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Umfang: 1 online resource (352 pages)

Ausgabe: 1st ed.

ISBN: 9789819968114

Anmerkung: Intro -- Design Dictionary -- Acknowledgements -- Contents -- List of Figures -- List of Tables -- Changemakers: Designers in Healthcare -- 1 Why a Design-Led Approach Is Needed in Healthcare -- 2 The Promise and Perils of Technology -- 3 The Challenge of Changing Healthcare -- 4 Designers as Agents of Change -- 5 How to Read this Book -- 6 Part 1: Placemakers -- 7 Part 2: Makers -- 8 Part 3: Advocates -- 9 Part 4: Strategists -- 10 Part 5: Instigators -- 11 Part 6: Practitioners -- References -- Part I: Placemakers -- Reference -- Parrot Murals and Feather Floors: Co-designing playful wayfinding in the Queensland Children's Hospital -- 1 Wayfinding in Children's Hospitals -- 2 Our Approach: Co-Designing Playful Wayfinding at the QCH -- 3 The Collaborative Design Ideation Process for Playful Wayfinding at QCH -- 4 Sharing Design Power: Tracing and Negotiating for Best Outcomes -- 5 The Lift Zones: Arrival Landmarks -- 6 The Value of Mock-Ups -- 7 The Final Design -- 8 Conclusion -- References -- 'It Takes a Village': The Power of Conceptual Framing in the Participatory Redesign of Family-Centred Care in a Paediatric Intensive Care Unit -- 1 The Design of Environments for Paediatric Family-Centred Care -- 2 The Queensland Children's Hospital (QCH) PICU Partnership Project Design Challenge -- 3 Defining the Conceptual Approach for Participation in the PICU Partnership Project -- 4 Participatory Design Methods -- 5 Outcomes -- 6 Reflections on the Importance of Design Concepts and Metaphors for Participatory Health Design Projects -- References -- Designing Hospital Emergency Departments for a Post Pandemic World: The Value of a BaSE Mindset-Biophilia (Natural), Salutogenesis (Healthy), and Eudaimonia (Contentment) in Architectural Design -- 1 Flexible and Adaptive Spatial Environments in Hospital Emergency Departments. , 2 The Importance of Healing Architecture -- 3 Biophilic Architecture: Element One of the BaSE Mindset -- 4 Salutogenic Architecture: Element Two of the BaSE Mindset -- 5 Eudaimonic Architecture: Element Three of the BaSE Mindset -- 6 Our HEAL Project -- 7 What Works (and What Doesn't) in Emergency Department Design? -- References -- Transforming the NICU Environment for Parent and Staff Wellbeing: A Holistic and Transdisciplinary Supportive Design Approach -- 1 Engaging Differently -- 2 A Holistic & -- Transdisciplinary Approach -- 2.1 Spatial Design -- 2.2 Visual Communication Design -- 2.3 Service Design -- 3 Developing Solutions with Cross-Benefits for Parents and Staff -- 4 Supportive Design Theory for Neonatal Environments -- 4.1 Application of Theory: Perceived Sense of Control -- 4.2 Application of Theory: Positive Distraction -- 4.3 Application of Theory: Social Support Opportunities -- 5 Transforming the Neonatal Unit: An Overview of Six Supportive Design Concepts -- 5.1 A Place for Parents: Re-Designing the Parent Hub for Dining, Working, and Resting -- 5.2 From Parent Craft to Parent Retreat: Transforming the Parent Craft into a 'home away from home' -- 5.3 Placemaking and Creative Wayfinding: Creating Zones and a Sense of Identity for the Neonatal Unit -- 5.4 Bringing the Outside-in: Fostering Connection to Nature through Photographic Artworks of Australian Native Flora -- 5.5 Creating a Comforting Place for Private Conversations: Re-Imagining the Xray Room -- 5.6 Creating a Place for Connection: Re-Imagining the Conference Room -- 6 The Challenges and Limitations of a Holistic & -- Transdisciplinary Supportive Design Approach for Creating Change within a NICU Environment -- References -- Part II: Makers -- Reference -- Prototyping for Healthcare Innovation -- 1 Understanding Prototyping in the Design Research Process. , 2 Design Thinking, Co-Design and Prototyping in Human-Centred-Design for Healthcare Innovation -- 3 The Value of Prototyping -- 4 My Approach as an Industrial Designer in Design for Health -- 5 Project a: PPE for Paediatric Wards-Co-Designing Child Friendly Facial PPE -- 5.1 The Need for Person-Centred Solutions: A Mix-Methods Approach -- 6 Project B: Assessing Pain in Paediatric Hospital Wards -- 6.1 The Need for Person-Centred Solutions: A Collaborative Approach to Designing TAME -- 7 Challenges in Design for Health Research -- 8 Challenges to the Process of Designing the Paediatric PPE -- 9 Challenges to the Process of Designing TAME -- 10 Design Thinking Prototyping in Design for Health: Emerging Principles -- 11 Principle 1: Making for Engaging-Prototyping Is Essential for Stakeholders' Engagement -- 12 Principle 2: Making Meaning: Prototyping Brings out Context and Knowledge -- 13 Principle 3: Making Stories: Prototyping Helps Envision Scenarios -- 14 Principle 4: Making Language: Prototyping Is 'Design Doing' in your Own Way -- 15 Conclusions -- References -- Graphics and Icons for Healthcare with a Focus on Cultural Appropriateness, Diversity, and Inclusion -- 1 A History of Medical Graphics and Icons -- 1.1 Universal Symbols in Medical Graphics -- 1.2 Cross-Cultural Understanding of Graphic Images and Information -- 2 Case Study: Innovating Healthcare Design for Diversity and Inclusion -- 2.1 Introduction -- 3 Project Overview -- 4 Design Intervention -- 5 Discussion -- 6 Design Process -- 7 Poster Layout -- 8 Typography -- 9 The Myriad Font -- 10 Colour Palette -- 11 Illustration and Iconography -- 12 The Final Poster -- References -- Agency and Access: Redesigning the Prison Health Care Request Process -- 1 How Prisoners Currently Access and Experience Healthcare -- 2 Why Prison Healthcare Matters: And Current Priorities. , 3 Rethinking the Prison Health Request Process: A Queensland Case Study -- 4 The Queensland Prison Health System -- 5 Barriers to Accessing Timely and Appropriate Health -- 6 Redesigning the Prison Health Request Form -- 7 The New Visual Form -- 8 Conclusion -- References -- Part III: Advocates -- In a Heartbeat: Animation as a Tool for Improving Cultural Safety in Hospitals -- 1 Why Animation? -- 2 Re-Defining the Problem and Designing an Intervention -- 3 Step 2: Working Together towards a Script and a Visual Style -- 4 Defining a Visual Style -- 5 Animation Resources, Camera Placement and Sound -- 6 Creating a Storyline -- 7 Connecting with Users -- 7.1 The Co-Design Workshop with Clinicians -- 7.2 Findings from the Workshop -- 8 Crafting the Experience -- 9 The Final Version /Presentation/Current Uses -- 10 Reflections -- 11 Conclusion -- References -- Co-creating Virtual Care for Chronic Disease -- 1 Process -- 1.1 Mapping -- 1.2 Collaboratively Designing -- 1.3 Sensemaking -- 1.4 Implementing -- 1.5 User Testing -- 1.6 Improving -- 1.7 Expanding -- 2 Outcomes -- 3 What we Learned -- 4 Conclusion -- References -- Improving Interpreter Service Uptake and Access to Just Healthcare for CALD Consumers: Reflections from Clinicians and Designers on Animation and Experience-Based Co-design (EBCD) -- 1 Context/Problem -- 2 Background/Literature -- 2.1 The Rise of Design in Healthcare -- 2.2 Embedding Lived Experience to Promote a Culture of Access and Inclusion -- 2.3 Education Animation in Healthcare for Informing Behaviour Change -- 3 Project -- 3.1 Design Process/Stages -- 4 Reflections on Co-design and Service Design Process -- 4.1 Ruby Chari, Multicultural Mental Health Coordinator -- 4.2 Karen Beaver, Multicultural Mental Health Coordinator -- 4.3 Janice Rieger, Designer -- 4.4 Sarah Johnstone, Designer -- 4.5 Thalia Brunner, Animator. , 5 Discussion -- 6 Conclusion -- References -- Co-designing the Palliative Care Hospital Experience with Clinicians, Patients, and Families: Reflections from a Co-design Workshop with Clinicians -- 1 The Palliative Care Context -- 2 The Value of Co-design -- 3 The Co-design Workshop for Clinicians -- 3.1 Step 1: Connection and Creativity-Creating a Psychologically Safe Space Which Fosters a Co-design Learning Mindset -- 3.2 Step 2: Personas and Empathy Mapping-Imagining and Learning About the User Group's Experience -- 3.3 Step 3: Creative Ideation-'Wild Ideas' for 'Disrupting the System' -- 3.4 Step 4: Identifying Barriers to Change-Staff, Space, Social, and System -- 3.5 Step 5: Idea-Storming-Brainstorming and Formulating Creative Solutions -- 3.6 Stage 6: Prototyping and Designing Change -- 4 Conclusion -- References -- Part IV: Strategists -- Empathy in Action: A Rapid Design Thinking Sprint for Paediatric Pain-Perspective-Storming, Pain Points, and the Power of Personas -- 1 This Design Sprint Challenge: Reducing Procedural Pain for Children -- 2 Design Sprints-Origins, Role, and Philosophical Underpinnings -- 3 Creating 'Liminal Spaces' for Transformative Learning Experiences -- 4 The Six Steps in This Design Thinking Sprint -- 4.1 Step 1: Empathy-User Personas and the Empathy Mapping Task -- 4.2 Step 2: Define -- 4.3 Step 3: Ideate -- 4.4 Step 4: Prototype -- References -- Asking the Right Questions: Cancer Wellness and Stroke Care -- 1 Case Study 1: Cancer Wellness -- 1.1 The Problem -- 1.2 The Process -- 1.2.1 Reimagining -- 1.2.2 Co-designing -- 1.2.3 Sensemaking -- 1.2.4 Developing -- 1.2.5 Evaluating -- 1.3 Learnings -- 2 Case Study 2: Stroke Care -- 2.1 The Problem -- 2.2 The Process -- 2.2.1 Sensemaking -- 2.2.2 Stakeholder Workshops -- 2.2.3 Outcomes -- 2.3 Learnings -- 3 Conclusion: Asking the Right Questions -- References. , The Art of Transformation: Enabling Organisational Change in Healthcare Through Design Thinking, Appreciative Inquiry, and Creative Arts-Based Visual Storytelling.

