Advancing the U.S. trade agenda : trade with Africa and the African Growth and Opportunity Act : hearing before the Subcommittee on Trade of the Committee on Ways and Means, U.S. House of Representatives, One Hundred Thirteenth Congress, second session, July 29, 2014

• Record Nr.: UNINA9910708488603321
• Pubbl/distr/stampa: Washington : , : U.S. Government Publishing Office, , 2016
• Descrizione fisica: 1 online resource (iii, 91 pages) : illustrations
• Soggetti: Investments, American - Africa, Sub-Saharan; Economic development - Africa, Sub-Saharan; Foreign trade regulation - United States; Commerce; Commercial policy; Economic development; Foreign trade regulation; Investments, American; Legislative hearings.; United States Commerce Africa, Sub-Saharan; Africa, Sub-Saharan Commerce United States; United States Commercial policy; Africa, Sub-Saharan Commercial policy; Africa, Sub-Saharan; United States
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Paper version available for sale by the Superintendent of Documents, United States Government Publishing Office.; "Serial nos. 113-TR07."
• Nota di bibliografia: Includes bibliographical references.

Structural, Syntactic, and Statistical Pattern Recognition : Joint IAPR International Workshops, S+SSPR 2022, Montreal, QC, Canada, August 26–27, 2022, Proceedings / / edited by Adam Krzyzak, Ching Y. Suen, Andrea Torsello, Nicola Nobile

• Record Nr.: UNINA9910639901403321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
• ISBN: 3-031-23028-0
• Edizione: [1st ed. 2022.]
• Descrizione fisica: 1 online resource (336 pages)
• Collana: Lecture Notes in Computer Science, , 1611-3349 ; ; 13813
• Disciplina: 006.37; 006.4
• Soggetti: Artificial intelligence; Computer science - Mathematics; Discrete mathematics; Algorithms; Computer graphics; Computer vision; Pattern recognition systems; Artificial Intelligence; Discrete Mathematics in Computer Science; Design and Analysis of Algorithms; Computer Graphics; Computer Vision; Automated Pattern Recognition
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Realization of Autoencoders by Kernel Methods -- 1 Introduction -- 2 Related Work -- 3 Autoencoders by Kernel Methods -- 3.1 Encoder and Decoder -- 3.2 Fundamental Mapping Without Loss -- 3.3 Kernelized Autoencoder -- 4 Comparison with Neural Networks -- 5 Applications -- 5.1 Denoising Autoencoders -- 5.2 Generative Autoencoders -- 6 ; Discussion -- 7 Conclusion -- References -- Maximal Independent Vertex Set Applied to Graph Pooling -- 1 Introduction -- 2 Related Work -- 2.1 Graph Pooling -- 3 Proposed Method -- 3.1 Maximal Independent Vertex Set (MIVS) -- 3.2 Adaptation of MIVS to Deep Learning -- 4 Experiments -- 4.1 Datasets -- 4.2 Model Architecture and Training Procedure -- 4.3 Ablation Studies -- 4.4 Comparison of MIVSPool According to Other Methods -- 5 Conclusion -- References -- Annotation-Free Keyword Spotting in Historical Vietnamese Manuscripts Using Graph Matching -- 1 Introduction -- 2 Kieu Database -- 3 Annotation-Free Keyword Spotting (KWS) -- 3.1 Synthetic Dataset Creation -- 3.2 Character Detection -- 3.3 Graph Extraction -- 3.4 Graph Matching -- 3.5 Keyword Spotting (KWS) -- 4 Experimental Evaluation -- 4.1 Task Setup and Parameter Optimization -- 4.2 Results -- 4.3 Ablation Study -- 5 Conclusions -- References -- Interactive Generalized Dirichlet Mixture Allocation Model -- 1 Introduction -- 2 Model Description -- 3 Variational Inference -- 4 Interactive Learning Algorithm -- 5 Experimental Results -- 6 Conclusion -- References -- Classifying Me Softly: A Novel Graph Neural Network Based on Features Soft-Alignment -- 1 Introduction -- 2 Related Work -- 3 Features Soft-Alignment Graph Neural Networks -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Ablation Study -- 4.3 Graph Classification Results -- 4.4 Graph Regression Results -- 5 Conclusion -- References.; Review of