Challenges in Applying AI-Based Data Science -- 1.4.1 Technical Issues in Applying AI-Based Data Science -- 1.4.2 Nontechnical Issues in Applying AI-Based Data Science -- 1.5 Common Mistakes -- 1.5.1 Believing the Hype -- 1.5.2 Neglecting to Estimate the Demand for AI-Based Data Science -- 1.5.3 Mass-Scale Introduction of AI-Based Data Science in a Business without Required Skillset Availability -- 1.5.4 Introducing Data Science Bureaucracy -- 1.6 Suggested Reading -- 1.7 Questions -- Chapter 2: Business Problems Dependent on Data -- 2.1 The Leading Role of Business Problems -- 2.1.1 ''Data Is the New Oil'' Hype -- 2.1.2 Problems-First Approach -- 2.2 Typical Business Problems Related to AI-Based Data Science -- 2.2.1 Typical Problems in Manufacturing -- 2.2.2 Typical Problems in Business -- 2.3 How to Find Data-Driven Business Problems -- 2.3.1 Understand Business Needs -- 2.3.2 Match Business Needs with Known Artificial Intelligence-Based Use Cases -- 2.4 The Slippery Terrain of Problem Definition -- 2.4.1 Structure of Problem Definition , 2.4.2 Example of Problem Definition -- 2.5 Value Creation Hypothesis -- 2.5.1 Sources of Value Creation -- 2.5.2 Metrics for Value Creation -- 2.6 Common Mistakes -- 2.6.1 Jumping to Solutions without Defining Business Problems -- 2.6.2 Neglecting the Importance of a Detailed Realistic Problem Definition -- 2.6.3 Ignoring Definition of Value Creation Metrics for the Problem -- 2.6.4 Believing in a Data First, Problems Second Approach -- 2.7 Suggested Reading -- 2.8 Questions -- Chapter 3: Artificial Intelligence-Based Data Science Solutions -- 3.1 Typical Solutions Related to Data Science -- 3.1.1 Prediction -- 3.1.2 Forecasting -- 3.1.3 Classification -- 3.1.4 Clustering -- 3.1.5 Optimization -- 3.1.6 Association -- 3.2 Advanced AI Solutions Related to Data Science -- 3.2.1 Natural Language Processing -- 3.2.2 Video/Image Processing -- 3.2.3 Sentiment Analysis -- 3.2.4 Artificial General Intelligence -- 3.3 Key AI Methods in a Nutshell -- 3.3.1 Neural Networks in a Nutshell -- 3.3.2 Deep Learning Networks in a Nutshell -- 3.3.3 Support Vector Machines in a Nutshell -- 3.3.4 Decision Trees in a Nutshell -- 3.3.5 Evolutionary Computation in a utshell -- 3.3.6 Swarm Intelligence in a Nutshell -- 3.3.7 Intelligent Agents in a Nutshell -- 3.4 Common Mistakes -- 3.4.1 Obsession with One Method -- 3.4.2 Focusing on Fashionable Methods -- 3.4.3 Lack of Knowledge about Broad Options for AI-Based Approaches -- 3.4.4 Lack of Knowledge about Cost of Implementation of Methods -- 3.5 Suggested Reading -- 3.6 Questions -- Chapter 4: Integrate and Conquer -- 4.1 The Integrate and Conquer Strategy in Applied Data Science -- 4.1.1 The Nasty Reality of Real-World Applications -- 4.1.2 Why Integration of Methods Is Critical for Real-World Applications -- 4.2 Integration Opportunities -- 4.2.1 Integration Between AI-Based Methods , 4.2.2 Integration with First-Principles Models -- 4.2.3 Integration with Statistical Models -- 4.2.4 Integration by Ensembles of Models -- 4.3 How to Select the Best Solutions for the Business Problem -- 4.3.1 Capabilities of Methods -- 4.3.2 Applicability of Methods -- 4.3.3 One Method Is Not Enough -- 4.4 Common Mistakes -- 4.4.1 Ignoring Integration of Methods -- 4.4.2 Lack of Knowledge of Strengths and Weaknesses of Methods -- 4.4.3 Lack of Knowledge about Selecting the Most Appropriate Methods for the Business Problem -- 4.5 Suggested Reading -- 4.6 Questions -- Chapter 5: The Lost-in-Translation Trap -- 5.1 Translation from Business Problems to Data Science Solutions -- 5.1.1 Select Best Experts in Problem Domain -- 5.1.2 Generic Problem Questionnaire Template -- 5.1.3 Problem Description by Domain Experts -- 5.1.4 Problem Understanding by Data Scientists -- 5.1.5 Create a Problem-Related Glossary -- 5.2 Translation from Data Science Solutions to Business Problems -- 5.2.1 Explain Data Science Work Process -- 5.2.2 Communicate Potential Data Science Solutions -- 5.2.3 Demonstrate Similar Data Science Use Cases -- 5.2.4 Explain Key Principles Related to Potential Data Science Solutions -- 5.2.5 Create a Solution-Related Glossary -- 5.3 Typical Lost-in-Translation Cases -- 5.3.1 Inexperienced Data Scientists -- 5.3.2 Resistance from Experts -- 5.3.3 Improper Problem Definition -- 5.3.4 Management Intervention -- 5.4 How to Avoid the-Lost-in-Translation Trap -- 5.4.1 Translators -- 5.4.2 Examples of Translators for AI-Methods -- 5.5 Common Mistakes -- 5.5.1 Ignoring the Dialog Between Domain Experts and Data Scientists -- 5.5.2 Ignoring the People Factor -- 5.5.3 Ignoring Team Building -- 5.6 Suggested Reading -- 5.7 Questions -- Part II: The AI-Based Data Science Toolbox -- Chapter 6: The AI-Based Data Science Workflow -- 6.1 Overview of Workflow , 6.1.1 Why we Need an Effective AI-Based Data Science Workflow -- 6.1.2 Why Is the Classical Scientific Process Not Enough? -- 6.1.3 Comparison with CRISP-DM -- 6.1.4 AI-Based Data Science Workflow Sequence -- 6.2 Key Steps of AI-Based Data Science Workflow -- 6.2.1 Problem Definition -- 6.2.2 Project Organization -- 6.2.3 Problem Knowledge Acquisition -- 6.2.4 Data Preparation -- 6.2.5 Data Analysis -- 6.2.6 Model Development -- 6.2.7 Model Deployment -- 6.2.8 Model Maintenance -- 6.2.9 Automation of AI-Based Data Science Workflow -- 6.3 Project Organization -- 6.3.1 Organizing Project Teams -- 6.3.2 Resources Allocation -- 6.3.3 Project Scheduling -- 6.3.4 Project Funding -- 6.4 Common Mistakes -- 6.4.1 Ignoring a Detailed Workflow -- 6.4.2 Ignoring some Steps in the Workflow -- 6.4.3 Insufficient Efforts on Cost Estimates -- 6.4.4 Not Documenting the Deliverables -- 6.5 Suggested Reading -- 6.6 