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.

We present a principled methodology for filtering news stories by formal measures of information novelty, and show how the techniques can be used to custom-tailor newsfeeds based on information that a user has already reviewed. We review methods for analyzing novelty and then describe Newsjunkie, a system that personalizes news for users by identifying the novelty of stories in the context of stories they have already reviewed. Newsjunkie employs novelty-analysis algorithms that represent articles as words and named entities. The algorithms analyze inter- and intra- document dynamics by considering how information evolves over time from article to article, as well as within individual articles. We review the results of a user study undertaken to gauge the value of the approach over legacy time-based review of newsfeeds, and also to compare the performance of alternate distance metrics that are used to estimate the dissimilarity between candidate new articles and sets of previously reviewed articles.

This research explores distributed sensing techniques for mobile devices using synchronous gestures. These are patterns of activity, contributed by multiple users (or one user with multiple devices), which take on a new meaning when they occur together in time, or in a specific sequence in time. To explore this new area of inquiry, this work uses tablet computers augmented with touch sensors and twoaxis linear accelerometers (tilt sensors). The devices are connected via an 802.11 wireless network and synchronize their time-stamped sensor data. This paper describes a few practical examples of interaction techniques using synchronous gestures such as dynamically tiling together displays by physically bumping them together, discusses implementation issues, and speculates on further possibilities for synchronous gestures.

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this paper, we describe an architecture that supports the building of context-aware services that assume context is ambiguous and allows for mediation of ambiguity by mobile users in aware environments. We discuss design guidelines that arise from supporting mediation over space and time, issues not present in the graphical user interface domain, where mediation has typically been used in the past. We illustrate the use of our architecture and the design guidelines and evaluate it through three example context-aware services, a word predictor system, an In/Out Board, and a reminder tool

A primary challenge to large-scale data integration is creating semantic equivalences between elements from different data sources that correspond to the same real-world entity or concept. Dataspaces propose a pay-as-you-go approach: automated mechanisms such as schema matching and reference reconciliation provide initial correspondences, termed candidate matches, and then user feedback is used to incrementally confirm these matches. The key to this approach is to determine in what order to solicit user feedback for confirming candidate matches. In this paper, we develop a decision-theoretic framework for ordering candidate matches for user confirmation using the concept of the value of perfect information (VPI). At the core of this concept is a utility function that quantifies the desirability of a given state; thus, we devise a utility function for dataspaces based on query result quality. We show in practice how to efficiently apply VPI in concert with this utility function to order user confirmations. A detailed experimental evaluation on both real and synthetic datasets shows that the ordering of user feedback produced by this VPI-based approach yields a dataspace with a significantly higher utility than a wide range of other ordering strategies. Finally, we outline the design of Roomba, a system that utilizes this decision-theoretic framework to guide a dataspace in soliciting user feedback in a pay-as-you-go manner.

We elaborate a proposal for capturing, extending, and reusing design knowledge gleaned through usability testing. The proposal is specifically targeted to address interface design for notification systems, but its themes can be generalized to any constrained and well-defined genre of interactive system design. We reiterate arguments for and against using critical parameters to characterize user goals and usability artifacts. Responding to residual arguments, we suggest that clear advantages for research cohesion, design knowledge reuse, and HCI education are possible if several challenges are overcome. As a first step, we recommend a slight variation to the concept of a critical parameter, which would allow both abstract and concrete knowledge representation. With this concept, we demonstrate a feasible approach by introducing equations that elaborate and allow evolution of notification system critical parameters, which is made operational with a variety of usability evaluation instruments. A case study illustrates how one general instrument allowed system designs to be meaningfully compared and resulted in valuable inferences for interface reengineering. Broad implications and conclusions about this approach will be of interest to others concerned with using critical parameters in interface design, development of notification systems interfaces, or approaches to design rationale and knowledge reuse.

this report. Kautz [79] modeled plan recognition logically in a manner that allowed goals and plans to be described at various levels of abstraction. Etzioni et al. [94, 95, 92, 93] developed a version space algorithm for plan recognition that is provably sound and polynomial time [94, 93]. Weld et al. developed goal recognition algorithms using inductive logic programming [90] and version-space algebra [89, 168, 88] in the context of programming by demonstration

Sensor-based statistical models promise to support a variety of advances in human-computer interaction, but building applications that use them is currently difficult and potential advances go unexplored. We present Subtle, a toolkit that removes some of the obstacles to developing and deploying applications using sensor-based statistical models of human situations. Subtle provides an appropriate and extensible sensing library, continuous learning of personalized models, fully-automated high-level feature generation, and support for using learned models in deployed applications. By removing obstacles to developing and deploying sensor-based statistical models, Subtle makes it easier to explore the design space surrounding sensor-based statistical models of human situations. Subtle thus helps to move the focus of human-computer interaction research onto applications and datasets, instead of the difficulties of developing and deploying sensor-based statistical models. Author Keywords Toolkits, Subtle, sensor-based statistical models, machine

We describe a new interaction technique, called a spotlight, for directing the visual attention of an audience when viewing data or presentations on large wall-sized displays. A spotlight is simply a region of the display where the contents are displayed normally while the remainder of the display is somewhat darkened. In this paper we define the behavior of spotlights, show unique affordances of the technique, and discuss design characteristics. We also report on experiments that show the benefit of using the spotlight a large display and standard desktop configuration. Our results suggest that the spotlight is preferred over the standard cursor and outperforms it by a factor of 3.4 on a wall-sized display.