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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

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Umfang: 1 online resource (107 pages)

Ausgabe: 1st ed.

ISBN: 9783030138455

Serie: Advances in Experimental Medicine and Biology Series ; v.1137

Anmerkung: Intro -- Preface -- Acknowledgments -- Contents -- Acronyms -- 1 Introduction -- Biomedical Data Repositories -- Scientific Text -- Amount of Text -- Ambiguity and Contextualization -- Biomedical Ontologies -- Programming Skills -- Why This Book? -- Third-Party Solutions -- Simple Pipelines -- How This Book Helps Health and Life Specialists? -- Shell Scripting -- Text Files -- Relational Databases -- What Is in the Book? -- Command Line Tools -- Pipelines -- Regular Expressions -- Semantics -- 2 Resources -- Biomedical Text -- What? -- Where? -- How? -- Semantics -- What? -- Languages -- Formality -- Gold Related Documents -- Where? -- OBO Ontologies -- Popular Controlled Vocabularies -- How? -- OWL -- URI -- Further Reading -- 3 Data Retrieval -- Caffeine Example -- Unix Shell -- Current Directory -- Windows Directories -- Change Directory -- Useful Key Combinations -- Shell Version -- Data File -- File Contents -- Reverse File Contents -- My First Script -- Line Breaks -- Redirection Operator -- Installing Tools -- Permissions -- Debug -- Save Output -- Web Identifiers -- Single and Double Quotes -- Comments -- Data Retrieval -- Standard Error Output -- Data Extraction -- Single and Multiple Patterns -- Data Elements Selection -- Task Repetition -- Assembly Line -- File Header -- Variable -- XML Processing -- Human Proteins -- PubMed Identifiers -- PubMed Identifiers Extraction -- Duplicate Removal -- Complex Elements -- XPath -- Namespace Problems -- Only Local Names -- Queries -- Extracting XPath Results -- Text Retrieval -- Publication URL -- Title and Abstract -- Disease Recognition -- Further Reading -- 4 Text Processing -- Pattern Matching -- Case Insensitive Matching -- Number of Matches -- Invert Match -- File Differences -- Evaluation Metrics -- Word Matching -- Regular Expressions -- Extended Syntax -- Alternation -- Basic Syntax. , Scope -- Multiple Alternatives -- Multiple Characters -- Spaces -- Groups -- Ranges -- Negation -- Quantifiers -- Optional -- Multiple and Optional -- Multiple and Compulsory -- All Options -- Position -- Beginning -- Ending -- Near the End -- Word in Between -- Full Line -- Match Position -- Tokenization -- Character Delimiters -- Wrong Tokens -- String Replacement -- Multi-character Delimiters -- Keep Delimiters -- Sentences File -- Entity Recognition -- Select the Sentence -- Pattern File -- Relation Extraction -- Multiple Filters -- Relation Type -- Remove Relation Types -- Further Reading -- 5 Semantic Processing -- Classes -- OWL Files -- Class Label -- Class Definition -- Related Classes -- URIs and Labels -- URI of a Label -- Label of a URI -- Synonyms -- URI of Synonyms -- Parent Classes -- Labels of Parents -- Related Classes -- Labels of Related Classes -- Ancestors -- Grandparents -- Root Class -- Recursion -- Iteration -- My Lexicon -- Ancestors Labels -- Merging Labels -- Ancestors Matched -- Generic Lexicon -- All Labels -- Problematic Entries -- Special Characters Frequency -- Completeness -- Removing Special Characters -- Removing Extra Terms -- Removing Extra Spaces -- Disease Recognition -- Performance -- Inverted Recognition -- Case Insensitive -- ASCII Encoding -- Correct Matches -- Incorrect Matches -- Entity Linking -- Modified Labels -- Ambiguity -- Surrounding Entities -- Semantic Similarity -- Measures -- DiShIn Installation -- Database File -- DiShIn Execution -- Large Lexicons -- MER Installation -- Lexicon Files -- MER Execution -- Further Reading -- Bibliography -- Index.

Weitere Ausg.: Print version: Couto, Francisco M. Data and Text Processing for Health and Life Sciences Cham : Springer International Publishing AG,c2019 ISBN 9783030138448

Sprache: Englisch

Fachgebiete: Informatik , Biologie

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Umfang: 1 online resource (358 pages)

