05501nam 22007815 450 99646599850331620200704172116.03-642-16184-710.1007/978-3-642-16184-1(CKB)2670000000056639(SSID)ssj0000446424(PQKBManifestationID)11327272(PQKBTitleCode)TC0000446424(PQKBWorkID)10496098(PQKB)10057121(DE-He213)978-3-642-16184-1(MiAaPQ)EBC3066075(PPN)149890699(EXLCZ)99267000000005663920101102d2010 u| 0engurnn|008mamaatxtccrDiscovery Science[electronic resource] 13th International Conference, DS 2010, Canberra, Australia, October 6-8, 2010, Proceedings /edited by Bernahrd Pfahringer, Geoff Holmes, Achim Hoffman1st ed. 2010.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2010.1 online resource (XIII, 384 p. 108 illus.) Lecture Notes in Artificial Intelligence ;6332Bibliographic Level Mode of Issuance: Monograph3-642-16183-9 Includes bibliographical references and index.Sentiment Knowledge Discovery in Twitter Streaming Data -- A Similarity-Based Adaptation of Naive Bayes for Label Ranking: Application to the Metalearning Problem of Algorithm Recommendation -- Topology Preserving SOM with Transductive Confidence Machine -- An Artificial Experimenter for Enzymatic Response Characterisation -- Subgroup Discovery for Election Analysis: A Case Study in Descriptive Data Mining -- On Enumerating Frequent Closed Patterns with Key in Multi-relational Data -- Why Text Segment Classification Based on Part of Speech Feature Selection -- Speeding Up and Boosting Diverse Density Learning -- Incremental Learning of Cellular Automata for Parallel Recognition of Formal Languages -- Sparse Substring Pattern Set Discovery Using Linear Programming Boosting -- Discovery of Super-Mediators of Information Diffusion in Social Networks -- Integer Linear Programming Models for Constrained Clustering -- Efficient Visualization of Document Streams -- Bridging Conjunctive and Disjunctive Search Spaces for Mining a New Concise and Exact Representation of Correlated Patterns -- Graph Classification Based on Optimizing Graph Spectra -- Algorithm for Detecting Significant Locations from Raw GPS Data -- Discovery of Conservation Laws via Matrix Search -- Gaussian Clusters and Noise: An Approach Based on the Minimum Description Length Principle -- Exploiting Code Redundancies in ECOC -- Concept Convergence in Empirical Domains -- Equation Discovery for Model Identification in Respiratory Mechanics of the Mechanically Ventilated Human Lung -- Mining Class-Correlated Patterns for Sequence Labeling -- ESTATE: Strategy for Exploring Labeled Spatial Datasets Using Association Analysis -- Adapted Transfer of Distance Measures for Quantitative Structure-Activity Relationships -- Incremental Mining of Closed Frequent Subtrees -- Optimal Online Prediction in Adversarial Environments -- Discovery of Abstract Concepts by a Robot -- Contrast Pattern Mining and Its Application for Building Robust Classifiers -- Towards General Algorithms for Grammatical Inference -- The Blessing and the Curse of the Multiplicative Updates.Lecture Notes in Artificial Intelligence ;6332Artificial intelligenceInformation storage and retrievalApplication softwareDatabase managementData miningAlgorithmsArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Information Storage and Retrievalhttps://scigraph.springernature.com/ontologies/product-market-codes/I18032Information Systems Applications (incl. Internet)https://scigraph.springernature.com/ontologies/product-market-codes/I18040Database Managementhttps://scigraph.springernature.com/ontologies/product-market-codes/I18024Data Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Algorithm Analysis and Problem Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/I16021Artificial intelligence.Information storage and retrieval.Application software.Database management.Data mining.Algorithms.Artificial Intelligence.Information Storage and Retrieval.Information Systems Applications (incl. Internet).Database Management.Data Mining and Knowledge Discovery.Algorithm Analysis and Problem Complexity.501Pfahringer Bernahrdedthttp://id.loc.gov/vocabulary/relators/edtHolmes Geoffedthttp://id.loc.gov/vocabulary/relators/edtHoffman Achimedthttp://id.loc.gov/vocabulary/relators/edtInternational Conference on Discovery ScienceBOOK996465998503316Discovery