Publications

From 2018 (Clear Search)

2018
Publication Details
  • CHI 2018
  • Apr 21, 2018

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Effective communication of activities and progress in the workplace is crucial for the success of many modern organizations. In this paper, we extend current research on workplace communication and uncover opportunities for technology to support effective work activity reporting. We report on three studies: With a survey of 68 knowledge workers followed by 14 in-depth interviews, we investigated the perceived benefits of different types of progress reports and an array of challenges at three stages: Collection, Composition, and Delivery. We show an important interplay between written and face-to-face reporting, and highlight the importance of tailoring a report to its audience. We then present results from an analysis of 722 reports composed by 361 U.S.-based knowledge workers, looking at the influence of the audience on a report’s language. We conclude by discussing opportunities for future technologies to assist both employees and managers in collecting, interpreting, and reporting progress in the workplace.
Publication Details
  • IUI 2018
  • Mar 7, 2018

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Activity recognition is a core component of many intelligent and context-aware systems. In this paper, we present a solution for discreetly and unobtrusively recognizing common work activities above a work surface without using cameras. We demonstrate our approach, which utilizes an RF-radar sensor mounted under the work surface, in two work domains; recognizing work activities at a convenience-store counter (useful for post-hoc analytics) and recognizing common office deskwork activities (useful for real-time applications). We classify seven clerk activities with 94.9% accuracy using data collected in a lab environment, and recognize six common deskwork activities collected in real offices with 95.3% accuracy. We show that using multiple projections of RF signal leads to improved recognition accuracy. Finally, we show how smartwatches worn by users can be used to attribute an activity, recognized with the RF sensor, to a particular user in multi-user scenarios. We believe our solution can mitigate some of users’ privacy concerns associated with cameras and is useful for a wide range of intelligent systems.
Publication Details
  • Multimedia Modeling 2018
  • Feb 5, 2018

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This paper examines content-based recommendation in domains exhibiting sequential topical structure. An example is educational video, including Massive Open Online Courses (MOOCs) in which knowledge builds within and across courses. Conventional content-based or collaborative filtering recommendation methods do not exploit courses' sequential nature. We describe a system for video recommendation that combines topic-based video representation with sequential pattern mining of inter-topic relationships. Unsupervised topic modeling provides a scalable and domain-independent representation. We mine inter-topic relationships from manually constructed syllabi that instructors provide to guide students through their courses. This approach also allows the inclusion of multi-video sequences among the recommendation results. Integrating the resulting sequential information with content-level similarity provides relevant as well as diversified recommendations. Quantitative evaluation indicates that the proposed system, \textit{SeqSense}, recommends fewer redundant videos than baseline methods, and instead emphasizes results consistent with mined topic transitions.

Rethinking Summarization and Storytelling for Modern Social Multimedia

Publication Details
  • Multimedia Modeling
  • Feb 5, 2018

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Traditional summarization initiatives have been focused on specific types of documents such as articles, reviews, videos, image feeds, or tweets, a practice which may result in pigeonholing the summarization task in the surrounding of modern, content-rich multimedia collections. Consequently, much of the research to date has revolved around mostly toy problems in narrow domains and working on single-source media types. We argue that summarization and story generation systems need to refocus the problem space in order to meet the information needs in the age of user-generated content in different formats and languages. Here we create a framework for flexible multimedia storytelling. Narratives, stories, and summaries carry a set of challenges in big data and dynamic multi-source media that give rise to new research in spatial-temporal representation, viewpoint generation, and explanation.
Publication Details
  • arXiv
  • Jan 24, 2018

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Tutorials are one of the most fundamental means of conveying knowledge. Ideally when the task involves physical or digital objects, tutorials not only describe each step with text or via audio narration but show it as well using photos or animation. In most cases, online tutorial authors capture media from handheld mobile devices to compose these documents, but increasingly they use wearable devices as well. In this work, we explore the full life-cycle of online tutorial creation and viewing using head-mounted capture and displays. We developed a media-capture tool for Google Glass that requires minimal attention to the capture device and instead allows the author to focus on creating the tutorial's content rather than its capture. The capture tool is coupled with web-based authoring tools for creating annotatable videos and multimedia documents. In a study comparing standalone (camera on tripod) versus wearable capture (Google Glass) as well as two types of multimedia representation for authoring tutorials (video-based or document-based), we show that tutorial authors have a preference for wearable capture devices, especially when recording activities involving larger objects in non-desktop environments. Authors preferred document-based multimedia tutorials because they are more straightforward to compose and the step-based structure translates more directly to explaining a procedure. In addition, we explored using head-mounted displays (Google Glass) for accessing tutorials in comparison to lightweight computing devices such as tablets. Our study included tutorials recorded with the same capture methods as in our access study. We found that although authors preferred head-mounted capture, tutorial consumers preferred video recorded by a camera on tripod that provides a more stable image of the workspace. Head-mounted displays are good for glanceable information, however video demands more attention and our participants made more errors using Glass than when using a tablet, which was easier to ignore. Our findings point out several design implications for online tutorial authoring and access methods.