ISBN: 9783319939353

Serie: The Information Retrieval Ser. ; v.39

Anmerkung: Intro -- Preface -- Website -- Contents -- Acronyms -- Notation -- 1 Introduction -- 1.1 What Is an Entity? -- 1.1.1 Named Entities vs. Concepts -- 1.1.2 Properties of Entities -- 1.1.3 Representing Properties of Entities -- 1.2 A Brief Historical Outlook -- 1.2.1 Information Retrieval -- 1.2.2 Databases -- 1.2.3 Natural Language Processing -- 1.2.4 Semantic Web -- 1.3 Entity-Oriented Search -- 1.3.1 A Bird's-Eye View -- 1.3.1.1 Users and Information Needs -- 1.3.1.2 Search Engine -- 1.3.1.3 Data -- 1.3.2 Tasks and Challenges -- 1.3.2.1 Entities as the Unit of Retrieval -- 1.3.2.2 Entities for Knowledge Representation -- 1.3.2.3 Entities for an Enhanced User Experience -- 1.3.3 Entity-Oriented vs. Semantic Search -- 1.3.4 Application Areas -- 1.4 About the Book -- 1.4.1 Focus -- 1.4.2 Audience and Prerequisites -- 1.4.3 Organization -- 1.4.4 Terminology and Notation -- References -- 2 Meet the Data -- 2.1 The Web -- 2.1.1 Datasets and Resources -- 2.2 Wikipedia -- 2.2.1 The Anatomy of a Wikipedia Article -- 2.2.1.1 Title -- 2.2.1.2 Infobox -- 2.2.1.3 Introductory Text -- 2.2.2 Links -- 2.2.3 Special-Purpose Pages -- 2.2.3.1 Redirect Pages -- 2.2.3.2 Disambiguation Pages -- 2.2.4 Categories, Lists, and Navigation Templates -- 2.2.4.1 Categories -- 2.2.4.2 Lists -- 2.2.4.3 Navigation Templates -- 2.2.5 Resources -- 2.3 Knowledge Bases -- 2.3.1 A Knowledge Base Primer -- 2.3.1.1 Knowledge Bases vs. Ontologies -- 2.3.1.2 RDF -- 2.3.2 DBpedia -- 2.3.2.1 Ontology -- 2.3.2.2 Extraction -- 2.3.2.3 Datasets and Resources -- 2.3.3 YAGO -- 2.3.3.1 Taxonomy -- 2.3.3.2 Extensions -- 2.3.3.3 Resources -- 2.3.4 Freebase -- 2.3.5 Wikidata -- 2.3.6 The Web of Data -- 2.3.6.1 Datasets and Resources -- 2.3.7 Standards and Resources -- 2.4 Summary -- References -- Part I Entity Ranking -- 3 Term-Based Models for Entity Ranking -- 3.1 The Ad Hoc Entity Retrieval Task. , 3.2 Constructing Term-Based Entity Representations -- 3.2.1 Representations from Unstructured Document Corpora -- 3.2.1.1 Document-Level Annotations -- 3.2.1.2 Mention-Level Annotations -- 3.2.2 Representations from Semi-structured Documents -- 3.2.3 Representations from Structured Knowledge Bases -- 3.2.3.1 Predicate Folding -- 3.2.3.2 From Triples to Text -- 3.2.3.3 Multiple Knowledge Bases -- 3.3 Ranking Term-Based Entity Representations -- 3.3.1 Unstructured Retrieval Models -- 3.3.1.1 Language Models -- 3.3.1.2 BM25 -- 3.3.1.3 Sequential Dependence Models -- 3.3.2 Fielded Retrieval Models -- 3.3.2.1 Mixture of Language Models -- 3.3.2.2 Probabilistic Retrieval Model for Semi-Structured Data -- 3.3.2.3 BM25F -- 3.3.2.4 Fielded Sequential Dependence Models -- 3.3.3 Learning-to-Rank -- 3.3.3.1 Features -- 3.3.3.2 Learning Algorithms -- 3.3.3.3 Practical Considerations -- 3.4 Ranking Entities Without Direct Representations -- 3.5 Evaluation -- 3.5.1 Evaluation Measures -- 3.5.2 Test Collections -- 3.5.2.1 TREC Enterprise -- 3.5.2.2 INEX Entity Ranking -- 3.5.2.3 TREC Entity -- 3.5.2.4 Semantic Search Challenge -- 3.5.2.5 INEX Linked Data -- 3.5.2.6 Question Answering over Linked Data -- 3.5.2.7 The DBpedia-Entity Test Collection -- 3.6 Summary -- 3.7 Further Reading -- References -- 4 Semantically Enriched Models for Entity Ranking -- 4.1 Semantics Means Structure -- 4.2 Preserving Structure -- 4.2.1 Multi-Valued Predicates -- 4.2.1.1 Parameter Settings -- 4.2.2 References to Entities -- 4.3 Entity Types -- 4.3.1 Type Taxonomies and Challenges -- 4.3.2 Type-Aware Entity Ranking -- 4.3.3 Estimating Type-Based Similarity -- 4.4 Entity Relationships -- 4.4.1 Ad Hoc Entity Retrieval -- 4.4.2 List Search -- 4.4.3 Related Entity Finding -- 4.4.3.1 Candidate Selection -- 4.4.3.2 Type Filtering -- 4.4.3.3 Entity Relevance -- 4.5 Similar Entity Search. , 4.5.1 Pairwise Entity Similarity -- 4.5.1.1 Term-Based Similarity -- 4.5.1.2 Corpus-Based Similarity -- 4.5.1.3 Distributional Similarity -- 4.5.1.4 Graph-Based Similarity -- 4.5.1.5 Property-Specific Similarity -- 4.5.2 Collective Entity Similarity -- 4.5.2.1 Structure-Based Method -- 4.5.2.2 Aspect-Based Method -- 4.6 Query-Independent Ranking -- 4.6.1 Popularity -- 4.6.2 Centrality -- 4.6.2.1 PageRank -- 4.6.2.2 PageRank for Entities -- 4.6.2.3 A Two-Layered Extension of PageRank for the Web of Data -- 4.6.3 Other Methods -- 4.7 Summary -- 4.8 Further Reading -- References -- Part II Bridging Text and Structure -- 5 Entity Linking -- 5.1 From Named Entity Recognition Toward Entity Linking -- 5.1.1 Named Entity Recognition -- 5.1.2 Named Entity Disambiguation -- 5.1.3 Entity Coreference Resolution -- 5.2 The Entity Linking Task -- 5.3 The Anatomy of an Entity Linking System -- 5.4 