Weitere Ausg.: Print version: Miller, Evonne How Designers Are Transforming Healthcare Singapore : Springer,c2024 ISBN 9789819968107

Sprache: Englisch

Schlagwort(e): Electronic books. ; Electronic books.

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Umfang: 1 online resource (249 pages)

Ausgabe: 1st ed.

ISBN: 9783031548277

Anmerkung: Intro -- Foreword by Florian Schütz -- Foreword by Jan Kleijssen -- Preface -- Acknowledgments -- Contents -- List of Contributors -- Reviewers -- Acronyms -- Part I Introduction -- 1 From Deep Neural Language Models to LLMs -- 1.1 What LLMs Are and What LLMs Are Not -- 1.2 Principles of LLMs -- 1.2.1 Deep Neural Language Models -- 1.2.2 Generative Deep Neural Language Models -- 1.2.3 Generating Text -- 1.2.4 Memorization vs Generalization -- 1.2.5 Effect of the Model and Training Dataset Size -- References -- 2 Adapting LLMs to Downstream Applications -- 2.1 Prompt Optimization -- 2.2 Pre-Prompting and Implicit Prompting -- 2.3 Model Coordination: Actor-Agents -- 2.4 Integration with Tools -- 2.5 Parameter-Efficient Fine-Tuning -- 2.6 Fine-Tuning -- 2.7 Further Pretraining -- 2.8 From-Scratch Re-Training -- 2.9 Domain-Specific Distillation -- References -- 3 Overview of Existing LLM Families -- 3.1 Introduction -- 3.2 Pre-Transformer LLMs -- 3.3 BERT and Friends -- 3.4 GPT Family Proper -- 3.5 Generative Autoregressors (GPT Alternatives) -- 3.6 Compute-Optimal Models -- 3.6.1 LLaMA Family -- 3.7 Full-Transformer/Sequence-to-Sequence Models -- 3.8 Multimodal and Mixture-of-Experts Models -- 3.8.1 Multimodal Visual LLMs -- 3.8.2 Pathways Language Model, PaLM -- 3.8.3 GPT-4 and BingChat -- References -- 4 Conversational Agents -- 4.1 Introduction -- 4.2 GPT Related Conversational Agents -- 4.3 Alternative Conversational Agent LLMs -- 4.3.1 Conversational Agents Without Auxiliary Capabilities -- 4.3.2 Conversational Agents With Auxiliary Capabilities -- 4.3.2.1 Models With Non-Knowledge Auxiliary Capabilities -- 4.4 Conclusion -- References -- 5 Fundamental Limitations of Generative LLMs -- 5.1 Introduction -- 5.2 Generative LLMs Cannot Be Factual -- 5.3 Generative LLMs With Auxiliary Tools Still Struggle To Be Factual. , 5.4 Generative LLMs Will Leak Private Information -- 5.5 Generative LLMs Have Trouble With Reasoning -- 5.6 Generative LLMs Forget Fast and Have a Short Attention Span -- 5.7 Generative LLMs Are Only Aware of What They Saw at Training -- 5.8 Generative LLMs Can Generate Highly Inappropriate Texts -- 5.9 Generative LLMs Learn and Perpetrate Societal Bias -- References -- 6 Tasks for LLMs and Their Evaluation -- 6.1 Introduction -- 6.2 Natural Language Tasks -- 6.2.1 Reading Comprehension -- 6.2.2 Question Answering -- 6.2.3 Common Sense Reasoning -- 6.2.4 Natural Language Generation -- 6.3 Conclusion -- References -- Part II LLMs in Cybersecurity -- 7 Private Information Leakage in LLMs -- 7.1 Introduction -- 7.2 Information Leakage -- 7.3 Extraction -- 7.4 Jailbreaking -- 7.5 Conclusions -- References -- 8 Phishing and Social Engineering in the Age of LLMs -- 8.1 LLMs in Phishing and Social Engineering -- 8.2 Case Study: Orchestrating Large-Scale Scam Campaigns -- 8.3 Case Study: Shā Zhū Pán Attacks -- References -- 9 Vulnerabilities Introduced by LLMs Through Code Suggestions -- 9.1 Introduction -- 9.2 Relationship Between LLMs and Code Security -- 9.2.1 Vulnerabilities and Risks Introduced by LLM-Generated Code -- 9.3 Mitigating Security Concerns With LLM-Generated Code -- 9.4 Conclusion and The Path Forward -- References -- 10 LLM Controls Execution Flow Hijacking -- 10.1 Faulting Controls: The Genesis of Execution Flow Hijacking -- 10.2 Unpacking Execution Flow: LLMs' Sensitivity to User-Provided Text -- 10.3 Examples of LLMs Execution Flow Attacks -- 10.4 Securing Uncertainty: Security Challenges in LLMs -- 10.5 Security by Design: Shielding Probabilistic Execution Flows -- References -- 11 LLM-Aided Social Media Influence Operations -- 11.1 Introduction -- 11.2 Salience of LLMs -- 11.3 Potential Impact -- 11.4 Mitigation -- References. , 12 Deep(er) Web Indexing with LLMs -- 12.1 Introduction -- 12.2 Innovation Through Integration of LLMs -- 12.3 Navigating Complexities: Challenges and Mitigation Strategies -- 12.3.1 Desired Behavior of LLM-Based Search Query Creation Tools -- 12.3.2 Engineering Challenges and Mitigations -- 12.3.2.1 Ethical and Security Concerns -- 12.3.2.2 Fidelity of Query Responses and Model Accuracy -- 12.3.2.3 Linguistic and Regulatory Variations -- 12.3.2.4 Handling Ambiguous Queries -- 12.4 Key Takeaways -- 12.5 Conclusion and Reflections -- References -- Part III Tracking and Forecasting Exposure -- 13 LLM Adoption Trends and Associated Risks -- 13.1 Introduction -- 13.2 In-Context Learning vs Fine-Tuning -- 13.3 Adoption Trends -- 13.3.1 LLM Agents -- 13.4 Potential Risks -- References -- 14 The Flow of Investments in the LLM Space -- 14.1 General Context: Investments in the Sectors of AI, ML, and Text Analytics -- 14.2 Discretionary Evidence -- 14.3 Future Work with Methods Already Applied to AI and ML -- References -- 15 Insurance Outlook for LLM-Induced Risk -- 15.1 General Context of Cyber Insurance -- 15.1.1 Cyber-Risk Insurance -- 15.1.2 Cybersecurity and Breaches Costs -- 15.2 Outlook for Estimating the Insurance Premia of LLMs Cyber Insurance -- References -- 16 Copyright-Related Risks in the Creation and Useof ML/AI Systems -- 16.1 Introduction -- 16.2 Concerns of Owners of Copyrighted Works -- 16.3 Concerns of Users Who Incorporate Content Generated by ML/AI Systems Into Their Creations -- 16.4 Mitigating the Risks -- References -- 17 Monitoring Emerging Trends in LLM Research -- 17.1 Introduction -- 17.2 Background -- 17.3 Data and Methods: Noun Extraction -- 17.4 Results -- 17.4.1 Domain Experts Validation and Interpretations -- 17.5 Discussion, Limitations and Further Research -- 17.6 Conclusion -- References -- Part IV Mitigation. , 18 Enhancing Security Awareness and Education for LLMs -- 18.1 Introduction -- 18.2 Security Landscape of LLMs -- 18.3 Foundations of LLM Security Education -- 18.4 The Role of Education in Sub-Areas of LLM Deployment and Development -- 18.5 Empowering Users Against Security Breaches and Risks -- 18.6 Advanced Security Training for LLM Users -- 18.7 Conclusion and the Path Forward -- References -- 19 Towards Privacy Preserving LLMs Training -- 19.1 Introduction -- 19.2 Dataset Pre-processing with Anonymization and De-duplication -- 19.3 Differential Privacy for Fine-Tuning Models -- 19.4 Differential Privacy for Deployed Models -- 19.5 Conclusions -- References -- 20 Adversarial Evasion on LLMs -- 20.1 Introduction -- 20.2 Evasion Attacks in Image Classification -- 20.3 Impact of Evasion Attacks on the Theory of Deep Learning -- 20.4 Evasion Attacks for Language Processing and Applicability to Large Language Models -- References -- 21 Robust and Private Federated Learning on LLMs -- 21.1 Introduction -- 21.1.1 Peculiar Challenges of LLMs -- 21.2 Robustness to Malicious Clients -- 21.3 Privacy Protection of Clients' Data -- 21.4 Synthesis of Robustness and Privacy -- 21.5 Concluding Remarks -- References -- 22 LLM Detectors -- 22.1 Introduction -- 22.2 LLMs' Salience -- 22.2.1 General Detectors -- 22.2.2 Specific Detectors -- 22.3 Potential Mitigation -- 22.3.1 Watermarking -- 22.3.2 DetectGPT -- 22.3.3 Retrieval Based -- 22.4 Mitigation -- References -- 23 On-Site Deployment of LLMs -- 23.1 Introduction -- 23.2 Open-Source Development -- 23.3 Technical Solution -- 23.3.1 Serving -- 23.3.2 Quantization -- 23.3.3 Energy Costs -- 23.4 Risk Assessment -- References -- 24 LLMs Red Teaming -- 24.1 History and Evolution of Red-Teaming Large Language Models -- 24.2 Making LLMs Misbehave -- 24.3 Attacks -- 24.3.1 Classes of Attacks on Large Language Models. , 24.3.1.1 Prompt-Level Attacks -- 24.3.1.2 Contextual Limitations: A Fundamental Weakness -- 24.3.1.3 Mechanisms of Distractor and Formatting Attacks -- 24.3.1.4 The Role of Social Engineering -- 24.3.1.5 Integration of Fuzzing and Automated Machine Learning Techniques for Scalability -- 24.4 Datasets -- 24.5 Defensive Mechanisms Against Manual and Automated Attacks on LLMs -- 24.6 The Future -- Appendix -- References -- 25 Standards for LLM Security -- 25.1 Introduction -- 25.2 The Cybersecurity Landscape -- 25.2.1 MITRE CVEs -- 25.2.2 CWE -- 25.2.3 MITRE ATT& -- CK and Cyber Kill Chain -- 25.3 Existing Standards -- 25.3.1 AI RMF Playbook -- 25.3.2 OWASP Top 10 for LLMs -- 25.3.3 AI Vulnerability Database -- 25.3.4 MITRE ATLAS -- 25.4 Looking Ahead -- References -- Part V Conclusion -- 26 Exploring the Dual Role of LLMs in Cybersecurity: Threats and Defenses -- 26.1 Introduction -- 26.2 LLM Vulnerabilities -- 26.2.1 Security Concerns -- 26.2.1.1 Data Leakage -- 26.2.1.2 Toxic Content -- 26.2.1.3 Disinformation -- 26.2.2 Attack Vectors -- 26.2.2.1 Backdoor Attacks -- 26.2.2.2 Prompt Injection Attacks -- 26.2.3 Testing LLMs -- 26.3 Code Creation Using LLMs -- 26.3.1 How Secure is LLM-Generated Code? -- 26.3.2 Generating Malware -- 26.4 Shielding with LLMs -- 26.5 Conclusion -- References -- 27 Towards Safe LLMs Integration -- 27.1 Introduction -- 27.2 The Attack Surface -- 27.3 Impact -- 27.4 Mitigation -- References.

