Live-streamed performances, in which the perform-ers and the audience are simultaneously present in separate physical spaces, lack the emotional intensity present in in-person performances. Motivated by the social effects of robots and the potential synergy between robots and art, we conducted a between-subject study to explore robots as mediators in live-streamed performances. As a mediator between the performers and the audience, the robot can solicit audience input and direct performers according to that input. We held three interactive musical performances to compare the audiences' experiences: one with a chatbot mediator and two with a NAO robot mediator. We did not find significant differences in the audience's experiences between mediators, but survey responses and chat activity pointed to useful design considerations.

Effect systems have been a subject of active research for nearly four decades, with the most notable practical example being checked exceptions in programming languages such as Java. While many exception systems support abstraction, aggregation, and hierarchy (e.g., via class declaration and subclassing mechanisms), it is rare to see such expressive power in more generic effect systems. We designed an effect system around the idea of protecting system resources and incorporated our effect system into the Wyvern programming language. Similar to type members, a Wyvern object can have effect members that can abstract lower-level effects, allow for aggregation, and have both lower and upper bounds, providing for a granular effect hierarchy. We argue that Wyvern’s effects capture the right balance of expressiveness and power from the programming language design perspective. We present a full formalization of our effect-system design, showing that it allows reasoning about authority and attenuation. Our approach is evaluated through a security-related case study.

Trigger-action programming (if-this-then-that rules) empowers non-technical users to automate services and smart devices. As a user’s set of trigger-action programs evolves, the user must reason about behavior differences between similar programs, such as between an original program and several modification candidates, to select programs that meet their goals. To facilitate this process, we co-designed user interfaces and underlying algorithms to highlight differences between trigger-action programs. Our novel approaches leverage formal methods to efficiently identify and visualize differences in program outcomes or abstract properties. We also implemented a traditional interface that shows only syntax differences in the rules themselves. In a between-subjects online experiment with 107 participants, the novel interfaces better enabled participants to select trigger-action programs matching intended goals in complex, yet realistic, situations that proved very difficult when using traditional interfaces showing syntax differences.

In smart homes, access-control policies increasingly depend on contexts, such as who is taking an action, whether there is an emergency, or whether an adult is nearby. The vast literature on context sensing could potentially be leveraged to support contextual access control, yet this literature mostly ignores attacks, adversaries, and privacy. In this paper, we reevaluate the literature on home context sensing through a security and privacy mindset. We first describe a novel threat model in smart homes focusing on the capabilities of non-technical adversaries. Replay, imitation, and shoulder-surfing attacks are much more likely in this model. We summarize contexts relevant to access control in homes, mapping them to existing sensors. We then systematize the sensing literature to construct a decision framework for home context sensing that considers security, privacy, and usability. Applying our framework, we find that current sensors do not fully mitigate likely threats in homes. Some sensors are susceptible to simple threats like physical denial-of-service attacks, making it easy to bypass policies relying on the absence of a characteristic. Many sensors collect more data than needed and are not effective for all groups of users or under all situations.

Smartphones must balance power and performance. While most smartphones offer a power-saving mode, they typically provide a binary choice between full performance and monolithic performance degradation (e.g., reducing both screen brightness and processing speed) to save power. Could smartphones improve the user experience by automatically degrading only selected features based on the usage context? To gauge whether preferences for power-saving strategies vary by context, we conducted a 304-participant, survey-based experiment. Each participant was assigned a context (e.g., navigation) and degradation level. They viewed a series of side-by-side simulations of one smartphone operating normally in that context and another operating with reduced GPS accuracy, processing speed, or screen brightness. Participants rated their willingness to accept each tradeoff to save power. Contrasting current power-saving modes, we found that participants’ preferences did indeed vary by context. Using factor analysis to cluster preferences, we identified key personas that pave the way toward context-aware and self-aware alternatives to smartphone power-saving modes.