論文アブストラクト： Interface designs on both small and large displays can encourage people to alter their physical distance to the display. Mobile devices support this form of interaction naturally, as the user can move the device closer or further away as needed. The current generation of mobile devices can employ computer vision, depth sensing and other inference methods to determine the distance between the user and the display. Once this distance is known, a system can adapt the rendering of display content accordingly and enable proximity-aware mobile interfaces. The dominant method of exploiting proximity-aware interfaces is to remove or superimpose visual information. In this paper, we investigate change blindness in such interfaces. We present the results of two experiments. In our first experiment we show that a proximity-aware mobile interface results in significantly more change blindness errors than a non-moving interface. The absolute difference in error rates was 13.7%. In our second experiment we show that within a proximity-aware mobile interface, gradual changes induce significantly more change blindness errors than instant changes---confirming expected change blindness behavior. Based on our results we discuss the implications of either exploiting change blindness effects or mitigating them when designing mobile proximity-aware interfaces.
論文アブストラクト： People engaged in complex searches such as planning a vacation or understanding their medical symptoms are often overwhelmed by opening and managing many tabs. These challenges are exacerbated as search moves to smartphones and mobile devices where screen real-estate is limited and tasks are frequently suspended, resumed, and interleaved. Rather than continue to utilize tab-based browsing for complex search, we introduce a new way of browsing through a scaffolded interface. The list of search results serves as a mutable workspace, where a user can track progress on a specific information query. The search query serves as a gateway into this workspace, accessed through a task-subtask hierarchy. We instantiate this in the Bento mobile search system and investigate its effectiveness in three studies. We find converging evidence that users were able to make progress on their complex searching tasks with this structure, and find it more organized and easier to revisit.
論文アブストラクト： We introduce BIGFile, a new fast file retrieval technique based on the Bayesian Information Gain framework. BIGFile provides interface shortcuts to assist the user in navigating to a desired target (file or folder). BIGFile's split interface combines a traditional list view with an adaptive area that displays shortcuts to the set of file paths estimated by our computationally efficient algorithm. Users can navigate the list as usual, or select any part of the paths in the adaptive area. A pilot study of 15 users informed the design of BIGFile, revealing the size and structure of their file systems and their file retrieval practices. Our simulations show that BIGFile outperforms Fitchett et al.'s AccessRank, a best-of-breed prediction algorithm. We conducted an experiment to compare BIGFile with ARFile (AccessRank instantiated in a split interface) and with a Finder-like list view as baseline. BIGFile was by far the most efficient technique (up to 44% faster than ARFile and 64% faster than Finder), and participants unanimously preferred the split interfaces to the Finder.