Please select "SIGCHI" as Society, "UbiComp / ISWC 2020" as Conference / Journal and "Ubicomp 2020 / ISWC Workshop: xyz" as the track in the submission page (with “xyz” being the name of the selected workshop).
In the submission page, please enter the title, authors, and abstract of the paper, and upload your workshop paper, and any supplemental files as required by the specific workshop.
Each workshop paper (independently of the selected workshop) will have to use the same ACM template detailed in the template information page.
If you have any further inquiries, please contact email@example.com, or the organizers of the specific workshop (see below).
AUTHORS TAKE NOTE: The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of the UbiComp / ISWC 2020 conference. The official publication date affects the deadline for any patent filings related to published work. (For those rare conferences whose proceedings are published in the ACM Digital Library after the conference is over, the official publication date remains the first day of the conference.)
Kazuya Murao (Ritsumeikan University, Japan), Yu Enokibori (Nagoya University, Japan), Hristijan Gjoreski (Ss. Cyril and Methodius University, Macedonia), Paula Lago (Kyushu Institute of Technology, Japan), Tsuyoshi Okita (Kyushu Institute of Technology, Japan), Pekka Siirtola (University of Oulu, Finland), Kei Hiroi (Nagoya University, Japan), Philipp M. Scholl (University of Freiburg, Germany), Mathias Ciliberto (University of Sussex, UK)
The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require large-scale human activity corpuses and much improved methods to recognize activities and the context in which they occur.
This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating systems in the real world. We wish to reflect on future methods, such as lifelong learning approaches that allow open-ended activity recognition.
The objective of this workshop is to share the experiences among current researchers around the challenges of real-world activity recognition, the role of datasets and tools, and breakthrough approaches towards open-ended contextual intelligence. This year HASCA will also welcome papers from participants to two challenges: the Third Sussex-Huawei Locomotion and Transportation Recognition Competition (http://www.shl-dataset.org/activity-recognition-challenge-2020/) and the Second Nurse Care Activity Recognition Challenge (https://abc-research.github.io/nurse2020/) in special sessions.
UbiTtention 2020: 5th International Workshop on Smart & Ambient Notification and Attention Management
Anja Exler (Karlsruhe Institute of Technology (KIT), Germany), Alexandra Voit (Adesso AG, Germany), Martin Gjoreski (Jozef Stefan Institute, Slovenia), Tine Kolenik (Jozef Stefan Institute, Slovenia), Niels van Berkel (Aalborg University, Denmark), Tadashi Okoshi (Keio University, Japan), Veljko Pejovic (University of Ljubljana, Slovenia)
In the advancing ubiquitous computing, users are increasingly confronted with a tremendous amount of information proactively provided via notifications from versatile applications and services, through multiple devices and screens in their environment. Thus, human’s attention has been getting a new significant bottleneck. Further, the latest computing trends with emerging new devices including versatile IoT devices, and contexts, such as smart cities, attention representation, sensing, prediction, analysis and adaptive behavior in the computer systems, are needed in our computing systems.
Following the successful UbiTtention 2016 to 2019 workshops, the UbiTtention 2020 workshop brings together researchers and practitioners from academia and industry to explore the management of human attention and smart and ambient notifications with versatile devices and situations to overcome information overload and overchoice. In this workshop, we want to focus on a larger understanding of the different roles notifications can play in a wide variety of computing environments including the office, the home, in cars, and other smart environments. In addition, we introduce an open-data machine learning challenge to advance the field of cognitive load inference in ubiquitous computing. The dataset is the first labelled dataset for cognitive load monitoring with a wristband and it will be fully released after the challenge.
5th International Workshop on Mental Health And Well-Being: Sensing And Intervention
Varun Mishra (Dartmouth College, USA), Akane Sano (Rice University, USA), Saeed Abdullah (Penn State, USA), Jakob E. Bardram (TU Denmark, Denmark), Sandra Servia (University of Cambridge, UK), Elizabeth L. Murnane (Stanford University, USA), Tanzeem Choudhury (Cornell University, USA), Mirco Musolesi (UC London, UK), Giovanna Nunes Vilaza (DTU, Denmark), Rajalakshmi Nandakumar (Cornell Tech, USA), Tauhidur Rahman (UMass Amherst, USA)
Mental health issues affect a significant portion of the world's population and can result in debilitating and life-threatening outcomes. To address this increasingly pressing healthcare challenge, there is a need to research novel approaches for early detection and prevention. Toward this, ubiquitous systems can play a central role in revealing and tracking clinically relevant behaviors, contexts, and symptoms. Further, such systems can passively detect relapse onset and enable the opportune delivery of effective intervention strategies.
However, despite their clear potential, the uptake of ubiquitous technologies into clinical mental healthcare is rare, and a number of challenges still face the overall efficacy of such technology-based solutions. The goal of this workshop is to bring together researchers interested in identifying, articulating, and addressing such issues and opportunities. Following the success this workshop in the last four years, we aim to continue facilitating the UbiComp community in developing novel approaches for sensing and intervention in the context of mental health.
Xinlei Chen (CMU, USA), Shijia Pan (UC Merced, USA), M. Hadi (Florida International University, USA)
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.
CML-IOT 2020: 2nd Workshop on Continual and multimodal learning for Internet of Things
Susu Xu (Qualcomm AI Research, USA), Tong Yu (Samsung Research America, USA), Shijia Pan (UC Merced, USA)
With the deployment of the Internet of Things (IoT), a large number of sensors are connected to the Internet, providing large-amount, streaming, and multimodal data. These data have distinct statistical characteristics over time and sensing modalities, which are hardly captured by traditional learning methods. Continual and multimodal learning allows integration, adaptation, and generalization of the knowledge learned from experiential data collected from distributed and heterogeneous IoT devices to new situations. Therefore, continual and multimodal learning is an important step to enable efficient ubiquitous computing on IoT devices.
We aim at bringing together researchers from different areas to establish a multidisciplinary community and share the latest research in continual learning and multimodal learning for various IoT applications.
WellComp 2020: 3rd International Workshop on Computing for Well-Being
Tadashi Okoshi (Keio University, Japan), Jin Nakazawa (Keio University, Japan), JeongGil Ko (Yonsei University, Republic of Korea), Fahim Kawsar (Nokia Bell Labs, UK), Susanna Pirttikangas (University of Oulu, Finland)
We have been experiencing that much of the influence from ubicomp technologies are both contributing to a better quality of life (QoL) of our individual and organizational lives, and causing new types of stress and pain at the same time. The term "well-being" has recently gained attention as a term that covers our general happiness and even more concrete good conditions in our lives, such as physical, psychological, and social wellness. Active research in various ubicomp research areas (systems, mobile/wearable sensing, persuasive apps, different viewpoints and layers of computing.
After two consecutive successful workshops in 2019 and 2020, WellComp2020 will share the latest research in such various areas related to users' physical, mental, and social well-being. Especially this year's special attention will be paid for "Well-Being Metrics" and "Well-Being Intervention towards behavior change".
Submission deadline: July 06, 2020 at 11:59 PM HAST