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CPD 2018 : Combining Physical and Data-Driven Knowledge in Ubiquitous Computing (part of Ubicomp'18)

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Link: https://ubicomp18.github.io/workshop18-cpd/
 
When Oct 12, 2018 - Oct 12, 2018
Where Singapore
Submission Deadline Aug 1, 2018
Notification Due Aug 14, 2018
Final Version Due Aug 20, 2018
Categories    ubiquitous computing   sensor network   domain knowledge
 

Call For Papers

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 of 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.

Related Resources

AAAI-MAKE 2019   AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering
ASUC 2019   10th International Conference on Ad hoc, Sensor & Ubiquitous Computing
PerFoT 2019   2019 International Workshop on Pervasive Flow of Things (Co-located with IEEE PerCom 2019)
IEEE EUC 2019   The 17th IEEE International Conference on Embedded and Ubiquitous Computing
CDKE 2019   Conversational Data and Knowledge Engineering
INAP 2019   22nd International Conference on Applications of Declarative Programming and Knowledge Management
AmI4IoT 2019   Trends and Advances in Ambient Intelligence for the Internet of Things, Special Issue of Sensors
COMPSAC 2019   COMPSAC 2019: Data Driven Intelligence for a Smarter World
ICMLA 2019   18th IEEE International Conference on Machine Learning and Applications
PECCS 2019   9th International Conference on Pervasive and Embedded Computing and Communication Systems