Weitere Ausg.: Print version: Kucharavy, Andrei Large Language Models in Cybersecurity Cham : Springer International Publishing AG,c2024 ISBN 9783031548260

Sprache: Englisch

Schlagwort(e): Electronic books. ; Electronic books.

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Umfang: 1 online resource (xv, 430 pages) : , digital, PDF file(s).

Ausgabe: 1st ed.

ISBN: 1-009-45382-3 , 0-511-78369-8

Serie: Studies in natural language processing

Inhalt: Distributional semantics develops theories and methods to represent the meaning of natural language expressions, with vectors encoding their statistical distribution in linguistic contexts. It is at once a theoretical model to express meaning, a practical methodology to construct semantic representations, a computational framework for acquiring meaning from language data, and a cognitive hypothesis about the role of language usage in shaping meaning. This book aims to build a common understanding of the theoretical and methodological foundations of distributional semantics. Beginning with its historical origins, the text exemplifies how the distributional approach is implemented in distributional semantic models. The main types of computational models, including modern deep learning ones, are described and evaluated, demonstrating how various types of semantic issues are addressed by those models. Open problems and challenges are also analyzed. Students and researchers in natural language processing, artificial intelligence, and cognitive science will appreciate this book.

Anmerkung: Title from publisher's bibliographic system (viewed on 08 Sep 2023). , Cover -- Halftitle page -- Series page -- Endorsements -- Title page -- Copyright page -- Contents -- Preface -- What Is Distributional Semantics? -- The Need for a Common Ground -- Outline of the Book -- Terminological Issues -- Acknowledgments -- Part I racktenTheory -- 1 From Usage to Meaning: The Foundations of Distributional Semantics -- 1.1 The Distributional Hypothesis -- 1.1.1 The Distributional Methodology in Structural Linguistics -- 1.1.2 Meaning as Use: The Echoes ofWittgenstein -- 1.1.3 Distributionalism and Corpus Linguistics -- 1.1.4 The Distributional Hypothesis in Psychology -- 1.2 Distributional Semantics in Language Research -- 1.2.1 Computational Linguistics -- 1.2.2 Semantic Theory -- 1.3 Summary -- 1.4 Further Reading -- 2 Distributional Representations -- 2.1 Corpus Selection and Processing -- 2.1.1 Word Frequency Distributions -- 2.1.2 Choosing the Training Corpus -- 2.1.3 Corpus Annotation -- 2.2 Extracting Co-occurrences -- 2.2.1 Contexts as Co-occurring Linguistic Units -- 2.2.2 Contexts as Documents -- 2.3 The Co-occurrence Matrix -- 2.3.1 Co-occurrence Weighting Functions -- 2.3.2 Context Selection -- 2.4 Distributional Vectors -- 2.4.1 Explicit Distributional Vectors -- 2.4.2 Implicit Distributional Vectors (Word Embeddings) -- 2.5 Reducing Vector Dimensionality -- 2.5.1 Singular Value Decomposition (SVD) -- 2.5.2 Principle Component Analysis (PCA) -- 2.5.3 Nonnegative Matrix Factorization (NMF) -- 2.6 Vector Similarity -- 2.6.1 Geometric Measures -- 2.6.2 Nongeometric Measures -- 2.7 Summary -- 2.8 Further Reading -- Part II racktenModels -- 3 Distributional Semantic Models -- 4 Matrix Models -- 4.1 Classical Matrix Models -- 4.1.1 Hyperspace Analogue to Language (HAL) -- 4.1.2 Latent Semantic Analysis (LSA) -- 4.1.3 Dependency Vectors (DV) -- 4.2 Latent Relational Analysis (LRA) -- 4.3 Distributional Memory (DM). , 4.3.1 Distributional Tuples and Tensors -- 4.3.2 From Tensors to Matrices -- 4.4 Topic Models (TMs) -- 4.4.1 Latent Dirichlet Allocation (LDA) -- 4.4.2 Representing Lexemes with Topic Models -- 4.5 Global Vectors (GloVe) -- 4.6 Summary -- 4.7 Further Reading -- 5 Random Encoding Models -- 5.1 The Johnson-Lindenstrauss Lemma -- 5.2 Random Projection -- 5.3 Random Indexing (RI) -- 5.3.1 Random Indexing as Random Projection -- 5.4 The BEAGLE Model -- 5.5 Encoding Sequences in RI by Random Permutations -- 5.6 Self-Organizing Maps (SOM) -- 5.7 Summary -- 5.8 Further Reading -- 6 Neural Network Models -- 6.1 Neural Networks: A Brief Introduction -- 6.2 Neural Language Models -- 6.2.1 Simple Recurrent Networks (SRN) -- 6.2.2 Feed-Forward Language Models -- 6.3 Word2vec: Skip-Gram (SG) and CBOW -- 6.3.1 TrainingWord2vec -- 6.3.2 Variations of Word2vec -- 6.4 Count or Predict? -- 6.5 Summary -- 6.6 Further Reading -- Part III racktenPractice -- 7 Evaluation of Distributional Semantic Models -- 7.1 Semantic Similarity and Relatedness -- 7.2 Intrinsic DSM Evaluation -- 7.2.1 Synonym Tests -- 7.2.2 Similarity and Relatedness Tests -- 7.2.3 Categorization Tests -- 7.2.4 Analogy Tests -- 7.2.5 Relation Tests -- 7.2.6 Psycholinguistic Tasks -- 7.3 Extrinsic DSM Evaluation -- 7.4 Quantitative Evaluation of Static DSMs -- 7.4.1 Model Selection and Training -- 7.4.2 Tasks and Datasets -- 7.4.3 Results and Analyses -- 7.4.4 Discussion -- 7.5 Representation Similarity Analysis of Semantic Spaces -- 7.6 Summary -- 7.7 Further Reading -- 8 Distributional Semantics and the Lexicon -- 8.1 Representing Lexical Meaning -- 8.2 Word Senses -- 8.2.1 Senses as Clusters of Contexts -- 8.2.2 Senses as Clusters of Neighbors -- 8.3 Paradigmatic Semantic Relations -- 8.3.1 Hypernymy -- 8.3.2 Antonymy -- 8.4 Cross-Lingual DSMs -- 8.4.1 Mapping Models -- 8.4.2 Joint Models. , 8.5 Connotative Meaning -- 8.5.1 Distributional Models of Affect -- 8.5.2 Cultural Biases and Stereotypes in DSMs -- 8.6 Semantic Change -- 8.7 Grounded Distributional Representations -- 8.7.1 Multimodal Distributional Semantics -- 8.8 Distributional Semantics in Cognitive Science -- 8.8.1 The Cognitive Plausibility of Distributional Representations -- 8.8.2 FromWord Embeddings to Semantic Features -- 8.8.3 Neurosemantic Decoding -- 8.9 Summary -- 8.10 Further Reading -- 9 Distributional Semantics beyond the Lexicon -- 9.1 Semantic Representations and Compositionality -- 9.1.1 The Problems of Fregean Compositionality -- 9.2 Vector Composition Functions -- 9.2.1 Predicting the Compositionality of Multiword Expressions -- 9.3 The Distributional Functional Model (DFM) -- 9.3.1 Matrix-Vector Recursive Neural Networks (MV-RNN) -- 9.4 Sentence Embeddings -- 9.4.1 Paragraph Vector (doc2vec) -- 9.4.2 Convolutional Neural Networks (CNNs) -- 9.4.3 Encoder-Decoder Models (seq2seq) -- 9.5 Evaluation of Compositional DSMs -- 9.6 Context-Sensitive Distributional Representations -- 9.6.1 Vector Contextualization -- 9.6.2 Exemplar DSMs -- 9.6.3 Contextual DSMs -- 9.7 Distributional Models of Selectional Preferences -- 9.7.1 Modeling Coercion: The Case of Logical Metonymy -- 9.8 Compositional Distributional Semantics: Limits and Prospects -- 9.9 Summary -- 9.10 Further Reading -- 10 Conclusions and Outlook -- 10.1 The Golden Age of Distributional Semantics -- 10.2 Are We Climbing the Right Hill? -- 10.3 Climbing Meaning with Distributional Semantics -- References -- Index.

