ISWC
NOTES & BRIEFS

The following ISWC notes and briefs will be presented at the UbiComp / ISWC 2020 virtual conference:

ISWC Notes

Obtaining a signal useful for continuous pointing input is still an open problem for wearables. While magnetic field sensing is one promising approach, there are significant limitations. Our key contribution in this work is a simulation of a system that tracks a magnet in 3D while also accounting for the ambient magnetic field. The simulated sensor data is processed and the position and rotation is determined by using magnetic field equations, a particle filter and a kinematic model of the hand.

https://doi.org/10.1145/3410531.3414304

Eyelid stickers are thin strips that temporarily create a crease when attached to the eyelid. The direct contact with the crease that increases and decreases the pressure on the eyelid sticker provides a novel opportunity for sensing blinking. We present Eslucent, an on-skin wearable capacitive sensing device that affords blink detection, building on the form factor of eyelid stickers. It consists of an art layer, conductive thread, fiber eyelid stickers, coated with conductive liquid, and applied the device onto the eyelid crease with adhesive temporary tattoo paper. Eslucent detects blinks during intentional blinking and four involuntary activities by a falling edge detection algorithm in a user study of 14 participants. The average precision was 82% and recall was 70% while achieving the precision and recall of more than 90% in intentional blinking. By embedding interactive technology into a daily beauty product, Eslucent explores a novel wearable form factor for blink detection.

https://doi.org/10.1145/3410531.3414298

Glasses are a suitable platform for embedding sensors and displays around our heads to support our daily lives. Furthermore, aesthetic features, durability, and portability are essential properties of glasses. However, designing such smart glasses is challenging, because connecting different glass frames both mechanically and electrically, result in smart glasses with bulky hinges.
To overcome this challenge, we propose a new design to embed inductively coupled coil pairs adjacent to glasses hinges to deliver power and data wirelessly to the frames.
Positioning the coils next to the hinges creates sufficient area for a large transmission and reception coil while maintaining the utility of the glasses.
Consequently, we were able to achieve over 85% power efficiency and a communication rate of 50~Mbps between coils that are small enough to be embedded inside the frame of conventional glasses, available on the market.

https://doi.org/10.1145/3410531.3414299

Recent advances in Automated Dietary Monitoring (ADM) with wearables have shown promising results in eating detection in naturalistic environments. However, determining what an individual is consuming remains a significant challenge. In this paper, we present results of a food type classification study based on a sub-centimeter scale wireless intraoral sensor that continuously measures temperature and jawbone movement. We explored the feasibility of classifying nine different types of foods into five classes based on their water-content and typical serving temperature in a controlled environment (n=4). We demonstrated that the system can classify foods into five classes with a weighted accuracy of 77.5% using temperature-derived features only and with a weighted accuracy of 85.0% using both temperature- and acceleration-derived features. Despite the limitations of our study, these results are encouraging and suggest that intraoral computing might be a viable direction for ADM in the future.

https://doi.org/10.1145/3410531.3414309

Rapid prototyping and fast manufacturing processes are critical drivers for implementing wearable devices. This paper shows an exemplary method for building flexible, fully elastomeric, vibrotactile electromagnetic actuators based on the Lorentz force law.
This paper also introduces the design parameters required for well-functioning actuators and studies the properties of such actuators. The crucial element of actuator is a helical planer coil manufactured from “capillary” silver TPU (Thermoplastic polyurethane), an ultra-stretchable conductor. This paper leverages the novel material to manufacture soft vibration actuators in fewer and simpler steps than previous approaches. Best practice and procedure for building a wearable actuator are reported. We show that dimension of actuators are easily configurable and can be printed in batch-size-one using 3D printing. Actuators can be attached directly to the skin as all the components of FLECTILE are made from biocompatible polymers. Tests on the driving properties have confirmed that the actuator could reach a broad scope of frequency up to 200 Hz with a small voltage (5 V) required. A user study showed that vibrations of the actuator are well perceivable by six study participants under an observing, hovering, and resting condition.

