Sports fans are increasingly using social media platforms like Twitter to express emotions and share their opinions while watching sports on TV. These commentaries describe a intense subjective experience of a fan watching a sport passionately. We see an opportunity to attend to these nostalgic moments by capturing them into a physical form. We present, Fantibles, personalized sports memorabilia that highlights an individual's commentary about sports on Twitter along with the uniqueness of each sports match. As a first case study, we investigate Fantibles for one popular sport, Cricket. We report insights from field deployments of Fantibles during an ODI Cricket match series between India and Bangladesh and offer reflections on the design in the form of four themes: self-expression, layered sense making, ad-hoc interactions and distributed social interactions. We believe our work opens up new interaction possibilities to support social sports viewing experience and design thinking on creating personalized sports memorabilia.

To encourage enterprise knowledge sharing especially, to facilitate the discovery and sharing of enterprise meetings, we develop an end-to-end enterprise meeting service called Agora that manages the full cycle of hosting web meetings and sharing multimedia recorded meeting artifacts. In this paper, we focus on Agora's composite search engine that allows users to seamlessly search distributed multimedia meeting artifacts. Agora has been deployed as a cloud service which allows selected IBM customers to test new collaborative technologies on IBM's Smart Cloud platform.

In this paper, we present Expediting Expertise, a system designed to provide structured support to the otherwise informal process of social learning in the enterprise. It employs a data-driven approach where online content is automatically analyzed and categorized into relevant topics, topic-specific user expertise is calculated by comparing the models of individual users against those of the experts, and personalized recommendation of learning activities is created accordingly to facilitate expertise development. The system's UI is designed to provide users with ongoing feedback of current expertise, progress, and comparison with others. Learning recommendation is visualized with an interactive treemap which presents estimated return on investment and distance to current expertise for each recommended learning activity. Evaluation of the system showed very positive results.

In this Note we summarize our research on increasing the information scent of video recordings that are shared via email in a corporate setting. We compare two types of email messages for sharing recordings: the first containing basic information (e.g. title, speaker, abstract) with a link to the video; the second with the same information plus a set of video thumbnails (hyperlinked to the segments they represent), which are automatically created by video summarization technology. We report on the results of two user studies. The first one compares the quality of the set of thumbnails selected by the technology to sets selected by 31 humans. The second study examines the clickthrough rates for both email formats (with and without hyperlinked thumbnails) as well as gathering subjective feedback via survey. Results indicate that the email messages with the thumbnails drove significantly higher clickthrough rates than the messages without, even though people clicked on the main video link more frequently than the thumbnails. Survey responses show that users found the email with the thumbnail set significantly more appealing and novel.

Corporate meetings are increasingly being held remotely using web technologies. With such remote meetings being recorded and made available after the fact, there is a pressing need for tools to access and utilize these recordings efficiently. Our work explores the utility of using annotations generated by meeting attendees to meet this need. We conducted a controlled lab study to evaluate the benefits of sharing annotations. Attendee-created annotations were shared with non-attendees to assist them on typical information retrieval tasks. Results indicate that (a) non-attendees given access to shared annotations performed about as well as attendees provided with their own and shared annotations, (b) non-attendees were more confident in their responses when they used shared annotations as access cues into the recording than when they directly skimmed the video, and (c) attendees utilized shared annotations more than their own, with similar success and confidence as using their own annotations.

We present Enterprise Priority Inbox Classifier (EPIC), an automatic personalized email prioritization system based on a topic-based user model built from the user's email data and relevant enterprise information. The user model encodes the user's topics of interest and email processing behaviors (e.g. read/reply/file) at the granularity of pair-wise interactions between the user and each of his/her email contacts. Given a new message, the user model is used in combination with the message metadata and content to determine the values of a set of contextual features. Contextual features include people-centric features representing information about the user's interaction history and relationship with the email sender, as well as message-centric features focusing on the properties of the message itself. Based on these feature values, EPIC uses a dynamic strategy to combine a global priority classifier with a user-specific classifier for determining the message's priority. An evaluation of EPIC based on 2,064 annotated email messages from 11 users, using 10-fold cross-validation, showed that the system achieves an average accuracy of 81.3%. The user-specific classifier contributed an improvement of 11.5%. Lastly we report on findings regarding the relative value of different contextual features for email prioritization.

