HeyTAP: Bridging the Gaps Between Users' Needs and Technology in IF-THEN Rules via Conversation

In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of IF-THEN rules. Unfortunately, how to make such a personalization effective and appreciated is still largely unknown. On the one hand, contemporary platforms to compose IF-THEN rules adopt representation models that strongly depend on the exploited technologies, thus making end-user personalization a complex task. On the other hand, the usage of technology-independent rules envisioned by recent studies opens up new questions, and the identification of available connected entities able to execute abstract users' needs become crucial. To this end, we present HeyTAP, a conversational and semantic-powered trigger-action programming platform able to map abstract users' needs to executable IF-THEN rules. By interacting with a conversational agent, the user communicates her personalization intentions and preferences. User's inputs, along with contextual and semantic information related to the available connected entities, are then used to recommend a set of IF-THEN rules that satisfies the user's needs. An exploratory study on 8 end users preliminary confirms the effectiveness and the appreciation of the approach, and shows that HeyTAP can successfully guide users from their needs to specific rules

The Post Anachronism: The Temporal Dimension of Facebook Privacy

This paper reports on two studies that investigate empirically how privacy preferences about the audience and emphasis of Facebook posts change over time. In a 63-participant longitudinal study, participants gave their audience and emphasis preferences for up to ten of their Facebook posts in the week they were posted, again one week later, and again one month later. In a 234-participant retrospective study, participants expressed their preferences about posts made in the past week, as well as one year prior. We found that participants did not want content to fade away wholesale with age; the audience participants wanted to be able to access posts remained relatively constant over time. However, participants did want a handful of posts to become more private over time, as well as others to become more visible. Participants ’ predictions about how their preferences would change correlated poorly with their actual changes in preferences over time, casting doubt on ideas for setting an expiration date for content. Although older posts were seen as less relevant and had often been forgotten, participants found value in these posts for reminiscence. Surprisingly, we observed few concerns about privacy or self-presentation for older posts. We discuss our findings ’ implications for retrospective privacy mechanisms

Implementing a semi-causal domain-specific language for context detection over binary sensors

International audienceIn spite of the fact that many sensors in use today are binary (i.e. produce only values of 0 and 1), and that useful context-aware applications are built exclusively on top of them, there is currently no development approach specifically targeted to binary sensors. Dealing with notions of state and state combinators, central to binary sensors, is tedious and error-prone in current approaches. For instance, developing such applications in a general programming language requires writing code to process events, maintain state and perform state transitions on events, manage timers and/or event histories. In another paper, we introduced a domain specific language (DSL) called Allen, specifically targeted to binary sensors. Allen natively expresses states and state combinations, and detects contexts on line, on incoming streams of binary events. Expressing state combinations in Allen is natural and intuitive due to a key ingredient: semi-causal operators. That paper focused on the concept of the language and its main operators, but did not address its implementation challenges. Indeed, online evaluation of expressions containing semi-causal operators is difficult, because semi-causal sub-expressions may block waiting for future events, thus generating unknown values, besides 0 and 1. These unknown values may or may not propagate to the containing expressions, depending on the current value of the other arguments. This paper presents a compiler and runtime for the Allen language, and shows how they implement its state combining operators, based on reducing complex expressions to a core subset of operators, which are implemented natively. We define several assisted living applications both in Allen and in a general scripting language. We show that the former are much more concise in Allen, achieve more effective code reuse, and ease the checking of some domain properties

Biometric authentication on iPhone and Android: Usability, perceptions, and influences on adoption

Abstract—While biometrics have long been promoted as the future of authentication, the recent introduction of Android face unlock and iPhone fingerprint unlock are among the first large-scale deployments of biometrics for consumers. In a 10-participant, within-subjects lab study and a 198-participant online survey, we investigated the usability of these schemes, along with users ’ experiences, attitudes, and adoption decisions. Participants in our lab study found both face unlock and fingerprint unlock easy to use in typical scenarios. The notable exception was that face unlock was completely unusable in a dark room. Most participants preferred fingerprint unlock over face unlock or a PIN. In our survey, most fingerprint unlock users perceived it as more secure and convenient than a PIN. In contrast, face unlock users had mixed experiences, and many had stopped using it. We conclude with design recommendations for biometric authentication on smartphones. I

A Cross-Cultural Framework for Protecting User Privacy in Online Social Media

Social media has become truly global in recent years. We argue that support for users ’ privacy, however, has not been extended equally to all users from around the world. In this paper, we survey existing literature on cross-cultural privacy issues, giving particular weight to work specific to online social networking sites. We then propose a framework for evaluating the extent to which social networking sites ’ privacy options are offered and communicated in a manner that supports diverse users from around the world. One aspect of our framework focuses on cultural issues, such as norms regarding the use of pseudonyms or posting of photographs. A second aspect of our framework discusses legal issues in cross-cultural privacy, including data-protection requirements and questions of jurisdiction. The final part of our framework delves into user expectations regarding the data-sharing practices and the communication of privacy information. The framework can enable service providers to identify potential gaps in support for user privacy. It can also help researchers, regulators, or consumer advocates reason systematically about cultural differences related to privacy in social media

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