The DASFAA 2024 Workshop on Emerging Results in Data Science and Engineering (ERDSE 2024)
Various systems and services, including generative AI, built on massive amounts of data, are revolutionizing our society.
For their development, not only technologies related to data science, such as machine learning, but also data engineering techniques for efficiently managing large amounts of data are essential.
In order for them to be accepted by society, various aspects of data management, such as privacy, security, and fairness, must also be taken into consideration.
This workshop aims to provide an opportunity for presentations and discussions, especially for young researchers and students, in the area of the convergence of data science and data engineering, where many new research topics and results are emerging.
The topics of the workshop include, but are not limited to:
Data Science and AI: Large Language Models (LLM), Explainable AI (XAI), Interpretable Machine Learning
Data Engineering and Databases: Big Data, Graph Data, Data Security, Data Privacy
Information Retrieval and Recommendation: Social Media, Information Credibility, Polarization and Echo Chambers
Multimedia Data: Media Understanding, AI Generated Content, Multimodal AI
Human Factors: Human Computation, Crowdsourcing, Human-in-the-Loop Methods, Bias and Fairness
Research Papers (*) (long presentation): They report significant and original results relevant to the scope of this workshop. We solicit innovative or thought-provoking work but they do not necessarily have to reach the level of completion. Each submitted paper should include an abstract up to 200 words and be no longer than 16 pages (including references, appendices, etc.) in LNCS (Lecture Notes in Computer Science) format. The expected length is between 12 and 16 pages.
Work-in-progress papers (short presentation): They present the goals, challenges, and preliminary results of research work in progress. Each submitted paper should include an abstract up to 200 words and be no longer than 8 pages (including references, appendices, etc.) in LNCS (Lecture Notes in Computer Science) format. The expected length is between 4 and 8 pages.
(*) Some of the papers submitted to the research paper category may be accepted as Work-in-progress papers and allotted to short presentation slots.
Paper Format
Paper submission must be in English.
There is no need for authors to mask their names and affiliations in the manuscript.
Submissions must be original (not previously published and not under review in other forums).
Please use one of the following templates for the LNCS (Lecture Notes in Computer Science) format: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines
Publication
The papers accepted and presented at the DASFAA workshops will be published as post-proceedings in a volume of Springer's Lecture Notes in Computer Science (LNCS) series.
Note: Please choose the TRACK "[Workshop] Emerging Results In Data Science And Engineering (ERDSE 2024)". Please prefix your submission category [Research Paper] or [Work-in-Progress Paper] to the Title of Paper field in the submission page. For example, for a paper with title "Using LLMs for Fake News Detection",
- If you would like to submit it as a research paper, you have to put "[Research Paper] Using LLMs for Fake News Detection" into the Title of Paper field.
- Otherwise, if it is a work-in-progress paper, you have to put "[Work-in-Progress Paper] Using LLMs for Fake News Detection" into the Title of Paper field.
Important Dates
Abstract submission deadline: 2024.3.29 2024.4.5, i.e., You can still submit new papers until the Paper submission deadline 2024.4.5, even if you did not submit an abstract until 2024.3.29
Paper submission deadline: 2024.3.22 2024.4.5
Acceptance notification: 2024.4.26 2024.5.3
(Anywhere on Earth)
Organizer
Satoshi Oyama (Nagoya City University)
Jiyi Li (University of Yamanashi)
Program Committee
Daisuke Kitayama, Kogakuin University
Dominik Köppl, University of Yamanashi
Hiroaki Shiokawa, University of Tsukuba
Hiroaki Ohshima, University of Hyogo
Makoto P. Kato, University of Tsukuba
Masafumi Oyamada, NEC
Jianwei Zhang, Iwate University
Kazutoshi Umemoto, University of Tokyo
Kento Sugiura, Nagoya University
Kei Wakabayashi, University of Tsukuba
Kejing Lu, Nagoya University
Shoko Wakamiya, Nara Institute of Science and Technology
