09093nam 2200517 450 99646450990331620220402184813.03-030-80253-1(CKB)5590000000517261(MiAaPQ)EBC6676269(Au-PeEL)EBL6676269(OCoLC)1258126081(PPN)25735882X(EXLCZ)99559000000051726120220402d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMulti-disciplinary trends in artificial intelligence 14th International Conference, MIWAI 2021, virtual event, July 2-3, 2021 : proceedings /Phatthanaphong Chomphuwiset, Junmo Kim, Pornntiwa Pawara (editors)Cham, Switzerland :Springer,[2021]©20211 online resource (202 pages)Lecture Notes in Artificial Intelligence ;128323-030-80252-3 Intro -- Preface -- Organization -- Bias, Trust, and Doing Good: The Impacts of Digital Technology on Human Ethics, and Vice Versa (Abstract of Keynote Speaker) -- Contents -- 3D Point Cloud Upsampling and Colorization Using GAN -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Data Pre-processing -- 3.2 Network Architecture -- 3.3 Objective -- 3.4 Post-processing -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Upsampling and Colorization Results -- 4.4 Only Colorization Results -- 4.5 Ablation Study -- 5 Evaluation -- 5.1 Evaluation Metrics -- 5.2 Evaluation Results -- 6 Conclusion and Discussion -- References -- Learning Behavioral Rules from Multi-Agent Simulations for Optimizing Hospital Processes -- 1 Introduction -- 1.1 Motivation -- 1.2 Related Work -- 2 Mutli-agent Simulation Setting -- 2.1 Scenario Description -- 2.2 Modeling of the Scenario -- 2.3 Simulation Results as Gantt Charts -- 3 Alternative Solution: Learning Behavioral Rules -- 3.1 Preliminaries -- 3.2 An Advanced Algorithm for Learning HKBs from Data -- 3.3 Application in the Hospital Process Multi-agent Simulation Setting -- 4 Demonstration and Basic Evaluation -- 5 Conclusion and Future Work -- References -- An Open-World Novelty Generator for Authoring Reinforcement Learning Environment of Standardized Toolkits -- 1 Introduction -- 2 System Architecture -- 3 Experimental Results -- 3.1 Domain Editor Results -- 3.2 Environment Editor Results -- 4 Discussion and Future Work -- References -- Book Cover and Content Similarity Retrieval Using Computer Vision and NLP Techniques -- 1 Introduction -- 2 Literature Review -- 3 Proposed Method -- 3.1 Image Preprocessing -- 3.2 Feature Extraction Using Speed-Up Robust Features (SURF Descriptor) -- 3.3 Word Segmentation -- 3.4 Stop Word Removal -- 3.5 Feature Generation -- 3.6 Similarity Measurements.3.7 Performance Evaluation -- 4 Experiment and Result -- 5 Conclusion -- References -- Fast Classification Learning with Neural Networks and Conceptors for Speech Recognition and Car Driving Maneuvers -- 1 Introduction -- 2 Background and Related Works -- 2.1 Recurrent Neural Networks -- 2.2 Echo State Networks and Conceptors -- 3 Classification with Conceptors -- 3.1 Conceptor Algebra -- 3.2 Classification -- 4 Case Studies -- 4.1 Speech Recognition -- 4.2 Car Driving Maneuvers -- 5 Evaluation -- 5.1 Speech Recognition -- 5.2 Car Driving Maneuvers -- 5.3 Identifying Essential Factors -- 6 Conclusions -- References -- Feature Group Importance for Automated Essay Scoring -- 1 Introduction -- 2 Related Work -- 3 Evaluation Methodology -- 3.1 Data Preprocessing -- 3.2 Learning Algorithms -- 3.3 Evaluation Metric for Learning Algorithm -- 3.4 Experimental Setup -- 3.5 Feature Influence -- 3.6 Feature Selection -- 4 Results and Discussion -- 4.1 QWK Scores Result for Comparison -- 4.2 Feature Selection Results -- 5 Conclusion -- References -- Feature Extraction Efficient for Face Verification Based on Residual Network Architecture -- 1 Introduction -- 2 Related Work -- 3 The Proposed Face Verification System -- 3.1 Ace Detection Using MMOD + CNN -- 3.2 Deep Feature Extraction Using ResNet-50 Architecture -- 4 Experiments -- 4.1 Face Databases -- 4.2 Evaluation Metrics -- 4.3 Evaluation -- 4.4 Discussion -- 5 Conclusion -- References -- Acquiring Input Features from Stock Market Summaries: A NLG Perspective -- 1 Introduction -- 2 Related Work -- 3 Dataset and Problem Formulation -- 3.1 Preliminary -- 3.2 Market Summaries Preprocessing -- 3.3 Dataset Statistics -- 3.4 Generating Input Features -- 3.5 Linearization -- 4 Experimental Setup -- 4.1 Results -- 4.2 Human Evaluation -- 4.3 Discussion and and Error Analysis -- 5 Conclusion -- References.A Comparative of a New Hybrid Based on Neural Networks and SARIMA Models for Time Series Forecasting -- 1 Introduction -- 2 Methodology -- 2.1 Decomposition Method -- 2.2 Seasonal Autoregressive Integrated Moving Average (SARIMA) Model -- 2.3 Artificial Neural Network (ANN) -- 2.4 Radial Basis Function (RBF) -- 2.5 Proposed Method -- 3 Data Preparation and Model Evaluation Criteria -- 3.1 Data Descriptions and Data Preparation -- 3.2 Model Evaluation Criteria -- 4 Results and Discussion -- 5 Conclusion and Future Research -- 5.1 Conclusion -- 5.2 Future