Mention Detection -- 5.4.1 Surface Form Dictionary Construction -- 5.4.2 Filtering Mentions -- 5.4.3 Overlapping Mentions -- 5.5 Candidate Selection -- 5.6 Disambiguation -- 5.6.1 Features -- 5.6.1.1 Prior Importance Features -- 5.6.1.2 Contextual Features -- 5.6.1.3 Entity-Relatedness Features -- 5.6.2 Approaches -- 5.6.2.1 Individual Local Disambiguation -- 5.6.2.2 Individual Global Disambiguation -- 5.6.2.3 Collective Disambiguation -- 5.6.3 Pruning -- 5.7 Entity Linking Systems -- 5.8 Evaluation -- 5.8.1 Evaluation Measures -- 5.8.2 Test Collections -- 5.8.2.1 Individual Researchers -- 5.8.2.2 INEX Link-the-Wiki -- 5.8.2.3 TAC Entity Linking -- 5.8.2.4 Entity Recognition and Disambiguation Challenge -- 5.8.3 Component-Based Evaluation -- 5.9 Resources -- 5.9.1 A Cross-Lingual Dictionary for English Wikipedia Concepts -- 5.9.2 Freebase Annotations of the ClueWeb Corpora -- 5.10 Summary -- 5.11 Further Reading -- References -- 6 Populating Knowledge Bases. , 6.1 Harvesting Knowledge from Text -- 6.1.1 Class-Instance Acquisition -- 6.1.1.1 Obtaining Instances of Semantic Classes -- 6.1.1.2 Obtaining Semantic Classes of Instances -- 6.1.2 Class-Attribute Acquisition -- 6.1.3 Relation Extraction -- 6.2 Entity-Centric Document Filtering -- 6.2.1 Overview -- 6.2.2 Mention Detection -- 6.2.3 Document Scoring -- 6.2.3.1 Mention-Based Scoring -- 6.2.3.2 Boolean Queries -- 6.2.3.3 Supervised Learning -- 6.2.4 Features -- 6.2.4.1 Document Features -- 6.2.4.2 Entity Features -- 6.2.4.3 Document-Entity Features -- 6.2.4.4 Temporal Features -- 6.2.5 Evaluation -- 6.2.5.1 Test Collections -- 6.2.5.2 Annotations -- 6.2.5.3 Evaluation Methodology -- 6.2.5.4 Evaluation Methodology Revisited -- 6.3 Slot Filling -- 6.3.1 Approaches -- 6.3.2 Evaluation -- 6.4 Summary -- 6.5 Further Reading -- References -- Part III Semantic Search -- 7 Understanding Information Needs -- 7.1 Semantic Query Analysis -- 7.1.1 Query Classification -- 7.1.1.1 Query Intent Classification -- 7.1.1.2 Query Topic Classification -- 7.1.2 Query Annotation -- 7.1.2.1 Query Segmentation -- 7.1.2.2 Query Tagging -- 7.1.3 Query Interpretation -- 7.2 Identifying Target Entity Types -- 7.2.1 Problem Definition -- 7.2.2 Unsupervised Approaches -- 7.2.2.1 Type-Centric Model -- 7.2.2.2 Entity-Centric Model -- 7.2.3 Supervised Approach -- 7.2.4 Evaluation -- 7.2.4.1 Evaluation Measures -- 7.2.4.2 Test Collections -- 7.3 Entity Linking in Queries -- 7.3.1 Entity Annotation Tasks -- 7.3.1.1 Named Entity Recognition -- 7.3.1.2 Semantic Linking -- 7.3.1.3 Interpretation Finding -- 7.3.2 Pipeline Architecture for Interpretation Finding -- 7.3.3 Candidate Entity Ranking -- 7.3.3.1 Unsupervised Approach -- 7.3.3.2 Supervised Approach -- 7.3.3.3 Gathering Additional Context -- 7.3.3.4 Evaluation and Test Collections -- 7.3.4 Producing Interpretations. , 7.3.4.1 Unsupervised Approach -- 7.3.4.2 Supervised Approach -- 7.3.4.3 Evaluation Measures -- 7.3.4.4 Test Collections -- 7.4 Query Templates -- 7.4.1 Concepts and Definitions -- 7.4.2 Template Discovery Methods -- 7.4.2.1 Classify& -- Match -- 7.4.2.2 QueST -- 7.5 Summary -- 7.6 Further Reading -- References -- 8 Leveraging Entities in Document Retrieval -- 8.1 Mapping Queries to Entities -- 8.2 Leveraging Entities for Query Expansion -- 8.2.1 Document-Based Query Expansion -- 8.2.2 Entity-Centric Query Expansion -- 8.2.3 Unsupervised Term Selection -- 8.2.4 Supervised Term Selection -- 8.2.4.1 Features -- 8.2.4.2 Training -- 8.3 Projection-Based Methods -- 8.3.1 Explicit Semantic Analysis -- 8.3.1.1 ESA Concept-Based Indexing -- 8.3.1.2 ESA Concept-Based Retrieval -- 8.3.2 Latent Entity Space Model -- 8.3.3 EsdRank -- 8.3.3.1 Features -- 8.3.3.2 Learning-to-Rank Model -- 8.4 Entity-Based Representations -- 8.4.1 Entity-Based Document Language Models -- 8.4.2 Bag-of-Entities Representation -- 8.4.2.1 Basic Ranking Models -- 8.4.2.2 Explicit Semantic Ranking -- 8.4.2.3 Word-Entity Duet Framework -- 8.4.2.4 Attention-Based Ranking Model -- 8.5 Practical Considerations -- 8.6 Resources and Test Collections -- 8.7 Summary -- 8.8 Further Reading -- References -- 9 Utilizing Entities for an Enhanced Search Experience -- 9.1 Query Assistance -- 9.1.1 Query Auto-completion -- 9.1.1.1 Leveraging Entity Types -- 9.1.2 Query Recommendations -- 9.1.2.1 Query-Flow Graph -- 9.1.2.2 Exploiting Entity Aspects -- 9.1.2.3 Entity Types -- 9.1.2.4 Entity Relationships -- 9.1.3 Query Building Interfaces -- 9.2 Entity Cards -- 9.2.1 The Anatomy of an Entity Card -- 9.2.2 Factual Entity Summaries -- 9.2.2.1 Fact Ranking -- 9.2.2.2 Summary Generation -- 9.3 Entity Recommendations -- 9.3.1 Recommendations Given an Entity -- 9.3.2 Personalized Recommendations. , 9.3.2.1 Entity-Based Method.