https://doi.org/10.1145/3410531.3414307

The ubiquitous availability of wearable sensing devices has rendered large scale collection of movement data a straightforward endeavor. Yet, annotation of these data remains a challenge and as such, publicly available datasets for human activity recognition (HAR) are typically limited in size as well as in variability, which constrains HAR model training and effectiveness. We introduce masked reconstruction as a viable self-supervised pre-training objective for human activity recognition and explore its effectiveness in comparison to state-of-the-art unsupervised learning techniques. In scenarios with small labeled datasets, the pre-training results in improvements over end-to-end learning on two of the four benchmark datasets. This is promising because the pre-training objective can be integrated “as is” into state-of-the-art recognition pipelines to effectively facilitate improved model robustness, and thus, ultimately, leading to better recognition performance.

https://doi.org/10.1145/3410531.3414306

Earable computing gains growing attention within research and becomes ubiquitous in society. However, there is an emerging need for prototyping devices as critical drivers of innovation. In our work, we reviewed the features of existing earable platforms. Based on 24 publications, we characterized the design space of earable prototyping. We used the open eSense platform (6-axis IMU, auditory I/O) to evaluate the problem-based learning usability of non-experts. We collected data from 79 undergraduate students who developed 39 projects. Our questionnaire-based results suggest that the platform creates interest in the subject matter and supports self-directed learning. The projects align with the research space, indicating ease of use, but lack contributions for more challenging topics. Additionally, many projects included games not present in current research. The average SUS score of the platform was 67.0. The majority of problems are technical issues (e.g., connecting, playing music).

https://doi.org/10.1145/3410531.3414302

Our ability to exploit low-cost wearable sensing modalities for critical human behaviour and activity monitoring applications in health and wellness is reliant on supervised learning regimes; here, deep learning paradigms have proven extremely successful in learning activity representations from annotated data. However, the costly work of gathering and annotating sensory activity datasets is labor intensive, time consuming and not scalable to large volumes of data. While existing unsupervised remedies of deep clustering leverage network architectures and optimization objectives that are tailored for static image datasets, deep architectures to uncover cluster structures from raw sequence data captured by on-body sensors remains largely unexplored. In this paper, we develop an unsupervised end-to-end learning strategy for the fundamental problem of human activity recognition (HAR) from wearables. Through extensive experiments, including comparisons with existing methods, we show the effectiveness of our approach to jointly learn unsupervised representations for sensory data and generate cluster assignments with strong semantic correspondence to distinct human activities.

https://doi.org/10.1145/3410531.3414312

Transfer Learning is becoming increasingly important to the Human Activity Recognition community, as it enables algorithms to reuse what has already been learned from models. It promises shortened training times and increased classification results for new datasets and activity classes. However, the question of what exactly is transferred is not dealt with in detail in many of the recent publications, and it is furthermore often difficult to reproduce the presented results. Therefore we would like to contribute with this paper to the understanding of transfer learning for sensor-based human activity recognition.
In our experiment use weight transfer to transfer models between two data sets, as well as between sensors from the same data set. As source- and target- datasets PAMAP2 and Skoda Mini Checkpoint are used. The utilized network architecture is based on a DeepConvLSTM.
The result of our investigation shows that transfer learning has to be considered in a very differentiated way, since the desired positive effects by applying the method depend very much on the data and also on the architecture used.

https://doi.org/10.1145/3410531.3414311

On-skin displays have emerged as a seamless form factor for visualizing information. However, the non-traditional form factor of these on-skin displays and how they present notifications on the skin may raise concerns for public wear. These perceptions will impact whether a device is eventually adopted or rejected by society. Therefore, researchers must consider the societal facets of device design. In this paper, we study social perceptions towards interacting with a color-changing on-skin display. We examined third-person perspectives through a 254-person online survey. The study was conducted in the United States and Taiwan to distill cross-cultural attitudes. This structured study sheds light on designing on-skin displays reflective of cultural considerations.

https://doi.org/10.1145/3410531.3414301

Fatigue is one of the key factors in the loss of work efficiency and health-related quality of life, and most fatigue assessment methods were based on self-reporting, which may suffer from many factors such as recall bias. To address this issue, we developed an automated system using wearable sensing and machine learning techniques for objective fatigue assessment. ECG/Actigraphy data were collected from subjects in free-living environments. Preprocessing and feature engineering methods were applied, before interpretable solution and deep learning solution were introduced. Specifically, for interpretable solution, we proposed a feature selection approach which can select less correlated and high informative features for better understanding system’s decision-making process. For deep learning solution, we used state-of-the-art self-attention model, based on which we further proposed a consistency self-attention (CSA) mechanism for fatigue assessment. Extensive experiments were conducted, and very promising results were achieved.

