Kooperativer Bibliotheksverbund

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Format: XXVI, 705 p. 248 illus., 222 illus. in color. , online resource.

Edition: 1st ed. 2021.

ISBN: 9783030922382

Series Statement: Theoretical Computer Science and General Issues ; 13110

Content: The four-volume proceedings LNCS 13108, 13109, 13110, and 13111 constitutes the proceedings of the 28th International Conference on Neural Information Processing, ICONIP 2021, which was held during December 8-12, 2021. The conference was planned to take place in Bali, Indonesia but changed to an online format due to the COVID-19 pandemic. The total of 226 full papers presented in these proceedings was carefully reviewed and selected from 1093 submissions. The papers were organized in topical sections as follows: Part I: Theory and algorithms; Part II: Theory and algorithms; human centred computing; AI and cybersecurity; Part III: Cognitive neurosciences; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning algorithms for biomedical data and imaging; applications; Part IV: Applications.

Note: Cognitive Neurosciences -- A Novel Binary BCI Systems Based on Non-oddball Auditory and Visual Paradigms -- A Just-In-Time Compilation Approach for Neural Dynamics Simulation -- STCN-GR: Spatial-Temporal Convolutional Networks for Surface-Electromyography-Based Gesture Recognition -- Gradient descent learning algorithm based on spike selection mechanism for multilayer spiking neural networks -- Learning to Coordinate via Multiple Graph Neural Networks -- A Reinforcement Learning Approach for Abductive Natural Language Generation -- DFFCN: Dual Flow Fusion Convolutional Network for Micro Expression Recognition -- AUPro: Multi-label Facial Action Unit Proposal Generation for Sequence-level Analysis -- Deep kernelized network for fine-grained recognition -- Semantic Perception Swarm Policy with Deep Reinforcement Learning -- Reliable, Robust, and Secure Machine Learning Algorithms Open-Set Recognition with Dual Probability Learning -- How Much Do Synthetic Datasets Matter In Handwritten Text Recognition -- PCMO: Partial Classification from CNN-Based Model Outputs -- Multi-branch Fusion Fully Convolutional Network for Person Re-Identification -- Fast Organization of Objects Spatial Positions in Manipulator Space from Single RGB-D Camera -- EvoBA: An Evolution Strategy as a Strong Baseline for Black-Box Adversarial Attacks -- A Novel Oversampling Technique for Imbalanced Learning Based on SMOTE and Genetic Algorithm -- Dy-Drl2Op: Learning Heuristics for TSP on the Dynamic Graph via Deep Reinforcement Learning -- Multi-label classification of hyperspectral images based on label-specific feature fusion -- A Novel Multi-Scale Key-Point Detector Using Residual Dense Block and Coordinate Attention -- Alleviating Catastrophic Interference in Online Learning via Varying Scale of Backward Queried Data -- Construction and Reasoning for Interval-Valued EBRB Systems -- Theory and Applications of Natural Computing Paradigms -- Brain-mimetic Kernel: A Kernel Constructed from Human fMRI Signals Enabling a Brain-mimetic Visual Recognition Algorithm -- Predominant Sense Acquisition with a Neural Random Walk Model -- Processing-response dependence on the on-chip readout positions in spin-wave reservoir computing -- Advances in deep and shallow machine learning algorithms for biomedical data and imaging -- A Multi-Task Learning Scheme for Motor Imagery Signal Classification -- An End-to-End Hemisphere Discrepancy Network for Subject-Independent Motor Imagery Classification -- Multi-domain Abdomen Image Alignment Based on Joint Network of Registration and Synthesis -- Coordinate Attention Residual Deformable U-Net for Vessel Segmentation -- Gated Channel Attention Network for Cataract Classification on AS-OCT Image -- Overcoming Data Scarcity for Coronary Vessel Segmentation Through Self-Supervised Pre-Training -- Self-Attention Long-Term Dependency Modelling in Electroencephalography Sleep Stage Prediction -- ReCal-Net: Joint Region-Channel-Wise Calibrated Network for Semantic Segmentation in Cataract Surgery Videos -- Enhancing Dermoscopic Features Classification in Images Using Invariant Dataset Augmentation and Convolutional Neural Networks -- Ensembles of Randomized Neural Networks for Pattern-based Time Series Forecasting -- Grouped Echo State Network with Late Fusion for Speech Emotion Recognition -- Applications -- MPANet: Multi-level Progressive Aggregation Network for Crowd Counting -- AFLLC: A Novel Active Contour Model based on Adaptive Fractional Order Differentiation and Local Linearly Constrained Bias Field -- DA-GCN: A Dependency-Aware Graph Convolutional Network for Emotion Recognition in Conversations -- Semi-Supervised Learning with Conditional GANs for Blind Generated Image Quality Assessment -- Uncertainty-Aware Domain Adaptation for Action Recognition -- Free-Form Image Inpainting with Separable Gate Encoder-decoder Network -- BERTDAN: Question-Answer Dual Attention Fusion Networks With Pre-trained Models for Answer Selection -- Rethinking the Effectiveness of Selective Attention in Neural Networks -- An Attention Method to Introduce Prior Knowledge in Dialogue State Tracking -- Effect of Input Noise Dimension in GANs -- Wiper Arm Recognition using YOLOv4 -- Context Aware Joint Modeling of Domain Classification, Intent Detection and Slot Filling with Zero-shot Intent Detection Approach -- Constrained Generative Model for EEG Signals Generation -- Top-Rank Learning Robust to Outliers -- Novel GAN Inversion Model with Latent Space Constraints for Face Reconstruction -- Edge Guided Attention Based Densely Connected Network for Single Image Super-Resolution -- An Agent-Based Market Simulator for Back-testing Deep Reinforcement Learning Based Trade Execution Strategies -- Looking beyond the haze: A Pyramid Fusion Approach -- DGCN-rs: a Dilated Graph Convolutional Networks Jointly Modelling Relation and Semantic for Multi-Event Forecasting -- Training Graph Convolutional Neural Network against Label Noise -- An LSTM-based Plagiarism Detection via Attention Mechanism and a Population-based Approach for Pre-Training Parameters with imbalanced Classes.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783030922375

Additional Edition: Printed edition: ISBN 9783030922399

Language: English

DOI: 10.1007/978-3-030-92238-2

URL: https://doi.org/10.1007/978-3-030-92238-2