Weitere Ausg.: Print version: Balog, Krisztian Entity-Oriented Search Cham : Springer International Publishing AG,c2018 ISBN 9783319939339

Sprache: Englisch

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

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Umfang: 1 online resource (746 pages)

Ausgabe: 1st ed.

ISBN: 94-6463-196-1

Serie: Advances in Intelligent Systems Research Series ; v.176

Anmerkung: Intro -- Preface -- Organization -- Contents -- Peer-Review Statements -- 1 Review Procedure -- 2 Quality Criteria -- 3 Key Metrics -- An Improved Computer Aided System for Lung Cancer Detection using Image Processing Techniques -- 1 Introduction -- 2 Literature survey -- 3 Methodology -- 4 Result -- 5 Conclusion -- References -- Automated Detection of Tuberculosis Based on Cantilever Biosensor -- 1 Introduction -- 2 Bio-Mems Cantilever Sensor -- 3 Experimental Details -- 4 Result and Discussion -- References -- Diagnosing Microscopic Blood Samples for Early Detection of Leukemia by Deep and Hybrid Learning Techniques -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Description of Two Datasets -- 3.2 Pre-processing -- 3.3 Convolutional Neural Networks (CNN) -- 3.4 Hybrid of Deep and Machine Learning -- 4 Experimental Result -- 4.1 Splitting Dataset -- 4.2 Evaluation Metrics -- 4.3 CNN Models Results -- 4.4 Results of the Hybrid CNN with SVM Algorithm -- 5 Discussion -- 6 Conclusion -- References -- Lung Cancer Nodules Detection Using Ideal Features Extraction Technique in CT Images -- 1 Introduction -- 2 Related Work -- 3 Methods and materials -- 3.1 Dataset -- 3.2 Image Preprocessing -- 3.3 Segmentation -- 3.4 Feature Extraction -- 3.5 Classification Using Hybrid-CNN -- 4 Result and Discussion -- 5 Conclusion -- References -- Fuzzy Level Set Search and Rescue Optimization (FLSSR) Based Segmentation of Pediatric Brain Tumor -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Preprocessing -- 3.2 Fuzzy Level Set Search and Rescue Optimization (FLSSR) for Segmentation Process: -- 4 Results and Discussions -- 5 Performance Evaluation -- 6 Conclusion -- References -- Investigating EEG Images of Cognitive Actions for Robotic Arm -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Participants. , 3.2 Technical Analysis -- 3.3 Analyzing .edf Files via EEGLAB -- 3.4 Robotic Arm Overview -- 3.5 Active Region Identification -- 3.6 ERD and ERS for the Components in Frontal Region -- 4 Performance evaluation of the Robotic Arm -- 5 Result -- 6 Conclusion -- References -- Localization of Intervertebral Discs Using Deep-Learning and Region Growing Technique -- 1 Introduction -- 2 Review of the Literature -- 3 Proposed Methodology -- 3.1 Data -- 3.2 Pre-processing -- 3.3 Proposed Method -- 4 Results and Discussion -- 4.1 Evaluation Matrices -- 4.2 Effect of Hourglass Attention Mechanism -- 5 Conclusion -- References -- Identification of Skin Disease Using Machine Learning -- 1 Introduction -- 2 Related Works -- 3 Method and Techniques -- 3.1 Input Images -- 3.2 Image Preprocessing -- 3.3 Filtering Techniques -- 3.4 Gaussian Filter -- 3.5 Image Segmentation -- 3.6 Support Vector Machine (SVM) -- 3.7 K-nearest Neighbor (KNN) -- 3.8 Feature Extraction -- 3.9 Color Moments -- 3.10 Texture Feature Extraction -- 4 Performance Measures -- 5 Result and Discussion -- 6 Conclusion -- References -- Apple Classification Based on MRI Images Using VGG16 Convolutional Deep Learning Model -- 1 Introduction -- 2 Literature Survey -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 VGG Model -- 4 Results and Discussion -- 5 Conclusion and Future Work -- References -- Design a Novel Detection Using KNN Classification Technique for Early Sign of Diabetic Maculopathy -- 1 Introduction -- 2 Methodology -- 2.1 Preprocessing -- 2.2 RGB Channel -- 2.3 Histogram -- 2.4 Enhancement -- 2.5 KNN Classification -- 3 Experiment Result -- 4 Conclusion -- References -- Extraction of Bank Cheque Fields Based on Faster R-CNN -- 1 Introduction -- 2 Related Work and Overview -- 2.1 Related Work -- 2.2 Faster RCNN -- 3 Methodology -- 3.1 ConvNet Layers -- 3.2 Region Proposal Networks. , 3.3 Region of Interest Pooling Layer -- 3.4 Classification Layers -- 4 Experiment and Results -- 4.1 Dataset -- 4.2 Experiment Results -- 5 Conclusion -- References -- Multimodal Deep Learning Based Score Level Fusion Using Face and Fingerprint -- 1 Introduction -- 2 Literature Survey -- 3 About Database -- 4 Experimental Setup -- 5 Proposed Methodology -- 5.1 Pre-processing -- 5.2 CNN -- 5.3 VGG16 -- 5.4 Features Classification -- 5.5 Score Level Fusion -- 6 Performance Analysis -- 6.1 Classification and Confusion Matrix -- 7 Result and Discussion -- 8 Conclusion -- 9 Contributions -- References -- Enhanced Technique for Exemplar Based Image Inpainting Method -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Input Image -- 3.2 Perform Cropping -- 3.3 Perform Inpainting by Criminisi Method -- 3.4 Finding Parameters -- 3.5 Perform Tensor Inpainting -- 4 Experimental Results -- 5 Conclusion -- References -- An Optimal (2, 2) Visual Cryptography Schemes For Information Security -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Experimental Result -- 5 Discussion and Performance Analysis -- 5.1 Pixel Expansion -- 5.2 Contrast and Statistical Analysis -- 5.3 Mean Square Error -- 5.4 Peak-Signal-to-Noise-Ratio -- 5.5 Universal-Index-Quality (UIQ) -- 5.6 Maximum Difference (MD) -- 5.7 Average Difference (AD) -- 6 Conclusion -- References -- A Numeral Script Identification from a Multi-lingual Printed Document Image -- 1 Introduction -- 1.1 Motivation -- 2 Proposed Method -- 3 Experimental Results -- 4 Conclusion -- References -- A Novel Approach for Object Detection Using Optimized Convolutional Neural Network to Assist Visually Impaired People -- 1 Introduction -- 2 Related Work -- 3 Architectural Description of the Proposed Object Detection Model for Visually Impaired (ODMVI) -- 3.1 Preprocessing -- 3.2 Segmentation. , 3.3 Feature Extraction -- 4 Optimal Feature Selection -- 5 Object Detection Using CNN -- 5.1 Convolution Layer -- 5.2 Pooling Layer -- 5.3 Fully Connected Layer -- 6 Dataset -- 7 Results and Discussion -- 7.1 Simulation Procedure -- 7.2 Convergence Analysis -- 7.3 Performance Evaluation of ODMVI -- 8 Conclusion and Future Scope -- References -- A Machine Learning Based Approach for Image Quality Assessment of Forged Document Images -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Pre-processing -- 3.2 Feature Extraction -- 3.3 Classification -- 4 Implementation and Results -- 4.1 Dataset -- 5 Statistical Test of Significance -- 6 Conclusion -- References -- Comparative Study of Grid-Inverted List Hybrid Indexing Techniques for Moving Objects and Queries -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Grid-Inverted List Hybrid Index -- 3.2 KNN Query -- 3.3 Hybrid Index Implementation with YPK-CNN Technique -- 3.4 Hybrid Index Implementation with SEA-CNN Technique -- 3.5 Hybrid Index Implementation with CPM Technique -- 3.6 Differences Between YPK-CNN, SEA-CNN and CPM Techniques -- 3.7 Proposed Algorithms -- 4 Experimental work -- 5 Results and Discussion -- 6 Conclusion -- References -- Text-Independent Source Identification of Printed Documents using Texture Features and CNN Model -- 1 Introduction -- 2 Review of Related Studies -- 3 Proposed Method -- 3.1 Data Collection -- 3.2 Pre-processing -- 3.3 Feature Extraction -- 4 Experimental Results and Discussion -- 4.1 Performance of Textual Features -- 4.2 Deep Learning CNN Performance Measurement -- 4.3 Comparison Analysis -- 5 Conclusion -- References -- A Vision-Based Sign Language Recognition using Statistical and Spatio-Temporal Features -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Data Collection -- 3.2 Feature Extraction and Learning. , 4 Results and Discussion -- 4.1 Raw Data Pre-processing -- 4.2 Extraction and Analysis of Statistical and Spatio-Temporal Features -- 4.3 Classification of Statistical Features -- 4.4 Early Fusion of Statistical and Spatio-Temporal Features -- 4.5 Comparison of Results -- 4.6 Conclusion and Future Work -- References -- Single Image Dehazing Using Haze Veil Analysis and CLAHE -- 1 Introduction -- 2 Methodology -- 3 Haze Veil Calculation -- 3.1 Computing Reflectance Image -- 4 Experimental Results -- 5 Conclusion -- References -- HiTEK Multilingual Speech Identification Using Combinatorial Model -- 1 Introduction -- 2 Literature Review -- 3 Challenges -- 4 Methodology -- 4.1 Hidden Markov Model- Gaussian Mixture Model -- 4.2 Hidden Markov Model- Artificial Neural Networks -- 4.3 Hidden Markov Model- Deep Neural Networks -- 4.4 POS Tagging -- 4.5 Tokenization and Stemming -- 4.6 Morphological Analysis -- 4.7 Syntactical Analysis -- 4.8 Semantic Analysis -- 4.9 Word Discourse Knowledge -- 5 Experimental Setup and Results -- 6 Conclusion and Future Scope -- References -- Devanagari License Plate Detection, Classification and Recognition -- 1 Introduction -- 1.1 Devanagari (Nepalese) License Plate -- 2 Literature Review -- 3 Proposed Method -- 3.1 LP Detection and Classification -- 3.2 Character Segmentation and Recognition -- 4 Result and Discussion -- 4.1 DLP Dataset -- 4.2 Detection and Classification Results -- 4.3 Character Recognition Results -- 4.4 Real-Time Implementation Results -- 5 Conclusion -- References -- Pre-trained Convolutional Neural Networks for Gender Classification -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Keras Models -- 3.2 Custom CNN -- 4 Results and Discussion -- 5 Conclusion -- References -- AVAO Enabled Deep Learning Based Person Authentication Using Fingerprint -- 1 Introduction -- 2 Motivation. , 2.1 Literature Review.

