01251nam--2200409---450-99000040800020331620031015163453.00-471-19135-30040800USA010040800(ALEPH)000040800USA01004080020010420d1998----km-y0itay0103----baengUSa|||||||001yyConstructing intelligence agents with Javaa programmer's guide to smarter applicationsJoseph P. Bigus and Jennifer BigusNew YorkJohn Wiley & Sonsc1998XXXV, 379 p.ill.24 cmcd rom2001001-------2001Intelligenza artificialeLinguaggio Java006.3BIGUS,Joseph P.544542BIGUS,Jennifer544543ITsalbcISBD990000408000203316006.3 BIG12085 Ing006.3BKTECPATTY9020010420USA01093120020403USA011649SIAV51020031015USA011634PATRY9020040406USA011628Constructing intelligence agents with Java874862UNISA00886cam0 22002771 450 SOBE0002453320150506090500.020120416d1984 |||||ita|0103 baengGBMilton: Paradise losta casebookedited by A. E. Dyson, J. LovelockLondonMacMillan1984254 p.22 cmCasebook Series001LAEC000189572001 *Casebook SeriesDyson, A. E. AF00012919070Lovelock, JulianSOBA00003629070ITUNISOB20150506RICAUNISOBUNISOB82048974SOBE00024533M 102 Monografia moderna SBNM820000863SI48974rovitoUNISOBUNISOB20120416145619.020150506090500.0AlfanoMilton, Paradise lost1531452UNISOB01995nam 2200433 450 991015484170332120230810001453.01-63472-200-0(CKB)3710000000972082(MiAaPQ)EBC5155330(EXLCZ)99371000000097208220180521d2017 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAres /by Virginia Loh-HaganAnn Arbor, Michigan :45th Parallel Press is an imprint of Cherry Lake Publishing,[2017]©20171 online resource (32 pages) illustrationsGods and goddesses of the ancient world1-63472-134-9 Includes bibliographical references (page 31) and index.God of war -- Warrior -- Bloodthirsty -- Death tools -- Father of a dragon."Ares in the Gods and Goddesses of the Ancient World series explores the fascinating drama, love stories, and destruction in the myths surrounding the god of war. Book includes history, myths, and a family tree. Written with a high interest level to appeal to a more mature audience and a lower level of complexity with clear visuals to help struggling readers along. Considerate text includes tons of fascinating information and wild facts that will hold the readers' interest, allowing for successful mastery and comprehension. A table of contents, glossary with simplified pronunciations, and index all enhance comprehension."--Provided by publisher.Gods, GreekJuvenile literatureMythology, GreekJuvenile literatureGods, GreekMythology, Greek292.2/113Loh-Hagan Virginia1225467MiAaPQMiAaPQMiAaPQBOOK9910154841703321Ares2848291UNINA13146nam 22008895 450 99655856880331620231017131919.03-031-45725-010.1007/978-3-031-45725-8(CKB)28519053700041(MiAaPQ)EBC30793248(Au-PeEL)EBL30793248(DE-He213)978-3-031-45725-8(PPN)272915149(EXLCZ)992851905370004120231017d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierComputer Vision, Imaging and Computer Graphics Theory and Applications[electronic resource] 17th International Joint Conference, VISIGRAPP 2022, Virtual Event, February 6–8, 2022, Revised Selected Papers /edited by A. Augusto de Sousa, Kurt Debattista, Alexis Paljic, Mounia Ziat, Christophe Hurter, Helen Purchase, Giovanni Maria Farinella, Petia Radeva, Kadi Bouatouch1st ed. 2023.Cham :Springer Nature Switzerland :Imprint: Springer,2023.1 online resource (343 pages)Communications in Computer and Information Science,1865-0937 ;18159783031457241 Intro -- Preface -- Organization -- Contents -- Automatic Threshold RanSaC Algorithms for Pose Estimation Tasks -- 1 Introduction -- 2 RanSaC Methods -- 2.1 Notation -- 2.2 History of RanSaC Algorithms -- 3 Adaptative RanSaC Algorithms -- 4 Data Generation Methodology -- 4.1 Models and Estimators -- 4.2 Semi-artificial Data Generation Method -- 5 Benchmark and Results -- 5.1 Performance Measures -- 5.2 Parameters -- 5.3 Results -- 5.4 Analysis and Comparison -- 6 Conclusion -- References -- Semi-automated Generation of Accurate Ground-Truth for 3D Object Detection -- 1 Introduction -- 2 Related Work on 3D Object Detection -- 2.1 Techniques for Early Object Detection -- 2.2 CNN-Based 3D Object Detection -- 2.3 Conclusions on Related Work -- 3 Semi-automated 3D Dataset Generation -- 3.1 Orientation Estimation -- 3.2 3D Box Estimation -- 4 Experiments -- 4.1 Experimental Setup and Configuration -- 4.2 Evaluation 1: Annotation-Processing Chain -- 4.3 Evaluation 2: 3D Object Detector Trained on the Annotation-Processing Configurations -- 4.4 Cross-Validation on KITTI Dataset -- 4.5 Unsupervised Approach -- 5 Conclusion -- References -- A Quantitative and Qualitative Analysis on a GAN-Based Face Mask Removal on Masked Images and Videos -- 1 Introduction -- 2 Related Works -- 2.1 Inpainting -- 2.2 Face Completion -- 3 Method -- 3.1 Pix2pix-Based Inpainting -- 3.2 Custom Loss Function -- 3.3 System Overview -- 3.4 Predicting Feature Points on a Face -- 4 Experiment -- 4.1 Image Evaluation -- 4.2 Video Evaluation -- 5 Discussion -- 5.1 Quality of Generated Images -- 5.2 Discriminating Facial Expressions -- 5.3 Generating Smooth Videos -- 5.4 Additional Quantitative Analyses -- 6 Limitations -- 7 Conclusion -- References -- Dense Material Segmentation with Context-Aware Network -- 1 Introduction -- 2 Related Works -- 2.1 Material Segmentation Datasets.2.2 Fully Convolutional Network -- 2.3 Material Segmentation with FCN -- 2.4 Global and Local Training -- 2.5 Boundary Refinement -- 2.6 Self-training -- 3 CAM-SegNet Architecture -- 3.1 Feature Sharing Connection -- 3.2 Context-Aware Dense Material Segmentation -- 3.3 Self-training Approach -- 4 CAM-SegNet Experiment Configurations -- 4.1 Dataset -- 4.2 Evaluation Metrics -- 4.3 Implementation Details -- 5 CAM-SegNet Performance Analysis -- 5.1 Quantitative Analysis -- 5.2 Qualitative Analysis -- 5.3 Ablation Study -- 6 Conclusions -- References -- Partial Alignment of Time Series for Action and Activity Prediction -- 1 Introduction -- 2 Related Work -- 3 Temporal Alignment of Action/Activity Sequences -- 3.1 Alignment Methods - Segmented Sequences -- 3.2 Alignment Methods - Unsegmented Sequences -- 3.3 Action and Activity Prediction -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Alignment-Based Prediction in Segmented Sequences -- 4.3 Alignment-Based Action Prediction in Unsegmented Sequences -- 4.4 Graph-Based Activity Prediction -- 4.5 Duration Prognosis -- 5 Conclusions -- References -- Automatic Bi-LSTM Architecture Search Using Bayesian Optimisation for Vehicle Activity Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Trajectory Representation and Analysis -- 2.2 Deep Neural Network Optimisation -- 3 Method -- 3.1 Qualitative Feature Representation -- 3.2 Automatic Bi-LSTM Architecture Search -- 3.3 Optimal Architecture Selection -- 3.4 VNet Modelling -- 4 Vehicle Activity Datasets -- 4.1 Highway Drone Dataset -- 4.2 Traffic Dataset -- 4.3 Vehicle Obstacle Interaction Dataset -- 4.4 Next Generation Simulation Dataset -- 4.5 Combined Dataset -- 5 Experiments and Results -- 5.1 Optimal Architecture Selection -- 5.2 Evaluation of the Optimal Architecture -- 6 Discussion -- 7 Conclusion -- References.ANTENNA: Visual Analytics of Mobility Derived from Cellphone Data -- 1 