Weitere Ausg.: ISBN 9781107004290

Sprache: Englisch

Fachgebiete: Komparatistik. Außereuropäische Sprachen/Literaturen

URL: https://doi.org/10.1017/9780511783692

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Umfang: 1 online resource (270 pages)

ISBN: 9783319916897

Serie: Fascinating Life Sciences Ser.

Anmerkung: Intro -- Foreword -- References -- Contents -- Chapter 1: Introduction: Studying Birds in Time and Space -- 1.1 Why and How to Study Bird Species -- 1.2 Physical and Behavioral Aspects of Birds -- 1.3 The Spatial Component -- 1.4 Ecology Matters: Bird Species in the Anthropocene -- References -- Chapter 2: Integrative Taxonomy of Birds: The Nature and Delimitation of Species -- 2.1 The Centrality of Species -- 2.2 Why Is There a Species Problem? -- 2.2.1 Monism vs. Pluralism -- 2.2.2 Realism vs. Anti-realism -- 2.2.3 Theoretical vs. Operational -- 2.2.4 Pattern vs. Process -- 2.2.5 Prospective vs. Historical -- 2.2.6 Concerns by End Users -- 2.3 The Lineage Concept -- 2.4 Corollaries of the Lineage Concept -- 2.5 Integrative Taxonomy -- 2.5.1 Why Multiple Data? -- 2.5.2 Why Integrate? -- 2.6 Strengths of Integrative Taxonomy -- 2.7 What Is Not Integrative Taxonomy? -- 2.7.1 Falsification by a ``Defining ́́Species Criterion -- 2.7.2 Standardization of Species Criteria -- 2.8 The Dynamics of Taxonomic Change -- 2.9 The Drivers of Taxonomic Change -- 2.10 Benefits of Integrative Taxonomy to Other Fields -- 2.10.1 Speciation Studies -- 2.10.2 Biogeography -- 2.10.3 Conservation -- 2.11 Remaining Issues -- References -- Suggestion for Further Reading -- Chapter 3: Studying Speciation: Genomic Essentials and Approaches -- 3.1 What Is an Avian Genome? -- 3.1.1 Structure of the Genetic Material -- 3.1.1.1 Noncoding and Coding Regions -- 3.1.1.2 Autosomes Versus Sex Chromosomes -- 3.1.1.3 Nuclear Genome and Mitochondrial Genome -- 3.1.2 The Chicken Model: History and Overview -- 3.2 How Does the Genome ``Work?́́ -- 3.2.1 Replication of the DNA -- 3.2.2 Transcription: RNA Synthesis -- 3.2.3 Translation -- 3.2.4 One Gene: One Function? -- 3.2.5 Categorical vs. Quantitative Traits -- 3.2.6 Phenotypic Plasticity -- 3.3 How Does the Genome Evolve?. , 3.3.1 Modification of the DNA -- 3.3.2 Mutation -- 3.3.3 Selection -- 3.3.4 Genetic Drift -- 3.3.5 Geographic Variation and Dispersal -- 3.3.6 Recombination and Migration -- 3.3.7 Gene Duplication -- 3.4 How to Study Speciation Using Genomic Features? -- 3.4.1 PCR-Based Molecular Markers -- 3.4.1.1 Ribosomal Genes -- 3.4.1.2 Mitochondrial DNA Markers -- 3.4.1.3 Microsatellites -- 3.4.2 Expressed Sequence Tags -- 3.4.3 Single Nucleotide Polymorphisms -- 3.4.4 Restriction-site-associated DNA sequencing -- 3.4.5 Genotyping by sequencing -- 3.4.6 Transcriptomics -- 3.4.7 ``Whole ́́Genome Sequencing -- 3.4.7.1 Different Strategies for Sequencing Genomes -- 3.4.7.2 Limitations of Analyzing Genomes -- 3.4.8 Epigenome -- 3.5 Closing Words -- References -- Chapter 4: Morphological Variation in Birds: Plasticity, Adaptation, and Speciation -- 4.1 General Aspects of Phenotypic Variation in Birds -- 4.2 The Historical Role of Morphological Criteria for Species Delimitation -- 4.3 Phenotypic Variation and Plasticity of Characters -- 4.4 Assessing Morphological Variation -- 4.5 Disentangling Phylogenetic and Adaptive Constraints -- 4.6 A Contemporary Perspective on Morphological Variation -- References -- Chapter 5: Song: The Learned Language of Three Major Bird Clades -- 5.1 Eager Birds: The Advanced Learners -- 5.2 Passerine Song -- 5.3 The Best Singer Takes It All: Female Preference and Sexual Selection -- 5.4 How It All Began: A Brief History of Bioacoustic Studies -- 5.5 Telltale Songs: Evolution and Phylogenetic Information of Vocalizations -- 5.6 Vocal Learning as a Pacemaker of Evolution -- 5.7 Dialects: Spatial Variation -- 5.8 Competition for Acoustic Space: The Role of Ecology -- 5.9 Dialects as a Language Barrier and Isolating Mechanism -- 5.10 Sympathy in Sympatry: Bilingual Birds in a Hybrid Zone -- References. , Chapter 6: Timing Matters: Allochronic Contributions to Population Divergence -- 6.1 Timing Is Everything! -- 6.2 Clockworks -- 6.3 Allochrony: Differences in Timing Between Individuals, Populations, and Species -- 6.4 Isolation by the Clock -- 6.5 Conclusions -- Further Reading -- References -- Chapter 7: (Micro)evolutionary Changes and the Evolutionary Potential of Bird Migration -- 7.1 History and Geographic Origins -- 7.2 Regulation -- 7.2.1 Variation in Migratory Strategy -- 7.2.2 Migratory Traits Are Inherited -- 7.2.3 Underlying Genetic Architecture: Simple and Common? -- 7.2.4 Marker-Based Approaches: Candidate Genes for Migration -- 7.2.5 Enhancing Scale and Resolution: Genome-Wide Approaches -- 7.3 Population Differentiation and Speciation -- References -- Chapter 8: Avian Diversity and Distributions and Their Evolution Through Space and Time -- 8.1 Spatiotemporal Diversification of Modern Birds -- 8.2 Global Distribution and Diversity Patterns -- 8.3 Geography of Speciation -- 8.4 Vicariance vs. Dispersal and the Dynamics of Range Evolution in Birds -- References -- Chapter 9: Modeling Avian Distributions and Niches: Insights into Invasions and Speciation in Birds -- 9.1 Introduction -- 9.2 The Conceptual Background of SDMs or What Is a Niche? -- 9.3 How to Build a Species Distribution Model? -- 9.3.1 Occurrence Data -- 9.3.2 Predictor Variables -- 9.3.3 Algorithms -- 9.3.4 Niche Comparisons -- 9.4 Niche Conservatism -- 9.5 Evaluating Avian Invasions -- 9.6 Speciation and Niche Evolution -- 9.7 Assisting Taxonomy -- References -- Chapter 10: Phylogeography and the Role of Hybridization in Speciation -- 10.1 Introduction -- 10.2 Some General Observations from Avian Phylogeography: Historical Population Size Changes and Introgression -- 10.3 Phylogeography, Sex Chromosomes, and Speciation. , 10.4 Bird Species with No Known or Very Few Genetic Differences -- 10.5 Hybrid Zones: A Closer Look -- 10.5.1 Suture Zones and Multiple Hybrid Zones -- 10.5.2 Detail Emerging from Single Species and Hybrid Zones: Three Case Studies -- 10.6 Mitonuclear Incompatibility, Hybridization, and Speciation -- 10.7 Ring Species as a Special Case of Divergence with Gene Flow: Are There Any Surviving Examples? -- 10.8 Hybrid Species -- 10.8.1 Hybrid Zones Sometimes Move -- 10.9 A View to the Future -- References -- Chapter 11: Ecological Speciation: When and How Variation Among Environments Can Drive Population Divergence -- 11.1 Approaches Toward the Study of Speciation -- 11.2 Four Ways to Increase Ecological Performance: Which May Each Drive Speciation -- 11.3 Ecological Speciation Driven by Natural Selection -- 11.4 Ecological Speciation Driven by Phenotypic Plasticity -- 11.5 Ecological Speciation Driven by Adjustment of the Environment -- 11.6 Ecological Speciation Driven by Selection of the Environment -- 11.7 Feedbacks Between Plasticity, Adjusting the Environment, Selection of the Environment, and Natural Selection -- References -- Chapter 12: Climate Change Impacts on Bird Species -- 12.1 Introduction -- 12.2 Birds and Climate Change: Is There an Impact? -- 12.2.1 Climate Change Indicators -- 12.3 What Are the Consequences of Climate Change for Birds? -- 12.4 Projections of Potential Climate Change Impacts: What Else Is Waiting for Us? -- 12.5 Do Niches and Interactions with Abiotic and Biotic Environment ``Evolve?́́ -- 12.6 Conservation Implications -- References -- Chapter 13: Impact of Urbanization on Birds -- 13.1 A Brief History of Urbanization -- 13.2 Birds and the City -- 13.2.1 Species Vanish from the City -- 13.2.2 Species Flourish or Persist in the City -- 13.2.3 Species Change -- 13.3 Urban Environment as a Barrier for Movement. , 13.4 The Urban Drivers -- 13.5 Phenotypic Changes and Responses as a Result of Urban Life -- 13.5.1 Physiology -- 13.5.1.1 Stress Physiology and Its Implications -- 13.5.1.2 Nutritional Physiology and Its Implications -- 13.5.2 Behavior -- 13.5.2.1 Behavioral Responses to Chemical Pollution -- 13.5.2.2 Behavioral Responses to Noise -- 13.5.2.3 Behavioral Responses to ALAN -- 13.6 Concluding Remarks -- References -- Glossary.

