Neural Information Processing : 29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part V / / edited by Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatowt

• Record Nr.: UNINA9910698641103321
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
• ISBN: 9789819916429; 9819916429
• Edizione: [1st ed. 2023.]
• Descrizione fisica: 1 online resource (XXXV, 609 p. 196 illus., 173 illus. in color.)
• Collana: Communications in Computer and Information Science, , 1865-0937 ; ; 1792
• Disciplina: 006.3
• Soggetti: Pattern recognition systems; Computer vision; Data mining; Social sciences - Data processing; Software engineering; Artificial intelligence; Automated Pattern Recognition; Computer Vision; Data Mining and Knowledge Discovery; Computer Application in Social and Behavioral Sciences; Software Engineering; Artificial Intelligence
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Theory and Algorithms II -- GCD-PKAug: A Gradient Consistency Discriminator-based Augmentation Method for Pharmacokinetics Time Courses -- ISP-FESAN: Improving Significant Wave Height Prediction with Feature Engineering and Self-Attention Network -- Binary Orthogonal Non-negative Matrix Factorization -- Shifted Chunk Encoder for Transformer Based Streaming End-to-End ASR -- Interpretable Decision Tree Ensemble Learning with Abstract ; Argumentation for Binary Classification -- Adaptive Graph Recurrent Network for Multivariate Time Series Imputation -- Adaptive Rounding Compensation for Post-Training Quantization -- More Efficient And Locally Enhanced Transformer -- ASLEEP: A Shallow neural modEl for knowlEdge graph comPletion -- A speech enhancement method combining two-branch communication and spectral subtraction -- A fast and robust Photometric redshift forecasting method using Lipschitz adaptive learning rate -- Generating Textual Description using Modified Beam Search -- Disentangling Exploration and Exploitation in Deep Reinforcement Learning Using Contingency Awareness -- Multi-Grained Fusion Graph Neural Networks for Sequential Recommendation -- Optimal Design of Cable-Driven Parallel Robots by Particle Schemes -- UPFP-growth++: An Efficient Algorithm to Find Periodic-Frequent Patterns in Uncertain Temporal Databases -- Active Learning with Weak Supervision for Gaussian Processes -- HPC based Scalable Logarithmic Kernelized Fuzzy Clustering Algorithms for Handling Big Data -- Cognitive Neurosciences -- RTS:A Regional Time Series Framework for Brain Disease Classification -- Deep Domain Adaptation for EEG-based Cross-subject Cognitive Workload Recognition -- Graph Convolutional Neural Network Based on Channel Graph Fusion for EEG Emotion Recognition -- Detecting Major Depressive Disorder by Graph Neural Network Exploiting Resting-state Functional MRI -- An Improved Stimulus Reconstruction Method for EEG-based Short-time Auditory Attention Detection -- Functional Connectivity of the Brain while Solving Scientific Problems with Uncertainty as Revealed by Phase Synchronization based on Hilbert Transform -- Optimizing pcsCPD with Alternating Rank-R and Rank-1 Least Squares: Application to Complex-Valued Multi-Subject fMRI Data -- Decoding Brain Signals with Meta-Learning -- Human Centered Computing -- Research on Answer Generation for Chinese Gaokao Reading Comprehension -- A Novel Graph Transformer Based Approach Toward Multi-hop Question Answering -- Logit Distillation via Student Diversity -- Causal connectivity transition from action observation to mentalizing network for understanding other’s action intention -- ND-NER: A Named Entity Recognition Dataset for OSINT towards the National Defense Domain -- Extractive Question Answering using Transformer-based LM -- Temporal dynamics of value integration in perceptual decisions: An EEG study -- Measuring Decision Confidence Levels from EEG Using a Spectral-Spatial-Temporal Adaptive Graph ConvolutionalNeural Network -- BPMCF: Behavior Preference Mapping Collaborative Filtering for Multi-Behavior Recommendation -- Neural Distinguishers on TinyJAMBU-128 and GIFT-64 -- Towards Hardware-friendly and Robust Facial Landmark Detection Method -- Few-shot Class-incremental Learning for EEG-based Emotion Recognition -- Motor Imagery BCI-based Online Control Soft Glove Rehabilitation System with Vibrotactile Stimulation -- Multi-level visual feature enhancement method for visual question answering -- Learning from Hindsight Demonstrations -- Hindsight Balanced Reward Shaping -- Emotion Recognition with Facial Attention and Objective Activation Functions -- M3S-CNN: Resting-state EEG based Multimodal and Multiscale Feature Extraction for Student Status Prediction in Class -- Towards Human Keypoint Detection in Infrared Images -- Multi-human intelligence in Instance-Based Learning -- How the Presence of Cognitive Biases in Phishing Emails Affects Human Decision-making? -- A simple memory module on reading comprehension -- Predicting Parkinson’s Disease Severity Using Patient-Reported Outcomes and Genetic Information -- Towards the Development of a Machine Learning-based Action Recognition Model to ; Support Positive Behavioural Outcomes in Students with Autism -- Safety Issues Investigation in Deep Learning based Chatbots Answers to Medical Advice Requests.
• Sommario/riassunto: The four-volume set CCIS 1791, 1792, 1793 and 1794 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022. The 213 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.