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Shijia Pan <[log in to unmask]>
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Wed, 13 Jun 2018 09:41:46 -0700
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*CPD 2018: The First Workshop on Combining Physical and Data-Driven
Knowledge in Ubiquitous Computing *

*****Part of Ubicomp 2018*****

Oct 12th, 2018




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 the
computing performance with limited (labeled) data. We aim to bring
researchers that explore this direction together and search for systematic
solutions across various applications.

Topics of interests include, but are not limited to, the follows:

- 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

The application areas include but not limited to:

- Human-centric sensing applications
- Environmental and structural monitoring
- Smart cities and urban health
- Health, wellness & medical

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.

*****Important Dates*****

Submission deadline: Aug 1, 2018, submit HERE <>

Notifications: Aug 14, 2018.

Camera-ready: Aug 20, 2018.

Workshop: Oct 12, 2018.

*****Submission Guidelines*****

Please submit short papers that are at most 4 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.

Workshop Chairs:

Shijia Pan (Carnegie Mellon University)

Xinlei Chen (Carnegie Mellon University)

Technical Program Committee:

Josiah Hester (Northwestern University)

Mi Zhang (Michigan State University)

Tam Vu (University of Colorado Boulder)

Yuan Tian (University of Virginia)

Kent Lyons (Tesla)

Yong Li (Tsinghua University)

Mo Li (Nanyang Technological University)

Jun Han (National University of Singapore)

Advising Committee:

Pei Zhang (Carnegie Mellon University)

Hae Young Noh (Carnegie Mellon University)

Jie Liu (Microsoft AI & Research)

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