Sprache: Englisch

Schlagwort(e): Electronic books.

URL: lizenzpflichtig

URL: Click to View

UID:

Umfang: XXIV, 548 p. 233 illus., 171 illus. in color. , online resource.

Ausgabe: 1st ed. 2023.

ISBN: 9789819907410

Serie: Lecture Notes on Data Engineering and Communications Technologies, 165

Inhalt: The book presents selected papers from International Conference on Data Science and Emerging Technologies (DaSET 2022), held online at UNITAR International University, Malaysia, during December 20-21, 2022. This book aims to present current research and applications of data science and emerging technologies. The deployment of data science and emerging technology contributes to the achievement of the Sustainable Development Goals for social inclusion, environmental sustainability, and economic prosperity. Data science and emerging technologies such as artificial intelligence and blockchain are useful for various domains such as marketing, health care, finance, banking, environmental, and agriculture. An important grand challenge in data science is to determine how developments in computational and social-behavioral sciences can be combined to improve well-being, emergency response, sustainability, and civic engagement in a well-informed, data-driven society. The topics of this book include, but not limited to: artificial intelligence, big data technology, machine and deep learning, data mining, optimization algorithms, blockchain, Internet of Things (IoT), cloud computing, computer vision, cybersecurity, augmented and virtual reality, cryptography, and statistical learning.

Anmerkung: Part I: Artificial Intelligence -- Extractive Text Summarization Using Syntactic Sub- Graph Models -- Analysis of Big Five Personality Factors to determine the Appropriate Type of Career using the C4.5 Algorithm -- Predicting Disaster Type from Social Media Imagery via Deep Neural Networks Directed by Visual Attention -- Dissemination Management for Official Statistics Using Artificial Intelligence-Based Media Monitoring -- Part II: Computational Vision -- A Naive but Effective Post-Processing Approach for Dark Channel Prior (DCP) -- COVID-19 Face Mask Classification Using Deep Learning -- Gender Classification Using Transfer Learning and Fine-Tuning -- Multi-Language Recognition Translator by Using the Convolutional Neural Network (CNN) Algorithm and Optical Character Recognition (OCR) -- Autonomous Driving Through Road Segmentation Based on Computer Vision Techniques -- Part III: Cybersecurity -- Phishing Attack Types and Mitigation: A Survey -- A Review of Privacy Protection Methods for Smart Homes Against Wireless Snooping Attack -- Development of Graph-Based Knowledge on Ransom-Ware Attacks Using Twitter Data -- Part IV: Big Data Analytics -- BigMDHealth: Supporting Multidimensional Big Data Management and Analytics over Big Healthcare Data via Effective and Efficient Multidimensional Aggregate Queries over Key-Value Stores (Prof Alredo's paper) -- Design and Implementation of Data Warehouse Solution at Kumpulan Wang Persaraan (KWAP) -- Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics -- Big Data Application on Prediction of HDD Manufacturing Process Performance -- Visualising Economic Situation Through Malaysia Economic Recovery Dashboard (MERD) -- Part V: Machine/Deep Learning -- Lung Nodules Classification Using Convolutional Neural Network with Transfer Learning -- Plant Growth Phase Classification Using Deep Neural Network -- The Implementation of Genetic Algorithm-Ensemble Learning on QSAR Study of Diacylglycerol Acyltransferase-1(DGAT1) Inhibitors as Anti-Diabetes -- Classification of Exercise Game Data for Rehabilitation Using Machine Learning Algorithms -- SDDLA: A New Architecture for Secured Decentralized Distributed Learning -- Gated Memory Unit: A Novel Recurrent Neural Network Architecture for Sequential Analysis -- Multi-Class Classification for Breast Cancer with High Dimensional Microarray Data Using Machine Learning Classifier -- Predicting Risks of Late Delivery to Online Shopping Customers Using Machine Learning Techniques -- Quora Insincere Questions Classification Using Attention Based Model -- Suicide Ideation Detection: A Comparative Study of Sequential and Transformer Hybrid Algorithms -- Well Log Data Preparation and Effective Utilization of Drilling Parameters Using Data Science Based Approaches -- Deep Learning-Based Approach for Classifying the Severity of Metal Corrosion Using SEM Images -- Insurance Risk Prediction Using Machine Learning -- Loan Default Forecasting Using StackNet -- Part VI: Statistical Learning -- Neural Network Autoregressive Model for Forecasting Malaysia Under-5 Mortality -- Robustness of Support Vector Regression and Random Forest Models: A Simulation Study -- The Impact of Restrictions Community Activities on COVID-19 Transmission: A Case Study in Sumatra Island, Indonesia -- Predicting Internet Usage for Digital Finance Services: Multitarget Classification Using Vector Generalized Additive Model with SMOTE-NC -- Part VII: Text Mining and Classification -- Identifying Topic Modeling Technique in Evaluating Textual Datasets -- Y-X-Y Encoding for Identifying Types of Sentence Similarity -- Evaluation of Extractive and Abstract Methods in Text Summarization.

In: Springer Nature eBook

Weitere Ausg.: Printed edition: ISBN 9789819907403

Weitere Ausg.: Printed edition: ISBN 9789819907427

Weitere Ausg.: Printed edition: ISBN 9789819907434

Sprache: Englisch

DOI: 10.1007/978-981-99-0741-0

URL: https://doi.org/10.1007/978-981-99-0741-0

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Umfang: 1 online resource (XV, 772 p. 1 illus.)