Questions -- Chapter 7: Problem Knowledge Acquisition -- 7.1 Importance of Problem Knowledge -- 7.1.1 Problem Knowledge in Problem Definition -- 7.1.2 Problem Knowledge in Data Preparation -- 7.1.3 Problem Knowledge in Data Analysis -- 7.1.4 Problem Knowledge in Model Development -- 7.1.5 Problem Knowledge in Model Deployment -- 7.2 Sources of Problem Knowledge -- 7.2.1 Subject Matter Experts -- 7.2.2 Problem-Related Documents -- 7.2.3 Publicly Available References -- 7.3 Problem Knowledge Acquisition Methods -- 7.3.1 Mind Mapping -- 7.3.2 Brainstorming Sessions -- 7.3.3 External Knowledge Acquisition -- 7.3.4 Knowledge Acquisition Skills -- 7.4 Problem Knowledge Integration -- 7.4.1 Define Recommended Assumptions -- 7.4.2 Define Normal/Abnormal Operating Conditions -- 7.4.3 Suggest Selection of Initial Variables -- 7.4.4 Define Qualitative Performance Metric -- 7.5 Definition of a Problem Solution Strategy -- 7.5.1 Define Solution Hypotheses , 7.5.2 Define a List of Potential Solutions -- 7.5.3 Define Issues and Limitations of Suggested Solutions -- 7.5.4 Define Needed Infrastructure -- 7.6 Common Mistakes -- 7.6.1 Focusing on Data and Ignoring Problem Knowledge -- 7.6.2 SMEs Are Not Involved -- 7.6.3 Not Validating SMEs Knowledge -- 7.6.4 Reinventing the Wheel -- 7.7 Suggested Reading -- 7.8 Questions -- Chapter 8: Data Preparation -- 8.1 Data Collection -- 8.1.1 Data Sources -- 8.2 Visual Data Exploration -- 8.2.1 Strange Data Patterns -- 8.2.2 Data Distributions -- 8.2.3 Univariate Plots -- 8.2.4 Bivariate Plots -- 8.2.5 Multivariate Plots -- 8.3 Data Preprocessing -- 8.3.1 Handling Missing Data -- 8.3.2 Handling Outliers -- 8.3.3 Data Transformation -- 8.3.4 Data Balance -- 8.3.5 Data Quality Assessment -- 8.4 Common Mistakes -- 8.4.1 GIGO 2.0 -- 8.4.2 Problem Solving with Insufficient Data -- 8.4.3 Problem Solving with Low-Quality Data -- 8.4.4 Low-Quality Data Preparation -- 8.5 Suggested Reading -- 8.6 Questions -- Chapter 9: Data Analysis -- 9.1 Translation of Data into Insight -- 9.1.1 Problem Knowledge Gain from Data Analysis -- 9.1.2 Insight from Multivariate View -- 9.1.3 Insight from Understanding Key Drivers -- 9.1.4 Insight from Discovered Features and Patterns -- 9.1.5 Insight from Data Analysis as the Final Problem Solution -- 9.1.6 Insight for Final Data Preparation for Modeling -- 9.2 Multivariate Data Analysis -- 9.2.1 Principal Component Analysis -- 9.2.2 Multivariate Patterns -- 9.3 Variable Selection -- 9.3.1 Variable Reduction -- 9.3.2 Handling Multicollinearity -- 9.3.3 Linear Variable Selection -- 9.3.4 Nonlinear Variable Selection -- 9.4 Feature Extraction -- 9.4.1 Feature Engineering -- 9.4.2 Automatically Generated Features -- 9.5 Data Visualization -- 9.5.1 Parallel Coordinates Plot -- 9.5.2 Chord Diagram -- 9.5.3 Contour Plot , 9.6 Data-Analysis-Driven Storytelling