https://doi.org/10.1145/3410531.3414308

Encounters with casual acquaintances are common in our daily lives. In such situations, people are sometimes unable to find an appropriate topic for conversation, and as such, an awkward silence follows. However, we believe that this awkward encounter can be an opportunity to build a good relationship with the acquaintance through a brief conversation if an appropriate topic is discovered. In this study, we examined a method to enrich casual conversations for an unintended encounter by following three strategies. (1) an online questionnaire survey that involves 10,750 participants to determine how they experience awkward encounters. (2) the design and implementation of a smartwatch-based topic suggestion that relies on finding a commonality in the users’ video-viewing histories. (3) demos and semi-structured interviews that involves 15 participants to evaluate this approach. This investigation demonstrates that this novel approach can help users overcome the awkwardness of conversations with casual acquaintances.

https://doi.org/10.1145/3410531.3414310

The COVID-19 pandemic dictated that wearing face masks during public interactions was the new norm across much of the globe. As the masks naturally occlude part of the wearer’s face, the part of communication that occurs through facial expressions is lost, and could reduce acceptance of mask wear. To address the issue, we created 2 face mask prototypes, incorporating simple expressive display elements and evaluated them in a user study. Aiming toexplore the potential for low-cost solutions, suitable for large-scale deployment, our concepts utilized bi-state electrochromic displays. One concept Mouthy Mask aimed to reproduce the image of the wearer’s mouth, whilst the Smiley Mask was symbolic in nature. The smart face masks were considered useful in public contexts to support short socially expected rituals. Generally a visualization directly representing the wearer’s mouth was preferred to an emoji style visualization. As a contribution, our work presents a stepping stone towards productizable solutions for smart face masks that potentially increase the acceptability of face mask wear in public.

https://doi.org/10.1145/3410531.3414303

This paper investigates the possibility of using soft smart textiles over the hair regions to detect chewing activities under episodes of snacking in a simulated scenario with everyday activities. The planar pressure textile sensors are used to perform mechanomyography of the temporalis muscles in the form of a cap. 10 participants contributed 30 recording sessions with time periods between 30 and 60 minutes. A frequency analysis method is developed to detect moments of snacking events with continuous sliding windows on 1-second time granularity. Our approach results in a baseline 80% accuracy, over 85% after outlier removal, and above 90% accuracy for some of the participants.

https://doi.org/10.1145/3410531.3414305

We present a wearable, oscillating magnetic field-based proximity sensing system to monitor social distancing as suggested to prevent COVID 19 spread (being between 1.5 and 2.0m) apart. We evaluate the system both in controlled lab experiments and in a real life large hardware store setting. We demonstrate that, due physical properties of the magnetic field, the system is much more robust than current BT based sensing, in particular being nearly 100% correct when it comes to distinguishing between distances above and below the 2.0m threshold.

https://doi.org/10.1145/3410531.3414313

We present the GastroDigitalShirt, a smart T-Shirt for capturing abdominal sounds produced during digestion. The garment prototype embeds an array of eight miniaturised microphones connected to a low-power wearable computer and is designed for long-term recording. We present the microphone integration and shirt wiring layout. With the GastroDigitalShirt we monitored the different digestion phases over six hours in four healthy participants with no prior gastro-intestinal diseases. The collected data were annotated by two independent raters to mark Bowel Sounds (BS) instances. The interrater agreement was substantial, with Cohen’s Kappa of 0.7, confirming a consistent labeling approach. Overall 3046 BS instances were individually annotated. The extracted BS were structured by Hierarchical Agglomerative Clustering. The analysis highlighted the presence of 4 BS types. The results show that our prototype can capture the main BS types reported in literature.

