October 8, 2023, Cancun
Combining Physical and Data-Driven Knowledge in Ubiquitous Computing
Ubicomp 2023 Workshop
ABOUT THE WORKSHOP
With the proliferation of connected devices with advanced sensing, computing, and communication capabilities, ubiquitous computing systems have become prevalent nowadays. They have the potential to revolutionize various industries by enabling new applications and services (e.g., patient monitoring, personalized recommendations, traffic control, home energy management). However, in real-world ubiquitous computing systems, data collection can be expensive or impossible. Due to the limited quantity and quality of data available, pure data-driven methods may not perform well. A promising approach to overcome these limitations is to utilize physical knowledge, including domain knowledge from experts, heuristics based on experience, and analytic models of physical phenomena.
CALL FOR PAPER
The theme of this workshop is to advance the theoretical understanding, algorithmic development and system implementations of ubiquitous computing systems that integrate physical knowledge with data-driven methods. The workshop will provide an inclusive gathering for researchers and practitioners from various fields and facilitate future collaborations. The overall goal is to grow the community who are dedicated to improving ubiquitous computing systems by fusing physical knowledge into data-driven methods. The workshop welcomes research papers as well as position papers.
Topics of Interests
Topics of interests include, but are not limited to:- - 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
- - Environmental and structural monitoring
- - Smart cities
- - Health, wellness and medical
- - Smart energy systems
- - Intelligent transportation networks
Authors must cite and relate their submissions to relevant prior publications of their own. If applicable, ethical approval for experiments with human subjects should be demonstrated as part of the submission.
Important Dates
Submission Deadline: June 15, 2023 (AoE) June 22, 2023 (AoE)
Notification of acceptance: June 30, 2023
Deadline of camera-ready papers: July 31, 2023
Workshop: October 8, 2023
Submission Guidelines
Submissions can be made at PCS.
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
Advising Committee
Workshop TPC Chairs
Weiwei Jiang Beijing University of Posts and Telecommunications
Amir H. Alavi University of Pittsburgh
Technical Programm Committee
Sicong Liu Xiamen University
Susu Xu Stony Brook University
Weixi Gu China Academy of Industrial Internet
Shuai Wang Southeast University
Yongpan Zou Shenzhen University
Linqi Song City University of Hong Kong
Wenbo Ding Tsinghua University
Zhengxiong Li University of Colorado Denver
Le Yang Xi’an Jiaotong University
Hailu Xu California State University
Yincheng Jin University at Buffalo
Yu Yang Lehigh University
Miao He Yanqi Lake Beijing Institute of Mathematical Sciences and Applications
Tan Li City University of Hong Kong
Weitao Xu City University of Hong Kong
Ray E. Sheriff Edge Hill University
Weiwei Jiang Beijing University of Posts and Telecommunications
Amir H. Alavi University of Pittsburgh
Jingao Xu Tsinghua University
Man Zhou Huazhong University of Science and Technology
Marzieh Khakifirooz Tecnológico de Monterrey
Tao Sun Peng Cheng Laboratory
Achyut Shankar University of Warwick
Web Chairs
Letian Zhang Middle Tennessee State University
Publicity Chair
AGENDA
8th October 2023 (Eastern Standard Time)
Oral Session 1 (8:30 AM - 10:10 AM): Sensing
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.
8:30-8:50 CaliFormer: Leveraging Unlabeled Measurements to Calibrate Sensors with Self-supervised Learning
Haoyang Wang, Tsinghua University, Yuxuan Liu, Tsinghua Berkeley Shenzhen Institute, Chenyu Zhao, Tsinghua University, Jiayou He, Hong Kong University of Science and Technology, Wenbo Ding, Tsinghua University and Pengcheng Lab, and Xinlei Chen, Tsinghua University and Pengcheng Lab.
08:50-09:10 SolareSkin: Self-powered Visible Light Sensing Through a Solar Cell E-Skin
Jiarong Li, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Peng Cheng Laboratory, Changshuo Ge, Tsinghua University, Jun Tao, Tsinghua University, Jingyang Wang, Tsinghua University, Xiaomin Xu, Tsinghua University andTsinghua-Berkeley Shenzhen Institute, Xinlei Chen, Tsinghua University and Peng Cheng Laboratory, Weihua Gui, Central South University andPeng Cheng Laboratory, Xiaojun Liang, Peng Cheng Laboratory, and Wenbo Ding Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, and Peng Cheng Laboratory.
09:10-09:30 A Wireless Integrated System with Hybrid Embedded Sensing for the Continuous Monitoring of Bird Flight
Shilong Mu, Tsinghua-Berkeley Shenzhen Institute, Ho Ngai Chow, Tsinghua University, Mi Zhou, Tsinghua-Berkeley Shenzhen Institute, Runze Zhao Tsinghua University, Kai Chong Lei, Tsinghua University, Zihan Geng, Tsinghua University, Yuxing Han Tsinghua-Berkeley Shenzhen Institute, and Wenbo Ding, Tsinghua-Berkeley Shenzhen Institute.