Weitere Ausg.: Print version: Tietze, Dieter Thomas Bird Species Cham : Springer International Publishing AG,c2018 ISBN 9783319916880

Sprache: Englisch

Fachgebiete: Biologie

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Anmerkung: Intro -- Preface -- Objectives -- Organisation of Book Chapters -- Intended Readers -- Limitations -- Book Project During Sabbatical Stay in Sydney -- Aims -- Acknowledgements -- Contents -- 1 Introduction -- 1.1 Early Work and Review Articles -- 2 The History of the Patient Record and the Paper Record -- 2.1 The Egyptians and the Greeks -- 2.2 The Arabs -- 2.3 The Swedes -- 2.4 The Paper Based Patient Record -- 2.5 Greek and Latin Used in the Patient Record -- 2.6 Summary of the History of the Patient Record and the Paper Record -- 3 User Needs: Clinicians, Clinical Researchers and Hospital Management -- 3.1 Reading and Retrieving Efficiency of Patient Records -- 3.2 Natural Language Processing on Clinical Text -- 3.3 Electronic Patient Record System -- 3.4 Different User Groups -- 3.5 Summary -- 4 Characteristics of Patient Records and Clinical Corpora -- 4.1 Patient Records -- 4.2 Pathology Reports -- 4.3 Spelling Errors in Clinical Text -- 4.4 Abbreviations -- 4.5 Acronyms -- 4.6 Assertions -- 4.6.1 Negations -- 4.6.2 Speculation and Factuality -- Levels of Certainty -- Negation and Speculations in Other Languages, Such as Chinese -- 4.7 Clinical Corpora Available -- 4.7.1 English Clinical Corpora Available -- 4.7.2 Swedish Clinical Corpora -- 4.7.3 Clinical Corpora in Other Languages than Swedish -- 4.8 Summary -- 5 Medical Classifications and Terminologies -- 5.1 International Statistical Classification of Diseases and Related Health Problems (ICD) -- 5.1.1 International Classification of Diseases for Oncology (ICD-O-3) -- 5.2 Systematized Nomenclature of Medicine: Clinical Terms (SNOMED CT) -- 5.3 Medical Subject Headings (MeSH) -- 5.4 Unified Medical Language Systems (UMLS) -- 5.5 Anatomical Therapeutic Chemical Classification (ATC) -- 5.6 Different Standards for Interoperability -- 5.6.1 Health Level 7 (HL7). , Fast Healthcare Interoperability Resources (FHIR) -- 5.6.2 OpenEHR -- 5.6.3 Mapping and Expanding Terminologies -- 5.7 Summary of Medical Classifications and Terminologies -- 6 Evaluation Metrics and Evaluation -- 6.1 Qualitative and Quantitative Evaluation -- 6.2 The Cranfield Paradigm -- 6.3 Metrics -- 6.4 Annotation -- 6.5 Inter-Annotator Agreement (IAA) -- 6.6 Confidence and Statistical Significance Testing -- 6.7 Annotation Tools -- 6.8 Gold Standard -- 6.9 Summary of Evaluation Metrics and Annotation -- 7 Basic Building Blocks for Clinical Text Processing -- 7.1 Definitions -- 7.2 Segmentation and Tokenisation -- 7.3 Morphological Processing -- 7.3.1 Lemmatisation -- 7.3.2 Stemming -- 7.3.3 Compound Splitting (Decompounding) -- 7.3.4 Abbreviation Detection and Expansion -- A Machine Learning Approach for Abbreviation Detection -- 7.3.5 Spell Checking and Spelling Error Correction -- Spell Checking of Clinical Text -- Open Source Spell Checkers -- Search Engines and Spell Checking -- 7.3.6 Part-of-Speech Tagging (POS Tagging) -- 7.4 Syntactical Analysis -- 7.4.1 Shallow Parsing (Chunking) -- 7.4.2 Grammar Tools -- 7.5 Semantic Analysis and Concept Extraction -- 7.5.1 Named Entity Recognition -- Machine Learning for Named Entity Recognition -- 7.5.2 Negation Detection -- Negation Detection Systems -- Negation Trigger Lists -- NegEx for Swedish -- NegEx for French, Spanish and German -- Machine Learning Approaches for Negation Detection -- 7.5.3 Factuality Detection -- 7.5.4 Relative Processing (Family History) -- 7.5.5 Temporal Processing -- TimeML and TIMEX3 -- HeidelTime -- i2b2 Temporal Relations Challenge -- Temporal Processing for Swedish Clinical Text -- Temporal Processing for French Clinical Text -- Temporal Processing for Portuguese Clinical Text -- 7.5.6 Relation Extraction -- 2010 i2b2/VA Challenge Relation Classification Task. , Other Approaches for Relation Extraction -- 7.5.7 Anaphora Resolution -- i2b2 Challenge in Coreference Resolution for Electronic Medical Records -- 7.6 Summary of Basic Building Blocks for Clinical Text Processing -- 8 Computational Methods for Text Analysis and Text Classification -- 8.1 Rule-Based Methods -- 8.1.1 Regular Expressions -- 8.2 Machine Learning-Based Methods -- 8.2.1 Features and Feature Selection -- Term Frequency-Inverse Document Frequency, tf-idf -- Vector Space Model -- 8.2.2 Active Learning -- 8.2.3 Pre-Annotation with Revision or Machine Assisted Annotation -- 8.2.4 Clustering -- 8.2.5 Topic Modelling -- 8.2.6 Distributional Semantics -- 8.2.7 Association Rules -- 8.3 Explaining and Understanding the Results Produced -- 8.4 Computational Linguistic Modules for Clinical Text Processing -- 8.5 NLP Tools: UIMA, GATE, NLTK etc -- 8.6 Summary of Computational Methods for Text Analysis and Text Classification -- 9 Ethics and Privacy of Patient Records for Clinical Text Mining Research -- 9.1 Ethical Permission -- 9.2 Social Security Number -- 9.3 Safe Storage -- 9.4 Automatic De-Identification of Patient Records -- 9.4.1 Density of PHI in Electronic Patient Record Text -- 9.4.2 Pseudonymisation of Electronic Patient Records -- 9.4.3 Re-Identification and Privacy -- Black Box Approach -- 9.5 Summary of Ethics and Privacy of Patient Records for Clinical Text Mining Research -- 10 Applications of Clinical Text Mining -- 10.1 Detection and Prediction of Healthcare Associated Infections (HAIs) -- 10.1.1 Healthcare Associated Infections (HAIs) -- 10.1.2 Detecting and Predicting HAI -- 10.1.3 Commercial HAI Surveillance Systems and Systems in Practical Use -- 10.2 Detection of Adverse Drug Events (ADEs) -- 10.2.1 Adverse Drug Events (ADEs) -- 10.2.2 Resources for Adverse Drug Event Detection -- 10.2.3 Passive Surveillance of ADEs. , 10.2.4 Active Surveillance of ADEs -- 10.2.5 Approaches for ADE Detection -- An Approach for Swedish Clinical Text -- An Approach for Spanish Clinical Text -- A Joint Approach for Spanish and Swedish Clinical Text -- 10.3 Suicide Prevention by Mining Electronic Patient Records -- 10.4 Mining Pathology Reports for Diagnostic Tests -- 10.4.1 The Case of the Cancer Registry of Norway -- 10.4.2 The Medical Text Extraction (Medtex) System -- 10.5 Mining for Cancer Symptoms -- 10.6 Text Summarisation and Translation of Patient Record -- 10.6.1 Summarising the Patient Record -- 10.6.2 Other Approaches in Summarising the Patient Record -- 10.6.3 Summarising Medical Scientific Text -- 10.6.4 Simplification of the Patient Record for Laypeople -- 10.7 ICD-10 Diagnosis Code Assignment and Validation -- 10.7.1 Natural Language Generation from SNOMED CT -- 10.8 Search Cohort Selection and Similar Patient Cases -- 10.8.1 Comorbidities -- 10.8.2 Information Retrieval from Electronic Patient Records -- 10.8.3 Search Engine Solr -- 10.8.4 Supporting the Clinician in an Emergency Department with the Radiology Report -- 10.8.5 Incident Reporting -- 10.8.6 Hypothesis Generation -- 10.8.7 Practical Use of SNOMED CT -- 10.8.8 ICD-10 and SNOMED CT Code Mapping -- 10.8.9 Analysing the Patient's Speech -- 10.8.10 MYCIN and Clinical Decision Support -- 10.8.11 IBM Watson Health -- 10.9 Summary of Applications of Clinical Text Mining -- 11 Networks and Shared Tasks in Clinical Text Mining -- 11.1 Conferences, Workshops and Journals -- 11.2 Summary of Networks and Shared Tasks in Clinical Text Mining -- 12 Conclusions and Outlook -- 12.1 Outcomes -- References -- Index.

Weitere Ausg.: Print version: Dalianis, Hercules Clinical Text Mining Cham : Springer International Publishing AG,c2018 ISBN 9783319785028

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Umfang: 1 online resource (226 pages)

Ausgabe: 1st ed.

ISBN: 9781040267660

Inhalt: This is a core resource for students and instructors of machine learning and data science looking for beginner-friendly material which offers real-world applications and takes ethical discussions into account.

