For authors whose papers are rejected by PRICAI 2024 or ICONIP 2024, please re-submit your paper to PKAW 2024 through this submission link ASAP. The submission link
is open until 31 August 2024.
Please submit the review report as a supplementary file. If you have significantly revised the manuscript based on the review report, please also include a response letter indicating how you resolved the
reviewers' comments in the supplementary file.
The proceedings of PKAW 2024 have been released on Springer. You can access from here for free until 31 December 2024.
Welcome to PKAW 2024
Welcome to the 2024 Principle and practice of data and Knowledge Acquisition Workshop (PKAW). In the past, the workshops have been held in Guilin (2006), Hanoi (2008), Daegu (2010), Kuching (2012), Gold Coast (2014), Phuket (2016), Nanjing (2018), Fiji (2019), Yokohama (2020, online), Shanghai (2022, hybrid), and Jakarta (2023, hybrid). PKAW 2024 will be collocated with the 21st Pacific Rim International Conference on Artificial Intelligence (PRICAI 2024) and held in Kyoto, Japan in November 2024.
PKAW has provided a forum for researchers and practitioners to discuss the state-of-the-art in the areas of knowledge acquisition and machine intelligence (MI, also Artificial Intelligence, AI). PKAW 2024 will continue the above focus and welcome the contributions to the multi-disciplinary approach of human and big data-driven knowledge acquisition and AI techniques and applications.
AI is changing the way in which organizations innovate and communicate their processes, products, and services. Also, in our daily life, AI-embedded devices such as smart speakers are about to become widely used, which extends the possibility of acquiring knowledge from users’ behavior observed through the interaction between those devices and their users. Knowledge acquisition and learning from big data are becoming more challenging than ever. Various knowledge can be acquired not only from human experts but also from heterogeneous data. Multidisciplinary research, including knowledge engineering, artificial intelligence and machine learning, human-computer interaction, etc., is required to meet the challenge. We invite authors to submit papers on all aspects of these areas.
Furthermore, not only in the engineering field but also in the social science field (e.g., economics, social networks, and sociology), recent progress in knowledge acquisition and data engineering techniques is realizing interesting applications. We also invite submissions that present applications tested and deployed in real-life settings and lessons learned during this process.
Proceedings of PKAW 2024 will be published by Springer as a volume of Lecture Notes in Artificial Intelligence (LNAI) series. For more details, please visit here.
News
(02 October) The tentative program for PKAW 2024 has been released.(16 September) The instruction for submitting Camera-ready paper has been released.
(01 August 2024) Program Committee Members has been released.
(07 July 2024) The submission date has been extended to 01 August 2024!
(05 May 2024) We have confirmed that the proceedings of PKAW 2024 will be published by Springer as a volume of Lecture Notes in Artificial Intelligence (LNAI) series.
(01 April 2024) "Call for Papers has been released.
Important Dates
- Paper Submission Deadline:
15 July 202401 August 2024 (UTC-12) - Acceptance Notification: 15 September 2024
- Camera-ready Submission: 22 September 2024
- Workshop Date: 18-19 November 2024
Supporting Organizations
For authors whose papers are rejected by PRICAI 2024 or ICONIP 2024, please re-submit your paper to PKAW 2024 through this submission link ASAP. The submission link
is open until 31 August 2024.
Please submit the review report as a supplementary file. If you have significantly revised the manuscript based on the review report, please also include a response letter indicating how you resolved the reviewers'
comments in the supplementary file.
CALL FOR PAPERS
PKAW (Principle and Practice of Data and Knowledge Acquisition Workshop) was established in 1980s as an integral part of PRICAI (Pacific Rim International Conference on Artificial Intelligence). PKAW 2024 will be held at the 21st Pacific Rim International Conference on Artificial Intelligence (PRICAI 2024) in Kyoto, Japan. A wide range of topics related to knowledge acquisition and representation are greatly welcome.
Important Dates
- Paper Submission:
15 July 202401 August 2024 (UTC-12) - Notification: 15 September 2024
- Camera-Ready Submission: 22 September 2024
- Workshop Date: 18-19 November 2024
Areas of Interest
All aspects of AI, machine learning, knowledge acquisition, data engineering and management for intelligent systems, including (but not restricted to):- Knowledge Acquisition
- Fundamental views on knowledge that affect the knowledge acquisition process and the use of knowledge in knowledge engineering
- Algorithmic approaches to knowledge acquisition
- Tools and techniques for knowledge acquisition, knowledge maintenance and knowledge validation
- Evaluation of knowledge acquisition techniques, tools and methods.