We present three studies that compare adoption and appropriation between China and the United States of BlogCentral, an internal blogging tool employed by a large global enterprise. We first analyzed 23 months of usage logs for users in both countries and found that compared to the U.S., Chinese users were much less active, with less activity and sparser user interaction. We then conducted 25 interviews and surveyed 213 bloggers in both countries to understand user motivations and behaviors of corporate blogging in more detail. The results show that Chinese employees use internal blogs for organizing personal work and short term team formation, unlike U.S. users who are driven more by the goal of sharing information to a broad community. The Chinese users were seeking (and not finding) greater social interaction and felt instead a sense of alienation from the global blogging community. We further identify a gap between Chinese users' requirement for local community and the existing global aspect of enterprise blogging tools. Lastly we discuss implications from the data analysis.

We present ICARUS, a contextual information retrieval system, which uses the current email message and a multi-tiered user model to retrieve relevant content and make it available in a sidebar widget embedded in the email client. The system employs a dynamic retrieval strategy to conduct automated contextual search across multiple information sources including the user's hard drive, online documents (wikis, blogs and files) and other email messages. It also presents the user with information about the sender of the current message, which varies in detail and degree based on how often the user interacts with this sender. We conducted a formative evaluation which compared three retrieval methods that used different context information: current message plus a multi-tiered user model; current message plus a single-tiered, aggregate user model; and lastly, cur-rent message only. Results indicate that the multi-tiered user modeling approach yields better retrieval performance than the other two. In addition, the study suggests that dynamically determining which sources to search, what query parameters to use, and how to filter/re-rank results can further improve the effectiveness of contextual IR.

We describe fieldwork in which we studied hospital ICU physicians and their strategies and documentation aids for composing patient progress notes. We then present a clinical documentation prototype, activeNotes, that supports the creation of these notes, using techniques designed based on our fieldwork. ActiveNotes integrates automated, context-sensitive patient data retrieval, and user control of automated data updates and alerts via tagging, into the documentation process. We performed a qualitative study of activeNotes with 15 physicians at the hospital to explore the utility of our information retrieval and tagging techniques. The physicians indicated their desire to use tags for a number of purposes, some of them extensions to what we intended, and others new to us and unexplored in other systems of which we are aware. We discuss the physicians' responses to our prototype and distill several of their proposed uses of tags: to assist in note content management, communication with other clinicians, and care delivery.

We present a system that supports seamless access to information contained in recorded meetings from the cornerstone points of a knowledge worker's daily life: mailbox and calendar. The solution supports granular search of meeting content from an enterprise email system and automatically displays recordings of meetings related to the message the user is currently viewing. Additionally thumbnail summaries of the meetings are added to the user's calendar entries after the meetings have taken place. Lastly our system supports easy sharing of videos associated with recorded meetings through the use of hot-linked thumbnail summaries which can be sent via email.

In this work, we introduce the use of tags to support the near synchronous use of instant messaging. As a proof-of-concept, we developed a plug-in in Lotus Sametime, an enterprise IM client. Our plug-in supports tasks that do not need immediate attention and tasks that have deadlines. A trial deployment and survey shows that users can see the potential usefulness of such a tagging system in their IM communication. Furthermore, users rated our design intuitive and easy to use. Longer study is needed to explore communication norms that results from its use.

This article reports on an experiment that critically tests user preference for an input modality (speech vs. Dual Tone Multiple Frequency[DTMF])in a phone-based message retrieval system. Unlike previous studies that compared these two modalities, the speech system used in this study was a fully functioning natural language system, and participants in this study were working professionals, rather than college students. Results indicate that (a) DTMF was more effective and efficient for linear tasks, whereas speech was better for nonlinear tasks; (b) speech was preferred to DTMF by a majority of users; (c) speech was judged as being more satisfying, more entertaining, and easier to use than DTMF; and (d) user preference for a particular modality was better predicted by user performance in nonlinear tasks rather than linear ones. Possible reasons for users' continuing preference for the speech modality even after experiencing fairly high recognition errors are discussed. Finally, the importance of examining speech user interfaces from other perspectives, in addition to efficiency maximization, is emphasized. The results of this study have theoretical, as well as practical, implications for the design of speech user interfaces and interactive voice response applications.

