PRICAI 2024 : trends in artificial intelligence : 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Kyoto, Japan, November 18-24, 2024, Proceedings. Part I / Rafik Hadfi, Patricia Anthony, Alok Sharma, Takayuki Ito, Quan Bai, editors.

Machine Learning. -- Quantitative Analysis of Training Methods, Data Size, and User-Specific Effectiveness in DL-Based Personalized Aesthetic Evaluation. -- EQUISCALE: Equitable Scaling for Abstention Learning. -- Unsupervised Clustering Using a Variational Autoencoder with Constrained Mixtures for Posterior and Prior. -- UTBoost: Gradient Boosted Decision Trees for Uplift Modeling. -- CodeMosaic Patch: Physical Adversarial Attacks Against Infrared Aerial Object Detectors. -- Sequential Clustering for Real-world Datasets. -- Dual-mode Contrastive Learning-Enhanced Knowledge Tracing. -- Leveraging Information Consistency in Frequency and Spatial Domain for Adversarial Attacks. -- Characterization of Similarity Metrics in Epistemic Logic. -- A Relaxed Symmetric Non-negative Matrix Factorization Approach for Community Discovery. -- Enhanced Cognitive Distortions Detection and Classification through Data Augmentation Techniques. -- Enhancing Music Genre Classification using Augmented Features Ensemble Learning Technique. -- A Multi-Layer Network Community Detection Method via Network Feature Augmentation and Contrastive Learning. -- Scene Text Recognition Based on Corner Point and Attention Mechanism. -- A Comprehensive Framework for Debiased Sample Selection across All Noise Types. -- A Traffic Flow Prediction Model Integrating Dynamic Implicit Graph Information. -- A Recursive Learning Algorithm for the Least Squares SVM. -- BDEL: A Backdoor Attack Defense Method Based on Ensemble Learning. -- Customizing Spatial-Temporal Graph Mamba Networks for Pandemic Forecasting. -- Distribution-aligned Sequential Counterfactual Explanation with Local Outlier Factor. -- T-FIA: Temporal-Frequency Interactive Attention Network for Long-term Time Series Forecasting. -- Multi-modal Food Recommendation using Clustering andSelf-supervised Learning. -- A quality assessment method of few-shot datasets based on the fusion of quantity and quality. -- Deep Learning. -- CSDCNet: A Semantic Segmentation Network for Tubular Structures. -- Neural Network Surrogate based on Binary Classification for Assisting Genetic Programming in Searching Scheduling Heuristic. -- HN-Darts:Hybrid Network Differentiable Architecture Search for Industrial Scenarios. High-Order Structure Enhanced Graph Clustering. -- CAFGO: Confidence-Adaptive Factor Graph Optimization Algorithm for Fusion Localization. -- MFNAS: Multi-Fidelity Exploration in Neural Architecture Search with Stable Zero-shot Proxy. -- DyAGL: A Dynamic-aware Adaptive Graph Learning Network for Next POI Recommendation. -- Acoustic classification of bird species using improved pre-trained models. -- Aspect Term Extraction via Dynamic Attention and a Densely Connected Graph Convolutional Network. -- NLDF: Neural Light Dynamic Fields for 3D Talking Head Generation. -- Enhanced Knowledge Tracing via Frequency Integration and Order Sensitivity. -- Position-Aware Dynamic Graph Convolutional Recurrent Network for Traffic Forecasting. -- Pose Preserving Landmark Guided Neural Radiation Fields for Talking Portrait Synthesis. -- Adaptive Optimisation of PyTorch Memory Pools for DNNs. -- Detaching Range from Depth: Personalized Recommendation Meets Personalized PageRank. -- Context-Aware Structural Adaptive Graph Neural Networks. -- multi-GAT: Integrative Analysis of scRNA-seq and scATAC-seq Data Using Graph Attention Networks for Cell Annotation.

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.

Includes author index.

Online resource; title from PDF title page (SpringerLink, viewed November 21, 2024).