ABOUT THE WORKSHOP
Real-world ubiquitous computing systems face the challenge of requiring a significant amount of data to obtain accurate information through pure data-driven approaches. The performance of these data-driven systems greatly depends on the quantity and `quality' of data. In ideal conditions, pure data-driven methods perform well due to the abundance of data. However, in real-world systems, collecting data can be costly or impossible due to practical limitations. Physical knowledge, on the other hand, can be used to alleviate these issues of data limitation. This physical knowledge can include domain knowledge from experts, heuristics from experiences, as well as analytic models of the physical phenomena.
This workshop aims to explore the intersection between (and the combination of) data and physical knowledge. The workshop will bring together domain experts that explore the physical understanding of the data, practitioners that develop systems and the researchers in traditional data-driven domains. The workshop welcomes papers addressing these issues in different applications/domains as well as algorithmic and systematic approaches to apply physical knowledge. Therefore, we further seek to develop a community that systematically analyzes the data quality regarding inference and evaluates the improvements from the physical knowledge. Preliminary and on-going work are welcomed.
CALL FOR PAPER
Real-world ubiquitous computing systems face the challenge of requiring a significant amount of data to obtain accurate information through pure data-driven approaches. The performance of these pure data-driven systems greatly depends on the quantity and `quality' of data. In ideal conditions, pure data-driven methods perform well due to the abundance of data. However, in real-world systems, collecting data can be costly or impossible due to practical limitations. Physical knowledge, on the other hand, can be used to alleviate these issues of data limitation. This physical knowledge can include 1) domain knowledge from experts, 2) heuristics from experiences, and 3) analytic models of the physical phenomena. With the physical knowledge, we can infer the target information 1) more accurately compared to the pure data-driven model, or 2) with limited (labeled) data, since it is often difficult to obtain a large amount of (labeled) data under various conditions. In recent years, researchers combine this physical knowledge with traditional data-driven approaches to improve computing performance with limited (labeled) data. We aim to bring researchers that explore this direction together and search for systematic solutions across various applications. The accepted papers are invited to be extended for a journal submission at Frontier Big Data.
Topics of Interests
- - Innovations in learning algorithms that combine physical knowledge or models for sensor perception and understanding
- - Experiences, challenges, analysis, and comparisons of sensor data in terms of its physical properties
- - Sensor data processing to improve learning accuracy
- - Machine learning and deep learning with physical knowledge on sensor data
- - Mobile and pervasive systems that utilize physical knowledge to enhance data acquisition
- - System services such as time and location estimation enhanced by additional physical knowledge
- - Heterogeneous collaborative sensing based on physical rules
- - Distributed sensing for cyber-physical systems
- - Advanced machine learning algorithms and solutions for efficient sensing
The application areas include but not limited to:
- - Human-centric sensing applications
- - Environmental and structural monitoring
- - Smart cities and urban health
- - Health, wellness and medical
- - Smart energy systems and intelligent transportation networks
Successful submissions will explain why the topic is relevant to the data limitation caused problem that may be solved through the physical understanding of domain knowledge. In addition to citing relevant, published work, authors must cite and relate their submissions to relevant prior publications of their own. Ethical approval for experiments with human subjects should be demonstrated as part of the submission.
July 3, 2020 July 10, 2020, submit HERE
Author Notifications: July 24, 2020
Camera-ready: July 31, 2020
Workshop Date: September 13, 2020
Please submit short papers that are approximately 4-8 single-spaced 8.5” x 11” pages, including figures and tables, but excluding references, two-column format, using 10-point type on 11-point (tight single-spaced) leading, with a maximum text block of 7” wide x 9” deep with an inter-column spacing of .25”. Submissions may include as many pages as needed for references.
ACM Template can be found here.
The accepted paper will be published in the proceedings with Ubicomp papers this year.
Technical Programm Committee
The CPD 2020 workshop is part of (co-located with) Ubicomp 2020, which will be held at Cancun.