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[null,null,"Titolo",null,"Computational Science – ICCS 2023 [[electronic resource] ] : 23rd International Conference, Prague, Czech Republic, July 3–5, 2023, Proceedings, Part I / / edited by Jiří Mikyška, Clélia de Mulatier, Maciej Paszynski, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M.A. Sloot"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023"]
[null,null,"ISBN",null,"3-031-35995-X"]
[null,null,"Edizione",null,"[1st ed. 2023.]"]
[null,null,"Descrizione fisica",null,"1 online resource (718 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 14073"]
[null,null,"Disciplina",null,"929.605"]
[null,null,"Soggetti",null,"Computer science"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Software engineering"]
[{"$nulls":4},"Computer science—Mathematics"]
[{"$nulls":4},"Theory of Computation"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Software Engineering"]
[{"$nulls":4},"Mathematics of Computing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part I -- ICCS 2023 Main Track Full Papers -- Improving the Resiliency of Decentralized Crowdsourced Blockchain Oracles -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 System Overview -- 3.2 Agents -- 3.3 Reputation -- 3.4 Threat Models -- 3.5 Rewards -- 3.6 Evaluation -- 4 Experiments and Simulation -- 4.1 Simulation Settings -- 4.2 Participation Control -- 4.3 Weighted Voting -- 4.4 Stratified Voting -- 5 Discussion -- 6 Conclusion -- References -- Characterization of Pedestrian Contact Interaction Trajectories -- 1 Introduction -- 2 Datasets -- 3 Data Analysis -- 4 Conclusion -- References -- Siamese Autoencoder-Based Approach for Missing Data Imputation -- 1 Introduction -- 2 Related Work -- 3 Siamese Autoencoder-Based "]
[{"$nulls":4},"Approach for Imputation -- 3.1 Deep Autoencoder Architecture -- 3.2 Custom Loss Function -- 3.3 Custom Triplet Mining -- 4 Experimental Setup -- 5 Results -- 6 Conclusions -- References -- An Intelligent Transportation System for Tsunamis Combining CEP, CPN and Fuzzy Logic -- 1 Introduction -- 2 Related Work -- 3 Application Scenario -- 4 The CEP Event Patterns -- 5 The Fuzzy Inference System -- 6 CPN Model -- 7 Conclusions and Future Work -- References -- Downscaling WRF-Chem: Analyzing Urban Air Quality in Barcelona City -- 1 Introduction -- 2 Data, Materials and Methods -- 2.1 Case Study -- 2.2 Model Description, Chemistry and Physics Schemes -- 3 Experimental Results -- 3.1 Meteorology Results -- 3.2 Air Quality Results -- 4 Conclusions -- References -- Influence of Activation Functions on the Convergence of Physics-Informed Neural Networks for 1D Wave Equation -- 1 Introduction -- 2 Wave Equation -- 3 Training -- 4 Numerical Results -- 5 Experiments -- 5.1 Parameters Tuning -- 5.2 Activation Functions -- 6 Results -- 7 Conclusions and Future Work -- References."]
[{"$nulls":4},"Accelerating Multivariate Functional Approximation Computation with Domain Decomposition Techniques -- 1 Introduction -- 1.1 Related Work -- 2 Approach -- 2.1 Numerical Background -- 2.2 Shared Knot Spans at Subdomain Interfaces -- 2.3 Solver Workflow -- 2.4 Implementation -- 3 Results -- 3.1 Error Convergence Analysis -- 3.2 Real Simulation Datasets -- 3.3 Parallel Scalability -- 4 Summary -- References -- User Popularity Preference Aware Sequential Recommendation -- 1 Introduction -- 2 Related Works -- 2.1 Sequential Recommendation -- 2.2 Popularity Aware Recommendation -- 2.3 Contrastive Learning -- 3 Proposed Method -- 3.1 Problem Statement -- 3.2 Basic Model -- 3.3 Sequential Popularity Perception Module -- 3.4 Popularity Contrastive Learning Module -- 3.5 Network Training -- 4 Experiment -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Implementation Details and Evaluation Metrics -- 4.4 Performance Comparison -- 4.5 Performance on Particular Users -- 4.6 Ablation Study -- 5 Conclusion -- References -- Data Heterogeneity Differential Privacy: From Theory to Algorithm -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Notations and Assumptions -- 3.2 Differential Privacy -- 4 Sharper Utility Bounds for DP-SGD -- 5 Performance Improving DP-SGD -- 5.1 Influence Function and Error Analysis -- 5.2 Performance Improving DP-SGD -- 5.3 Privacy Guarantees -- 5.4 Utility Analysis -- 6 Comparison with Related Work -- 7 Experimental Results -- 8 Conclusions -- References -- Inference of Over-Constrained NFA of Size k+1 to Efficiently and Systematically Derive NFA of Size k for Grammar Learning -- 1 Introduction -- 2 The NFA Inference Problem -- 2.1 Notations -- 2.2 A ``Meta-model'' -- 2.3 Some Previous Models -- 3 k_NFA Extensions -- 3.1 Building a (k+1)_NFA from a k_NFA -- 3.2 (k+1)_NFA+ Extension -- 3.3 k_NFA Extension -- 3.4 Complexity."]