Ausgabe: 1st ed. 2020.

ISBN: 3-030-44914-9

Serie: Theoretical Computer Science and General Issues, 12075

Inhalt: This open access book constitutes the proceedings of the 29th European Symposium on Programming, ESOP 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The papers deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems. .

Anmerkung: Intro -- ETAPS Foreword -- Preface -- Organization -- Formal Methods for Evolving Database Applications (Abstract of Keynote Talk) -- Contents -- Trace-Relating Compiler Correctness and Secure Compilation -- Introduction -- Trace-Relating Compiler Correctness -- Property Mappings -- Trace Relations and Property Mappings -- Preservation of Subset-Closed Hyperproperties -- Instances of Trace-Relating Compiler Correctness -- Undefined Behavior -- Resource Exhaustion -- Different Source and Target Values -- Abstraction Mismatches -- Trace-Relating Compilation and Noninterference Preservation -- Trace-Relating Secure Compilation -- Trace-Relating Secure Compilation: A Spectrum of Trinities -- Instance of Trace-Relating Robust Preservation of Trace Properties -- Instances of Trace-Relating Robust Preservation of Safety and Hypersafety -- Related Work -- Conclusion and Future Work -- Acknowledgements -- Bibliography -- Runners in action -- 1 Introduction -- 2 Algebraic effects, handlers, and runners -- 2.1 Algebraic effects and handlers -- 2.2 Runners -- 3 Programming with runners -- 3.1 The user and kernel monads -- 3.2 Runners as a programming construct -- 4 A calculus for programming with runners -- 4.1 Types -- 4.2 Values and computations -- 4.3 Type system -- 4.4 Equational theory -- 5 Denotational semantics -- 5.1 Semantics of types -- 5.2 Semantics of values and computations -- 5.3 Coherence, soundness, and finalisation theorems -- 6 Runners in action -- 7 Implementation -- 8 Related work -- 9 Conclusion and future work -- References -- On the Versatility of Open Logical Relations -- 1 Introduction -- 2 The Playground -- 3 A Fundamental Gap -- 4 Warming Up: A Containment Theorem -- 5 Automatic Differentiation -- 6 On Refinement Types and Local Continuity -- 6.1 A Refinement Type System Ensuring Local Continuity -- 6.2 Basic Typing Rules. , 6.3 Typing Conditionals -- 6.4 Open-logical Predicates for Refinement Types -- 7 Related Work -- 8 Conclusion and Future Work -- References -- Constructive Game Logic -- 1 Introduction -- 2 Related Work -- 3 Syntax -- 3.1 Example Games -- 4 Semantics -- 4.1 Realizers -- 4.2 Formula and Game Semantics -- 4.3 Demonic Semantics -- 5 Proof Calculus -- 6 Theory: Soundness -- 7 Operational Semantics -- 8 Theory: Constructivity -- 9 Conclusion and Future Work -- References -- Optimal and Perfectly Parallel Algorithms for On-demand Data-flow Analysis -- 1 Introduction -- 2 Preliminaries -- 2.1 The IFDS Framework -- 2.2 Trees and Tree Decompositions -- 3 Problem definition -- 4 Treewidth-based Data-ow Analysis -- 4.1 Preprocessing -- 4.2 Word Tricks -- 4.3 Answering Queries -- 4.4 Parallelizability and Optimality -- 5 Experimental Results -- 6 Conclusion -- References -- Concise Read-Only Specifications for Better Synthesis of Programs with Pointers -- 1 Introduction -- 1.1 Correct Programs that Do Strange Things -- 1.2 Towards Simple Read-Only Specifications for Synthesis -- 1.3 Our Contributions -- 2 Program Synthesis with Read-Only Borrows -- 2.1 Basics of SSL-based Deductive Program Synthesis -- 2.2 Reducing Non-Determinism with Read-Only Annotations -- 2.3 Composing Read-Only Borrows -- 2.4 Borrow-Polymorphic Inductive Predicates -- 3 BoSSL: Borrowing Synthetic Separation Logic -- 3.1 BoSSL rules -- 3.2 Memory Model -- 3.3 Soundness -- 4 Implementation and Evaluation -- 4.1 Experimental Setup -- 4.2 Performance and Quality of the Borrowing-Aware Synthesis -- 4.3 Stronger Correctness Guarantees -- 4.4 Robustness under Synthesis Perturbations -- 5 Limitations and Discussion -- 6 Related Work -- 7 Conclusion -- References -- Soundness conditions for big-step semantics -- 1 Introduction -- 2 A meta-theory for big-step semantics -- 3 Extended semantics. , 3.1 Traces -- 3.2 Wrong -- 4 Expressing and proving soundness -- 4.1 Expressing soundness -- 4.2 Conditions ensuring soundness-must -- 4.3 Conditions ensuring soundness-may -- 5 Examples -- 5.1 Simply-typed -calculus with recursive types -- 5.2 MiniFJ& -- -λ -- 5.3 Intersection and union types -- 5.4 MiniFJ& -- O -- 6 The partial evaluation construction -- 7 Related work -- 8 Conclusion and future work -- Acknowledgments -- References -- Liberate Abstract Garbage Collection from the Stack by Decomposing the Heap -- 1 Introduction -- 1.1 Examples -- 1.2 Generalizing the Approach -- 2 A-Normal Form λ- Calculus -- 3 Background -- 3.1 Semantic Domains -- 3.2 Concrete Semantics -- 3.3 Abstracting Abstract Machines with Garbage Collection -- 3.4 Stack-Precise CFA with Garbage