09:30-09:50 Field Reconstruction-Based Non-Rendezvous Calibration for Mobile Air Pollution Sensors
Ji Luo, Tsinghua-Berkeley Shenzhen Institute, Yiling Hu, Shenzhen Environmental Thinking Science and Technology (ETST) Company Ltd., Chengzhao Yu, Tsinghua-Berkeley Shenzhen Institute, Chaopeng Hong, Tsinghua University, Xiao-Ping Zhang, Tsinghua University, and Xinlei Chen, Tsinghua University and Pengcheng Lab.
09:50-10:10 WSTac: Interactive Surface Perception based on Whisker-Inspired and Self-Illuminated Vision-Based Tactile Sensor
Kai Chong Lei, Tsinghua University, Kit Wa Sou, Tsinghua-Berkeley Shenzhen Institute, Wang Sing Chan, Tsinghua-Berkeley Shenzhen institute, Jiayi Yan, Tsinghua University, Siqi Ping, Tsinghua-Berkeley Shenzhen Institute, Dengfeng Peng, Shenzhen University, Wenbo Ding, Tsinghua-Berkeley Shenzhen Institute, and Xiao-Ping Zhang, Tsinghua University.
5-min break
Oral Session 2 (10:15 AM - 11:35 AM): Machine Learning I — Forecasting and Classification
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.
10:15-10:35 LSTM Based Short-Term Data Center Electrical Consumption Forecasting
Feiyang Chen, The Chinese University of Hong Kong, Shenzhen, Chenye Wu, The Chinese University of Hong Kong, Shenzhen, Jiasheng Zhang, Tsinghua University, and Guanchi Liu, Tencent Inc.
10:35-10:55 Data Center Peak Electrical Demand Forecasting: A Multi-Feature SARIMA-LSTM Model
Zeyu Yang, The Chinese University of Hong Kong, Shenzhen, Chenye Wu, The Chinese University of Hong Kong, Shenzhen, Guanchi Liu, Tencent Inc., and Jiasheng Zhang, Tsinghua University.
10:55-11:15 Cross-domain Feature Distillation Framework for Enhancing Classification in Ear-EEG Brain-Computer Interfaces
Ying Sun, Beihang University, Xiaolin Liu, Beihang University, ui Na, Beijing Institute of Technology, Shuai Wang, Beihang University, Dezhi Zheng, Beihang university, and Shangchun Fan, Beihang University.
11:15-11:35 Machine Learning-based Multi-Class Traffic Management for Smart Grid Communication Network
Weiwei Jiang Beijing University of Posts and Telecommunications, Haoyu Han, Beijing University of Posts and Telecommunications, Miao He, Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, and Weixi Gu, China Academy of Industrial Internet.
5-min break
Oral Session 3 (11:40 AM - 01:20 PM): Machine Learning II — Reinforcement Learning and Federated Learning
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.
11:40-12:00 Ground-to-air Heterogeneous Data Communication for Disaster-Response by Attention-based Graph Reinforcement Learning
Jiyuan Ren S, Tsinghua University, Yanggang Xu, Tsinghua University, Zuxin Li, Tsinghua-Berkeley Shenzhen Institute, Chaopeng Hong, Tsinghua University, Xiao-Ping Zhang, Tsinghua University, and Xinlei Chen, Tsinghua University and Pengcheng Lab.
12:00-12:20 SmoothLander: A Quadrotor Landing Control System with Smooth Trajectory Guarantee Based on Reinforcement Learning
Chenyu Zhao, Tsinghua University, Haoyang Wang, Tsinghua University, Jiaqi Li, Tsinghua University, Fanhang Man, Tsinghua-Berkeley Shenzhen Institute, Shilong Mu, Tsinghua-Berkeley Shenzhen Institute, Wenbo Ding, Tsinghua-Berkeley Shenzhen Institute, Xiao-Ping Zhang, Tsinghua University, Xinlei Chen, Tsinghua University and Pengcheng Lab.
12:20-12:40 Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for Resource Management in Space-Air-Ground Integrated Networks
Hengxi Zhang, Tsinghua University, Huaze Tang, Tsinghua University, Wenbo Ding, Tsinghua-Berkeley Shenzhen Institute, and Xiao-Ping Zhang, Tsinghua University and Ryerson University.
12:40-01:00 Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt
Zihao Zhao, Tsinghua-Berkeley Shenzhen Institute, Zhenpeng Shi, Tsinghua University, Yang Liu, Institute for AI Industry Research, and Wenbo Ding, Tsinghua-Berkeley Shenzhen Institute.
01:00-01:20 Client Clustering for Energy-Efficient Clustered Federated Learning in Wireless Networks
Jieming Bian, University of Miami and Jie Xu, University of Miami.
THE VENUE
The CPD 2023 workshop is part of Ubicomp 2023, which will be held at Cancun.