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PRICAI 2024: Trends in Artificial Intelligence: 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Kyoto, Japan, November 1824, 2024, Proceedings, Part II 2025 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 465 pages, height x width: 235x155 mm, 126 Illustrations, color; 17 Illustrations, black and white; XXV, 465 p. 143 illus., 126 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 15282
  • Izdošanas datums: 17-Nov-2024
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 9819601185
  • ISBN-13: 9789819601189
  • Mīkstie vāki
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  • Formāts: Paperback / softback, 465 pages, height x width: 235x155 mm, 126 Illustrations, color; 17 Illustrations, black and white; XXV, 465 p. 143 illus., 126 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 15282
  • Izdošanas datums: 17-Nov-2024
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 9819601185
  • ISBN-13: 9789819601189
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 1824, 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
.- 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 Users 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.