[{"$nulls":4},"4 Properties of the Extensions -- 4.1 (k+1)_NFA+ -- 4.2 (k+1)_NFA -- 5 Experimentation -- 5.1 Context for Reproductibility -- 5.2 Simplified Models -- 5.3 Results and Discussions -- 6 Conclusion -- References -- Differential Dataset Cartography: Explainable Artificial Intelligence in Comparative Personalized Sentiment Analysis -- 1 Introduction -- 2 Background -- 2.1 Personalization in NLP -- 2.2 Explainable AI -- 3 Datasets -- 4 Personalized Architectures -- 5 HumAnn -- 6 Differential Data Maps -- 7 Experimental Setup -- 8 Results -- 9 Conclusions and Future Work -- References -- Alternative Platforms and Privacy Paradox: A System Dynamics Analysis -- 1 Introduction -- 2 Theoretical Background -- 2.1 Privacy as a Social Issue -- 2.2 Social Theory Based Explanations of the Privacy Paradox -- 3 A System Dynamics Model of the Privacy Paradox -- 3.1 Problem Articulation and "]
[{"$nulls":4},"Dynamic Hypothesis -- 4 Model Development -- 4.1 Model Structure -- 4.2 Model Parameters -- 4.3 Model Testing and Validation -- 5 Simulation Results -- 5.1 Simulation Experiment 1 -- 5.2 Simulation Experiment 2 -- 5.3 Simulation Experiment 3 -- 6 Concluding Discussion -- References -- The First Scientiffic Evidence for the Hail Cannon -- 1 Introduction -- 2 Experimental Verification -- 3 Numerical Simulations -- 4 IGA-ADS Simulation of the Hail Cannon -- 5 Conclusion and Future Work -- References -- Constituency Parsing with Spines and Attachments -- 1 Introduction -- 2 Headed Constituencies -- 3 The Dataset -- 4 Proposed Parsing Technique -- 4.1 Spines -- 4.2 Spine Based Parsing -- 5 Parser Architecture -- 6 Related Work -- 7 Evaluation -- 8 Conclusions -- References -- Performing Aerobatic Maneuver with Imitation Learning -- 1 Introduction -- 2 Related Work -- 3 Data Analysis -- 3.1 Maneuvers Description -- 3.2 Evaluation Metrics -- 3.3 Maneuvers Evaluation."]