Research -- References -- Cartpole Problem with PDL and GP Using Multi-objective Fitness Functions Differing in a Priori Knowledge -- 1 Introduction -- 2 Cartpole Problem with Pdl -- 2.1 PDL and Genetic Programming -- 2.2 Experiment &amp -- Fitness Function Design -- 3 Results -- 4 Conclusions and Future Work -- References -- Learning Robot Arm Controls Using Augmented Random Search in Simulated Environments -- 1 Introduction -- 2 Estimating Policy Using Random Search -- 2.1 Policy Space Search Using Augmented Random Search -- 3 Empirical Set up Using Robot Arm Domain -- 3.1 Designing Robot Arm-Reaching Tasks -- 3.2 State Representations -- 3.3 Training a Robot Arm Using ARS -- 4 Empirical Results and Discussion -- 5 Conclusion -- References -- An Analytical Evaluation of a Deep Learning Model to Detect Network Intrusion -- 1 Introduction -- 2 Literature Review -- 3 Dataset Overview -- 4 Research Methodology -- 4.1 Preprocessing -- 4.2 Feature Selection -- 4.3 Class Imbalance Handling -- 4.4 Long Short Term Memory (LSTM) -- 4.5 Machine Learning Models -- 5 Results and Discussions -- 6 Conclusion -- References -- Application of Machine Learning Techniques to Predict Breast Cancer Survival -- 1 Introduction -- 2 Material and Method -- 2.1 Machine Learning Techniques -- 2.2 Methods.3 Experiment Results -- 3.1 Insight Model Performance -- 3.2 Overall Model Performance -- 4 Discussion and Conclusion -- References -- Thai Handwritten Recognition on BEST2019 Datasets Using Deep Learning -- 1 Introduction -- 2 Related Works -- 2.1 Thai Language Property -- 2.2 Thai Handwritten Recognition -- 3 The Datasets -- 4 Methodology of Thai Handwritten Recognition -- 4.1 Text Localization -- 4.2 Model Generation -- 4.3 Connectionist Temporal Classification (CTC) -- 4.4 Character Error Rate (CER) -- 5 Experiment and Results -- 6 Conclusion -- References -- Comparing of Multi-class Text Classification Methods for Automatic Ratings of Consumer Reviews -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 The Method of Multi-class Classifiers Modelling -- 4.1 Pre-processing of Movie Reviews -- 4.2 Feature Selection and Text Representation -- 4.3 Term Weighting -- 4.4 Multi-class Classifiers Modelling -- 5 Experimental Results -- 6 Conclusion -- References -- Improving Safety and Efficiency for Navigation in Multiagent Systems -- 1 Introduction -- 2 Fundamentals -- 2.1 Reciprocal Velocity Obstacles (RVO) -- 2.2 3D Reciprocal Velocity Obstacle (3DRVO) -- 2.3 Three Dimensional Collision Avoidance -- 3 Believe-Desire-Intention Architecture -- 3.1 Planning Strategy with Sub-goal -- 4 K-D Tree Algorithms -- 4.1 3D-Tree Algorithms -- 5 Experiment and Results -- 5.1 The Scenes -- 5.2 Results -- 6 Conclusion and Future Work -- References -- Correction to: Thai Handwritten Recognition on BEST2019 Datasets Using Deep Learning -- Correction to: Chapter "Thai Handwritten Recognition on BEST2019 Datasets Using Deep Learning" in: P. Chomphuwiset et al. (Eds.): Multi-disciplinary Trends in Artificial Intelligence, LNAI 12832, https://doi.org/10.1007/978-3-030-80253-0_14 -- Author Index.Lecture notes in computer science.Lecture notes in artificial intelligence ;12832.Artificial intelligenceCongressesArtificial intelligenceArtificial intelligenceArtificial intelligence.006.3Phatthanaphong ChomphuwisetKim JunmoPawara PornntiwaMiAaPQMiAaPQMiAaPQBOOK996464509903316Multi-disciplinary Trends in Artificial Intelligence2214287UNISA02052nam 2200433 450 991070279270332120141104134220.0(CKB)5470000002430268(OCoLC)894355801(EXLCZ)99547000000243026820141104d2014 ua 0engurmn|||||||||rdacontentrdamediardacarrierFederal real property, improved transparency could help efforts to manage agencies' maintenance and repair backlogs : report to Chairman, Committee on Homeland Security and Governmental Affairs, U.S. Senate[Washington, D.C.] :United States Government Accountability Office,2014.1 online resource (ii, 35 pages) color illustrationsTitle from title screen (viewed Oct. 21, 2014)."January 2014.""GAO-14-188."Includes bibliographical references.Federal real property, improved transparency could help efforts to manage agencies' maintenance and repair backlogs Administrative agenciesUnited StatesRules and practiceReal propertyUnited StatesMaintenance and repairManagementPublic buildingsUnited StatesMaintenance and repairManagementTransparency in governmentUnited StatesAdministrative agenciesReal propertyMaintenance and repairManagement.Public buildingsMaintenance and repairManagement.Transparency in governmentUnited States.Congress.Senate.Committee on Homeland Security and Governmental Affairs,GPOGPOBOOK9910702792703321Federal real property, improved transparency could help efforts to manage agencies' maintenance and repair backlogs : report to Chairman, Committee on Homeland Security and Governmental Affairs, U.S. Senate3186052UNINA