Awareness of and connections to a local community are important for building social capital, sharing resources, and providing physical support, but have been elusive to create in dense urban environments. We describe the design and implementation of MoveMeant, a system aimed to increase local community awareness through shared location traces. MoveMeant securely uses anonymized location data generated automatically by mobile devices to display aggregate, community-level location data. We report findings from interviews with residents in the Bronx, New York City who participated in a deployment of MoveMeant over a 6-week period. Our findings show that people use the anonymous information to make judgments about the people and places in their community, while opting to reveal their identity for third places where there is an opportunity to connect socially.

Peer-to-peer indirect exchange services, such as sharing sites that facilitate the lending and borrowing of physical goods among neighbors (such as NeighborGoods and Peerby), have not been as widely adopted as direct exchange systems, such as peer-to-peer platforms that facilitate the exchange goods and services for money (such as Uber and Airbnb). In order to understand contributing factors to this lack of adoption, we examined attitudes towards and usage of peer-to-peer resource-sharing sites among 37 residents of New York City, 9 of whom had previously used a peer-to-peer sharing site. In addition, to more deeply understand the role of trust on willingness to lend, we also conducted a survey with 195 respondents. Our findings show that people expressed concerns about violating norms of the kinds of objects suitable for sharing, about potential risks involved with entrusting a possession to somebody else, and about a dearth of available items that would be useful. Building upon previous technology acceptance models, critical mass theory, and prior research on peer economies, we propose a technology acceptance model for indirect exchange systems that includes generalized trust and ease of coordination.

Graphics like maps and models are important learning materials. With recently developed projects, we can use 3D printers to make tactile graphics that are more accessible to blind people. However, current 3D printed graphics can only convey limited information through their shapes and textures. We present Magic Touch, a computer vision-based system that augments printed graphics with audio files associated with specific locations, or hotspots, on the model. A user can access an audio file associated with a hotspot by touching it with a pointing gesture. The system detects the user's gesture and determines the hotspot location with computer vision algorithms by comparing a video feed of the user's interaction with the digital representation of the model and its hotspots. To enable MT, a model designer must add a single tracker with fiducial tags to a model. After the tracker is added, MT only requires an RGB camera, so it can be easily deployed on many devices such as mobile phones, laptops and smart glasses.

This paper contributes novel measures of user engagement in mobile music retrieval, linking these to work in music psychology, and illustrating resulting design guidelines in a demonstrator system. The large music collections available to users today can be overwhelming in mobile settings, they offer 'too-much-choice' to users, who often resort to shuffle-based playback. Work in music psychology has introduced the concept of music engagement -- listeners vary in their desired control over their music listening, and engagement varies with listening context. We develop a series of metrics to capture music listening behaviour from users' interaction logs. In a survey of 94 music listeners, we show significant correlations between music engagement from questionnaires and the presented quantitative metrics. We show how music retrieval can adapt to this engagement, developing a tablet-based demonstrator system, with an exploratory evaluation.

In this paper we present the results of a study investigating the control of simultaneous non-dominant hand (NDH) pressure input and dominant hand (DH) pinch, swipe and rotate gesture input on a touchscreen tablet device. We investigate the effect of NDH pressure input on DH gesture accuracy, and vice versa, to support the design of bimanual interaction techniques for tablet devices. Our results suggest that pressure negatively affects DH touchscreen gesture performance; however, pressure targeting accuracy for each gesture was still high (86% overall). Our results show that participants were able to achieve high levels of accuracy using DH swipe gestures (the simplest gesture in the study) suggesting that the ability to perform a simultaneous combination of pressure and touchscreen gesture input depends on the complexity of the dominant hand action involved.

A novel investigation of pressure input is presented where it is characterised as a transient modality, one that has a natural inverse, bounce-back and a state that only persists during interaction. Three empirical studies are described that evaluate pressure for use as a non-dominant hand input modality, where the ability to target and maintain pressure while simultaneously performing a dominant-hand targeting task is investigated. Pressure accuracy was high (93%) and the impact on dominant-hand targeting was low. Mean pressure accuracy when selecting targets by releasing pressure was also high (89%) as was selecting targets by applying pressure from a non-zero starting point (94.4%). The ability to accurately maintain pressure over time was better with larger target pressures. Example applications and design guidelines are presented that enable designers to exploit the transient properties of pressure input in interaction design.

In this paper we argue that Musicons, short samples from pieces of music are a useful way to present private but memorable reminder messages. We investigated accuracy, memorability and response times for short, medium, and long Musicons. User performance on the Musicons was also compared to short spoken reminders. The study consisted of two sessions a week apart. Quantitative measures were augmented with qualitative questions about associations and memories. Overall, participants achieved a high level of accuracy (89%) on the Musicons. Recognition was stable at 90% or better across sessions for users who were able to construct meaningful links between Musicons and the associated tasks. Optimal response times were achieved for medium-length 0.5 sec. Musicons. We conclude that Musicons are a viable option for alarms and notifications that combine the high learnability of Auditory Icons with the more private nature of Earcons.