Collection -- 3.5 The k-CFA Context Abstraction -- 4 From Threaded to Compositional Stores -- 4.1 Threaded-Store Semantics -- 4.2 Threaded-Store Semantics with Effect Log -- 4.3 Compositional-Store Semantics -- 4.4 Compositional-Store Semantics with Garbage Collection -- 5 Abstract Compositional-Store Semantics with Garbage Collection -- 6 Discussion -- 6.1 The Effects of Treating the Store Compositionally -- 6.2 The Effect of Treating the Time Compositionally -- 7 Related Work -- 8 Conclusion and Future Work -- References -- SMT-Friendly Formalization of the Solidity Memory Model -- 1 Introduction -- 2 Background -- 2.1 Ethereum -- 2.2 Solidity -- 2.3 SMT-Based Programs -- 3 Formalization -- 3.1 Types -- 3.2 Local Storage Pointers -- 3.3 Contracts, State Variables, Functions -- 3.4 Statements -- 3.5 Assignments -- 3.6 Expressions -- 4 Evaluation -- 5 Related Work -- 6 Conclusion -- References -- Exploring Type-Level Bisimilarity towards More Expressive Multiparty Session Types -- 1 Introduction -- 2 Overview of our Approach -- 3 An MPST Theory with +, ∃, and ll. , 3.1 Types as Process Algebraic Terms -- 3.2 Global Types and Local Types -- 3.3 End-Point Projection: from Global Types to Local Types -- 3.4 Weak Bisimilarity of Global Types, Local Types, and Groups -- 3.5 Well-formedness of Global Types -- 3.6 Correctness of Projection under Well-Formedness -- 3.7 Decidability of Checking Well-Formedness -- 3.8 Discussion of Challenges -- 4 Practical Experience with the Theory -- 4.1 Implementation -- 4.2 Evaluation of the Approach -- 5 Related Work -- 6 Conclusion -- References -- Verifying Visibility-Based Weak Consistency -- 1 Introduction -- 2 Weak Consistency -- 2.1 Weak-Visibility Specifications -- 2.2 Consistency against Weak-Visibility Specifications -- 3 Establishing Consistency with Forward Simulation -- 3.1 Reducing Consistency to Safety Verification -- 3.2 Verifying Implementations -- 4 Proof Methodology -- 5 Implementation and Evaluation -- 6 Related Work -- 7 Conclusion and Future Work -- A Appendix: Proofs to Theorems and Lemmas -- References -- Local Reasoning for Global Graph Properties -- 1 Introduction -- 2 The Foundational Flow Framework -- 2.1 Preliminaries and Notation -- 2.2 Flows -- 2.3 Flow Graph Composition and Abstraction -- 3 Proof Technique -- 3.1 Encoding Flow-based Proofs in SL -- 3.2 Proof of the PIP -- 4 Advanced Flow Reasoning and the Harris List -- 4.1 The Harris List Algorithm -- 4.2 Product Flows for Reasoning about Overlays -- 4.3 Contextual Extensions and the Replacement Theorem -- 4.4 Existence and Uniqueness of Flows -- 4.5 Proof of the Harris List -- 5 Related Work -- 6 Conclusions and Future Work -- References -- Aneris: A Mechanised Logic for Modular Reasoning about Distributed Systems -- 1 Introduction -- 2 The Core Concepts of Aneris -- 2.1 Local and Thread-Local Reasoning -- 2.2 Node-Local Reasoning -- 2.3 Example: An Addition Service -- 2.4 Example: A Lock Server. , 3 AnerisLang -- 4 The Aneris Logic -- 4.1 The Program Logic -- 4.2 Adequacy for Aneris -- 5 Case Study 1: A Load Balancer -- 6 Case Study 2: Two-Phase Commit -- 6.1 A Replicated Log -- 7 Related Work -- 8 Conclusion -- Acknowledgments -- Bibliography -- Continualization of Probabilistic Programs With Correction -- 1 Introduction -- 2 Example -- 2.1 Continualization -- 2.2 Parameter Synthesis -- 2.3 Improving Inference -- 3 Syntax and Semantics of Programs -- 3.1 Source Language Syntax -- 3.2 Semantics -- 4 Continualizing Probabilistic Programs -- 4.1 Overview of the Algorithm -- 4.2 Distribution and Expression Transformations -- 4.3 Inuence Analysis and Control-Flow Correction of Predicates -- 4.4 Bringing it all together: Full Program Transformations -- 5 Synthesis of Continuity Correction Parameters -- 5.1 Optimization Framework -- 5.2 Optimization Algorithm -- 6 Methodology -- 6.1 Benchmarks -- 6.2 Experimental Setup -- 7 Evaluation -- 7.1 RQ1: Benefits of Continualization -- 7.2 RQ2: Impact of Smoothing Factors -- 7.3 RQ3: Extending Results to Other Systems -- 8 Related Work -- 9 Conclusion -- References -- Semantic Foundations for Deterministic Dataflow and Stream Processing -- 1 Introduction -- 2 Monoids as Types for Streams -- 3 Stream Transductions -- 4 Model of Computation -- 5 Combinators for Deterministic Dataow -- 6 Algebraic Reasoning for Optimizing Transformations -- 7 Related Work -- 8 Conclusion -- References -- Connecting Higher-Order Separation Logic to a First-Order Outside World -- 1 Introduction -- 2 Background: Ghost State in Separation Logic -- 2.1 Ghost Algebras -- 3 External State as Ghost State -- 4 Verifying C Programs with I/O in VST -- 5 Soundness of External-State Reasoning -- 6 Connecting VST to CertiKOS -- 6.1 CertiKOS Specifications -- 6.2 Relating OS and User State -- 6.3 Soundness of VST + CertiKOS. , 7 From syscall-level to hardware-level interactions. , English

