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PRICAI 2022: Trends in Artificial Intelligence de Sankalp Khanna

PRICAI 2022: Trends in Artificial Intelligence: Lecture Notes in Computer Science

Editat de Sankalp Khanna, Jian Cao, Quan Bai, Guandong Xu
en Limba Engleză Paperback – 4 noi 2022
This three-volume set, LNAI 13629, LNAI 13630, and LNAI 13631 constitutes  the thoroughly refereed proceedings of the 19th Pacific Rim Conference on Artificial Intelligence, PRICAI 2022, held in Shangai, China, in November 10–13, 2022.

The 91 full papers and 39 short papers presented in these volumes were carefully reviewed and selected from 432 submissions. PRICAI covers a wide range of topics in the areas of social and economic importance for countries in the Pacific Rim: artificial intelligence, machine learning, natural language processing, knowledge representation and reasoning, planning and scheduling, computer vision, distributed artificial intelligence, search methodologies, etc.
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Specificații

ISBN-13: 9783031208645
ISBN-10: 3031208641
Pagini: 568
Ilustrații: XX, 546 p. 143 illus., 133 illus. in color.
Dimensiuni: 155 x 235 x 31 mm
Greutate: 0.85 kg
Ediția:1st edition 2022
Editura: Springer
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

​Knowledge Representation and Reasoning.- Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information.- Source-Free Implicit Semantic Augmentation for Domain Adaptation.- Role-Oriented Network Embedding Method Based on Local Structural Feature and Commonality.- Dynamic Refining Knowledge Distillation Based on Attention Mechanism.-  Entity Representation by Neighboring Relations Topology for Inductive Relation Prediction.- Entity Similarity-based Negative Sampling for Knowledge Graph Embedding.- Label Enhancement using Inter-Example Correlation Information.- Link Prediction via Fused Attribute Features Activation with Graph Convolutional Network.- Multi-Subspace Attention Graph Pooling.- Embedding for Temporal Knowledge Graph Reasoning.- Natural Language Processing.- M2FNet: Multi-granularity Feature Fusion Network for Medical Visual Question Answering.- Noise-robust Semi-supervised Multi-modal Machine Translation.- SETFF: A Semantic Enhanced Table Filling Framework for Joint Entity and Relation Extraction.- PEKIN: Prompt-based External Knowledge Integration Network for Rumor Detection on Social Media.-  Entity-aware Social Media Reading Comprehension.- Analysis via Virtual Node Augmented Graph Convolutional Networks.- Bidirectional Macro-level Discourse Parser based on Oracle Selection.- Evidence-Based Document-Level Event Factuality Identification.- Named Entity Recognition Model of Power Equipment Based on Multi-feature Fusion.- Improving Abstractive Multi-document Summarization with Predicate-Argument Structure Extraction.- A Structure-aware Method for Cross-domain Text Classification.- SICM: A Supervised-based Identification and Classification Model for Chinese Jargons Using Feature Adapter Enhanced BERT.- HS2N: Heterogeneous Semantics-Syntax Fusion Network for Document-level Event Factuality Identification.-  Pay Attention to the ”Tails”: A Novel Aspect-Fusion Model for Long-Tailed Aspect Category Detection.- Choice-driven Contextual Reasoning for Commonsense Question Answering.- Implicit Discourse Relation Recognition Based on Multi-granularity Context Fusion Mechanism.- Chinese Medical Named Entity Recognition Using External Knowledge.- Neural Networks and Deep Learning.- Trajectory Prediction With Heterogeneous Graph Neural Network.- EEF1-NN: Efficient and EF1 allocations through Neural Networks.- Weighted Adaptive Perturbations Adversarial Training for Improving Robustness.- Improved Network Pruning via Similarity-Based Regularization.- Dynamic-GTN: Learning an Node Efficient Embedding in Dynamic Graph with Transformer.- ICDT: Incremental Context Guided Deliberation Transformer for Image Captioning.- Semantic-Adversarial Graph Convolutional Network for Zero-shot Cross-modal Retrieval.- DAST: Depth-Aware Assessment and Synthesis Transformer for RGB-D Salient Object Detection.-  A Vehicle Re-ID Algorithm Based on Channel Correlation Self-Attention and Lstm Local Information Loss.-  A Self-Supervised Graph Autoencoder with Barlow Twins.- Few-shot Image Classification Method Based on Fusion of Important Features of Different Scales.-  Group Residual Dense Block for Key-Point Detector with One-level Feature.