The growing need to manage and exploit the proliferation of online data sources is opening up new opportunities for bringing people closer to the resources they need. For instance, consider a recommendation service through which researchers can receive daily pointers to journal papers in their elds of interest. We survey some of the known approaches to the problem of technical paper recommendation and ask how they can be extended to deal with multiple information sources. More speci cally, we focus on a variant of this problem { recommending conference paper submissions to reviewing committee members { which o ers us a testbed to try di erent approaches. Using WHIRL { an information integration system { we are able to implement di erent recommendation algorithms derived from information retrieval principles. We also use a novel autonomous procedure for gathering reviewer interest information from the Web. We evaluate our approach and compare it to other methods using preference data provided by members of the AAAI-98 conference reviewing committee along with data about the actual submissions.1. The data were obtained with permission from AAAI, the AAAI reviewers, and when appropriate, from the authors of the submitted papers.
The training objective for urban warfare includes acquisition and perfection of a set of diverse skills in support of kinetic and non-kinetic operations. The US Marines (USMC) employ long-duration acted scenarios with verbal training feedback provided sporadically throughout the training session and at the end in a form of an after-action review (AAR). The inherent characteristic of training ranges for urban warfare is that they are the environments with a high level of physical occlusion, which causes many performances not to be seen by a group of instructors who oversee the training. We describe BASE-IT (Behavioral Analysis and Synthesis for Intelligent Training), a system in development that aims to automate capture of training data and their analysis, performance evaluation, and AAR report generation. The goal of this effort is to greatly increase the amount of observed behavior and improve the quality of the AAR. The system observes training with stationary cameras and personal tracking devices. It then analyzes movement and body postures, measures individual and squad-level performance, and compares it to standards and levels of performance expected in given situations. An interactive visualization component delivers live views augmented with real-time analytics and alerts; it also generates a personalized AAR review in a three-dimensional virtual or mixed reality environment, indexed by automatically extracted salient events and accompanied by summary statistics of unit performance. The approaches presented in the system have the potential to radically change the analysis and performance assessment on physical training ranges and ultimately this type of training itself.
One of the major hurdles in maintaining long-lived electronic systems is that electronic parts become obsolete, no longer available from the original suppliers. When this occurs, an engineer is tasked with resolving the problem by finding a replacement that is "as similar as possible" to the original part. The current approach involves a laborious manual search through several electronic portals and data books. The search is difficult because potential replacements may differ from the original and from each other by one or more parameters. Worse still, the cumbersome nature of this process may cause the engineers to miss appropriate solutions amid the many thousands of parts listed in industry catalogs.In this paper, we address this problem by introducing the notion of a parametric "distance" between electronic components. We use this distance to search a large parts data set and recommend likely replacements. Recommendations are based on an adaptive nearest-neighbor search through the parametric data set. For each user, we learn how to scale the axes of the feature space in which the nearest neighbors are sought. This allows the system to learn each user's judgment of the phrase "as similar as possible."
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