Science772321UNISA11067nam 2200529 450 99646684650331620231110213116.03-030-77517-8(CKB)5360000000050064(MiAaPQ)EBC6739246(Au-PeEL)EBL6739246(OCoLC)1273427075(PPN)258298766(EXLCZ)99536000000005006420220629d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierProceedings of the 2019 International Conference of The Computational Social Science Society of the Americas /Zining Yang and Elizabeth von BriesenCham, Switzerland :Springer International Publishing,[2021]©20211 online resource (403 pages)Springer Proceedings in Complexity 3-030-77516-X Intro -- Contents -- Editors and Contributors -- Social Impression of Faces: From Prediction to Modification -- 1 Introduction -- 2 Predicting Social Attributes of Faces -- 3 Creating a Large-Scale Facial Impression Dataset -- 4 Validating the Algorithm-Augmented Dataset -- 5 ModifAE: A Modification Model of Social Impressions -- 5.1 Architecture -- 5.2 ModifAE Training Procedure -- 5.3 How ModifAE Works -- 5.4 Qualitative Evaluation -- 5.5 Quantitative Evaluation -- 5.6 Qualitative Interpretations -- 6 Discussion -- References -- Corruption and the Effects of Influence Within Social Networks: An Agent-Based Model of the ``Lava Jato'' Scandal -- 1 Introduction -- 2 Background -- 3 The Model -- 3.1 Generation of Networks -- 3.2 Agents -- 3.3 Initialization of the Model and Agent Interactions -- 4 Results -- 5 Conclusion -- References -- Resistance of Communities Against Disinformation -- 1 Introduction -- 2 Opinion Dynamics -- 3 Model of Conspirators -- 4 Results -- 5 Discussion -- References -- Assessing the Potential of Crowd-Shipping for Food Rescue Logistics Using Agent-Based Modeling -- 1 Introduction -- 2 Models of Crowd-Shipping Systems -- 3 Agent-Based Model -- 3.1 Model Overview -- 3.2 Sub-Model 1: Restaurant Agent Decision-Making -- 3.3 Sub-Model 2: Shelter Assignment -- 3.4 Sub-Model 3: Crowd-Shipper Agent Decision-Making -- 3.5 Initialization -- 4 Experimentation and Results -- 5 Conclusion -- References -- Exogenous Shocks Lead to Increased Responsiveness and Shifts in Sentimental Resilience in Online Discussions -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Data -- 3.2 Conversation Dynamics -- 3.3 Sentiment Transfer -- 3.4 Transfer Entropy -- 4 Results -- 4.1 Conversation Dynamics -- 4.2 Sentiment Transfer -- 4.3 Total Transfer Entropy -- 5 Conclusion -- References -- The Cat and Mouse of Getting Around the Law.1 Introduction -- 2 Results -- 3 Optimizing Strategies -- 3.1 Fast and Strong ("All in") -- 3.2 Slow and Strong ("Slow and Aggressive") -- 3.3 Fast and Weak -- 3.4 Slow and Weak ("Do Nothing") -- 4 Discussion -- 5 Conclusion -- References -- The Degree-Dependent Threshold Model: Towards a Better Understanding of Opinion Dynamics on Online Social Networks -- 1 Introduction -- 2 Methods -- 2.1 Data Set and Twitter Analysis Results -- 2.2 Generating Networks -- 2.3 Assigning Thresholds -- 2.4 Running Simulations -- 3 Simulation Results -- 4 Conclusion -- References -- Modeling Genocide: An Agent-Based Model of Bystander Motivations and Societal Restraints -- 1 Introduction -- 1.1 Review of Prior Work -- 1.2 Addressing the Research Gap -- 2 Research Approach -- 2.1 Social Science Theories -- 2.2 ABM Implementation -- 3 Results -- 3.1 Experiment 1: Effect of Contagion and Fear on Violence -- 3.2 Experiment 2: Sensitivity Analysis-System-Level Factors of Restraint () -- 4 Conclusions and Future Work -- References -- Global News Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology and Experimentation -- 4 Results -- 4.1 Initial Observation -- 4.2 Comparison of Four Sentiment Analysis Tools -- 4.3 Three Article Processing Approaches -- 5 Discussion -- 6 Conclusion -- References -- An Agent-Based Model of Social Fabric Seen as an Emergent Behavior -- 1 Introduction -- 2 Social Fabric -- 3 Modeling Social Fabric in Cities -- 3.1 Conceptual Model -- 3.2 Agent-Based Model -- 4 Case Study: The Miramar Region -- 4.1 Mobility Patterns and Its Relationship with Perception -- 4.2 Encounters -- 5 Conclusion -- References -- Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks -- 1 Introduction -- 2 Challenge Problem Description -- 3 Methodology -- 3.1 Deep Agent Framework: Architecture and