Connectionists: [CFP] MMM 2024 - special session ICDAR

[dao at nict.go.jp]

https://mmm2024.org/specialpaper.html#s4

ICDAR: Intelligent Cross-Data Analysis and Retrieval

Data has become a critical component of human life in the digital age, 
where it can be collected from various sources and in real-time, 
providing valuable insights into our living environment. However, these 
data sources only represent a small piece of the larger puzzle of life. 
Therefore, the ability to collect and analyze data across multiple 
domains, modalities, and platforms is crucial to solving this puzzle 
faster. Recent research has focused on multimodal data analytics, but 
there is a lack of investigation into cross-data analysis and retrieval. 
This research direction includes cross-modal data, cross-domain, and 
cross-platform data analysis and retrieval. For example, cross-modal 
retrieval systems use a textual query to look for images, while air 
quality index can be predicted using lifelogging images, and daily 
exercises and meals can help predict sleeping quality.

To promote intelligent cross-data analytics and retrieval research and 
create a smarter, sustainable society, we invite submissions to a 
special article collection on "Intelligent Cross-Data Analysis and 
Retrieval." We welcome submissions from diverse research domains and 
disciplines, including well-being, disaster prevention and mitigation, 
mobility, climate change, tourism, healthcare, and food computing. Join 
us in exploring the exciting field of cross-data analysis and retrieval!

This Research Topic welcomes submissions from diverse research domains 
and disciplines such as well-being, disaster prevention and mitigation, 
mobility, climate change, tourism, healthcare, and food computing. 
Example topics of interest include, but are not limited to:

- Event-based cross-data retrieval
- Data mining and AI technology
- Complex event processing for linking sensors data from individuals, 
regions to broad areas dynamically
Transfer Learning and Transformers
- Hypotheses development of the associations within the heterogeneous 
data
- Realization of a prosperous and independent region in which people and 
nature coexist
- Applications leveraging intelligent cross-data analysis for a 
particular domain
- Cross-datasets for repeatable experimentation
- Federated Analytics and Federated Learning for cross-data
- Privacy-public data collaboration
- Integration of diverse multimodal data

Organizers
- Minh-Son Dao, National Institute of Information and Communications 
Technology, Japan
- Michael Alexander Riegler, Simula Metropolitan Center for Digital 
Engineering, Norway
- Duc Tien Dang Nguyen, University of Bergen, Norway
- Thanh-Binh Nguyen, University of Science, Vietnam National University 
in HCM City