Anmerkung: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- About the authors -- Preface -- Acknowledgments -- Glossary -- Chapter 1: Fundamentals of machine learning -- 1.1 What is machine learning? -- 1.2 A brief history of machine learning -- 1.3 Types of machine learning algorithms -- 1.3.1 Supervised learning -- 1.3.1.1 Types of supervised learning -- 1.3.2 Unsupervised learning -- 1.3.2.1 Types of unsupervised learning -- 1.3.2.1.1 Clustering -- 1.3.2.1.2 Association Rules -- 1.3.3 Semi-supervised learning -- 1.3.4 Reinforcement learning -- 1.4 Relationship between machine learning and other computer science disciplines -- 1.4.1 Machine learning and artificial intelligence -- 1.4.2 Machine learning and data science -- 1.4.3 Machine learning and traditional programming -- 1.4.4 Machine learning and deep learning -- 1.4.5 Machine learning and natural language processing -- 1.4.6 Machine learning and computer vision -- 1.4.7 Machine learning and generative AI -- 1.5 The importance of machine learning -- 1.6 When do we need machine learning? -- 1.7 Machine learning skills -- 1.7.1 Essential technical skills for machine learning professionals -- 1.7.2 Essential soft skills for machine learning professionals -- 1.8 What do machine learning professionals do? -- 1.9 Real-world applications of machine learning -- 1.10 Machine learning and ethical concerns -- 1.11 Summary -- Further Reading -- Chapter 2: Mathematics for machine learning -- 2.1 Linear algebra -- 2.1.1 Scalars -- 2.1.2 Vectors -- 2.1.2.1 Vector addition -- 2.1.2.2 Vector subtraction -- 2.1.2.3 Vector multiplication -- 2.1.3 Matrix -- 2.1.3.1 Matrix addition -- 2.1.3.2 Matrix subtraction -- 2.1.3.3 Matrix multiplication -- 2.1.3.4 Matrix transpose -- 2.1.3.5 Square and rectangular matrix -- 2.1.3.6 Triangular matrix -- 2.1.3.7 Diagonal matrix -- 2.1.3.8 Identity matrix. , 2.1.3.9 Matrix determinant -- 2.1.3.10 Adjugate of a matrix -- 2.1.3.11 Singular and non-singular matrix -- 2.1.3.12 Matrix inversion -- 2.1.3.13 Eigenvectors and eigenvalues -- 2.2 Statistics concepts -- 2.2.1 Use of statistics in machine learning -- 2.2.2 Types of statistics -- 2.2.2.1 Descriptive statistics -- 2.2.2.2 Inferential statistics -- 2.2.3 Types of data -- 2.2.3.1 Numerical data -- 2.2.3.2 Categorical data -- 2.2.4 Data distribution -- 2.2.4.1 Normal distribution in statistics -- 2.2.4.2 Skewness -- 2.2.4.3 Central limit theorem -- 2.2.5 Applied statistical inference -- 2.2.5.1 Linear regression -- 2.3 Probability theory -- 2.3.1 Sample spaces and events -- 2.3.2 Probability -- 2.3.3 Probability measures -- 2.3.4 Conditional probability -- 2.3.5 Bayes' theorem -- 2.3.6 Random variables -- 2.3.7 Expectation -- 2.3.8 Variance -- 2.3.9 Standard deviation -- 2.3.9.1 Cumulative distribution function -- 2.3.9.2 Probability mass function -- 2.3.9.3 Probability density function -- 2.3.9.4 Discrete distributions -- 2.3.9.5 Bernoulli distribution -- 2.3.9.6 Binomial distribution -- 2.3.9.7 Poisson distribution -- 2.3.9.8 Uniform distribution -- 2.3.9.9 Continuous distributions -- 2.3.9.10 Normal distribution (Gaussian distribution) -- 2.3.9.11 Uniform distribution -- 2.4 Calculus -- 2.4.1 Differentiation -- 2.4.2 Integration -- 2.4.3 Gradient -- 2.4.4 Linear function -- 2.4.5 Quadratic function -- 2.4.6 Sigmoid function -- 2.5 Geometry and trigonometry -- 2.5.1 Geometry in data representation -- 2.5.2 Trigonometric geometry in model optimization -- 2.6 Information theory -- 2.6.1 Entropy and information content -- 2.6.2 Mutual information and feature selection -- 2.6.3 Cross-entropy and model evaluation -- 2.7 Clustering -- 2.7.1 K-Means clustering algorithm -- 2.8 Summary -- Further Reading -- Chapter 3: Data preparation. , 3.1 Overview of machine learning process -- 3.2 Business problem identification -- 3.3 Success criteria definition -- 3.4 Data collection -- 3.4.1 Nature of data -- 3.4.2 Data sources -- 3.4.3 Data curation -- 3.4.4 Data labeling -- 3.4.5 Ethical considerations in data collection -- 3.5 Data preprocessing -- 3.5.1 Data cleaning -- 3.5.1.1 Removing duplicate or irrelevant values -- 3.5.1.2 Fixing structural errors -- 3.5.1.3 Detecting and removing outliers -- 3.5.1.4 Handling missing values -- 3.5.1.5 Validation -- 3.5.2 Data Transformation -- 3.5.2.1 Binning -- 3.5.2.2 Encoding -- 3.5.2.3 Data normalization -- 3.5.2.4 Standardization -- 3.5.3 Exploratory data analysis -- 3.5.3.1 Data summarization -- 3.5.3.2 Data visualization -- 3.5.4 Types of exploratory data analysis -- 3.5.4.1 Univariate -- 3.5.4.2 Bivariate -- 3.5.5 Multivariate -- 3.5.6 Dimensionality reduction -- 3.5.6.1 Feature selection -- 3.5.6.1.1 Backward feature elimination -- 3.5.6.1.2 Forward feature selection -- 3.5.6.2 Feature extraction -- 3.5.7 Data balancing -- 3.6 Summary -- Further Reading -- Chapter 4: Machine learning operations -- 4.1 Model development -- 4.1.1 Dataset splitting -- 4.1.1.1 Hold-out -- 4.1.1.2 Cross-validation -- 4.1.2 Choosing an algorithm -- 4.1.2.1 Problem understanding -- 4.1.2.2 Algorithm capabilities -- 4.1.2.3 Computational resources -- 4.1.3 Model training -- 4.1.4 Model evaluation -- 4.1.5 Overfitting and underfitting -- 4.1.6 Model optimization -- 4.1.6.1 Exhaustive search -- 4.1.6.2 Gradient descent -- 4.1.6.3 Stochastic gradient descent -- 4.1.6.4 Evolutionary optimization algorithms -- 4.2 Model deployment -- 4.3 Model monitoring -- 4.4 Ethical considerations in machine learning operations (MLOps) -- 4.5 Summary -- Further Reading -- Chapter 5: Machine learning software and hardware requirements -- 5.1 Programming languages. , 5.1.1 Python programming language -- 5.1.1.1 Python code editors and IDEs -- 5.1.1.2 Python libraries -- 5.1.2 R programming language -- 5.1.2.1 R programming code editors and IDEs -- 5.1.2.2 R programming libraries -- 5.1.3 MATLAB -- 5.1.3.1 MATLAB code editors and IDEs -- 5.1.3.2 MATLAB libraries -- 5.1.4 Other programming languages -- 5.1.4.1 Java programming -- 5.1.5 Java programming code editors and IDEs -- 5.1.6 Java ML libraries -- 5.1.6.1 C++ programming -- 5.1.7 C++ programming code editors and IDEs -- 5.1.8 C++ programming libraries -- 5.1.9 Criteria for choosing programming language for machine learning -- 5.1.9.1 Library and framework support -- 5.1.9.2 Robust and extensive community support -- 5.1.9.3 Ease of learning and use -- 5.1.9.4 Flexibility, scalability, and efficiency -- 5.1.9.5 Integration with other tools and software -- 5.1.9.6 Industry adoption -- 5.2 No-code tools -- 5.3 Experiment tracking tools -- 5.4 Pre-trained models repositories -- 5.5 Datasets and model tracking tools -- 5.6 AutoML hyperparameter optimization tools -- 5.7 Machine learning life cycle tools -- 5.8 User interface development tools -- 5.9 Explainable AI tools -- 5.10 Version control systems -- 5.11 Machine learning hardware requirements -- 5.12 Operating systems requirements -- 5.13 Processor and memory requirements -- 5.13.1 CPU -- 5.13.2 GPU -- 5.13.3 TPU -- 5.13.4 RAM -- 5.13.5 Storage -- 5.14 Cloud computing services for machine learning -- 5.15 Summary -- Further Reading -- Chapter 6: Responsible AI and explainable AI -- 6.1 Responsible AI -- 6.2 Explainable AI -- 6.3 Privacy concerns in machine learning -- 6.4 Ethical implications of machine learning -- 6.5 Accountability and trust in AI -- 6.6 Global case studies on AI governance and regulation -- 6.6.1 Formulation of AI strategies and guidelines in Africa -- 6.6.2 European Union AI Act. , 6.6.3 Global partnership on AI -- 6.6.4 China AI ethics guidelines -- 6.7 Human-centric artificial intelligence -- 6.8 Responsible AI best practices -- 6.9 AI impact assessment case studies -- 6.10 Artificial intelligence sovereignty -- 6.11 Summary -- Further Reading -- Chapter 7: Artificial general intelligence -- 7.1 Categories of artificial intelligence -- 7.2 What makes an intelligence general? -- 7.3 Approaches for developing AGI -- 7.4 Philosophy of mind -- 7.5 Challenges of artificial general intelligence -- 7.6 Potential benefits and risks of artificial general intelligence -- 7.7 Indicators of the presence of artificial general intelligence -- 7.8 Robotics and embodied intelligence -- 7.9 Artificial super intelligence -- 7.10 Summary -- Further Reading -- Chapter 8: Machine learning step-by-step practical examples -- 8.1 Case study 1: Classification problem -- 8.1.1 Problem definition -- 8.1.1.1 Description of the dataset -- 8.1.2 Loading libraries -- 8.1.3 Loading dataset -- 8.1.4 Data summary -- 8.1.4.1 Descriptive statistics -- 8.1.4.2 Data visualization -- 8.1.5 Data preprocessing -- 8.1.5.1 Data cleaning -- 8.1.5.1.1 Outliers -- 8.1.5.1.2 Missing values -- 8.1.5.2 Data standardization -- 8.1.6 Split-out the dataset -- 8.1.7 Choosing classification algorithm -- 8.1.8 Training the model -- 8.1.8.1 Model evaluation -- 8.1.8.2 Saving the model -- 8.1.8.3 Inferencing -- 8.2 Case study 2: Regression problem -- 8.2.1 Problem definition -- 8.2.1.1 Description of the dataset -- 8.2.2 Loading libraries -- 8.2.3 Loading dataset -- 8.2.4 Data summary -- 8.2.4.1 Descriptive statistics -- 8.2.4.2 Data visualization -- 8.2.5 Data preprocessing -- 8.2.5.1 Data cleaning -- 8.2.5.1.1 Outliers -- 8.2.5.1.2 Missing values -- 8.2.5.2 Feature selection -- 8.2.5.3 Data transformation -- 8.2.6 Choosing regression algorithm -- 8.2.7 Training the model. , 8.2.7.1 Model equation.

