Combining Physical and Data-Driven Knowledge in Ubiquitous Computing
Ubicomp 2021 Workshop
Ubicomp 2021 Workshop
In the real-world ubiquitous computing systems, it is difficult to require a significant amount of data to obtain accurate information through pure data-driven methods. The performance of data-driven methods relies on the quantity and ‘quality’ of data. They perform well when a sufficient amount of data is available, which is regarded as ideal conditions. However, in real-world systems, collecting data can be costly or impossible due to practical limitations. On the other hand, it is promising to utilize physical knowledge to alleviate these issues of data limitation. The physical knowledge includes domain knowledge from experts, heuristics from experiences, analytic models of the physical phenomena and etc. We aim to bring researchers that explore this direction together and search for systematic solutions across various applications.
The goal of the workshop is to explore the intersection between (and the combination of) data and physical knowledge. The workshop aims to 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, which focuses on addressing these issues in different applications/domains as well as algorithmic and systematic approaches to applying physical knowledge. Therefore, we further seek to develop a community that systematically analyzes the data quality regarding inference and evaluates the improvements from physical knowledge. Preliminary and on-going work is welcomed.
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.
Submission Deadline: June 29, 2021
Notifications: July 15, 2021
Camera-ready: July 31, 2021
Workshop: September 27, 2021
Submissions can be made at PCS. The workshop paper for CPD can be submitted via: SIGCHI -> UbiComp/ISWC 2021 -> UbiComp/ISWC 2021 Workshop: CPD
Please submit short papers that are at most 5 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.
Abstract: With the advances of hardware, software, and artificial intelligence (AI), there is a new computing paradigm shift from centralized intelligence in the cloud to distributed intelligence on the edge. In the era of edge computing, it is critical to infuse AI to empower diverse edge devices and applications. This talk overviews the challenges and opportunities of on-device deep learning and introduces our recent research work on making on-device deep-learning more efficient, focusing on how to build affordable AI models customized for diverse edge devices and how to maximize the performance of on-device model inference by fully utilizing the heterogeneous computing resources.
The CPD 2021 workshop is part of Ubicomp 2021.