Weitere Ausg.: ISBN 3-030-44913-0

Sprache: Englisch

Fachgebiete: Informatik

Schlagwort(e): Konferenzschrift

DOI: 10.1007/978-3-030-44914-8

URL: kostenfrei

UID:

Umfang: 1 online resource (361 pages)

Ausgabe: 1st ed. 2023.

ISBN: 3-031-37720-6

Serie: Lecture Notes in Networks and Systems, 737

Inhalt: The book includes papers about various problems of dependable operation of computer systems and networks, which were presented during the 18th DepCoS-RELCOMEX conference. Their collection can be an interesting source material for scientists, researchers, practitioners, and students who are dealing with design, analysis, and engineering of computer systems and networks and must ensure their dependable operation. The increasing role of artificial intelligence algorithms and tools in modern information technology and computer engineering, especially rapid expansion of tools based on deep learning methods, calls for extending our view on system dependability. Selection of papers in these proceedings not only illustrates a wide-ranging variety of multidisciplinary topics which should be considered in this context but also proves that virtually all areas of contemporary computer systems and networks must take into account an aspect of dependability.

Anmerkung: Line Segmentation of Handwritten Documents Using Direct Tensor Voting -- Practical Approach to Introducing Parallelism in Sequential Programs -- The digital twin to train a neural network detecting headlamps failure of motor vehicles -- Dynamic change of tasks in multiprocessor scheduling -- Regression models evaluation of short-term traffic flow prediction -- Performance analysis of a real-time data warehouse system implementation based on open-source technologies -- Evaluating the Different Factors Affecting the Educational Attainment of Students at the Faculty of Information Technology at Jordan Universities -- Hammering test on a concrete wall using Neural Network -- Artificial intelligence methods in email marketing - a survey -- Detection of oversized objects in a video stream, exploiting an image classification approach using deep neural networks -- Reliability Model of Bioregenerative Reactor of Life Support System for Deep Space Habitation -- Safety assessment of maintained control systems with cascade two-version 2oo3/1oo2 structures considering version faults -- CPU signal rank-based disaggregation in Cloud computing environments -- Efficient Clustering-based Neighbourhood in Recommender Systems -- New approach to constructive induction - Towards Deep Discrete Learning -- Softcomputing Approach to Music Generation -- Identification of the Language UsingStatistical and Neural Approaches -- Smart data logger with continuous ECG signalmonitoring -- Movement Tracking in Augmented and Mixed Realities Impacting the User Activity in Medicine and Healthcare -- General provisioning strategy for local specialized cloud computing environments -- Tabular structures detection on scanned VAT invoices -- Automation of deanonymization queries for the Bitcoin investigations -- Structural models for fault detection of Moore finite state machines -- Application of generative models to augment IMU signals in gait biometric -- Ant colony optimization algorithm for finding the maximum number of d-size cliques in a graph with not always m-vertices in its d parts -- Partitioning of a m-part weighted graph with n vertices in each its part into n cliques with m vertices and the total minimum sum of their edges weights using ant algorithms -- A Study of Architecture Optimization Techniques for Convolutional Neural Networks -- Scheduling Resource to Deploy Monitors in Automated Driving Systems -- Power Analysis of BLAKE3 Pipelined Implementations in FPGA Devices -- Deep Learning ECG Signal Analysis: Description and Preliminary Results -- Deployment of Deep Models in NLP Infrastructure -- Analysis of handwritten texts to detect selected psychological characteristics of a person -- Architecting Cloud-Based Business Software - A Practitioner's Perspective.

Weitere Ausg.: Print version: Zamojski, Wojciech Dependable Computer Systems and Networks Cham : Springer,c2023 ISBN 9783031377198

Sprache: Englisch

DOI: 10.1007/978-3-031-37720-4

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Umfang: 51, 7 Bl. : , graph. Darst.

Serie: Technische Universität 〈Berlin, West〉 / Fachbereich Kybernetik: Bericht. 76,20

Anmerkung: Erscheint auch als No. 76,6 von Semantic network project report

Sprache: Englisch

Fachgebiete: Informatik

Schlagwort(e): Programmiersprache ; ADQL

UID:

Umfang: 98, 7 Bl. : , graph. Darst.

Serie: Technische Universität 〈Berlin, West〉 / Fachbereich Kybernetik: Bericht. 76,25

Anmerkung: Erscheint auch als No. 76,7 von Semantic network project report

Sprache: Englisch

Fachgebiete: Informatik , Komparatistik. Außereuropäische Sprachen/Literaturen

Schlagwort(e): Programmiersprache ; ADQL ; ADQL

UID:

Umfang: 1 online resource (465 p.)

Ausgabe: 1st edition

ISBN: 1-281-07813-1 , 9786611078133 , 0-08-052798-1

Serie: The Morgan Kaufmann series in multimedia information and systems

Inhalt: Life science data integration and interoperability is one of the most challenging problems facing bioinformatics today. In the current age of the life sciences, investigators have to interpret many types of information from a variety of sources: lab instruments, public databases, gene expression profiles, raw sequence traces, single nucleotide polymorphisms, chemical screening data, proteomic data, putative metabolic pathway models, and many others. Unfortunately, scientists are not currently able to easily identify and access this information because of the variety of semantics, interfaces,

Anmerkung: Description based upon print version of record. , Cover; Bioinformatics: Managing Scientific Data; Copyright Page; Contents; Preface; Chapter 1. Introduction; 1.1 Overview; 1.2 Problem and Scope; 1.3 Biological Data Integration; 1.4 Developing a Biological Data Integration System; References; Chapter 2. Challenges Faced in the Integration of Biological Information; 2.1 The Life Science Discovery Process; 2.2 An Information Integration Environment for Life Science Discovery; 2.3 The Nature of Biological Data; 2.4 Data Sources in Life Science; 2.5 Challenges in Information Integration; Conclusion; References , Chapter 3. A Practitioner's Guide to Data Management and Data Integration in Bioinformatics3.1 Introduction; 3.2 Data Management in Bioinformatics; 3.3 Dimensions Describing the Space of Integration Solutions; 3.4 Use Cases of Integration Solutions; 3.5 Strengths and Weaknesses of the Various Approaches to Integration; 3.6 Tough Problems in Bioinformatics Integration; 3.7 Summary; Acknowledgments; References; Chapter 4. Issues to Address While Designing a Biological Information System; 4.1 Legacy; 4.2 A Domain in Constant Evolution; 4.3 Biological Queries; 4.4 Query Processing , 4.5 Visualization4.6 Conclusion; Acknowledgments; References; Chapter 5. SRS: An Integration Platform for Databanks and Analysis Tools in Bioinformatics; 5.1 Integrating Flat File Databanks; 5.2 Integration of XML Databases; 5.3 Integrating Relational Databases; 5.4 The SRS Query Language; 5.5 Linking Databanks; 5.6 The Object Loader; 5.7 Scientific Analysis Tools; 5.8 Interfaces to SRS; 5.9 Automated Server Maintenance with SRS Prisma; 5.10 Conclusion; References; Chapter 6. The Kleisli Query System as a Backbone for Bioinformatics Data Integration and Analysis; 6.1 Motivating Example , 6.2 Approach6.3 Data Model and Representation; 6.4 Query Capability; 6.5 Warehousing Capability; 6.6 Data Sources; 6.7 Optimizations; 6.8 User Interfaces; 6.9 Other Data Integration Technologies; 6.10 Conclusions; References; Chapter 7. Complex Query Formulation Over Diverse Information Sources in TAMBIS; 7.1 The Ontology; 7.2 The User Interface; 7.3 The Query Processor; 7.4 Related Work; 7.5 Current and Future Developments in TAMBIS; Acknowledgments; References; Chapter 8. The Information Integration System K2; 8.1 Approach; 8.2 Data Model and Languages; 8.3 An Example; 8.4 Internal Language , 8.5 Data Sources8.6 Query Optimization; 8.7 User Interfaces; 8.8 Scalability; 8.9 Impact; 8.10 Summary; Acknowledgments; References; Chapter 9. P/FDM Mediator for a Bioinformatics Database Federation; 9.1 Approach; 9.2 Analysis; 9.3 Conclusions; Acknowledgment; References; Chapter 10. Integration Challenges in Gene Expression Data Management; 10.1 Gene Expression Data Management: Background; 10.2 The GeneExpress System; 10.3 Managing Gene Expression Data: Integration Challenges; 10.4 Integrating Third-Party Gene Expression Data in GeneExpress; 10.5 Summary; Acknowledgments; Trademarks , References , English

Weitere Ausg.: ISBN 1-55860-829-X

Sprache: Englisch