Weitere Ausg.: Print version: Nyamawe, Ally S. Practical Machine Learning Boca Raton : CRC Press LLC,c2025 ISBN 9781032770291

Sprache: Englisch

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ISBN: 9781108997461 , 1108997465 , 9781108997669 , 110899766X , 9781108990424 , 1108990428

Serie: Methodological tools in the social sciences

Inhalt: Much training in quantitative social science focuses on data analysis and fails to equip researchers with the skills to prepare the data required for this. This book is a comprehensive introduction to simple and advanced tools for data management, drawing on established concepts and techniques from computer science.

Anmerkung: Also issued in print: 2023. , Cover -- Half-title -- Series information -- Title page -- Copyright information -- Dedication -- Contents -- Preface -- Part I Introduction -- 1 Motivation -- 1.1 Data Processing and the Research Cycle -- 1.2 What We Do (and Don't Do) in this Book -- 1.3 Why Focus on Data Processing? -- 1.4 Data in Files vs. Data in Databases -- 1.5 Target Audience, Requirements and Software -- 1.6 Plan of the Book -- 2 Gearing Up -- 2.1 R and RStudio -- 2.2 Setting Up the Project Environment for Your Work -- 2.3 The PostgreSQL Database System -- 2.4 Summary and Outlook -- 3 Data = Content + Structure -- 3.1 What Is Data? -- 3.2 Data Content and Structure -- 3.3 Tables, Tables, Tables -- 3.4 The Structure of Tables Matters -- 3.5 Summary and Outlook -- Part II Data in Files -- 4 Storing Data in Files -- 4.1 Text and Binary Files -- 4.2 File Formats for Tabular Data -- 4.3 Transparent and Efficient Use of Files -- 4.4 Summary and Outlook -- 5 Managing Data in Spreadsheets -- 5.1 Application: Spatial Inequality -- 5.2 Spreadsheet Tables and (the Lack of) Structure -- 5.3 Retrieving Data from a Table -- 5.4 Changing Table Structure and Content -- 5.5 Aggregating Data from a Table -- 5.6 Exporting Spreadsheet Data -- 5.7 Results: Spatial Inequality -- 5.8 Summary and Outlook -- 6 Basic Data Management in R -- 6.1 Application: Inequality and Economic Performance in the US -- 6.2 Loading the Data -- 6.3 Merging Tables -- 6.4 Aggregating Data from a Table -- 6.5 Results: Inequality and Economic Performance in the US -- 6.6 Summary and Outlook -- 7 R and the tidyverse -- 7.1 Application: Global Patterns of Inequality across Regime Types -- 7.2 A New Operator: The Pipe -- 7.3 Loading the Data -- 7.4 Merging the WID and Polity IV Datasets -- 7.5 Grouping and Aggregation -- 7.6 Results: Global Patterns of Inequality across Regime Types. , 7.7 Other Useful Functions in the tidyverse -- 7.8 Summary and Outlook -- Part III Data in Databases -- 8 Introduction to Relational Databases -- 8.1 Database Servers and Clients -- 8.2 SQL Basics -- 8.3 Application: Electoral Disproportionality by Country -- 8.4 Creating a Table with National Elections -- 8.5 Computing Electoral Disproportionality -- 8.6 Results: Electoral Disproportionality by Country -- 8.7 Summary and Outlook -- 9 Relational Databases and Multiple Tables -- 9.1 Application: The Rise of Populism in Europe -- 9.2 Adding the Tables -- 9.3 Joining the Tables -- 9.4 Merging Data from the PopuList -- 9.5 Maintaining Referential Integrity -- 9.6 Results: The Rise of Populism in Europe -- 9.7 Summary and Outlook -- 10 Database Fine-Tuning -- 10.1 Speeding Up Data Access with Indexes -- 10.2 Collaborative Data Management with Multiple Users -- 10.3 Summary and Outlook -- Part IV Special Types of Data -- 11 Spatial Data -- 11.1 What Is Spatial Data? -- 11.2 Application: Patterns of Violence in the Bosnian Civil War -- 11.3 Reading and Visualizing Spatial Data in R -- 11.4 Spatial Data in a Relational Database -- 11.5 Results: Patterns of Violence in the Bosnian Civil War -- 11.6 Summary and Outlook -- 12 Text Data -- 12.1 What Is Textual Data? -- 12.2 Application: References to (In)equality in UN Speeches -- 12.3 Working with Strings in (Base) R -- 12.4 Natural Language Processing with quanteda -- 12.5 Using PostgreSQL to Manage Documents -- 12.6 Results: References to (In)equality in UN Speeches -- 12.7 Summary and Outlook -- 13 Network Data -- 13.1 What Is Network Data? -- 13.2 Application: Trade and Democracy -- 13.3 Exploring Network Data in R with igraph -- 13.4 Network Data in a Relational Database -- 13.5 Results: Trade and Democracy -- 13.6 Summary and Outlook -- Part V Conclusion -- 14 Best Practices in Data Management. , 14.1 Two General Recommendations -- 14.2 Collaborative Data Management -- 14.3 Disseminating Research Data and Code -- 14.4 Summary and Outlook -- Bibliography -- Index.

Weitere Ausg.: ISBN 9781108964784

Weitere Ausg.: ISBN 1108964788

Weitere Ausg.: ISBN 9781108845670

Weitere Ausg.: ISBN 1108845673

Sprache: Englisch

Fachgebiete: Allgemeines , Soziologie

DOI: 10.1017/9781108990424

URL: https://doi.org/10.1017/9781108990424

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

UID:

Umfang: XXIII, 631 p. 340 illus., 252 illus. in color. , online resource.

Ausgabe: 1st ed. 2024.

ISBN: 9783031479427

Serie: Signals and Communication Technology,

Inhalt: This book is a collection of best selected research papers presented at the 2nd Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication (MDCWC 2023) held during June 22-24th, 2023, at the National Institute of Technology Tiruchirappalli, India. The presented papers are grouped under the following topics (a) Machine learning, Deep learning and Computational intelligence algorithms (b) Wireless communication systems and (c) Mobile data applications. The topics include the latest research and results in the areas of network prediction, traffic classification, call detail record mining, mobile health care, mobile pattern recognition, natural language processing, automatic speech processing, mobility analysis, indoor localization, wireless sensor networks (WSN), energy minimization, routing, scheduling, resource allocation, multiple access, power control, malware detection, cyber security, flooding attacks detection, mobile apps sniffing, MIMO detection, signal detection in MIMO-OFDM, modulation recognition, channel estimation, MIMO nonlinear equalization, super-resolution channel and direction-of-arrival estimation. The book is a rich reference material for academia and industry. Presents research works in various fields of computational intelligence and machine learning; Discusses results of MDCWC 2023 held at National Institute of Technology Tiruchirappalli, India; Serves as a reference for researchers and practitioners in academia and industry.