https://doi.org/10.1145/3410531.3414297

More than one million people in the US suffer from hemianopia, which blinds the vision in one half of the peripheral vision in both eyes. Hemianopic patients are often not aware of what they cannot see and frequently bump into walls, trip over objects, or walk into people on the side where the peripheral vision is diminished. We present an augmented reality based assistive technology that expands the peripheral vision of hemianopic patients at all distances. In a pilot trial, we evaluate the utility of this assistive technology for ten hemianopic patients. We measure and compare outcomes related to target identification and visual search in the participants. Improvements in target identification are noted in all participants ranging from 18% to 72%. Similarly, all the participants benefit from the assistive technology in performing a visual search task with an average increase of 24% in the number of successful searches compared to unaided trials. The proposed technology is the first instance of an electronic vision enhancement tool for hemianopic patients and is expected to maximize the residual vision and quality of life in this growing, yet largely overlooked population.

https://doi.org/10.1145/3410531.3414296

ISWC Briefs

Sound can provide important information about the environment, human activity, and situational cues but can be inaccessible to deaf or hard of hearing (DHH) people. In this paper, we explore a wearable tactile technology to provide sound feedback to DHH people. After implementing a wrist-worn tactile prototype, we performed a four-week field study with 12 DHH people. Participants reported that our device increased awareness of sounds by conveying actionable cues (e.g., appliance alerts) and ‘experiential’ sound information (e.g., bird chirp patterns).

https://doi.org/10.1145/3410531.3414291

Theatre provides a unique environment in which to obtain detailed data on social interactions in a controlled and repeatable manner. This work introduces a method for capturing and characterising the underlying emotional intent of performers in a scripted scene using in-ear accelerometers. Each scene is acted with different underlying emotional intentions using the theatrical technique of Actioning. The goal of the work is to uncover characteristics in the joint movement patterns that reveal information on the positive or negative valence of these intentions. Preliminary findings over 3×12 (Covid-19 restricted) non-actor trials suggests people are more energetic and more in-sync when using positive versus negative intentions.

https://doi.org/10.1145/3410531.3414292

Dental braces are a semi-permanent dental treatment that are in direct contact with our metabolism (saliva), food and liquids we ingest, and our environment while smiling or talking. This paper introduces braceIO, biochemical ligatures on dental braces that change colors depending on saliva concentration levels (pH, nitric oxide and acid uric), and can be read by an external device. This work presents our fabrication process of the ligatures and external device, and the technical evaluation of the absorption time, colorimetric measurement tests and the color map to the biosensor level in the app. This project aims to maintain the shape, wearability and aesthetics of traditional ligatures but with embedded biosensors. We propose a novel device that senses metabolism changes with a different biosensor ligature worn in each tooth to access multiple biodata and create seamless interactive devices.

https://doi.org/10.1145/3410531.3414290

People who are deaf and hard of hearing often have difficulty realizing when someone is attempting to get their attention, especially when mobile. Speech recognition coupled with a head-worn display (HWD) may aid in awareness of when someone calls the user’s name. As our intended users are often oversubscribed with experiments, we chose to test non-deaf and hard of hearing subjects while refining our procedures. Preliminary findings from three hearing participants wearing sound masking headphones and performing a mobile task suggest that a HWD display may be faster than, and preferred to, a smartphone for displaying captions for attending to one’s name being called.

https://doi.org/10.1145/3410531.3414293

IMPORTANT DATES

Virtual Conference: September 12-17, 2020

Paper Sessions: September 14-17, 2020

CONTACT

These 9 workshops will be held as part of the UbiComp / ISWC 2020 virtual conference:

Saturday’s Workshops


W1 HASCA 2020

8th International Workshop on Human Activity Sensing Corpus and Applications
http://hasca2020.hasc.jp

W2 BEYOND STEPS

Challenges and Opportunities in Fitness Tracking
https://beyondsteps2020.github.io

W4 UBITTENTION 2020

5th International Workshop on Smart & Ambient Notification and Attention Management
https://www.ubittention.org/2020/

W5 MENTAL HEALTH AND WELL-BEING

5th International Workshop on Mental Health And Well-Being: Sensing And Intervention
https://ubicomp-mental-health.github.io

Sunday’s Workshops


W7 APPLENS 2020

3rd International Workshop on Mining and Learning from Smartphone Apps
http://www.shazhao.net/applens2020/

W8 UPA 2020

5th International Workshop on Ubiquitous Personal Assistance
https://upa20.weebly.com

W9 CPD 2020

3rd Workshop on Combining Physical and Data-driven Knowledge in Ubiquitous Computing
https://ubicomp-cpd.com/2020

W11 CML-IOT 2020

2nd Workshop on Continual and multimodal learning for Internet of Things
https://cmliot2020.github.io

W13 WELLCOMP 2020

3rd International Workshop on Computing for Well-Being
http://wellcomp.org/2020

How To Submit

Workshop papers for the accepted workshops should be submitted electronically through https://new.precisionconference.com/submissions.