Analysis.3.2 Agent-Based Models -- 4 Dataset Description -- 4.1 Evaluation Events and Metrics -- 5 Experimental Results -- 6 Conclusion and Future Work -- References -- Electoral College: Emergent Battlegrounds An Agent-Based Model of Campaign Behavior Change with District Allocation of Electors -- 1 Introduction -- 2 Background -- 3 Methodology -- 4 The Model -- 5 Validation -- 6 Results -- 7 Discussion and Conclusions -- References -- Social Viscosity, Fluidity, and Turbulence in Collective Perceptions of Color: An Agent-Based Model of Color Scale Convergence -- 1 Introduction: Social Viscosity, Fluids, and Turbulence -- 2 Social Meaning and the Analysis of Proximity -- 2.1 Meaning and Viscosity in Color Agents -- 3 An Agent-Based Model of Color Proximity -- 4 Case Study: Nucleation in Circular Color Scales -- 4.1 Methods -- 4.2 All-to-all Interactions -- 4.3 N Nearest-Neighbor Interactions -- 5 Discussion -- 6 Conclusions and Further Work -- References -- Inside the Mind of the Nonfiler: An Agent-Based Modeling Approach -- 1 Introduction -- 1.1 Brief Overview of Nonfilers and IRS Enforcement/Outreach -- 1.2 Current IRS Nonfiler Research -- 1.3 Past Uses of Agent-Based Models (ABMs) at RAAS -- 2 Methods -- 2.1 Agent-Based Model Overview -- 2.2 Econometric Modeling -- 3 Data -- 4 Results -- 4.1 Agent-Based Model -- 5 Discussion -- 5.1 Agent-Based Model -- 6 Conclusions and Future Work -- 6.1 Future Work -- Appendix-Overview, Design Concepts and Details, and Decision Making (ODD + D) Protocol Documentation of the Nonfiler Decision Model -- Purpose -- Entities, State Variables, Scales -- Process Overview and Scheduling -- Design Concepts -- Initialization -- Input Data -- Submodels -- References -- Capturing the Effects of Gentrification on Property Values: An Agent-Based Modeling Approach -- 1 Introduction -- 2 Related Work -- 3 Model Design -- 3.1 Data.3.2 Environment -- 3.3 Agent Classes -- 3.4 Gentrification Calculation -- 3.5 Verification -- 4 Results -- 4.1 Gentrification by Supply -- 4.2 Gentrification by Demand -- 5 Discussion and Further Works -- 5.1 Main Results -- 6 Conclusion -- References -- Negative Influence Gradients Lead to Lowered Information Processing Capacity on Social Networks -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- A Complex-Systems Agenda for Influencing Policy Studies -- 1 Introduction -- 1.1 Purpose -- 1.2 Our Definitions -- 1.3 Additional Discussion of Definitions -- 2 Worldview -- 2.1 System Thinking -- 2.2 Confronting Complexity and Wicked Problems -- 2.3 Decision-Making as a Continuing and Messy Process -- 2.4 The Values and Objectives that Drive Decisions -- 2.5 Knowledge, Uncertainty, and Disagreement -- 2.6 Moderation in the Search for Control -- 3 Basis for Reasoning and Inference -- 3.1 Theory, Data, Association, and Causation -- 3.2 Types of Theory -- 4 Analytic Style -- 4.1 Analysis and Reductionism, Both Good and Bad -- 4.2 Character of Analysis: Quantitative Versus Qualitative, and Matters of Rigor -- 4.3 Confronting Uncertainty and Disagreement -- 4.4 Comparing Options -- 4.5 Changing the Questions Asked of Simulation -- 5 Character of Models and Model-Based Analysis -- 5.1 Different Classes of Model -- 5.2 Purpose of Models and Related Issues of Validity -- 5.3 Measures of Outcome -- 5.4 Predict and Act Versus Anticipate Possibilities and Act Adaptively -- 6 Conclusions -- 6.1 Implications for Teaching Policy Studies -- 6.2 Implications for Computational Social Science -- References -- Wealth Dynamics in the Presence of Network Structure and Primitive Cooperation -- 1 Introduction -- 2 Model -- 2.1 Mathematical Formulation -- 2.2 Implementation -- 3 Verification.3.1 Agent and Proto Life History -- 3.2 Gini Coefficient History -- 4 Analysis -- 4.1 Gini Coefficient -- 4.2 Life Expectancy -- 4.3 Interpretations, Conjectures, and Next Steps -- 5 Conclusion and Future Work -- References -- Social Primitives: Exploring Spark of Life Collective Behavior in Agent-Based Models -- 1 Introduction -- 2 Examples of Agent-Based Social Primitives -- 2.1 Ring World -- 2.2 Conway-Game of Life -- 2.3 Complexity Adaptive Systems-Page and Miller -- 2.4 Hammond-Tobacco and Policy-Low-Dimensional and High-Dimensional