PRICAI 2024: Trends in Artificial Intelligence : 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Kyoto, Japan, November 18-24, 2024, Proceedings, Part II (Lecture Notes in Computer Science) (2025)

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PRICAI 2024: Trends in Artificial Intelligence : 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Kyoto, Japan, November 18-24, 2024, Proceedings, Part II (Lecture Notes in Computer Science) (2025)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 465 p.
  • 言語 ENG
  • 商品コード 9789819601189

Full Description

The five-volume proceedings set LNAI 15281-15285, constitutes the refereed proceedings of the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, held in Kyoto, Japan, in November 18-24, 2024.

The 145 full papers and 35 short papers included in this book were carefully reviewed and selected from 543 submissions. 

The papers are organized in the following topical sections:

Part I: Machine Learning, Deep Learning

Part II: Deep Learning, Federated Learning, Generative AI, Natural Language Processing, Large Language Models, 

Part III: Large Language Models, Computer Vision

Part IV: Computer Vision, Autonomous Driving, Agents and Multiagent Systems, Knowledge Graphs, Speech Processing, Optimization

Part V: Optimization, General Applications, Medical Applications, Theoretical Foundations of AI

Contents

.- Deep Learning.

.- STLB-GN: Spatio-Temporal Dual Graph Network with Learnable Bases.

.- Rethinking the Reliability of Post-hoc Calibration Methods under Subpopulation Shift.

.- Zero-shot Heterogeneous Graph Embedding via Semantic Extraction.

.- TG-PhyNN: An Enhanced Physically-Aware Graph Neural Network framework for forecasting Spatio-Temporal Data.

.- Stock Market Index Movement Prediction using Partial Contextual Embedding BERT-LSTM.

.- SCBC: A Supervised Single-cell Classification Method Based on Batch Correction for ATAC-seq Data.

.- TS-CATMA: A Lung Cancer Electronic Nose Data Classification Method Based on Adversarial Training and Multi-Scale Attention.

.- Visualizing the Unseen: Arabic Image-to-Story Generation Using Deep Learning Techniques.

.- Federated Learning.

.- Federated Prompt Tuning: When is it Necessary?.

.- Dirichlet-Based Local Inconsistency Query Strategy for Active Domain Adaptation.

.- FedSD: Cross-Heterogeneous Federated Learning Based on Self-Distillation.

.- Personalized Federated Learning with Feature Alignment via Knowledge Distillation.

.- Multi-Party Collaborative Hate Speech Study on Social Media via Personalized Federated Learning.

.- Preserving Individual User's Right to be Forgotten in Enterprise-Level Federated Learning.

.- Generative AI.

.- Dance Generation From Music with Enhanced Beat.

.- Contrastive Prototype Network for Generative Zero-Shot learning.

.- Steganography: An improved robust model for deep hidden network.

.- Human- and AI-Generated Marketing Content Comparison Corpus, Evaluation, and Detection.

.- Natural Language Processing.

.- Mongolian-Chinese Cross-lingual Topic Detection Based on Knowledge Distillation and Contrastive Learning Methods.

.- Emergence of Grounded Language Representations for Continuous Object Properties through Decentralized Embodied Learning.

.- AI-facilitation for consensus-building by virtual discussion using large language models.

.- False Positive Detection for Text-based Person Retrieval.

.- An End-to-End Method for Chinese Spelling Error Detection and Correction.

.- Dialogue Summarization based on Feature Extraction and Commonsense Injection.

.- SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq .- Personalized Generation with Causal Inference.

.- Document-Level Relation Extraction Model Based On Boundary Distance Loss And Long-Tail Relation Enhancement.

.- MCQG: Reading Comprehension Multiple Choice Questions Generation based on Pre-trained Language Models.

.- ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification.

.- EC-PEFT: An Expertise-Centric Parameter-Efficient Fine-Tuning Framework for Large Language Models.

.- Enhanced Classification of Delay Risk Sources in Road Construction Using Domain- Knowledge-Driven.

.- Modeling the Structural and Semantic Features for Japanese Lyrics Generation of J-pop Songs.

.- FINE-LMT: Fine-grained Feature Learning for Multi-Modal Machine Translation.

.- Segmentation Strategies and Data Enrichment for Improved Abstractive Summarization of Burmese Language.

.- Constrained Reasoning Chains for Enhancing Theory-of-Mind in Large Language Models.

.- Spatial-Temporal Union Channel Enhancement for Continuous Sign Language Recognition.

.- KLoB: a Benchmark for Assessing Knowledge Localization Methods in Language Models.

.- Cross-lingual Entity Alignment Model based on Multi-entity Enhancement and Semantic Information.

.- Large Language Models.

.- A Decomposed-Distilled Sequential Framework for Text-to-Table Task with LLMs.

.- Are Dense Retrieval Models Few-Shot Learners?.

.- An Empirical Study of Leveraging PLMs and LLMs for Long-Text Summarization.

.- A Novel MLLMs-based Two-stage Model for Zero-shot Multimodal Sentiment Analysis.

.- DeepTTS: Enhanced Transformer-Based Text Spotter via Deep Interaction Between Detection and Recognition Tasks.

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