Computational Science - ICCS 2021 21st International Conference, Krakow, Poland, June 16-18, 2021, Proceedings, Part I

• Main Authors: Paszynski, Maciej, Kranzlmüller, Dieter, Krzhizhanovskaya, Valeria V, Dongarra, Jack J, Sloot, Peter M. A
• Format: eBook Conference Proceeding
• Language: English
• Published: Cham Springer International Publishing AG 2021; Springer International Publishing
• Edition: 1
• Series: Lecture Notes in Computer Science
• Subjects: Artificial Intelligence; Computer Science; Computer science-Congresses; Computer Systems Organization and Communication Networks; Image Processing and Computer Vision; Mathematics of Computing; Theory of Computation

The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.*The total of 260 full papers and 57 short papers presented in this book set were carefully...

Table of Contents:

• 6.3 Surge Capability -- 7 Conclusion -- References -- Scientific Workflow Management on Hybrid Clouds with Cloud Bursting and Transparent Data Access -- 1 Introduction -- 2 Related Work -- 3 Cloud Bursting Solution for Scientific Workflows -- 3.1 Architecture -- 3.2 Workflow Partitioning -- 4 Evaluation -- 4.1 Workflow Partitioning Analysis -- 4.2 Experimental Runs -- 4.3 Discussion -- 5 Conclusions and Future Work -- References -- Scaling Simulation of Continuous Urban Traffic Model for High Performance Computing System -- 1 Introduction -- 2 Scalable Traffic Simulations -- 3 Towards HPC-Grade Traffic Simulation -- 3.1 Initialization of the Simulation -- 3.2 Data Scalability -- 3.3 Massive Parallelism Issues -- 3.4 Simulation Results Processing -- 4 Evaluation of Traffic Simulation Scalability -- 4.1 Strong Scalability -- 4.2 Weak Scalability -- 5 Conclusions and Further Work -- References -- A Semi-supervised Approach for Trajectory Segmentation to Identify Different Moisture Processes in the Atmosphere -- 1 Introduction -- 2 Background -- 2.1 Trajectory Clustering -- 2.2 Research Data -- 3 Experimental Evaluation -- 4 Discussion -- 5 Conclusion -- References -- mRelief: A Reward Penalty Based Feature Subset Selection Considering Data Overlapping Problem -- 1 Introduction -- 2 Preliminaries -- 2.1 Relief -- 2.2 ReliefF -- 2.3 SURF and SURF* -- 2.4 MultiSURF* and MultiSURF -- 3 Modified Relief (mRelief) -- 3.1 Candidate Feature Selection -- 3.2 Final Feature Subset Selection -- 3.3 An Illustrative Example -- 4 Result Analysis and Discussions -- 4.1 Dataset Description -- 4.2 Implementation Detail -- 4.3 Comparison of mRelief with the State-of-the-Art Methods -- 5 Conclusion -- References -- Reconstruction of Long-Lived Particles in LHCb CERN Project by Data Analysis and Computational Intelligence Methods -- 1 Introduction
• Music Genre Classification: Looking for the Perfect Network -- 1 Introduction -- 1.1 Related Work -- 1.2 Contribution and Paper Structure -- 2 Deep Learning Models -- 2.1 Convolutional Neural Network -- 2.2 1-Dimensional Convolutional Recurrent Neural Network -- 2.3 2-Dimensional Convolutional Recurrent Neural Network -- 2.4 Recurrent Neural Network with Long Short-Term Memory Cells (LSTM) -- 2.5 Ensembles -- 3 Experiments -- 3.1 Dataset and Hardware Setup -- 3.2 Additional Settings -- 3.3 Quantitative Results -- 3.4 Qualitative Results -- 3.5 Comparison of the Outcomes -- 4 Conclusions -- References -- Big Data for National Security in the Era of COVID-19 -- 1 Introduction -- 2 