[{"$nulls":4},"4 Controllers Training -- 4.1 Results -- 4.2 Discussion of Results -- 5 Circuit Controller -- 6 Conclusion -- References -- An Application of Evolutionary Algorithms and Machine Learning in Four-Part Harmonization -- 1 Introduction -- 1.1 State of the Art -- 1.2 Contribution -- 2 Soprano Harmonization Problem -- 3 Algorithmic Approach -- 3.1 Genetic Algorithm -- 3.2 Bayesian Network -- 3.3 Hybrid Algorithm -- 4 Test Results -- 5 Conclusions -- References -- Predicting ABM Results with Covering Arrays and Random Forests -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Heatbugs Model -- 3.2 Choosing Parameters via Covering Arrays -- 3.3 Machine Learning -- 4 Experimental Setup -- 4.1 Data Gathering and Preparation -- 4.2 Machine Learning in All Experiments -- 5 Results -- 5.1 Experiment A: Low Unhappiness and Low Variation -- 5.2 Experiment B: Steady Unhappiness -- 5.3 Experiment C: Average Unhappiness -- 5.4 Feature Importance -- 6 Conclusions -- References -- Vecpar - A Framework for Portability and Parallelization -- 1 Introduction -- 2 State of the Art and Related Work -- 3 Proposed Approach -- 4 Evaluation -- 4.1 BabelStream Benchmark -- 4.2 Vecpar Internal Benchmark -- 4.3 Track Reconstruction Use Cases -- 5 Conclusions and Future Work -- References -- Self-supervised Deep Heterogeneous Graph Neural Networks with Contrastive Learning -- 1 Introduction -- 2 Related Work -- 2.1 Heterogeneous Graph Neural Networks -- 2.2 Contrastive Learning -- 3 Preliminary -- 4 The Proposed DHG-CL Model -- 4.1 Node Transformation -- 4.2 Cross-Layer Semantic Encoder -- 4.3 Graph-Based Contrastive Learning -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Node Classification -- 5.3 Node Clustering -- 5.4 Visualization -- 5.5 Variant Analysis -- 5.6 Parameter Analysis -- 6 Conclusion and Future Work -- References."]
[{"$nulls":4},"First-Principles Calculation to N-type Beryllium Related Co-doping and Beryllium Doping in Diamond -- 1 Introduction -- 2 Calculation Methods -- 3 Results and Discussion -- 3.1 Impurity Formation Energy (Ef) -- 3.2 Ionization Energies -- 3.3 Electronic Structure -- 3.4 Band Structure -- 4 Conclusions -- References -- Machine Learning Detects Anomalies in OPS-SAT Telemetry -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Detecting OPS-SAT Anomalies Using Machine Learning -- 3 Experimental Validation -- 3.1 Experiment 1: Exploiting Original Training Dataset -- 3.2 Experiment 2: Augmenting Training Datasets -- 4 Conclusions and Future Work -- References -- Wildfire Perimeter Detection via Iterative Trimming Method -- 1 Introduction -- 2 Thermal Infrared Image of a Wildfire -- 3 Delaunay Triangulation and Iterative Trimming -- 3.1 Delaunay Triangulation -- 3.2 Iterative Trimming Method -- 4 Results and Discussion -- 4.1 Iterative Trimming Method -- 4.2 Canny Edge Detector -- 4.3 Graph-"]
[{"$nulls":4},"Cut Method -- 4.4 Level Set Method -- 5 Conclusions -- References -- Variable Discovery with Large Language Models for Metamorphic Testing of Scientific Software -- 1 Introduction -- 2 State of the Art -- 3 Discovering I/O Variables with an LLM -- 3.1 LLM-Based Workflow -- 3.2 Prompt Construction and LLM Particulars -- 4 Evaluation -- 4.1 Ground Truth and Experiment Setup -- 4.2 Results -- 4.3 Discussion and Threats to Validity -- 5 Conclusion and Future Work -- References -- On Irregularity Localization for Scientific Data Analysis Workflows -- 1 Introduction -- 2 Motivation and Background -- 2.1 General Framework for Outcome-Preserving Input Reduction -- 3 Instantiation of the General Framework -- 4 Investigated Reduction Strategies -- 4.1 Baseline (Leave-One-Out) -- 4.2 Delta Debugging (dd-min) -- 4.3 Probabilistic Delta Debugging (prob-dd)."]
[{"$nulls":4},"4.4 Similarity-Based Isolation (similarity-iso)."]
[null,null,null,"Sommario/riassunto","The five-volume set LNCS 14073-14077 constitutes the proceedings of the 23rd International Conference on Computational Science, ICCS 2023, held in Prague, Czech Republic, during July 3-5, 2023. The total of 188 full papers and 94 short papers presented in this book set were carefully reviewed and selected from 530 submissions. 54 full and 37 short papers were accepted to the main track; 134 full and 57 short papers were accepted to the workshops/thematic tracks. The theme for 2023, \"Computation at the Cutting Edge of Science\", highlights the role of Computational Science in assisting multidisciplinary research. This conference was a unique event focusing on recent developments in scalable scientific algorithms, advanced software tools; computational grids; advanced numerical methods; and novel application areas. These innovative novel models, algorithms, and tools drive new science through efficient application in physical systems, computational and systems biology, environmental systems, finance, and others."]