This paper describes the wearable Remembrance Agent, a continuously running proactive memory aid that uses the physical context of a wearable computer to provide notes that might be relevant in that context. A currently running prototype is described, along with future directions for research inspired by using the prototype. 1 Introduction With computer chips getting smaller and cheaper the day will soon come when the desk-top, lap-top, and palm-top computer will all disappear into a vest pocket, wallet, shoe, or anywhere else a spare centimeter or two are available. As the price continues to plummet, these devices will enable all kinds of applications, from consumer electronics to personal communicators to field-operations support. Given that the primary use of today's palm-top computers is as day-planners, address books, and notebooks, one can expect memory aids will be an important application for wearable computers as well. Current computer-based memory aids are written to make l...

Wearable computing moves computation from the desktop to the user. We are forming a community of networked wearable computer users to explore, over a long period, the augmented realities that these systems can provide. By adapting its behavior to the user's changing environment, a body-worn computer can assist the user more intelligently, consistently, and continuously than a desktop system. A text-based augmented reality, the Remembrance Agent, is presented to illustrate this approach. Video cameras are used both to warp the visual input (mediated reality) and to sense the user's world for graphical overlay. With a camera, the computer tracks the user's finger, which acts as the system's mouse; performs face recognition; and detects passive objects to overlay 2.5D and 3D graphics onto the real world. Additional apparatus such as audio systems, infrared beacons for sensing location, and biosensors for learning about the wearer's affect are described. Using the input from these interfac...

A specific query establishes a rigid qualification and is concerned only with data that match it precisely. A vague query establishes a target qualification and is concerned also with data that are close to this target. Most conventional database systems cannot handle vague queries directly, forcing their users to retry specific queries repeatedly with minor modifications until they match data that are satisfactory. This article describes a system called VAGUE that can handle vague queries directly. The principal concept behind VAGUE is its extension to the relational data model with data metrics, which are definitions of distances between values of the same domain. A problem with implementing data distances is that different users may have different interpretations for the notion of distance. VAGUE incorporates several features that enable it to adapt itself to the individual views and priorities of its users.

We use data from the TREC routing experiments to explore how relevance feedback can be applied incrementally --- using a few judged documents each time --- to achieve results that are as good as if the feedback occurred in one pass. We show that relatively few judgments are needed to get highquality results. We also demonstrate methods that reduce the amount of information archived from past judged documents without adversely affecting effectiveness. A novel simulation shows that such techniques are useful for handling long-standing queries with drifting notions of relevance.

We survey the major techniques for information retrieval. In the rst part, weprovide an overview of the traditional ones (full text scanning, inversion, signature les and clustering). In the second part we discuss attempts to include semantic information (natural language processing, latent semantic indexing and neural networks).

Several visualizations have emerged which attempt to visualize all or part of the World Wide Web. Those visualizations, however, fail to present the dynamically changing ecology of users and documents on the Web. We present new techniques for Web Ecology and Evolution Visualization (WEEV). Disk Trees represent a discrete time slice of the Web ecology. A collection of Disk Trees forms a Time Tube, representing the evolution of the Web over longer periods of time. These visualizations are intended to aid authors and webmasters with the production and organization of content, assist Web surfers making sense of information, and help researchers understand the Web. Keywords World Wide Web, visualization, log file analysis, temporal analysis, information ecologies, hypertext, documents.

The combination of different text representations and search strategies has become a standard technique for improving the effectiveness of information retrieval. Combination, for example, has been studied extensively in the TREC evaluations and is the basis of the “meta-search” engines used on the Web. This paper examines the development of this technique, including both experimental results and the retrieval models that have been proposed as formal frameworks for combination. We show that combining approaches for information retrieval can be modeled as combining the outputs of multiple classifiers based on one or more representations, and that this simple model can provide explanations for many of the experimental results. We also show that this view of combination is very similar to the inference net model, and that a new approach to retrieval based on language models supports combination and can be integrated with the inference net model.

Statistically based ranked retrieval of records using keywords provides many advantages over traditional Boolean retrieval methods, especially for end users. This approach to retrieval, however, has not seen wide-spread use in large operational retrieval systems. To show the feasibility of this retrieval methodology, re-search was done to produce very fast search tech-niques using these ranking algorithms, and then to test the results against large databases with many end users. The results show not only response times on the order of 1 and l/2 seconds for 806 megabytes of text, but also very favorable user reaction. Novice users were able to consistently obtain good search results after 5 minutes of training. Additional work was done to de-vise new indexing techniques to create inverted files for large databases using a minicomputer. These techniques use no sorting, require a working space of only about 20 % of the size of the input text, and produce indices that are about 14 % of the input text size.

Recall and precision are often used to evaluate the effectiveness of information retrieval systems. They are easy to define if there is a single query and if the retrieval result generated for the query is a linear ordering. However, when the retrieval results are weakly ordered, in the sense that several documents have an identical retrieval status value with respect to a query, some probabilistic notion of precision has to be introduced. Relevance probability, expected precision, and so forth, are some alternatives mentioned in the literature for this purpose. Furthermore, when many queries are to be evaluated and the retrieval results averaged over these queries, some method of interpolation of precision values at certain preselected recall levels is needed. The currently popular approaches for handling both a weak ordering and interpolation are found to be inconsistent, and the results obtained are not easy to interpret. Moreover, in cases where some alternatives are available, no comparative analysis that would facilitate the selection of a particular strategy has been provided. In this paper, we systematically investigate the various problems and issues associated with the use of recall and precision as measures of retrieval system performance. Our motivation is to provide a comparative analysis of methods available for defining precision in a probabilistic sense and to promote a better understanding of the various issues involved in retrieval performance evaluation.

The paper is concerned with applying learning to rank to document retrieval. Ranking SVM is a typical method of learning to rank. We point out that there are two factors one must consider when applying Ranking SVM, in general a “learning to rank” method, to document retrieval. First, correctly ranking documents on the top of the result list is crucial for an Information Retrieval system. One must conduct training in a way that such ranked results are accurate. Second, the number of relevant documents can vary from query to query. One must avoid training a model biased toward queries with a large number of relevant documents. Previously, when existing methods that include Ranking SVM were applied to document retrieval, none of the two factors was taken into consideration. We show it is possible to make modifications in conventional Ranking SVM, so it can be better used for document retrieval. Specifically, we modify the “Hinge Loss ” function in Ranking SVM to deal with the problems described above. We employ two methods to conduct optimization on the loss function: gradient descent and quadratic programming. Experimental results show that our method, referred to as Ranking SVM for IR, can outperform the conventional Ranking SVM and other existing methods for document retrieval on two datasets.