Introduction -- 2 Related Work -- 2.1 Reconstruction and Extraction of Trajectories -- 2.2 Visual Analytics of Movement -- 3 System Overview -- 3.1 Backend and Frontend -- 4 Data -- 4.1 Database -- 4.2 Processing Pipeline -- 5 ANTENNA's Visualization -- 5.1 Tasks and Design Requirements -- 5.2 Visual Query -- 5.3 Grid Aggregation Mode -- 5.4 Road Aggregation Mode -- 6 Usage Scenarios -- 6.1 Scenario 1: Inter-Urban Movements -- 6.2 Scenario 2: Group Movements -- 7 User Testing -- 7.1 Methodology -- 7.2 Tasks -- 7.3 Results -- 8 Discussion -- 9 Conclusion -- References -- Influence of Errors on the Evaluation of Text Classification Systems -- 1 Introduction -- 2 Setup -- 2.1 Models and Dataset -- 2.2 Explanation Methods -- 2.3 Evaluation of the Models -- 2.4 System Output and Explanation Visualization -- 3 Experiment 1: Effect on the Evaluation of One System -- 3.1 Experiment Design -- 3.2 Task and Questionnaire -- 3.3 Participant Recruitment -- 3.4 Results -- 3.5 Qualitative Results -- 4 Experiment 2: Effect on the Comparison of Two Systems -- 4.1 Experiment Design -- 4.2 Task and Questionnaire -- 4.3 Participant Recruitment -- 4.4 Results -- 5 Experiment 3: Effect of the Comparison of Two Systems (Bias Error Pattern) -- 5.1 Experiment Design -- 5.2 Results -- 6 Experiment 4: Effect of Incorrect Examples (with a Different Language) -- 6.1 Experiment Design -- 6.2 Task and Questionnaire -- 6.3 Participant Recruitment -- 6.4 Translation -- 6.5 Results -- 6.6 Qualitative Results -- 7 Discussion -- 7.1 Limitations -- 8 Conclusion -- References -- Autonomous Navigation Method Considering Passenger Comfort Recognition for Personal Mobility Vehicles in Crowded Pedestrian Spaces -- 1 Introduction -- 2 Process of Passenger Comfort Recognition.3 Investigation of Passenger Comfort Recognition -- 3.1 Passenger Comfort Evaluation Experiment -- 3.2 Effects of Current Situation on Comfort Recognition -- 3.3 Effects of Future Status on Comfort Recognition -- 3.4 Characteristics of Passenger Comfort Recognition -- 4 Proposal of an Autonomous Navigation Method Considering Passenger Comfort Recognition -- 4.1 Design -- 4.2 Validation -- 5 Conclusions -- References -- The Electrodermal Activity of Player Experience in Virtual Reality Games: An Extended Evaluation of the Phasic Component -- 1 Introduction -- 2 Background -- 2.1 Related Work -- 3 Methodology -- 3.1 EDA Data Capture and Phasic Component Calculation -- 3.2 Phasic Component Analysis -- 3.3 Game Experience Analysis -- 3.4 Statistical Analyses -- 3.5 Implementation Tools -- 3.6 Ethical Considerations -- 4 Results -- 4.1 Peaks per Minute -- 4.2 Average Peak Amplitude -- 4.3 Game Experience -- 4.4 Correlation Analysis -- 5 Discussion -- 6 Conclusion and Future Work -- References -- MinMax-CAM: Increasing Precision of Explaining Maps by Contrasting Gradient Signals and Regularizing Kernel Usage -- 1 Introduction -- 2 Related Work -- 3 Contrasting Class Gradient Information -- 3.1 Intuition -- 3.2 Definition -- 3.3 Reducing Noise by Removing Negative Contributions -- 4 Reducing Shared Information Between Classifiers -- 4.1 Counterbalancing Activation Vanishing -- 5 Experimental Setup -- 5.1 Evaluations over Architectures and Problem Domains -- 5.2 Training Procedure -- 5.3 Evaluation Metrics -- 6 Results -- 6.1 Comparison Between Architectures -- 6.2 Evaluation over Distinct Problem Domains -- 6.3 Kernel Usage Regularization -- 7 Conclusions -- References -- DIAR: Deep Image Alignment and Reconstruction Using Swin Transformers -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 