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Data Science: Foundations and Applications

29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025, Proceedings, Part VI

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  • © 2025
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Overview

Editors:
  1. Xintao Wu

    1. University of Arkansas, Fayetteville, USA

  2. Myra Spiliopoulou

    1. Otto-von-Guericke-University Magdeburg, Magdeburg, Germany

  3. Can Wang

    1. Griffith University, Nathan, Australia

  4. Vipin Kumar

    1. University of Minnesota, Minneapolis, USA

  5. Longbing Cao

    1. Macquarie University, Macquarie Park, Australia

  6. Xiangmin Zhou

    1. RMIT University, Melbourne, Australia

  7. Guansong Pang

    1. Singapore Management University, Singapore, Singapore

  8. Joao Gama

    1. University of Porto, Porto, Portugal

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 15875)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: PAKDD 2025.

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About this book

The two-volume set LNAI 15875 + 15876 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025 Special Session, held in Sydney, NSW, Australia, during June 10–13, 2025.

The 68 full papers included in this set were carefully reviewed and selected from 696 submissions. They were organized in topical sections as follows: survey track; machine learning; trustworthiness; learning on complex data; graph mining; machine learning applications; representation learning; scientific/business data analysis; and special track on large language models.

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Table of contents (34 papers)

  1. Front Matter

    Pages i-xxx
    Download chapter PDF

  2. Survey Track

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Large Language Models for Cybersecurity Education: A Survey of Current Practices and Future Directions

      • Nan Sun, Yuantian Miao, Xiaoxing Mo, Jun Zhang
      Pages 3-20

    3. A Comprehensive Survey on Deep Learning Solutions for 3D Flood Mapping

      • Wenfeng Jia, Bin Liang, Yuxi Lu, Muhammad Arif Khan, Lihong Zheng
      Pages 21-38

    4. A Survey of Foundation Models for Environmental Science

      • Runlong Yu, Shengyu Chen, Yiqun Xie, Xiaowei Jia
      Pages 39-57

    5. A Survey on Efficient Graph Reachability Queries

      • Huangleshuai He, Zhengyi Yang, Dong Wen, Wenqian Zhang, Michael Yu, Wenke Yang et al.
      Pages 58-77

  3. Machine Learning

    1. Front Matter

      Pages 79-79
      Download chapter PDF

    2. Disentangled Representation Learning for Geospatial-Temporal Data Modeling

      • Siyuan Ren, Guannan Chang, Luqi Jing, Zhenghong Wu, Shuailin Chen, Qianru Wang
      Pages 81-93

    3. Treatment Effect Estimation for Graph-Structured Targets

      • Shonosuke Harada, Ryosuke Yoneda, Hisashi Kashima
      Pages 94-109

    4. Dynamic DropConnect: Enhancing Neural Network Robustness Through Adaptive Edge Dropping Strategies

      • Yuan-Chih Yang, Hung-Hsuan Chen
      Pages 110-121

    5. The Brownian Integral Kernel: A New Kernel for Modeling Integrated Brownian Motions

      • Béla H. Böhnke, Edouard Fouché, Klemens Böhm
      Pages 122-134

    6. Fed-ARIMA-OPARBFN: An Ensemble Model for Cross-Domain Crop Yield Time Series Prediction Based on Federated Learning

      • Shiqi Xu, Shuqin Li, Tianyi Song
      Pages 135-146

    7. S-CPD: Topological Smoothing-Based Change Point Detection

      • Harindu Sugathadasa, Sarah Monazam Erfani, Christopher Leckie
      Pages 147-158

    8. VDASI: VAE-Enhanced Degradation-Aware System Identification Using Constrained Latent Spaces

      • Saumya Karunadhika, Ling Luo, Bastian Oetomo, Michele Discepola, Uwe Aickelin
      Pages 159-171

    9. Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning

      • Kohei Obata, Taichi Murayama, Zheng Chen, Yasuko Matsubara, Yasushi Sakurai
      Pages 172-184

    10. PFformer: A Position-Free Transformer Variant for Extreme-Adaptive Multivariate Time Series Forecasting

      • Yanhong Li, David C. Anastasiu
      Pages 185-197

    11. Advancing Long-Term High-Frequency Dissolved Oxygen Forecasting for Australian Rivers

      • Amelia Genova, Huidong Jin, John Taylor, Joel Janek Dabrowski, Peter Toscas, Klaus Joehnk
      Pages 198-209

    12. CNO-Former: Chaotic Neural Oscillatory Transformer for Social Media Text Generation

      • Nuobei Shi, Jiapeng Yu, Hengyi Luo, Jin Ma, Raymond Lee
      Pages 210-222

    13. Multilingual Non-factoid Question Answering with Answer Paragraph Selection

      • Ritwik Mishra, Sreeram Vennam, Rajiv Ratn Shah, Ponnurangam Kumaraguru
      Pages 223-236

    14. Turning Uncertainty to Information by Intervals in Ensemble Classifiers

      • Mansoureh Maadi, Uwe Aickelin, Hadi Akbarzadeh Khorshidi, Michael Kirley
      Pages 237-249
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Other volumes

  1. Trends and Applications in Knowledge Discovery and Data Mining

  2. Advances in Knowledge Discovery and Data Mining

  3. Advances in Knowledge Discovery and Data Mining

  4. Advances in Knowledge Discovery and Data Mining

  5. Advances in Knowledge Discovery and Data Mining

  6. Advances in Knowledge Discovery and Data Mining

  7. Data Science: Foundations and Applications

  8. Data Science: Foundations and Applications

Editors and affiliations

  • University of Arkansas, Fayetteville, USA

    Xintao Wu

  • Otto-von-Guericke-University Magdeburg, Magdeburg, Germany

    Myra Spiliopoulou

  • Griffith University, Nathan, Australia

    Can Wang

  • University of Minnesota, Minneapolis, USA

    Vipin Kumar

  • Macquarie University, Macquarie Park, Australia

    Longbing Cao

  • RMIT University, Melbourne, Australia

    Xiangmin Zhou

  • Singapore Management University, Singapore, Singapore

    Guansong Pang

  • University of Porto, Porto, Portugal

    Joao Gama

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