September 21-26, 2021, All over the world (virtual)
Combining Physical and Data-Driven Knowledge in Ubiquitous Computing
Ubicomp 2021 Workshop
ABOUT THE 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.
CALL FOR PAPER
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.
Topics of Interests
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
- - Distributed sensing for cyber-physical systems
- - Advanced machine learning algorithms and solutions for efficient sensing
- - 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 29, 2021
Notifications: July 15, 2021
Camera-ready: July 31, 2021
Workshop: September 27, 2021
Submission Guidelines
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.
ORGANIZERS
Workshop Chairs
Wenbo Ding Tsinghua-Berkeley Shenzhen Institute
Chenshu Wu University of Maryland
Weitao Xu City University of Hong Kong
Advising Committee
Pei Zhang University of Michigan
Hae Young Noh Stanford University
Jie Liu IEEE Fellow Harbin Institute of Technology
Ercan Engin Kuruoglu National Research Council of Italy
Workshop TPC Chairs
Xinlei Chen Tsinghua University
Shijia Pan University of California Merced
Sicong Liu Xiamen University
Technical Programm Committee
Amir H. Alavi University of Pittsburgh
Heba Aly Amazon
Ishara Dharmasena Loughborough University, UK
Di Jin Amazon Alexa AI
Abdelwahed Khamis University of New South Wales
Guohao Lan Duke University
Chengwen Luo Shenzhen University
Joao Palotti Qatar Computing Research Institute, HBKU
Parth Pathak George Mason University
Longfei Shangguan Microsoft Cognition
Stephan Sigg Aalto University
Shuai Wang Southeast University
Bo Wei Northumbria University
Yaxiong Xie Princeton University
Susu Xu Qualcomm AI Research
Fengli Xu University of Chicago
Wanli Xue University of New South Wales
Zimu Zhou Singapore Management University
Web Chair
Zihan Wang Tsinghua-Berkeley Shenzhen Institute
Publicity Chair
Le Liang Southeast University
AGENDA
27th September 2021 (UTC+8)
Keynote Speech (9:00-9:40)
Efficient On-Device Deep Learning: towards Ubiquitous Intelligence on the Edge
Speaker: Prof. Yunxin Liu, Tsinghua University
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.
Oral Session 1 (9:40-11:40)
All presenters need to enter the online conference room at least 10 minutes in advance, and ensure all the audio and video devices are working properly.
9:40-10:00 Electromagnetic Vibration Tactile Feedback for Biological and Artificial Wave Signals
Presenter: Xiaosa Li, Wuhan University
10:00-10:20 Understanding Structural Hole Spanners in Location-Based Social Networks: A Data-Driven Study
Presenter: Xiaoxin He, Fudan University
10:20-10:40 Quantity or Quality: Data Enabled Online Energy Dispatch
Presenter: Jingshi Cui, Tsinghua University
10:40-11:00 MassHog: Weight-Sensitive Occupant Monitoring for Pig Pens using Actuated Structural Vibrations
Presenter: Jesse R Codling, University of Michigan
11:00-11:20 PIWIMS: Physics Informed Warehouse Inventory Monitory via Synthetic Data Generation
Presenter: Prabh Simran Singh Baweja, Carnegie Mellon University
11:20-11:40 TACNet: Task-aware Electroencephalogram Classification for Brain-Computer Interface through A Novel Temporal Attention Convolutional Network
Presenter: Xiaolin Liu, Beihang University
Oral Session 2 (13:00-17:20)
All presenters need to enter the online conference room at least 10 minutes in advance, and ensure all the audio and video devices are working properly.
13:00-13:20 Deep Learning Based Underwater Acoustic Channel Estimation Exploiting Physical Knowledge on Channel Sparsity
Presenter: Longjie Gao, Xiamen University
13:20-13:40 Physical Knowledge Driven Multi-scale Temporal Receptive Field Network for Compressed Video Action Recognition
Presenter: Lijun He, Xi'an Jiaotong University
13:40-14:00 Three-Dimensional Indoor Visible Light Localization: A Learning-Based Approach
Presenter: Danping Su, Xiamen University
14:00-14:20 Blind Calibration by Maximizing Correlation
Presenter: Guodong Li, Tsinghua University
14:20-14:40 Generative Adversarial Network Enabled Sparse Signal Compression and Recovery for Internet of Medical Things
Presenter: Tiankuo Wei, Xiamen University
14:40-15:00 A Simple and Fast Human Activity Recognition System Using Radio Frequency Energy Harvesting
Presenter: Tao Ni, City University of Hong Kong
15:00-15:20 Few-Shot Cross Domain Battery Capacity Estimation
Presenter: Zihao Zhou, Tsinghua-Berkeley Shenzhen Institute
15:20-15:40 Mobility Data-driven Complete Dispatch Framework for the Ride-hailing Platform
Presenter: Jiaman Wu, Tsinghua University
15:40-16:00 Dark-Channel Mixed Attention Based Neural Networks for Smoke Detection in Fog Environment
Presenter: Le Yang, Xi’an Jiaotong University
16:00-16:20 Data-driven Clustering in Ad-hoc Networks based on Community Detection
Presenter: Shufan Huang, Shanghai Jiao Tong University
16:20-16:40 HTPad: Hexagon-fractal TENG Pad for Scalable Touch Control
Presenter: Xu Yang, Tsinghua University
16:40-17:00 TIP-Air: Tracking Pollution Transfer for Accurate Air Quality Prediction
Presenter: Yun Cheng, ETH Zurich
17:00-17:20 TriboGait: A deep learning enabled triboelectric gait sensor system for human activity recognition and individual identification
Presenter: Jiarong Li, Tsinghua University
THE VENUE
The CPD 2021 workshop is part of Ubicomp 2021.