September 13, 2020, Mexico, Cancun

Combining Physical and Data-Driven Knowledge in Ubiquitous Computing

Ubicomp 2020 Workshop

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.

Please check our CPD-18 workshop and CPD-19 workshop.

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.

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.

Important Dates

Submission Deadline: June 19, 2020, submit HERE

Author Notifications: July 3, 2020

Camera-ready: July 17, 2020

Workshop Date: September 13, 2020

Submission Guidelines

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.

ORGANIZERS

Workshop Chairs

Xinlei Chen Carnegie Mellon University

Shijia Pan University of California Merced

M. Hadi Amini Florida International University

Advising Committee

Pei Zhang Carnegie Mellon University

Hae Young Noh Carnegie Mellon University

Technical Programm Committee

Mohammad Mozaffari Ericsson Research

Mostafa Mirshekari Stanford University

Susu Xu Qualcomm AI Research

Yong Zhuang Carnegie Mellon University

Shuai Wang Southeast University

Huandong Wang Massachusetts Institute of Technology

Fengli Xu University of Chicago

Weitao Xu City University of Hong Kong

Wan Du University of California, Merced

Jason Shuo Zhang University of Colorado Boulder

Chris Xiaoxuan Lu Liverpool University

Weixi Gu China Academy of Industrial Internet

Miadreza Shafie-khah University of Vaasa

Shahab Bahrami The University of British Columbia

Marzieh Khakifirooz Tecnológico de Monterrey, Mexico

Publicity Chair

Ahmed Imteaj Florida International University

Web Chair

Billy Zhengwei Wu University of California, Santa Barbara

AGENDA

TBA

THE VENUE

The CPD 2020 workshop is part of (co-located with) Ubicomp 2020, which will be held at Cancun.