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ORCID20th PKAW 2024: Kyoto, Japan
- Shiqing Wu
, Xing Su, Xiaolong Xu, Byeong Ho Kang:
Knowledge Management and Acquisition for Intelligent Systems - 20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, Kyoto, Japan, November 18-19, 2024, Proceedings. Lecture Notes in Computer Science 15372, Springer 2025, ISBN 978-981-96-0025-0 - Vanha Tran, Thiloan Bui, Thaigiang Do, Hoangan Le:
Mining Prevalent Co-location Patterns with Multiple Minimum Prevalence Thresholds. 1-14 - Taosheng Qiu, Ryutaro Ichise:
Computable Relations Mapping with Horn Clauses for Inductive Program Synthesis. 15-28 - Guan Wang, Weihua Li, Edmund M.-K. Lai, Quan Bai:
Aspect-Adaptive Knowledge-based Opinion Summarization. 29-41 - Ashesh Mahidadia, Michael Bain, Hendra Suryanto, Byeong Kang, Charles Guan, Paul Compton:
Towards Responsible Decisions with Limited Training Data Using Human-in-the-Loop. 42-54 - Jinglong Duan, Ziyu Li, Xiaodan Wang, Weihua Li, Quan Bai, Minh Nguyen:
Intent-Spectrum BotTracker: Tackling LLM-Based Social Media Bots Through an Enhanced BotRGCN Model with Intention and Entropy Measurement. 55-67 - Haruto Domoto, Takahiro Uchiya, Ichi Takumi:
Improving User Satisfaction Through Approaches that Balance Recommendation Accuracy and Serendipity Tailored to Individual Preferences. 68-79 - Muhammad S. Battikh, Artem Lensky, Dillon Hammill, Matthew Cook:
kNN-Res: Residual Neural Network with kNN-Graph Coherence for Point Cloud Registration. 80-93 - Trung Phan Hoang Tuan, Khoa Tran Dang, Nghiem Thanh Pham, Nam Tran Ba, Ngan Nguyen Thi Kim, Hieu Doan Minh, Loc Van Cao Phu:
Revolutionizing Organic Product Supply Chains: Blockchain, RSA-Encrypted NFTs, and IPFS for Ethical and Transparent Supply Chains. 94-106 - Vanha Tran, Vanluan Nguyen:
Efficient Redundancy Elimination to Discovering Concise Prevalent Co-location Patterns. 107-119 - Mengshu Li, Zheyuan Yang, Yuai Hua, Jinyong Cheng:
EBcGAN: An Edge-Based Conditional Generative Adversarial Network for Image Fusion. 120-135 - Huiwen Wu, Shuo Zhang:
A Variational Approach to Personalized Federated Learning and Its Improvement. 136-148 - Pitchayagan Temniranrat, Natsuda Kaothanthong, Sanparith Marukatat:
Natural Language Integration for Multimodal Few-Shot Class-Incremental Learning: Image Classification Problem. 149-163 - Dieu-Hien Nguyen, Nguyen-Khang Le, Minh Le Nguyen:
Multi-target Contrastive Objective for Learning Property-Aware Vision-Language Representation. 164-175 - Tatsuya Hori, Koichiro Yamauchi:
Low Cost Active Learning Framework for Short Answer Scoring. 176-189 - Huiwen Wu:
Fast and Robust Differential Private Stochastic Gradient Descent with Preconditioner. 190-202 - Kim Tigchelaar, Seyed Sahand Mohammadi Ziabari, Jeroen Mulder:
The Integration of Federated Learning Techniques in Predictive Aircraft Maintenance Using Cloud Services. 203-213 - Shuzo Kitano, Akimasa Ebihara, Tomohide Sawada, Niken Prasasti Martono, Hayato Ohwada:
Precision 3D Motion Capture Using Pose Estimation Techniques: Application in Sports Video Analysis. 214-225 - Triet Minh Nguyen, Bang Le Khanh, Hong Khanh Vo, Nhi Truc Le, Nghiem Pham Thanh, Khiem Huynh Gia, Nam Tran Ba, Ngan Nguyen:
A Cross-Chain Analysis of NFT-Based Personal Data Marketplaces: Evaluating EVM-Supported Platforms for Transparent of Data Trading. 226-235 - Libo Zhang, Yuly Wu, Weidong Li, Song Yang, Yang Chen, Kaiqi Zhao, Jiamou Liu:
Optimizing Resource Distribution Towards Energy Justice in Resilient Smart Grids. 236-245 - Peng Xia, Ni Li, Xinying Wang, Yucong Duan, Zeyu Yang, Qi Qi:
A Novel Adaptive Multi-Channel Fusion Network Based on Deep Learning for Diabetes Diagnosis and Readmission Prediction. 246-255 - Xiangrui Liu, Shushi hong, Yucheng Fang, Ruirui Li:
Category-Aware Keypoint Masking to Address Biases in Semi-supervised 2D Pose Estimation. 256-265 - Nancy Bhutani, Soumen Pachal, Avinash Achar:
Seq2Seq RNNs for Bus Arrival Time Prediction. 266-275 - Nozomi Kitagawa, Koichiro Yamauchi:
Virtual Learning Machine for Tiny Devices. 276-288 - Nao Souma, Yui Obara, Yasuhiko Yokote, Yutaka Ishikawa, Kimio Kuramitsu:
Distributed Dataset Framework for Large Language Models Pre-training. 289-297
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