E-grāmata: 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

.- 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.