ACM SIGCHI General Interest Announcements (Mailing List)


Options: Use Forum View

Use Monospaced Font
Show Text Part by Default
Show All Mail Headers

Message: [<< First] [< Prev] [Next >] [Last >>]
Topic: [<< First] [< Prev] [Next >] [Last >>]
Author: [<< First] [< Prev] [Next >] [Last >>]

Print Reply
Shijia Pan <[log in to unmask]>
Reply To:
Shijia Pan <[log in to unmask]>
Wed, 19 Jun 2019 07:07:27 -0700
text/plain (165 lines)
[Apologies if you got multiple copies of this email.]

Dear Colleagues,
We would like to sincerely invite you to submit your paper to our workshop.
The deadline is extended to June 30th now.


CPD 2019: The Second Workshop on Combining Physical and Data-Driven
Knowledge in Ubiquitous Computing

******Part of Ubicomp 2019******

Sept 9 and 10, 2019

London, UK



Many real-world ubiquitous computing systems make use of data-driven
algorithms that require a significant amount of data to obtain good
performance. System performance of these pure, data-driven systems largely
depends on the quantity and quality of the data they use. Under ideal
conditions – representative, large, balanced and labeled data – pure
data-driven methods perform very well.  However, in real-world systems,
collecting data can be costly or impossible due to practical limitations.
Physical knowledge (strong priors), can help alleviate issues that emerge
when good data is limited. This includes 1) domain knowledge from experts,
2) experience-driven heuristics, and 3) analytic models of physical
phenomena. With physical knowledge, we can infer target information more
accurately compared to purely data-driven models.  These priors can also
improve performance and robustness when limited labeled data is available.
In recent years, researchers have combined physical knowledge with
traditional, data-driven approaches to improve model accuracy and system
performance. We aim to attract researchers that are exploring fundamental
questions about the integration of physical knowledge and data in
real-world systems and deployments.  We also aim to identify solutions and
methodologies that generalize across various application domains.

******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

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: June 30, 2019, submit HERE

Notifications: July 8, 2019.

Camera-ready: July 12, 2019.

Workshop: Sept 9 and 10, 2019.

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

Please submit short papers of varying length from *2 to **8 pages* in the
ACM *SIGCHI* format. Submissions may include as many pages as needed for
references. The submissions should not be anonymous. The ACM template can
be found here <>. The
guideline can be found here


*Workshop Chairs:*

Xinlei Chen (Carnegie Mellon University)

Shijia Pan (Carnegie Mellon University)

Jorge Ortiz (Rutgers University)

*Technical Program Committee:*

Yong Li (Tsinghua University)

Jun Han (National University of Singapore)

Roozbeh Jafari (Texas A&M University)

Dezhi Hong (University of California San Diego)

Mi Zhang (Michigan State University)

Yuan Tian (University of Virginia)

Yanjun Han (Stanford University)

Bing Liu (Facebook AI)

Ming Zeng (Facebook Inc.)

Pan Hu (Stanford University)

Wen Hu (The University of New South Wales)

*Advising Committee:*

Jie Liu (Microsoft Research, Harbin Institute of Technology)

Pan Hui (Hong Kong University of Science and Technology
University of Helsinki)

Rasit Eskicioglu (University of Manitoba <>)

Pei Zhang (Carnegie Mellon University)

Hae Young Noh (Carnegie Mellon University)

    For news of CHI books, courses & software, join CHI-RESOURCES
     mailto: [log in to unmask]

    To unsubscribe from CHI-ANNOUNCEMENTS send an email to
     mailto:[log in to unmask]

    For further details of CHI lists see