  • 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 workshops-2020@iswc.hosting2.acm.org, or the organizers of the specific workshop (see below).

Publication Date

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.)

Saturday September 12, 2020

HASCA 2020: 8th International Workshop On Human Activity Sensing Corpus And Applications

http://hasca2020.hasc.jp

ORGANIZERS

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)

WORKSHOP SUMMARY

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 the Third Sussex-Huawei Locomotion and Transportation Recognition Competition (http://www.shl-dataset.org/activity-recognition-challenge-2020/) as part of a special session.

Beyond Steps: Challenges And Opportunities In Fitness Tracking

https://beyondsteps2020.github.io

ORGANIZERS

Rushil Khurana (CMU, USA), Abdelkareem Bedri (CMU, USA), Patrick Carrington (CMU, USA), Daniel A. Epstein (UC Irvine, USA), Rúben Gouveia (University of Twente, The Netherlands), Jochen Meyer (OFFIS Institute for Information Technology, Germany), Julian Ramos (CMU, USA), Jason Wiese (University of Utah, USA), Paweł Woźniak (Utrecht University, The Netherlands)

WORKSHOP SUMMARY

The quantified-self is a positive and prevalent aspect of our culture that has progressed during the last decade propelled by technological advances in health and fitness tracking. Prior research has shown that self tracking has a myriad of benefits. And we have the ability to sense and track various aspects of fitness and well-being. However one key challenge that remains is what data needs to be shown to the user, and how to present it to the user. Moreover, when is the right time to deliver key information to the user. Secondly, we have noticed that self-monitoring and tracking research has mostly evolved in isolation i.e., researchers have separately studied or built systems for various aspects of fitness like exercise tracking, diet or sleep monitoring. While in reality many of these areas are intertwined and depend on each other: Poor sleep can lead to overeating and consequently weight gain.

In this workshop, we propose to highlight and address these two challenges and explore opportunities to expand beyond the current paradigm of single health factors tracking to a more comprehensive fitness tracking.

UbiTtention 2020: 5th International Workshop On Smart & Ambient Notification And Attention Management

https://www.ubittention.org/2020

ORGANIZERS

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)

WORKSHOP SUMMARY

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

https://ubicomp-mental-health.github.io

ORGANIZERS

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)

WORKSHOP SUMMARY

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.

Sunday September 13, 2020

AppLens 2020: 3rd International Workshop On Mining And Learning From Smartphone Apps

http://www.shazhao.net/applens2020/

ORGANIZERS

Sha Zhao (Zhejiang University, China), Yong Li (Tsinghua University, China), Sasu Tarkoma (University of Helsinki, Finland), Zhiwen Yu (Northwestern Polytechnical University, China), Anind Dey (University of Washington, USA), and Gang Pan (Zhejiang University, China)

WORKSHOP SUMMARY

Smartphone apps are becoming ubiquitous in our everyday life. Apps on smartphones sense users’ behaviors and activities, providing a lens for understanding users, which is an important point in the community of ubiquitous computing.

The 3rd International workshop AppLens 2020 at UbiComp/iSWC 2020 will fosters discussions covering methodologies and tools, theories and models, design, descriptions or analysis of smartphone app data. We seek participants interested in profiling users from their use of smartphone apps, discovering cultural and social phenomenon by analyzing app usage, modeling app usage behaviors, studying smartphone apps, user privacy issues, etc.

In order to attract more participants, we will open two app datasets consisting of app usage records. This workshop will include paper sessions, invited talks, a panel session, and Best Paper Award, to provide a forum for the participants to communicate and discuss issues to promote the emerging research field. Moreover, we will select a few accepted papers to be extended and published in a prestigious journal special issue.

