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  1. As IoT devices begin to permeate our environment, our interaction with these devices are starting to have a real potential to transform our daily lives. Therefore, there exists an incredible opportunity for intelligent user interfaces to simplify the task of controlling such devices. The goal of IUIoT workshop was to serve as a platform for researchers who are working towards the design of IoT systems from an intelligent, human-centered perspective. The workshop accepted a total of five papers: two position and three extended abstracts. These papers were presented by the authors and discussed among the workshop attendees with an aim of exploring future directions and improving existing approaches towards designing intelligent User Interfaces for IoT environments.
  2. Research has shown that privacy decisions are affected by heuristic influences such as default settings and framing, and such effects are likely also present in smarthome privacy de- cisions. In this paper we pose the challenge question: How exactly do defaults and framing influence smarthome users’ privacy decisions? We conduct a large-scale scenario-based study with a mixed fractional factorial design, and use sta- tistical analysis and machine learning to investigate these effects. We discuss the implications of our findings for the designers of smarthome privacy-setting interfaces.
  3. The Internet of Things provides household device users with an ability to connect and manage numerous devices over a common platform. However, the sheer number of possible privacy settings creates issues such as choice overload. This article outlines a data-driven approach to understand how users make privacy decisions in household IoT scenarios. We demonstrate that users are not just influenced by the specifics of the IoT scenario, but also by aspects immaterial to the decision, such as the default setting and its framing.
  4. User testing is often used to inform the development of user interfaces (UIs). But what if an interface needs to be developed for a system that does not yet exist? In that case, existing datasets can provide valuable input for UI development. We apply a data-driven approach to the development of a privacy-setting interface for Internet-of-Things (IoT) devices. Applying machine learning techniques to an existing dataset of users' sharing preferences in IoT scenarios, we develop a set of "smart" default profiles. Our resulting interface asks users to choose among these profiles, which capture their preferences with an accuracy of 82%---a 14% improvement over a naive default setting and a 12% improvement over a single smart default setting for all users.