Published papers

© 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM. 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. The human's attention is becoming a new significant bottleneck. Further, the latest computing trends with emerging new devices including versatile IoT devices, and contexts, such as smart cities and vehicles, are even accelerating this situation. In such situations, “attention management”, including attention representation, sensing, prediction, analysis, and adaptive behavior in the computer systems, are needed in our computing system. Following last years' successful UbiTtention 2016 and UbiTtention 2017 workshops with up to 50 participants, the UbiTtention 2018 workshop brings together researchers and practitioners from academy and industry to explore the management of human attention and notifications with versatile devices and smart situations to overcome information overload and overchoice.

In the advancing ubiquitous computing where users have been having an increasing amount of push-style information provision from lots of intelligent proactive services, detecting the users' current attentional status and/or in-terruptibility has been a significant issue. In our previous study[7] on interruptibility detection in "Yahoo! JAPAN" real world product for 21 days with more than 680,000 users, the result showed significant lower user response time in exchange for notification delivery delay for about 4 minutes on average. In this paper, we further analyze the features in the interruptibility detection model trained and updated during this study in nightly basis for 21 days and report the results. Copyright held by the owner/author(s).

© 2016 IEEE. This paper proposes ''SmileWave, a system for revealing smile-based emotional contagion, propagation effect of the similar emotion through smiley facial expression, on the social network where users interact each other through web-based user interface rather than in-person interaction. SmileWave is a picture-based networking service and detects the change of smile degree when the user looks at posted smile images of others. Our extensive user study with 50 participants for 30 days confirmed the emotional contagion effect on SmileWave. Users' smile degree improved by 27% when the user looked at posted smile images. The result also proved that there is a stronger effect on smile-based emotional contagion when the examinee and the person in the image are in close relationship.

© 2017 Copyright held by the owner/author(s). We propose "SmileWave", the first selfie social networking service to reveal the existence of emotional cognation through smiling selfies on the social network. We conducted multiple rounds of in-the-wild user studies with 86 cumulative total users for total duration of 5 weeks. Throughout the entire study, we confirmed the occurrence of smile-based emotional contagion over social network, not only in the momentary duration but in longer term period.

We present QueueVadis, a system that addresses the problem of estimating, in real-time, the properties of queues at commonplace urban locations, such as coffee shops, taxi stands and movie theaters. Abjuring the use of any queuing-specific infrastructure sensors, QueueVadis uses participatory mobile sensing to detect both (i) the individual-level queuing episodes for any arbitrarily-shaped queue (by a characteristic locomotive signature of short bursts of "shuffling forward" between periods of "standing") and (ii) the aggregate-level queue properties (such as expected wait or service times) via appropriate statistical aggregation of multi-person data. Moreover, for venues where multiple queues are too close to be separated via location estimates, QueueVadis also uses a novel disambiguation technique to separate users into multiple distinct queues. User studies, performed with 138 cumulative total users observed at 23 different real-world queues across Singapore and Japan, show that QueueVadis is able to (a) identify all individual queuing episodes, (b) predict service and wait times fairly accurately (with median estimation errors in the 10%-20% range), independent of the queue's shape, (c) separate users in multiple proximate queues with close to 80% accuracy and (d) provide reasonable estimates when the participation rate (the fraction of QueueVadis-equipped people in the queue) is modest.

Copyright © 2015 ACM. In today's ubiquitous computing environment where users carry, manipulate, and interact with an increasing number of networked devices, applications and web services, human attention is the new bottleneck in computing. It is therefore important to minimize a user's mental effort due to notifications, especially in situations where users are mobile and using multiple wearable and mobile devices. To this end, we propose Attelia II, a novel middleware that identifies breakpoints in users' lives while using those devices, and delivers notifications at these moments. Attelia II works in real-Time and uses only the mobile and wearable devices that users naturally use and wear, without any modifications to applications, and without any dedicated psycho-physiological sensors. Our in-The-wild evaluation in users' multi-device environment (smart phones and smart watches) with 41 participants for 1 month validated the effectiveness of Attelia. Our new physical activity-based breakpoint detection, in addition to the UI Event-based breakpoint detection, resulted in a 71.8% greater reduction of users' perception of workload, compared with our previous system that used UI events only. Adding this functionality to a smart watch reduced workload perception by 19.4% compared to random timing of notification deliveries. Our multi-device breakpoint detection across smart phones and watches resulted in about 3 times greater reduction in workload perception than our previous system.

We present Senbay, a novel smartphone-based platform for capturing and sharing synchronously-recorded video/sensor data stream using animated two-dimensional barcode. In the ubicomp environment, mobile devices have enabled us to capture video and various types of sensor data easily. However, currently we do not have a framework to instantly integrate video and those sensor data on smartphones and share them easily with other users on the net. In this paper, we present Senbay, a platform to integrate video and sensor data stream into single video on smartphones. With Senbay, sensor data stream will be embedded into a video as animated QR code in real-time, thus users can share the \sensor-federated" video easily on the web, through lots of popular video sharing services. This paper introduces the architecture and evaluation of Senbay and discusses future vision of the sensor-federated video sharing platform along with several applications.

Today, many users of mobile devices are continuously confronted with a huge variety of information: notifications from Facebook, new application updates, won badges, or reminders. This leads to an information overload, which makes it hard to stay focused. This workshop will investigate approaches towards smart attention management systems. We will discuss the fundamental challenges of smart notifications and the design of proactive notification mechanisms. We invite submissions that focus on the understanding of users and their current, mobile information handling. We further appreciate contributions that propose design concepts for the interaction with smart attention management systems. The expected workshop outcome is a summary of emerging challenges in the design and development of smart attention management systems as well as approaches to address them.