Published papers

© 2016 ACM. Mobile phones allow us to reach people anywhere, anytime. In addition to the benefits for end users, researchers and developers can also benefit from the powerful devices that people carry on a daily basis. Collectively, mobile phones form a ubiquitous computer. The Ubiquitous Mobile Instrumentation (UbiMI) workshop focuses on using mobile devices as instruments to collect data and conduct mobile user studies, to understand human-behavior and routines, and to gather users' context.

本研究は，ユーザが何らかのアプリケーション操作をともなってユビキタスコンピューティングにおける目標行動を行う環境における，"Application as a Sensor" (AaaS) アプローチに基づくユーザの注意状態の検知を提案する．本システムは，ユーザの注意に対するシステムの適応的動作実現のため，ユーザの現在の注意対象と "breakpoint"? を，ユーザが操作する複数のデバイス上のアプリケーションそれぞれに特化した情報を使って検知する．またその検知において，モバイルデバイス上でリアルタイムに，生体センサを用いずに実現する点を特徴とする．

In mobile computing environment, where the dynamic changes of the computing environment and the computing resources in quality and quantity occurs, the provision of"Continuous Operation"and"Quality Of Service"for the users is important. We developed"AQUARIUS", the adaptive QOS architecture for dynamically reconfigurable systems. AQUARIUS provides the QOS functions and the mobility which supports Continuous Operation for the applications. Using AQUARIUS, application programmers can add the QOS adaptation and the mobility to their applications easily. In this paper, we describe the design, implementation and the evaluation of AQUARIUS.

In mobile computing environment, where the dynamic changes of the computing environment and the computing resources in quality and quantity occurs, the provision of "Continuous Operation" and "Quality Of Service" for the users is important. We developed "AQUARIUS", the adaptive QOS architecture for dynamically reconfigurable systems. AQUARIUS provides the QOS functions and the mobility which supports Continuous Operation for the applications. Using AQUARIUS, application programmers can add the QOS adaptation and the mobility to their applications easily. In this paper, we describe the design, implementation and the evaluation of AQUARIUS.

© 2015 IEEE. In today's ubiquitous computing environment where the number of devices, applications and web services are ever increasing, human attention is the new bottleneck in computing. To minimize user cognitive load, we propose Attelia, a novel middleware that identifies breakpoints in user interaction and delivers notifications at these moments. Attelia works in realtime and uses only the mobile devices that users naturally use and wear, without any modifications to applications, and without any dedicated psycho-physiological sensors. Our evaluation proved the effectiveness of Attelia. A controlled user study showed that notifications at detected breakpoint timing resulted in 46% lower cognitive load compared to randomly-timed notifications. Furthermore, our 'in-the-wild' user study with 30 participants for 16 days further validated Attelia's value, with a 33% decrease in cognitive load compared to randomly-timed notifications.

In progressing ubiquitous computing where number of devices, applications and the web services are everincreasing, human user's attention is a new bottleneck in computing. This paper proposes Attelia, a novel middleware that senses user's attention status on user'ssmart phones in real-time, without any dedicated psycho-physiological sensors. To find better delivery timings of interruptive notifications from various applications and services to mobile users, Attelia detects breakpoint[16] of user's activity on the smart phones, with our novel "Application as a Sensor"(AsaS) approach and machine learning technique. Our initial evaluation of Attelia shows it can detect breakpoints of users with accuracy of 80 - 90%.

© 2017 IEEE. In today's advancing ubiquitous computing age, with its ever-increasing amount of information from various applications and services available for consumption, the management of people's attention has become very important. In particular, the high volume of notifications on mobile devices has become a major cause of interruption of users. There has been much research aimed at detecting the opportune moment to present such information to users with in a way that lowers the cognitive load or frustration. However, evaluation of such systems in the real-world production environment with real users and notifications, and evaluation on user's engagement to the presented notification beyond simple responsiveness have not been adequately studied. To the best of our knowledge, this study is the first to investigate user interruptibility and engagement using a real-world large-scale mobile application and real-world notifications consisting of actual news content. We equipped the Yahoo! JAPAN Android app, one of the most popular applications on the national market, with our mobile-sensing and machine-learning-based interruptibility estimation logic. We conducted a large-scale in-the-wild user study with more than 680,000 users for three weeks. The results show that in most cases delaying the notification delivery until an interruptible moment is detected is beneficial to users and results in significant reduction of user response time (49.7%) compared to delivering the notifications immediately. We also observed a higher number of notifications opened in our system as well as constant improvement in user engagement levels throughout the entire study period.