Current systems often create socially awkward interruptions or unduly demand attention because they have no way of knowing if a person is busy and should not be interrupted. Previous work has examined the feasibility of using sensors and statistical models to estimate human interruptibility in an office environment, but left open some questions about the robustness of such an approach. This paper examines several dimensions of robustness in sensor-based statistical models of human interruptibility. We show that real sensors can be constructed with sufficient accuracy to drive the predictive models. We also create statistical models for a much broader group of people than was studied in prior work. Finally, we examine the effects of training data quantity on the accuracy of these models and consider tradeoffs associated with different combinations of sensors. As a whole, our analyses demonstrate that sensor-based statistical models of human interruptibility can provide robust estimates for a variety of office workers in a range of circumstances, and can do so with accuracy as good as or better than people. Integrating these models into systems could support a variety of advances in human computer interaction and computer-mediated communication. Author Keywords Situationally appropriate interaction, managing human attention, sensor-based interfaces, context-aware computing, machine learning.

Abstract This paper outlines the results of commercial field trial of a multimodal (speech and GUI) platform using natural language technologies in a mobile environment. During the trial we compared the user experience of the multimodal interface to the user experience with a comparable application providing only unimodal (GUI) interaction. A key objective of the analysis following the trial was to understand the etiology of the difference in user experience with the two systems. Speech-only usage was used to complete tasks 71% of the time on average across all participants and tasks. The remaining 29% was distributed among GUI-only usage and multimodal usage. These results indicate that for input, the natural language speech was the major contributor to the increased quality of user experience. 1. Introduction Accessing email from a telephone is an application that has been available in various forms for many years. The first systems gave users speech-only access over a standard telephone. These were later adapted for use on cell phones by incorporating new acoustic models. Once WAP phones became more widely available more unimodal systems emerged, these were largely GUI only, for access to email when away from a traditional computer. With the arrival of third generation networks (3G) we now have the ability to create multimodal solutions (voice and GUI) to access information in a mobile environment. This paper describes one such solution, as well as the successful field trial associated with the solution. We believe this implementation to be first instance of a natural language understanding multimodal application on an unmodified cell phone.

The advent of mobile devices and the wireless Internet is having a profound impact on the way people communicate, as well as on the user interaction paradigms used to access information that was traditionally accessible only through visual interfaces. Applications for mobile devices entail the integration of various data sources optimized for delivery to limited hardware resources and intermittently connected devices through wireless networks. Although telephone interfaces arise as one of the most prominent pervasive applications, they present interaction challenges such as the augmentation of speech recognition through natural language (NL) understanding and high-quality text-to-speech conversion. This article presents an experience in building an automated assistant that is natural to use and could become an alternative to a human assistant. The Mobile Assistant (MA) can read e-mail messages, book appointments, take phone messages, and provide access to personal-organizer information. Key components are a conversational interface, enterprise integration, and notifications tailored to user preferences. The focus of the research has been on supporting the pressing communication needs of mobile workers and overcoming technological hurdles such as achieving high accuracy speech recognition in noisy environments, NL understanding, and optimal message presentation on a variety of devices and modalities. The article outlines findings from the 2 broad field trials and lessons learned regarding the support of mobile workers with pervasive computing devices and emerging technologies.

In this paper we describe a statistical technique for aligning sentences with their translations in two parallel corpora.In addition to certain anchor points that are available in our da.ta, the only information about the sentences that we use for calculating alignments is the number of tokens that they contain.Because we make no use of the lexical details of the sentence, the alignment computation is fast and therefore practical for application to very large collections of text.We have used this technique to align several million sentences in the English-French Hans~trd corpora and have achieved an accuracy in excess of 99% in a random selected set of 1000 sentence pairs that we checked by hand.We show that even without the benefit of anchor points the correlation between the lengths of aligned sentences is strong enough that we should expect to achieve an accuracy of between 96% and 97%.Thus, the technique may be applicable to a wider variety of texts than we have yet tried.