- Ontology and its role in knowledge acquisition
- Knowledge acquisition applications tested and deployed in real-life settings
- Knowledge Representation and Discovering
- Knowledge representation learning
- Temporal knowledge graph
- Data linkage
- Data analytics and mining
- Big data acquisition and analysis
- Machine learning/deep learning
- Semantic Web, the Linked Data and the Web of Data
- Responsible Data/Knowledge Management and System
- Transparency, explainability, trust, and accountability
- Privacy and security
- Other ethical concerns
- Knowledge-aware Application
- Question answering
- Recommendation system
- Domain-related application
- Human-centric Knowledge Engineering
- Human-machine collaboration, integration, interaction, delegation, dialog
- Hybrid approaches combining knowledge engineering and machine learning
- Other Topics
- Experience and Lesson learned
- Reproducibility and negative results of knowledge engineering
- Innovative user interfaces
- Crowd-sourcing for data generation and problem solving
Paper Submission
PKAW will not accept any paper that, at the time of submission, is under review for, has already been published in, or has already been accepted for publication in, a journal or another venue with formally published proceedings. If part of the work has been previously published, authors are strongly encouraged to cite and compare/contrast the new contributions with the parts that were already published before. The paper must substantially extend the previously published work.
PKAW 2024 will adopt single-blind rule for the reviewing process, i.e., the authors do not know the names of the reviewers, but the reviewers can infer the names of the authors from the submission.
Proceedings of PKAW 2024 will be published by Springer as a volume of Lecture Notes in Artificial Intelligence (LNAI) series. All papers for the review should be submitted electronically using the conference management tool in PDF format and formatted using the Springer LNAI template. The main content of the paper should not exceed 12 pages long (excluding references). For accepted papers, the latex source files and a camera-ready version are required to be submitted using the Springer LNAI template. For Springer LNAI format templates, please visit the Springer’s website below.
Contact
For any questions, please contact Dr. Shiqing Wu (shiqing.wu@uts.edu.au) and Dr. Xing Su (xingsu@bjut.edu.cn).ORGANIZING COMMITTEE
Workshop Chairs
Shiqing Wu
University of Technology Sydney, Australia
Weihua Li
Auckland University of Technology, New Zealand
Xing Su
Beijing University of Technology, China
Program Chairs
Xiaolong Xu
Nanjing University of Information Science and Technology, China
Byeong Kang
University of Tasmania, Australia
Publicity Chairs
Md Rafiqul Islam
Australian Institute of Higher Education, Australia
Jiaxing Shen
Lingnan University, China
Yu Yang
The Hong Kong Polytechnic University, China
Honorary Chairs
Paul Compton
University of New South Wales, Australia
Hiroshi Motoda
Osaka University, Japan
Advisory Committee
Quan Bai
University of Tasmania, Australia
Qing Liu
Data61, CSIRO, Australia
Kenichi Yoshida
University of Tsukuba, Japan
Maria R Lee
Shih Chien University, China
Deborah Richards
Macquarie University, Australia
PC MEMBERS
University of Technology Sydney, Australia
Japan Advanced Institute of Science and Technology, Japan
University of Tasmania, Australia
Australian Institute of Higher Education, Australia
Jilin University of Finance and Economics, China
Independent Researcher, Japan
Auckland University of Technology, New Zealand
Auckland University of Technology, New Zealand
Auckland University of Technology, New Zealand
Central China Normal University, China
Aoyama Gakuin University, Japan
Nihon University, Japan
University of Wollongong, Australia
University of Technology Sydney, Australia
Lingnan University, China
Keio University, Japan
University of Tasmania, Australia
Hefei University of Technology, China
The Hong Kong Polytechnic University, China
University of Wollongong, Australia
Nara Women's University, Japan
PROGRAM
The details of the conference venue can be found here.
Presentation length: 15 minutes for Long papers and 10 minutes for Short papers. The time includes Q&A.