3.1 Aligned Dataset -- 3.2 Misaligned Dataset.4 Deep Image Alignment -- 5 Architecture -- 5.1 Deep Residual Sets -- 5.2 Video Swin Transformer -- 5.3 Image Reconstruction Using Swin Transformers -- 5.4 Training -- 6 Evaluation -- 6.1 Aggregation -- 6.2 Image Reconstruction -- 6.3 Alignment and Reconstruction: -- 7 Conclusion -- References -- Active Learning with Data Augmentation Under Small vs Large Dataset Regimes for Semantic-KITTI Dataset -- 1 Introduction -- 1.1 State of the Art -- 2 Methodology -- 3 Validation and Results -- 3.1 Class Based Learning Efficiency -- 3.2 Dataset Size Growth: 1/4 Semantic-KITTI vs Full Semantic-KITTI -- 3.3 t-SNE Problem Analysis -- 4 Conclusion -- 4.1 Challenges and Future Scope -- References -- Transformers in Unsupervised Structure-from-Motion -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Monocular Unsupervised SfM -- 3.2 Architecture -- 3.3 Intrinsics -- 3.4 Appearance-Based Losses -- 4 Experiments -- 4.1 Datasets -- 4.2 Architecture -- 4.3 Implementation Details -- 4.4 Evaluation Metrics -- 4.5 Impact of Architecture -- 4.6 Generalizability -- 4.7 Auxiliary Tasks -- 4.8 Depth Estimation with Learned Camera Intrinsics -- 4.9 Efficiency -- 4.10 Comparing Performance -- 5 Conclusion -- References -- A Study of Aerial Image-Based 3D Reconstructions in a Metropolitan Area -- 1 Introduction -- 2 Previous Work -- 3 Urban Environment -- 3.1 Ground Truth -- 3.2 Image Sets -- 3.3 Urban Categorization -- 4 Experimental Setup -- 4.1 3D Reconstruction Techniques -- 4.2 Pipelines Under Study -- 4.3 Alignment -- 5 Experimental Results -- 5.1 Scene Level Evaluation -- 5.2 Urban Category Centric Evaluation -- 5.3 General Pipeline Evaluation -- 6 Conclusion -- References -- Author Index.This book constitutes the referred proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2022, Virtual Event, February 6–8, 2022. The 15 full papers included in this book were carefully reviewed and selected from 392 submissions. The purpose of VISIGRAPP is to bring together researchers and practitioners interested in both theoretical advances and applications of computer vision, computer graphics and information visualization. VISIGRAPP is composed of four co-located conferences, each specialized in at least one of the aforementioned main knowledge areas, namely GRAPP, IVAPP, HUCAPP and VISAPP. .Communications in Computer and Information Science,1865-0937 ;1815Image processingDigital techniquesComputer visionComputer engineeringComputer networksArtificial intelligenceApplication softwareUser interfaces (Computer systems)Human-computer interactionComputer Imaging, Vision, Pattern Recognition and GraphicsComputer Engineering and NetworksArtificial IntelligenceComputer and Information Systems ApplicationsUser Interfaces and Human Computer InteractionImage processingDigital techniques.Computer vision.Computer engineering.Computer networks.Artificial intelligence.Application software.User interfaces (Computer systems).Human-computer interaction.Computer Imaging, Vision, Pattern Recognition and Graphics.Computer Engineering and Networks.Artificial Intelligence.Computer and Information Systems Applications.User Interfaces and Human Computer Interaction.006de Sousa A. Augusto1434380Debattista Kurt911082Paljic Alexis1434381Ziat Mounia1434382Hurter Christophe1434383Purchase Helen1434384Farinella Giovanni Maria1262568Radeva Petia1434385Bouatouch Kadi1434386MiAaPQMiAaPQMiAaPQBOOK996558568803316Computer Vision, Imaging and Computer Graphics Theory and Applications3588030UNISA