COVID-19 and National Security -- 3 An Overview of the Framework -- 3.1 Event Polarisation and Early Warning Alert (Q1) -- 3.2 Radical Behaviour (Q2, Q3 and Q4) -- 3.3 Ideology (Q5) -- 3.4 Web Insights (Q6) -- 4 Analysing Two COVID-19 Disruptive Events -- 4.1 Data Collection and Cleansing -- 4.2 Early Warning Alert (Q1) -- 4.3 Radical Behaviour (Q2, Q3 and Q4) -- 4.4 Ideology (Q5) -- 4.5 Web Insights (Q6) -- 5 Conclusions -- References -- Efficient Prediction of Spatio-Temporal Events on the Example of the Availability of Vehicles Rented per Minute -- 1 Introduction -- 1.1 Contribution -- 2 Related Work -- 3 Model and Problem Definition -- 4 Dataset -- 4.1 Performance Measures -- 5 Results -- 6 Conclusion -- References -- Grouped Multi-Layer Echo State Networks with Self-Normalizing Activations -- 1 Introduction -- 2 ESN Architectures -- 2.1 Shallow Echo State Network -- 2.2 Self-Normalizing Activation Function on Hyper-Sphere -- 2.3 Grouped Deep Echo State Network -- 3 AutoESN Library -- 4 Results -- 5 Conclusion -- References -- SGAIN, WSGAIN-CP and WSGAIN-GP: Novel GAN Methods for Missing Data Imputation -- 1 Introduction -- 2 Imputation Methods Based on GAN
• Evaluating Energy-Aware Scheduling Algorithms for I/O-Intensive Scientific Workflows -- 1 Introduction -- 2 Background -- 2.1 Scientific Workflows -- 2.2 Power and Energy Consumption -- 3 Analysis of Energy-Aware Workflow Scheduling Algorithms -- 3.1 SPSS-EB -- 3.2 EnReal -- 3.3 Workflow Energy Consumption Analysis -- 4 Energy-Aware Scheduling of I/O-Intensive Workflows -- 4.1 I/O- and Energy-Aware Scheduling -- 4.2 Experimental Evaluation -- 5 Related Work -- 6 Conclusion and Future Work -- References -- A Job Shop Scheduling Problem with Due Dates Under Conditions of Uncertainty -- 1 Introduction -- 2 Problem Formulation -- 3 Uncertainty -- 4 Computational Experiments -- 5 Summary -- References -- New Variants of SDLS Algorithm for LABS Problem Dedicated to GPGPU Architectures -- 1 Introduction -- 2 New Variants of SDLS Search Algorithms for LABS -- 2.1 The SDLS-2 Algorithm with Extended Neighbourhood -- 2.2 Sequential Version of the SDLS-DT Algorithm -- 2.3 Parallel Version of SDLS-DT for GPGPU -- 3 Effectiveness of the Proposed Algorithms -- 4 Conclusions and Further Work -- References -- Highly Effective GPU Realization of Discrete Wavelet Transform for Big-Data Problems -- 1 Introduction -- 2 Calculation of One-Dimensional DWT -- 2.1 Calculations Based on the Convolution -- 2.2 Calculations Based on the Lattice Structure -- 3 The Proposed Lattice Structure -- 4 Experimental Analysis -- 5 Conclusions -- References -- A Dynamic Replication Approach for Monte Carlo Photon Transport on Heterogeneous Architectures -- 1 Introduction -- 2 Background -- 3 Related Works -- 4 Our Method -- 4.1 Step 1: Assignment -- 4.2 Step 2: Distribution -- 4.3 Step 3: Mapping -- 5 Experiment Overview -- 5.1 Hardware and Software -- 5.2 Experimental Factors -- 5.3 Measurements -- 6 Results -- 6.1 Algorithm Performance -- 6.2 Load Balance Efficiency
• 2 Long-Lived Particles in LHCb
