論文アブストラクト： Commodity mobile devices are now equipped with high-resolution front-facing cameras, allowing applications in biometrics (e.g., FaceID in the iPhone X), facial expression analysis, or gaze interaction. However, it is unknown how often users hold devices in a way that allows capturing their face or eyes, and how this impacts detection accuracy. We collected 25,726 in-the-wild photos, taken from the front-facing camera of smartphones as well as associated application usage logs. We found that the full face is visible about 29% of the time, and that in most cases the face is only partially visible. Furthermore, we identified an influence of users' current activity; for example, when watching videos, the eyes but not the entire face are visible 75% of the time in our dataset. We found that a state-of-the-art face detection algorithm performs poorly against photos taken from front-facing cameras. We discuss how these findings impact mobile applications that leverage face and eye detection, and derive practical implications to address state-of-the art's limitations.
論文アブストラクト： An individual's trust propensity - i.e., a dispositional willingness to rely on others" - mediates multiple socio-technical systems and has implications for their personal, and societal, well-being. Hence, understanding and modeling an individual's trust propensity is important for human-centered computing research. Conventional methods for understanding trust propensities have been surveys and lab experiments. We propose a new approach to model trust propensity based on long-term phone use metadata that aims to complement typical survey approaches with a lower-cost, faster, and scalable alternative. Based on analysis of data from a 10-week field study (mobile phone logs) and "ground truth" survey involving 50 participants, we: (1) identify multiple associations between phone-based social behavior and trust propensity; (2) define a machine learning model that automatically infers a person's trust propensity. The results pave way for understanding trust at a societal scale and have implications for personalized applications in the emerging social internet of things.
論文アブストラクト： While the proliferation of mobile devices has rendered mobile notifications ubiquitous, researchers are only slowly beginning to understand how these technologies affect everyday social interactions. In particular, the negative social influence of mobile interruptions remains unexplored from a methodological perspective. This paper contributes a mixed-method evaluation procedure for assessing the disruptive impact of mobile interruptions in conversation. The approach combines quantitative eye tracking, qualitative analysis, and a simulated conversation environment to enable fast assessment of disruptiveness. It is intended to be used as a part of an iterative interaction design process. We describe our approach in detail, present an example of its use to study a new call declining technique, and reflect upon the pros and cons of our approach.
論文アブストラクト： We present APPropriate -- a novel mobile design to allow users to temporarily annex any Android device for their own use. APPropriate is a small, cheap storage pod, designed to be easily carried in a pocket or hidden within clothing. Its purpose is simple: to hold a copy of the local content an owner has on their mobile, liberating them from carrying a phone, or allowing them to use another device that provides advantages over their own. Picking up another device when carrying APPropriate transfers all pertinent content to the borrowed device (using local no-cost WiFi from the APPropriate device), transforming it to give the impression that they are using their own phone. While APPropriate is useful for a wide range of contexts, the design was envisaged through a co-design process with resource-constrained emergent users in three countries. Lab studies and a subsequent deployment on participants' own devices identified key benefits of the approach in these contexts, including for security, resource sharing, and privacy.