Additional Edition: Erscheint auch als Druck-Ausgabe Kordon, Arthur K. Applying Data Science Cham : Springer International Publishing AG,c2020 ISBN 9783030363741

Language: English

Subjects: Computer Science

Keywords: Künstliche Intelligenz ; Big Data

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: XVI, 158 S.

ISBN: 0792393767

Series Statement: The Kluwer international series in engineering and computer science 242 : Natural language processing and machine translation

Language: English

Subjects: Computer Science , Mathematics

Keywords: Syntaktische Analyse ; Funktionale Programmierung

URL: Inhaltsverzeichnis

URL: Inhaltstext

UID:

Format: XV, 228 S.

ISBN: 3540613099

Series Statement: Lecture notes in computer science 1083

Content: This comprehensive state-of-the-art book is the first devoted to the important and timely issue of evaluating NLP systems. It addresses the whole area of NLP system evaluation, including aims and scope, problems and methodology. The authors provide a wide-ranging and careful analysis of evaluation concepts, reinforced with extensive illustrations; they relate systems to their environments and develop a framework for proper evaluation. The discussion of principles is completed by a detailed review of practice and strategies in the field, covering both systems for specific tasks, like translation, and core language processors. The methodology lessons drawn from the analysis and review are applied in a series of example cases

Content: The book also refers NLP system evaluation to the neighbouring areas of information and speech processing, and addresses issues of tool and data provision for evaluation. A comprehensive bibliography and subject index are included as well as a term glossary. This monograph will be a valuable source of inspiration in research, practice, and teaching

Language: German

Subjects: Computer Science

Keywords: Natürlichsprachiges System ; Leistungsbewertung

Author information: Sparck Jones, Karen 1935-2007

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Format: xiv, 519 Seiten , Illustrationen , 24 cm

ISBN: 9780262042840 , 0262042843

Series Statement: Adaptive computation and machine learning series

Content: "The book provides a technical perspective on the most contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. It also includes background in the salient linguistic issues, as well as computational representations and algorithms. The first section of the book explores what can be with individual words. The second section concerns structured representations such as sequences, trees, and graphs. The third section highlights different approaches to the representation and analysis of linguistic meaning. The final section describes three of the most transformative applications of natural language processing: information extraction, machine translation, and text generation. The book describes the technical foundations of the field, including the most relevant machine learning techniques, algorithms, and linguistic representations. From these foundations, it extends to contemporary research in areas such as deep learning. Each chapter contains exercises that include paper-and-pencil analysis of the computational algorithms and linguistic issues, as well as software implementations"--

Language: English

Subjects: Computer Science , Comparative Studies. Non-European Languages/Literatures

Keywords: Sprachverarbeitung ; Informatik

URL: Inhaltsverzeichnis

URL: Klappentext

URL: Inhaltsverzeichnis

UID:

Format: xi, 544 Seiten , Illustrationen, Diagramme

Edition: Second edition

ISBN: 9781107500556 , 9781107102545

Content: "This textbook provides a comprehensive overview of the human-computer interface in clear, non-technical language, making it an ideal introduction for students of both psychology and computer science. Covering the past, present, and future developments in technology and psychology, it combines cutting-edge academic research with engaging illustrations and examples that show students how the material relates to their lives. Topics addressed include: human factors of input devices, and the basics of sensation and perception; memory and cognitive issues of users navigating their way through interfaces; communication via programming languages and natural speech interaction; cyberpathologies such as techno-stress and Internet addiction disorders; and challenges surrounding automation and artificial intelligence. This thoroughly updated second edition features new chapters on virtual reality and cybersecurity; expanded coverage of social media, mobile computing, e-learning, and video games; and end-of-chapter review questions that ensure students have mastered key objectives"...

Content: "Psychology as a science and a discipline must do more than merely acknowledge that we live in a digital environment with computers and automation. It must do more than add a footnote, chapter, or illustration to current texts while perpetuating theories developed in the pre-digital world. Instead, it must rethink its basic theories in every area - from sensory and perception to social and clinical. Fortunately, this is already occurring in many areas. Cognitive science and neuroscience were founded in the digital age, and human factors psychology has embraced the interaction with computers, but some areas have fallen behind. Rather than make too much of this now, instead we will develop and push these areas forward as we go through the successive chapters of this book. In doing so, we will try to cover the full range of psychology"...

Language: English

Subjects: Computer Science , Psychology , General works

Keywords: Mensch-Maschine-Kommunikation ; Cyberpsychologie ; Neue Medien ; Computerunterstützte Kommunikation ; Einführung

URL: Inhaltsverzeichnis

URL: Inhaltsverzeichnis