A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time. E-mail and news articles are two natural examples of such streams, each characterized by topics that appear, grow in intensity for a period of time, and then fade away. The published literature in a particular research field can be seen to exhibit similar phenomena over a much longer time scale. Underlying much of the text mining work in this area is the following intuitive premise --- that the appearance of a topic in a document stream is signaled by a "burst of activity," with certain features rising sharply in frequency as the topic emerges.

A person seeking someone else's attention is normally able to quickly assess how interruptible they are. This assessment allows for behavior we perceive as natural, socially appropriate, or simply polite. On the other hand, today's computer systems are almost entirely oblivious to the human world they operate in, and typically have no way to take into account the interruptibility of the user. This paper presents a Wizard of Oz study exploring whether, and how, robust sensor-based predictions of interruptibility might be constructed, which sensors might be most useful to such predictions, and how simple such sensors might be. The study simulates a range of possible sensors through human coding of audio and video recordings. Experience sampling is used to simultaneously collect randomly distributed self-reports of interruptibility. Based on these simulated sensors, we construct statistical models predicting human interruptibility and compare their predictions with the collected self-report data. The results of these models, although covering a demographically limited sample, are very promising, with the overall accuracy of several models reaching about 78%. Additionally, a model tuned to avoiding unwanted interruptions does so for 90% of its predictions, while retaining 75% overall accuracy.

We introduce utility-directed procedures for mediating the flow of potentially distracting alerts and communications to computer users. We present models and inference procedures that balance the context-sensitive costs of deferring alerts with the cost of interruption. We describe the challenge of reasoning about such costs under uncertainty via an analysis of user activity and the content of notifications. After introducing principles of attention-sensitive alerting, we focus on the problem of guiding alerts about email messages. We dwell on the problem of inferring the expected criticality of email and discuss work on the Priorities system, centering on prioritizing email by criticality and modulating the communication of notifications to users about the presence and nature of incoming email. 1 Introduction Multitasking computer systems provide great value to users by hosting numerous processes and applications simultaneously. However, the ongoing execution of mu...

Introduction One of the main results of Twentieth-century Cognitive Psychology is that, despite the overall impressive abilities of people to sense, remember, and reason about the world, our cognitive abilities are extremely limited in well-characterized ways. In particular, psychologists have found that people grapple with scarce attentional resources and limited working memory. Such limitations become salient when people are challenged with remembering more than a handful of new ideas or items in the short term [20,28], recognizing important targets against a background pattern of items [5,26], or interleaving multiple tasks [6,26]. These results indicate that we cannot help but to inspect the world via a limited spotlight of attention. As such, we often generate clues implicitly and explicitly about what we are selectively attending to and how deeply we are focusing. Given constraints on attentional resources, it is no surprise that communication among people relies deeply on atte

The growing interest in multimodal interface design is inspired in large part by the goals of supporting more transparent, flexible, efficient, and powerfully expressive means of humancomputer interaction than in the past. Multimodal interfaces are expected to support a wider range of diverse applications, to be usable by a broader spectrum of the average population, and to function more reliably under realistic and challenging usage conditions. In this paper, we summarize the emerging architectural approaches for interpreting speech and pen-based gestural input in a robust manner--- including early and late fusion approaches, and the new hybrid symbolic/statistical approach. We also describe a diverse collection of state-of-the-art multimodal systems that process users' spoken and gestural input. These applications range from map-based and virtual reality systems for engaging in simulations and training, to field medic systems for mobile use in noisy environments, to web-based transactions and standard text-editing applications that will reshape daily computing and have a significant commercial impact. To realize successful multimodal systems of the future, many key research challenges remain to be addressed. Among these challenges are the development of cognitive theories to guide multimodal system design, and the development of effective natural language processing, dialogue processing, and error handling techniques. In addition, new multimodal systems will be needed that can function more robustly and adaptively, and with support for collaborative multi-person use. Before this new class of systems can proliferate, toolkits also will be needed to promote software development for both simulated and functioning systems. Multimodal Speech and Gesture Interfaces 3 CONT...

Adjustable autonomy refers to entities dynamically varying their own autonomy, transferring decision-making control to other entities (typically agents transferring control to human users) in key situations. Determining whether and when such transfers-of-control should occur is arguably the fundamental research problem in adjustable autonomy. Previous work has investigated various approaches to addressing this problem but has often focused on individual agent-human interactions. Unfortunately, domains requiring collaboration between teams of agents and humans reveal twokey shortcomings of these previous approaches. First, these approaches use rigid one-shot transfers of control that can result in unacceptable coordination failures in multiagent settings. Second, they ignore costs (e.g., in terms of time delays or eects on actions) to an agent's team due to such transfers-ofcontrol.

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We present methods employed in COORDINATE, a prototype service that supports collaboration and communication by learning predictive models that provide forecasts of users ’ presence and availability. We describe how data is collected about user activity and proximity from multiple devices, in addition to analysis of the content of users ’ calendars, the time of day, and day of week. We review applications of presence forecasting embedded in the PRIORITIES application and then present details of the COORDINATE service that was informed by the earlier efforts. 1

This study examines a novel interface design for heavilyfeatured productivity software. The design includes two interfaces between which the user can easily toggle: (1) an interface personalized by the user containing desired features only, and (2) the default interface with all the standard features. This design was prototyped as a frontend to a commercial word processor and evaluated in a comprehensive field study. The study tested the effects of different interface designs on users' satisfaction and their perceived ability to navigate, control, and learn the software. There were two conditions: a commercial word processor with adaptive menus and our two-interface prototype with adaptable menus for the same word processor. Results showed that participants were better able to navigate through the menus and toolbars and were better able to learn with our prototype. There were also significant differences in satisfaction and control with our design.

When an automating application needs a user's input or has feedback or other information for that user, it typically engages the user immediately, interrupting the user's current task. To empirically validate why unnecessarily interrupting a user's task should be avoided, we designed an experiment measuring the effects of an interruption on a user's task performance, annoyance, and anxiety. Fifty subjects participated in the experiment. The results demonstrate that an interruption has a disruptive effect on both a user's task performance and emotional state, and that the degree of disruption depends on the user's mental load at the point of interruption. We discuss the implications of these results in terms of building a system to better coordinate interactions between the user and applications competing for that user's attention.