Models -- 3 Taxonomy of Social Primitives -- 3.1 Agent Description -- 3.2 Coding Agents and the Challenge of Implicit Assumptions -- 3.3 Environment Description -- 3.4 Coding the Environment and the Challenge of Implicit Assumptions -- 4 Coding a Social Primitives Model: The SPECscape Model -- 4.1 Agent to Agent Interaction -- 4.2 Agent-Environment Interaction -- 4.3 Anthropomorphic Crossover -- 5 Running the SPECscape Model -- 5.1 SPECscape Model and Gini Index -- 5.2 Four Landscape Starting Points -- 5.3 The Effect of Exogenous Shocks at Various Stages of Model Evolution -- 5.4 Models with and without Proto-institutions -- 6 Conclusion -- 6.1 Elements of Collective Function -- 6.2 Determining Anthropomorphic Leaps -- 6.3 Determining a Parameter-Based Measure of Model Complexity -- 6.4 Possibilities for Increasing the Complexity of SPECscape Model -- Appendix -- References -- Capturing the Production of Innovative Ideas: An Online Social Network Experiment and ``Idea Geography'' Visualization -- 1 Introduction -- 2 Online Social Network Experiment -- 2.1 Experimental Procedure -- 2.2 Collective Design Tasks -- 3 Data Analysis Methods -- 3.1 Doc2Vec -- 3.2 Principal Component Analysis -- 3.3 Idea Geography -- 4 Results -- 4.1 Experiment Session I: Catch Phrase Design -- 4.2 Experiment Session II: Story Design.5 Discussions.Springer Proceedings in Complexity BiotechnologyBiotechnologySafety measuresBiotechnology.BiotechnologySafety measures.660.6Yang Zining1072720Von Briesen ElizabethMiAaPQMiAaPQMiAaPQBOOK996466846503316Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas2895356UNISA03809oam 2200853I 450 991096275240332120251117080419.01-4665-5897-00-429-25110-61-4398-4591-310.1201/b11655(CKB)2670000000151405(EBL)863104(OCoLC)819594631(SSID)ssj0000611660(PQKBManifestationID)11362703(PQKBTitleCode)TC0000611660(PQKBWorkID)10646765(PQKB)11018593(MiAaPQ)EBC863104(OCoLC)785074709(EXLCZ)99267000000015140520180331d2012 uy 0engur|n|---|||||txtccrDeath scene investigation procedural guide /Michael S. Maloney1st ed.Boca Raton, Fla. :CRC Press,2012.1 online resource (361 p.)Description based upon print version of record.1-4665-4181-4 1-322-62778-9 1-4398-4590-5 Front Cover; Table of Contents; Preface; Acknowledgments; Author; Foreword; Chapter 1: Initial Response; Chapter 2: Scene Evaluation, Analysis, Strategy, and Direction; Chapter 3: Natural Deaths; Chapter 4: Accidental Deaths; Chapter 5: Suicidal Deaths; Chapter 6: Homicidal Deaths; Chapter 7: The Role of Medical Examiner; Appendix A: Universal Precautions for Blood Borne Pathogens; Appendix B: Death Scene Notes; Appendix C: Death Scene Notes: Supplemental; Appendix D: Biological Evidence Notes; Appendix E: Friction Ridge Evidence; Appendix F: Impression Evidence NotesAppendix G: Trace Evidence NotesAppendix H: Death Scene Entry log; Appendix I: Photographic Log; Appendix J: Photographic Head Slate; Appendix K: Immersion Burn Worksheet; Appendix L: SUID Scene Worksheet; Appendix M: Firearms Documentation worksheet; Appendix N: Post Blast Worksheet; Appendix O: Druggist Fold; Appendix P: Post Mortem Indicators Worksheet; Back Cover""Death Scene Investigation: Procedural Guide is the answer to a long recognized dilemma: how to have every death investigated by an experienced death investigator."" - Tom Bevel, author of Practical Crime Scene Analysis and Reconstruction and Bloodstain Pattern Analysis with an Introduction to Crime Scene Reconstruction, Third Edition Those tasked with investigating death scenes come from a variety of backgrounds and varying levels of experience. Death Scene Investigation: Procedural Guide gives the less experienced investigatorForensic pathologyCausesDeathForensic MedicinePathologyMedicineForensic SciencesHealth OccupationsCriminologySocial SciencesForensic PathologyPublic HealthHILCCHealth & Biological SciencesHILCCLegal & Forensic MedicineHILCCForensic pathologyCauses.Death.Forensic Medicine.Pathology.Medicine.Forensic Sciences.Health Occupations.Criminology.Social Sciences.Forensic Pathology.Public HealthHealth & Biological SciencesLegal & Forensic Medicine614.1Maloney Michael1866635AU-PeELAU-PeELAU-PeELBOOK9910962752403321Death scene investigation4474050UNINA