Principle and practice of data and Knowledge Acquisition Workshop (PKAW 2024)18 November 2024Venue: Clock Tower Centennial Hall, Kyoto University, Kyoto, Japan Room: Centennial Hall |
|
|---|---|
| Time |
Session |
| 9:30-9:40 | Opening address |
| 9:40-10:30 |
Keynote: Measuring urban bilateral job-housing balance based on big data: A case study of multiple cities in China Speaker: Zhou He Session Chair: Weihua Li |
| 10:30-11:05 |
Session 1: Computer Vision Session Chair: Niken Prasasti Martono EBcGAN: An Edge-Based Conditional Generative Adversarial Network for Image Fusion (Long) Mengshu Li, Zheyuan Yang, Yuai Hua, and Jinyong Cheng Category-Aware Keypoint Masking to Address Biases in Semi-Supervised 2D Pose Estimation Xiangrui Liu, Shushi hong, Yucheng Fang, and Ruirui Li Precision 3D Motion Capture Using Pose Estimation Techniques: Application in Sports Video Analysis Shuzo Kitano, Akimasa Ebihara, Tomohide Sawada, Niken Prasasti Martono, and Hayato Ohwada |
| 11:05-11:30 | Break (Morning Tea) |
| 11:30-13:05 |
Session 2: Natural Language Processing Session Chair: Weihua Li Natural Language Integration for Multimodal Few-Shot Class-Incremental Learning: Image Classification Problem (Long) Pitchayagan Temniranrat, Natsuda Kaothanthong, and Sanparith Marukatat Aspect-Adaptive Knowledge-based Opinion Summarization (Long) Guan Wang, Weihua Li, Edmund M-K Lai, and Quan Bai Multi-Target Contrastive Objective for Learning Property-Aware Vision-Language Representation (Long) Dieu-Hien Nguyen, Nguyen-Khang Le, and Le Minh Nguyen Low Cost Active Learning Framework for Short Answer Scoring (Long) Tatsuya Hori and Koichiro Yamauchi Intent-Spectrum BotTracker: Tackling LLM-based Social Media Bots through an Enhanced BotRGCN Model with Intention and Entropy Measurement (Long) Jinglong Duan, Ziyu Li, Xiaodan Wang, Weihua Li, Quan Bai, and Minh Nguyen Distributed Dataset Framework for Large Language Models Pre-training Nao Souma, Yui Obara, Yasuhiko Yokote, Yutaka Ishikawa, and Kimio Kuramitsu Virtual Learning Machine for Tiny Devices Nozomi Kitagawa and Koichiro Yamauchi |
| 13:05-14:30 | Break (Lunch) |
| 14:30-16:00 |
Session 3: Data Mining and Prediction Session Chair: TBA Mining Prevalent Co-location Patterns with Multiple Minimum Prevalence Thresholds (Long) Vanha Tran, Thiloan Bui, Thaigiang Do, and Hoangan Le Improving User Satisfaction through Approaches that Balance Recommendation Accuracy and Serendipity Tailored to Individual Preferences (Long) Haruto Domoto, Takahiro Uchiya, and Ichi Takumi Towards Responsible Decisions with Limited Training Data Using Human-in-the-Loop (Long) Ashesh Mahidadia, Michael Bain, Hendra Suryanto, Byeong Kang, Charles Guan, and Paul Compton Efficient Redundancy Elimination to Discovering Concise Prevalent Co-location Patterns (Long) Vanha Tran and Vanluan Nguyen Seq2Seq RNNs for Bus Arrival Time Prediction Nancy Bhutani, Soumen Pachal, and Avinash Achar Optimizing Resource Distribution Towards Energy Justice in Resilient Smart Grids Libo Zhang, Yuly Wu, Weidong Li, Song Yang, Yang Chen, Kaiqi Zhao, and Jiamou Liu A Novel Adaptive Multi-channel Fusion Network Based on Deep Learning for Diabetes Diagnosis and Readmission Prediction Peng Xia, Ni Li, Xinying Wang, Yucong Duan, Zeyu Yang, and Qi Qi |
| 16:00-16:30 | Break (Afternoon Tea) |
| 16:30-18:05 |
Session 4: Blockchains, Cloud, and Cybersecurity Session Chair: Huiwen Wu Computable Relations Mapping with Horn Clauses for Inductive Program Synthesis (Long) Taosheng Qiu and Ryutaro Ichise kNN-Res: Residual Neural Network with kNN-Graph Coherence for Point Cloud Registration (Long) Muhammad S. Battikh, Artem Lensky, Dillon Hammill, and Matthew Cook Revolutionizing Organic Product Supply Chains: Blockchain, RSA-Encrypted NFTs, and IPFS for Ethical and Transparent Supply Chains (Long) Trung Phan Hoang Tuan, Khoa Tran Dang, Nghiem Thanh Pham, Nam Tran Ba, Ngan Nguyen Thi Kim, Hieu Doan Minh, and Loc Van Cao Phu A Variational Approach to Personalized Federated Learning and its Improvement (Long) Huiwen Wu and Shuo Zhang Fast and Robust Differential Private Stochastic Gradient Descent with Preconditioner (Long) Huiwen Wu The Integration of Federated Learning Techniques in Predictive Aircraft Maintenance using Cloud Services Kim Tigchelaar, Seyed Sahand Mohammadi Ziabari, and Jeroen Mulder A Cross-Chain Analysis of NFT-Based Personal Data Marketplaces: Evaluating EVM-Supported Platforms for Transparent of Data Trading Triet Minh Nguyen, Bang Le Khanh, Khanh Hong Vo, Nhi Truc Le, Nghiem Pham Thanh, Khiem Huynh Gia, Nam Tran Ba, and Ngan Nguyen |
| 18:05-18:15 | Closing remark |
REGISTRATION
Registration is now available for both the author(s) and general participants. You can go to PRICAI website to complete registration.