AppLens 2020: 3rd International Workshop On Mining And Learning From Smartphone Apps

http://www.shazhao.net/applens2020/

ORGANIZERS

Sha Zhao (Zhejiang University, China), Yong Li (Tsinghua University, China), Sasu Tarkoma (University of Helsinki, Finland), Zhiwen Yu (Northwestern Polytechnical University, China), Anind Dey (University of Washington, USA), and Gang Pan (Zhejiang University, China)

WORKSHOP SUMMARY

Smartphone apps are becoming ubiquitous in our everyday life. Apps on smartphones sense users’ behaviors and activities, providing a lens for understanding users, which is an important point in the community of ubiquitous computing.

The 3rd International workshop AppLens 2020 at UbiComp/iSWC 2020 will fosters discussions covering methodologies and tools, theories and models, design, descriptions or analysis of smartphone app data. We seek participants interested in profiling users from their use of smartphone apps, discovering cultural and social phenomenon by analyzing app usage, modeling app usage behaviors, studying smartphone apps, user privacy issues, etc.

In order to attract more participants, we will open two app datasets consisting of app usage records. This workshop will include paper sessions, invited talks, a panel session, and Best Paper Award, to provide a forum for the participants to communicate and discuss issues to promote the emerging research field. Moreover, we will select a few accepted papers to be extended and published in a prestigious journal special issue.

AppLens 2020: 3rd International Workshop On Mining And Learning From Smartphone Apps

http://www.shazhao.net/applens2020/

ORGANIZERS

Sha Zhao (Zhejiang University, China), Yong Li (Tsinghua University, China), Sasu Tarkoma (University of Helsinki, Finland), Zhiwen Yu (Northwestern Polytechnical University, China), Anind Dey (University of Washington, USA), and Gang Pan (Zhejiang University, China)

WORKSHOP SUMMARY

Smartphone apps are becoming ubiquitous in our everyday life. Apps on smartphones sense users’ behaviors and activities, providing a lens for understanding users, which is an important point in the community of ubiquitous computing.

The 3rd International workshop AppLens 2020 at UbiComp/iSWC 2020 will fosters discussions covering methodologies and tools, theories and models, design, descriptions or analysis of smartphone app data. We seek participants interested in profiling users from their use of smartphone apps, discovering cultural and social phenomenon by analyzing app usage, modeling app usage behaviors, studying smartphone apps, user privacy issues, etc.

In order to attract more participants, we will open two app datasets consisting of app usage records. This workshop will include paper sessions, invited talks, a panel session, and Best Paper Award, to provide a forum for the participants to communicate and discuss issues to promote the emerging research field. Moreover, we will select a few accepted papers to be extended and published in a prestigious journal special issue.

CML-IOT 2020: 2nd Workshop On Continual And Multimodal Learning For Internet Of Things

https://cmliot2020.github.io

ORGANIZERS

Susu Xu (Qualcomm AI Research, USA), Tong Yu (Samsung Research America, USA), Shijia Pan (UC Merced, USA)

WORKSHOP SUMMARY

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

http://wellcomp.org/2020/

ORGANIZERS

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)

WORKSHOP SUMMARY

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”.

IMPORTANT DATES

Submission deadline:
July 06, 2020 at 11:59 PM HAST


Notification date:
July 24, 2020


Camera-ready deadline:
July 31, 2020


Virtual Conference:
September 12-16, 2020

Past Conferences

The ACM international joint conference on pervasive and ubiquitous computing (ubicomp) is the result of a merger of the two most renowned conferences in the field: pervasive and ubicomp. while it retains the name of the latter in recognition of the visionary work of mark weiser, its long name reflects the dual history of the new event. a complete list of both ubicomp and pervasive past conferences is provided below.

ISWC 2019, London, England

ISWC 2018, Singapore

ISWC 2017, Maui, USA

ISWC 2016, Heidelberg, Germany

ISWC 2015, Osaka, Japan

ISWC 2014, Seattle, USA

ISWC 2013, Zurich, Switzerland

ISWC 2012, Newcastle, England

ISWC 2011, San Francisco, USA

ISWC 2010, Seoul, Korea

View All