• 3 Novel Generative Imputation Methods -- 3.1 Notation and Problem Formulation -- 3.2 Slim GAIN -- 3.3 Wasserstein Slim GAIN with Clipping Penalty -- 3.4 Wasserstein Slim GAIN with Gradient Penalty -- 4 Experimental Results -- 4.1 Response Times -- 4.2 RMSE Performance -- 4.3 AUROC Performance -- 5 Conclusions -- References -- Deep Learning Driven Self-adaptive Hp Finite Element Method -- 1 Introduction -- 2 Self-adaptive hp-FEM with Neural Network -- 3 Conclusions -- References -- Machine-Learning Based Prediction of Multiple Types of Network Traffic -- 1 Introduction -- 2 Related Work -- 3 Data Analysis -- 4 Proposed Models and Algorithms -- 5 Numerical Experiments -- 6 Conclusions and Future Work -- References -- Scalable Handwritten Text Recognition System for Lexicographic Sources of Under-Resourced Languages and Alphabets -- 1 Introduction -- 2 Related Work -- 3 Data -- 3.1 Original Data -- 3.2 Synthetic Data for Training -- 3.3 Manually Labelled Subsets -- 4 Methodological Workflow -- 4.1 Detection of the Index Word -- 4.2 Recognition -- 4.3 Transfer Learning -- 4.4 Postprocessing -- 5 Application -- 6 Results -- 7 Discussion -- 8 Conclusion -- References -- Out-Plant Milk-Run-Driven Mission Planning Subject to Dynamic Changes of Date and Place Delivery -- 1 Introduction -- 2 Literature Review -- 3 An Ordered Fuzzy Numbers Framework -- 4 Problem Formulation -- 5 OFN-Based Constraint Satisfaction Problem -- 6 Dynamic Mission Planning -- 7 Computational Experiments -- 8 Conclusions -- References -- An Efficient Hybrid Planning Framework for In-Station Train Dispatching -- 1 Introduction -- 2 The In-Station Train Dispatching Problem -- 3 Description of the Framework -- 3.1 Preprocessor -- 3.2 Optimised Planning Engine -- 3.3 Visualisation Tool -- 4 Experimental Analysis -- 5 Related Work and Conclusions -- References
• Intro -- Preface -- Organization -- Contents - Part I -- ICCS 2021 Main Track -- Smoothing Speed Variability in Age-Friendly Urban Traffic Management -- 1 Introduction -- 2 Related Work -- 3 System Model -- 4 CoMAAPRAS -- 4.1 Update Road Pheromone -- 4.2 Collaborative Decision Making -- 5 Experiment Design -- 6 Results -- 6.1 Speed Variability -- 6.2 Waiting Time -- 6.3 Fuel Consumption -- 6.4 Travel Time -- 7 Conclusions -- References -- An Innovative Employment of the NetLogo AIDS Model in Developing a New Chain Code for Compression -- 1 Introduction -- 2 Method -- 3 Results -- 4 Discussion and Conclusion -- References -- Simulation Modeling of Epidemic Risk in Supermarkets: Investigating the Impact of Social Distancing and Checkout Zone Design -- 1 Introduction -- 2 Modeling Infection Transmission -- 3 Simulation Setup -- 4 Results -- 5 Conclusions -- References -- A Multi-cell Cellular Automata Model of Traffic Flow with Emergency Vehicles: Effect of a Corridor of Life -- 1 Introduction -- 2 Proposed Model -- 2.1 Road Structure, Types of Vehicles and Real Data -- 2.2 The Corridor of Life -- 2.3 Safe Distance Between Cars and the Friction Conflict -- 2.4 The Rules of Traffic Movement -- 3 Results -- 4 Conclusions -- References -- HSLF: HTTP Header Sequence Based LSH Fingerprints for Application Traffic Classification -- 1 Introduction -- 2 Background and Related Work -- 2.1 Traffic Classification Solutions -- 2.2 Research on HTTP Headers -- 3 Preprocessing -- 3.1 Weight Assignment -- 3.2 HTTP Header Sequence -- 4 HTTP Header Sequence Based LSH Fingerprints -- 4.1 LSH Fingerprint -- 4.2 Fingerprint Database -- 4.3 Application Identification -- 5 Experiments -- 5.1 Dataset -- 5.2 Multi-class Classification Performance -- 5.3 Compared with Classic Machine Learning Models -- 5.4 Comparison with Other Approaches -- 6 Conclusion -- References