Handwriting Analysis for Predicting Personality Traits -- 1 Introduction -- 1.1 History -- 1.2 Applications -- 1.3 Requirements -- 2 Research Progress -- 2.1 Advantages -- 2.2 Disadvantages -- 3 Research Steps -- 3.1 Database -- 3.2 Pre-processing -- 3.3 Feature Extraction -- 3.4 Personality Trait -- 3.5 Prediction Model -- 3.6 Performance Measurement -- 4 Experiment and Future Work -- 4.1 Experiment -- 4.2 Future Work -- References -- Graph Reduction Neural Networks for Structural Pattern Recognition -- 1 Introduction and Related Work -- 2 Graph Matching on GNN Reduced Graphs -- 2.1 Graph Reduction Neural Network (GReNN) -- 2.2 Classification of GReNN Reduced Graphs -- 3 Empirical Evaluations -- 3.1 Datasets and Experimental Setup -- 3.2 Analysis of the Structure of the Reduced Graphs -- 3.3 Classification Results -- 3.4 Ablation Study -- 4 Conclusions and Future Work -- References -- Sentiment Analysis from User Reviews Using a Hybrid Generative-Discriminative HMM-SVM Approach -- 1 Introduction -- 2 Related Work -- 3 Hybrid Generative-Discriminative Approach with Fisher Kernels -- 3.1 Hidden Markov Models -- 3.2 Inference on Hidden States: Forward-Backward Algorithm -- 3.3 Fisher Kernels -- 4 Experiments -- 4.1 Problem Modeling -- 4.2 Datasets -- 4.3 Results -- 5 Conclusion -- References -- Spatio-Temporal United Memory for Video Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Dual-Flow Structure Based on Autoencoder -- 2.2 Memory -- 3 Methodology -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Comparison with Existing Methods -- 4.3 Ablation Experiments -- 4.4 Running Time -- 5 Conclusion -- References -- A New Preprocessing Method for Measuring Image Visual Quality Robust to Rotation and Spatial Shifts -- 1 Introduction -- 2 Proposed Preprocessing Method -- 3 Experimental Results -- 4 Conclusions.; References -- Learning Distances Between Graph Nodes and Edges -- 1 Introduction -- 2 Related Work -- 2.1 Graph Edit Distance -- 2.2 Learning the Edit Costs -- 3 Method -- 3.1 The Learning Method -- 3.2 The Algorithm -- 4 Practical Experiments -- 5 Conclusions -- References -- Self-supervised Out-of-Distribution Detection with Dynamic Latent Scale GAN -- 1 Introduction -- 2 Out-of-; Distribution Detection with DLSGAN -- 3 Experiments -- 3.1 Experiments Settings -- 3.2 Experiments Results -- 4 Conclusion -- References -- A Novel Graph Kernel Based on the Wasserstein Distance and Spectral Signatures -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Spectral Signatures: HKS and WKS -- 3.2 Wasserstein Distance -- 4 From Spectral Signatures to Graph Kernels -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Sensitivity Study -- 5.3 Graph Classification Results -- 6 Conclusion -- References -- Discovering Respects for Visual Similarity -- 1 Introduction -- 2 Method -- 2.1 Dataset Selection -- 2.2 Image Representation -- 2.3 What -- 2.4 Why -- 3 Evaluations -- 3.1 Automatic Interpretability and Human Validation -- 3.2 Human Assessment of Cluster Quality -- 4 Conclusion -- References -- Graph Regression Based on Graph Autoencoders -- 1 Introduction -- 2 Related Work -- 2.1 Graph Embedding and Graph Regression -- 2.2 Autoencoders and Graph Autoencoders -- 2.3 Prediction of Chemical Compound Properties -- 3 The Method -- 4 Motive and Practical Application -- 4.1 Database -- 4.2 Architecture Configuration -- 4.3 Energy Prediction -- 4.4 Runtime Analysis -- 5 Conclusions and Future Work -- References -- Distributed Decision Trees -- 1 Introduction -- 2 Different Tree Architectures -- 2.1 Hard Decision Trees -- 2.2 Soft Decision Trees -- 2.3 Budding Trees -- 3 Distributed Budding Trees -- 4 Experiments -- 5 Visualization -- 6 Conclusions -- References.; A Capsule Network for Hierarchical Multi-label Image Classification -- 1 Introduction -- 2 Hierarchical Multi-label Capsules -- 3 Experiments -- 3.1 Implementation Details and Datasets -- 3.2 Experimental