Signing up for an account with the ConfAid is required before proceeding with your registration. You will find registration options specifically for PKAW in the system. The discount for PKAW participants is available until 22 September.
You will be asked to provide personal details and the paper information for the registration. Please enter your paper ID using the PKAW ID displayed in CMT3, such as "PKAW-01".
Camera-ready Submission
Submission Instruction
To smoothly publish your work in the proceedings of PKAW 2024, you must submit the camera-ready paper, source files, and the signed and completed copyright transfer form by 22 September 2024 (UTC-12). You need to submit the files using your CMT3 account.
Please submit only one .zip archive and name it using your "PKAW-[your submission ID]". For example, PKAW-01. Please also ensure the archive includes the following files:
- Camera-ready version of your paper - a pdf file of your paper.
- Source files - all your LaTeX sources (LaTeX files with all the associated style files, special fonts, eps files and the underlying bib file for the references);OR a Word file in RTF format
- Signed copyright transfer form
Camera-ready Copy and Source files
Please strictly follow the "Springer Author Instructions" when preparing the final version. Download and use the correct template (either in Latex or MS Word) to prepare your final camera-copy paper. Papers must be in trouble-free, high-resolution PDF format. Use only the arabic numbers system (i.e. [1], [3-5], [4-6,9]) for your references. Please also pay attention to Springer's Editorial Policies when preparing your final copy.
The page limit given in the proceedings is strict as follows. Note that NO appendix is allowed.
- Long papers: 12 pages (excluding references)
- Short papers: 8 pages (excluding references)
After submitting your paper, a corresponding author must be available to carry out a proof check of the paper. The corresponding author should be clearly marked as such in the header of the paper. He or she is also the one who signs the consent-to-publish form on behalf of all of the authors. Our publisher has recently introduced an extra control loop: once data processing is finished, they will contact all corresponding authors and ask them to check their papers. We expect this to happen shortly before the printing of the proceedings. At that time your quick interaction with Springer will be greatly appreciated.
Copyright Form
Please upload a signed and completed copyright form in EasyChair as soon as possible using the pre-filled Springer copyright form. Please do not change the main content and pre-filled content.
It is sufficient for one corresponding author to sign the copyright form. Springer requests that the corresponding author, who should match the corresponding author marked on the paper, must have the full right, power, and authority to sign the agreement on behalf of all of the authors of a particular paper, and accepts responsibility for releasing this material on their behalf. Spring does not accept digital signatures on the consent-to-publish forms at present.
KEYNOTE SPEAKERS
Measuring urban bilateral job-housing balance based on big data: A case study of multiple cities in China
Associate Professor Zhou He, University of Chinese Academy of Sciences, China
Abstract: The job-housing balance (JHB) has an essential impact on the residents’ sense of happiness and high-quality development of cities. This study proposes a new method for measuring job-housing balance and analyzing the influencing factors of different types of cities. First, based on the concept of “happy commuting”, this study constructs bilateral dynamic job-housing balance indicators: job-balance ratio (JBR) and residence-balance ratio (RBR). Then, this study uses big data to calculate the two indicators of 653 sub-districts (or jiedao in Chinese) in seven major cities in China, including Beijing, Shanghai, Xi’an, Wuhan, Shenyang, Shenzhen, and Xiamen. By combining visual analysis and difference value analysis of the two indicators, this study uses hierarchical clustering to classify the seven cities into four types: Job-centric with overall balance (Beijing), Balanced (Shanghai, Shenzhen, Xiamen), Residential-centric with overall balance (Xi’an, Wuhan), and Disjointed (Shenyang). Finally, based on the different types of cities, this study analyzes the influencing factors of urban job-housing balance with spatial distribution differences from three aspects: urban structure, urban facilities, and demographic characteristics. Based on empirical results, we proposed some policy recommendations for the sustainable and high-quality development of different types of cities.
Bio: Zhou He is an associate professor at the School of Economics and Management, University of Chinese Academy of Sciences. His research interests include agent-based modeling and policy simulation of complex management systems such as supply chains, sharing economy, and finance. He serves as an editorial board member of SCI/SSCI-indexed journals such as Flexible Services and Manufacturing Journal, Humanities & Social Sciences Communications, and Systems. He also serves as the Secretary-General of the Asian Social Simulation Association and the Convenor of ISO Supply Chain Research Ad Hoc Group. He has won the Beijing Science and Technology Progress Award, the Excellence Award of the Global Operational Optimization Challenge, and the Excellent Doctoral Dissertation of the Chinese Academy of Sciences.