Anmerkung: Part 1: Machine Learning, Deep Learning and Computational Intelligence -- Chapter 1. A novel hierarchical clustering technique to analyse style and content factorization during image recognition -- Chapter 2. Exploring Model-Level Transfer Learning to Improve the Recognition of Sinhala Speech -- Chapter 3. Metaheuristics Feature Selection Algorithms for Identification and Classification of Mango Pests Diseases -- Chapter 4. An Integrated Optimization Technique with SVM for Feature Selection -- Chapter 5. A Constant Binomial Coefficient Difference Equation Based FIR Predictor System for Signal Processing Data mining -- Chapter 6. Generating Synthetic Text Data for Improving Class Balance in Personality Prediction -- Chapter 7. CPSNet: Channel Pixel Spatial Feature Fusion Network for Image Dehazing -- Chapter 8. ATCBBC: A novel optimizer for neural network Architectures -- Chapter 9. Application of Dempster-Shafer Theory in Sensor Data Fusion -- Chapter 10.Examining Ant Colony Algorithm with The Cat Swarm Algorithm to Improve Energy Efficiency -- Chapter 11. Machine Learning based Online Visual Tracking with Multi-featured Adaptive Kernal Correlation filter -- Part 2: Wireless Communication -- Chapter 12. Integrating long-short term memory and particle swarm optimization for intrusion detection in 5G technologies -- Chapter 13. Performance Analysis of Polar Decoding Using Linear Code Mapping with Feedback Based Ann Architecture -- Chapter 14. Packet Classification Using Improved Random Forest Algorithm -- Chapter 15. A Machine Learning Perspective of optimal data transmission in Wireless Sensor Networks (WSN) -- Chapter 16. Blockchain and IPFS based solution for KYC -- Chapter 17. Analysis and Comparison of BER Performance of OFDM-IM under various fading channels using Conventional Detectors -- Chapter 18. A Survey of Non-Orthogonal Multiple Access for Internet of Things and Future Wireless Networks -- Chapter 19. Improved 3D Wireless Indoor Localization with Deep Learning Algorithms -- Chapter 20. Implementing Machine Learning for design and evaluation of Antenna Parameters -- Chapter 21. A MIMO dual mode homogenous OFDM IM system for next-generation communications -- Chapter 22. Probability Boosted Regression for Intrusion Detection in Cyber active Space -- Part 3: Mobile data application -- Chapter 23. Surface Water-body Extraction for Landsat-8 (OLI) Imagery using Water-Indices Methods and SCM Techniques -- Chapter 24. Automated Age-related Macular Degeneration Diagnosis in Retinal Fundus Images via ViT -- Chapter 25. Traffic Light Simulation Using ALP -- Chapter 26. DNN-ILD: A Transfer Learning-based Deep Neural Network for Automated Classification of Interstitial Lung Disease from CT Images -- Chapter 27. Image Manipulation Detection using Augmentation and Convolutional Neural Networks -- Chapter 28. Smart Women Safety Solution with Alert System Using Machine Learning Approach -- Chapter 29. Cyberbullying Detection Using BILSTM Model -- Chapter 30. Smart Agriculture using Internet of Things -- Chapter 31. Breast Cancer Detection from Mammograms using Deep Learning and Bio-Inspired Optimization Algorithm -- Chapter 32. Detection of Echinoderms Underwater using Deep Learning Network -- Chapter 33. Malaria Parasite Detection Using Deep Learning -- Chapter 34. FiltDeepNet: Architecture for Covid Detection based on Chest X-ray Images -- Chapter 35. Novel approaches to Automatic License Plate Reading (ALPR) -- Chapter 36. Crop Prediction Expert System with Ensemble Machine Learning Technique -- Chapter 37. A Comparative Analysis of Single Image-Based Face Morphing Attack Detection -- Chapter 38. Classifiers For Sentiment Analysis of Youtube Comments - A Comparative Study -- Chapter 39. Traffic Monitoring System Detecting Overspeed and Accidents from Video Input using OpenCV and Digital Image Processing -- Chapter 40. An experimental comparative analysis of human abnormal action identification on "SAIAZ" video dataset using SVM, ResNet50, and LSTM model -- Chapter 41. Comparative study on malicious URL using classifiers and boosting algorithms -- Chapter 42. Automation in Natural Rubber Latex Harvesting Field: A Review -- Chapter 43. Feature Selection based Machine Learning Model for Malware Detection -- Chapter 44. Application of Machine learning algorithm for prediction of Diabetes and Heart diseases -- Chapter 45. Damage Detection on Historical Structure using Image Processing -- Chapter 46. Eye Disease Classification Using VGG-19 Architecture -- Chapter 47. Automatic Photo Enhancer Using Machine Learning and Deep Learning with Python -- Chapter 48. Robust Touch Free Palm Vein Sensor Authentication System -- Chapter 49. An Effective Machine Learning-based Malware Detection Approach -- Chapter 50. Exploring The Performance of Various Classification Algorithms for Masked Face Recognition Using Cnn Based Features -- Chapter 51. Chess Game using Assembly Language Programming -- Chapter 52. Deep Artificial neural Network for Software Effort Estimation -- Chapter 53. 'Legal Owl', An Application for Machine Generated Legal Aid Using NLP.

In: Springer Nature eBook

Weitere Ausg.: Printed edition: ISBN 9783031479410

Weitere Ausg.: Printed edition: ISBN 9783031479434

Weitere Ausg.: Printed edition: ISBN 9783031479441

Sprache: Englisch

DOI: 10.1007/978-3-031-47942-7

URL: https://doi.org/10.1007/978-3-031-47942-7

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Umfang: 1 online resource (604 p.)

ISBN: 1-281-78948-8 , 9786611789480 , 0-08-086737-5

Serie: Advances in psychology ; 83

Inhalt: This collection of 33 papers represents the most current thinking andresearch on the study of cognitive processing in bilingual individuals. Thecontributors include well-known figures in the field and promising newscholars, representing four continents and work in dozens of languages.Instead of the social, political, or educational implications ofbilingualism, the focus is on how bilingual people (mostly adults) thinkand process language.

Anmerkung: Description based upon print version of record. , Front Cover; Cognitive Processing in Bilinguals; Copyright Page; Preface; Table of Contents; PART I: WHO ARE THE BILINGUALS?; Chapter 1. Bilingualism: Not the Exception Any More; Chapter 2. History of Bilingualism Research in Cognitive Psychology; Chapter 3. Another View of Bilingualism; Chapter 4.The Role of Language Background in Cognitive Processing; PART II: BILINGUAL MEMORY; Chapter 5. Bilingual Memory Revisited; Chapter 6. Cognitive Psychology and Second-language Processing: The Role of Short-term Memory; Chapter 7. Working Memory Capacity as a Constraint on L2 Development , Chapter 8. Linguistic Relativity Revisited: The Bilingual Word-length Effect in Working Memory during Counting, Remembering Numbers, and Mental CalculationChapter 9. The Representation of Translation Equivalents in Bilingual Memory; Chapter 10. The Influence of Semantic Cues in Learning among Bilinguals at Different Levels of Proficiency in English; Chapter 11. Lexical and Conceptual Memory in Fluent and Nonfluent Bilinguals; PART III: LEXICAL ACCESS AND WORD RECOGNITION IN BILINGUALS; Chapter 12. On the Representation and Use of Language Information in Bilinguals , Chapter 13. Orthographic and Lexical Constraints in Bilingual Word RecognitionChapter 14. Phonological Processing in Bilingual Word Recognition; Chapter 15. Lexical Processing in Bilingual or Multilingual Speakers; Chapter 16. Language as a Factor in the Identification of Ordinary Words and Number Words; Chapter 17. Word Recognition in Second-language Reading; Chapter 18. A Functional View of Bilingual Lexicosemantic Organization; PART IV: THE ROLE OF SYNTAX IN BILINGUAL COGNITIVE PROCESSING; Chapter 19. Changes in Sentence Processing as Second Language Proficiency Increases , Chapter 20. On-line Integration of Grammatical Information in a Second LanguageChapter 21. Non-native Features of Near-native Speakers: On the Ultimate Attainment of Childhood L2 Learners; PART V: LANGUAGE TRANSFER AND CODE-SWITCHING IN BILINGUALS; Chapter 22. Competition and Transfer in Second Language Learning; Chapter 23. An Overview of Cross-language Transfer in Bilingual Reading; Chapter 24. Auditory and Visual Speech Perception in Alphabetic and Nonalphabetic Chinese-Dutch Bilinguals , Chapter 25. A Study of Interlingual and Intralingual Stroop Effect in Three Different Scripts: Logograph, Syllabary, and AlphabetChapter 26. Code-switching and Language Dominance; Chapter 27. Cultural Influences of a Reading Text on the Concept Formation of Second-language Learners of Two Nigerian Ethnic Groups; PART VI: METALINGUISTIC SKILLS IN BILINGUALS; Chapter 28. Language Awareness and Language Separation in the Young Bilingual Child; Chapter 29. Selective Attention in Cognitive Processing: The Bilingual Edge; Chapter 30. Translation Ability: A Natural Bilingual and Metalinguistic Skill , Chapter 31. Metalinguistic Awareness in Second- and Third-language Learning , English

Weitere Ausg.: ISBN 0-444-88922-1

Sprache: Englisch

Fachgebiete: Komparatistik. Außereuropäische Sprachen/Literaturen , Psychologie

Schlagwort(e): Aufsatzsammlung

URL: Volltext

UID:

Umfang: 1 online resource (XIV, 203 p. 162 illus., 75 illus. in color.)

Ausgabe: 1st ed. 2025.

ISBN: 3-031-89810-9

Serie: Communications in Computer and Information Science, 2443

Inhalt: This book constitutes the refereed proceedings of the 18th International Conference on Automatic Processing of Natural-Language Electronic Texts with NooJ, NooJ 2024, held in Bergamo, Italy, during June 5-7, 2024. The 16 full papers included in this book were carefully reviewed and selected from 64 submissions. These papers have been organized under the following topical sections: Lexical and Morphological Resources; Syntactic Resources; Corpus Linguistics and Discourse Analysis; Natural Language Processing Applications.

Anmerkung: -- Lexical and Morphological Resources. -- Formalizing Abstract Nouns with “-pen” in Rromani. -- Recognizing Verbs in Medieval Latin. -- Formalizing Persian Verbs Conjugation. -- Exposing Diminutive and Pejorative Verbs in Croatian. -- Syntactic Resources. -- Local Syntactic Grammars for Quechua Negation and Negative Sentences. -- Conditional Clauses versus Concessive Clauses in Spanish: An Automatic Treatment with the NooJ Platform. -- Automatic Grammatical Disambiguation in Belarusian and Russian Legal Domain. -- Corpus Linguistics and Discourse Analysis. -- Corpus-Based Exploration of Defense-Related Political Speeches. -- A Collocation Analysis of Lemma 〈lahir〉 in the Indonesian Translation of the Holy Quran. -- Using NooJ in Action Research: the Case of Personal Expression in Professional Situations. -- How (Not) to Talk About Femicide and Gender-based Violence: An Analysis of Narrative Strategies Adopted by Italian Newspapers. -- Syntactic Analysis of Media Reporting on Femicides and Gender-Based Violence in Modern Italy. -- Exploring Metanarrative Cues in Literary Texts with NooJ: the Case of Les Amours de Psyché et De Cupidon by Jean de La Fontaine. -- Natural Language Processing Applications. -- From the social network data to the document-oriented NoSQL Data warehouse using the NooJ platform. -- Annotation of Qualitative Linguistic Features of Text Readability in Institutional Italian Texts. -- Automatic Processing of Free Word Combinations Containing Quantitative Expressions in NooJ.

Weitere Ausg.: ISBN 3-031-89809-5

Sprache: Englisch

DOI: 10.1007/978-3-031-89810-5

URL: Volltext  (URL des Erstveröffentlichers)