Setup -- 3.3 Results -- 4 Conclusions -- References -- Monte Carlo Dropout for Uncertainty Analysis and ECG Trace Image Classification -- 1 Introduction -- 2 Literature Review -- 2.1 ECG Classification -- 2.2 Uncertainty Estimation in Medical Image Analysis -- 3 Proposed Methodology -- 3.1 Dataset -- 3.2 Data Preprocessing -- 3.3 CNN Architecture -- 3.4 Monte Carlo Dropout -- 4 Experimental Results -- 4.1 Experimentation -- 4.2 Result Analysis and Discussion -- 5 Conclusion and Future Work -- References -- One-Against-All Halfplane Dichotomies -- 1 Introduction -- 2 Prior Work -- 3 One-Against-All Halfplane Dichotomies -- 4 A Geometric Perspective -- 5 Pairwise Attribute Difference Vectors -- 6 Linear Programming and Neural Networks -- 7 Ranking -- 8 Summary -- References -- Fast Distance Transforms in Graphs and in Gmaps -- 1 Introduction -- 1.1 Notations and Definitions -- 2 Distance Transform in a Graph -- 2.1 Geodesic Distance Transform -- 3 Distance Transforms in n-Gmaps -- 4 Results -- 5 Conclusions -- References -- Retargeted Regression Methods for Multi-label Learning -- 1 Introduction -- 2 Proposal: Retargeted Multi-label Least Square Regression -- 2.1 Notations -- 2.2 Brief Review of ReLSR -- 2.3 Problem Formulation -- 2.4 Optimization -- 2.5 Computational Complexity -- 2.6 Learning Threshold -- 3 Experiments -- 3.1 Dataset and Evaluation Measurement -- 3.2 Settings -- 3.3 Results -- 4 Conclusion -- References -- Transformer with Spatio-Temporal Representation for Video Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Video Anomaly Detection -- 2.2 Transformer -- 3 Methodology -- 4 Experiments.; 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Comparison with Existing State-of-the-Arts -- 4.4 Ablation Experiments -- 4.5 Running Time -- 5 Conclusion -- References -- Efficient Leave-One-Out Evaluation of Kernelized Implicit Mappings -- 1 Introduction -- 2 Kernelized Implicit Mapping -- 3 LOO Evaluation of KIM -- 4 Positioning of this Study -- 5 Kernels -- 6 Multiple Applications of LOO Matrix -- 6.1 Visualization and Model Selection -- ; 6.2 Nonlinear Classification -- 6.3 Recovery -- 7 Analysis of LOO Matrix -- 8 Discussion -- 9 Conclusion -- References -- Graph Similarity Using Tree Edit Distance -- 1 Introduction -- 2 Motivation and Basic Concepts -- 3 Algorithm -- 4 Experimental Results -- 5 Conclusion -- References -- Data Augmentation on Graphs for Table Type Classification -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Preprocessing -- 3.2 Data Augmentation -- 3.3 Model -- 4 Experiments -- 4.1 The Tab2Know Dataset -- 4.2 Using the Dataset -- 4.3 Results -- 5 Conclusions -- References -- Improved Training for 3D Point Cloud Classification -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Preliminaries -- 3.2 Proposed Method -- 3.3 Proposed Training Protocol -- 4 Experimental Results -- 4.1 Hyper-parameter Sensitivity -- 4.2 Results for Proposed Model Variants -- 4.3 Results for Explored Loss Functions -- 4.4 Explored Training Protocol -- 4.5 Effect of Augmentation -- 4.6 Transfer Learning -- 4.7 Confusion Matrix -- 5 Conclusions -- References -- On the Importance of Temporal Features in Domain Adaptation Methods for Action Recognition -- 1 Introduction -- 2 Related Works -- 3 Recalls Basics of an Architecture for Domain Adaptation -- 4 The New Designed Architecture -- 5 Experiments and Results -- 5.1 Datasets and Metrics -- 5.2 Parameters Setting Details -- 5.3 Results and Comments -- 6 Conclusions.; References.
• Sommario/riassunto: This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2022, held in Montreal, QC, Canada, in August 2022. The 30 papers together with 2 invited talks presented in this volume were carefully reviewed and selected from 50 submissions. The workshops presents papers on topics such as deep learning, processing, computer vision, machine learning and pattern recognition and much more.