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    Face Identification by a Cascade of Rejection Classifiers

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    Nearest neighbor search is commonly employed in face recognition but it does not scale well to large dataset sizes. A strategy to combine rejection classifiers into a cascade for face identification is proposed in this paper. A rejection classifier for a pair of classes is defined to reject at least one of the classes with high confidence. These rejection classifiers are able to share discriminants in feature space and at the same time have high confidence in the rejection decision. In the face identification problem, it is possible that a pair of known individual faces are very dissimilar. It is very unlikely that both of them are close to an unknown face in the feature space. Hence, only one of them needs to be considered. Using a cascade structure of rejection classifiers, the scope of nearest neighbor search can be reduced significantly. Experiments on Face Recognition Grand Challenge (FRGC) version 1 data demonstrate that the proposed method achieves significant speed up and an accuracy comparable with the brute force Nearest Neighbor method. In addition, a graph cut based clustering technique is employed to demonstrate that the pairwise separability of these rejection classifiers is capable of semantic grouping.National Science Foundation (EIA-0202067, IIS-0329009); Office of Naval Research (N00014-03-1-0108

    A Utility-Theoretic Approach to Privacy in Online Services

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    Online offerings such as web search, news portals, and e-commerce applications face the challenge of providing high-quality service to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by introducing methods to personalize services based on special knowledge about users and their context. For example, a user's demographics, location, and past search and browsing may be useful in enhancing the results offered in response to web search queries. However, reasonable concerns about privacy by both users, providers, and government agencies acting on behalf of citizens, may limit access by services to such information. We introduce and explore an economics of privacy in personalization, where people can opt to share personal information, in a standing or on-demand manner, in return for expected enhancements in the quality of an online service. We focus on the example of web search and formulate realistic objective functions for search efficacy and privacy. We demonstrate how we can find a provably near-optimal optimization of the utility-privacy tradeoff in an efficient manner. We evaluate our methodology on data drawn from a log of the search activity of volunteer participants. We separately assess users’ preferences about privacy and utility via a large-scale survey, aimed at eliciting preferences about peoples’ willingness to trade the sharing of personal data in returns for gains in search efficiency. We show that a significant level of personalization can be achieved using a relatively small amount of information about users

    The Daisystat: A model to explore multidimensional homeostasis

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    The Homeostat was a physical device that demonstrated Ashby’s notion of ‘ultrastability’. The components interact in such a way as to maintain sets of essential variables to within critical ranges in the face of an externally imposed regime of perturbations. The Daisystat model is presented that bears a number of similarities to Ashby’s Homeostat but which can also be considered as a higher dimensional version of the Watson & Lovelock Daisyworld model that sought to explain how homeostasis operating at the planetary scale may arise in the absence of foresight or planning. The Daisystat model features a population of diverse individuals that affect and are affected by the environment in different ways. The Daisystat model extends Daisyworld in that homeostasis is observed with systems comprised of four environmental variables and beyond. It is shown that the behaviour of the population is analogous to the ‘uniselector’ in the Homeostat in that rapid changes in the population allows the system to ‘search’ for stable states. This allows the system to find and recover homeostatic states in the face of externally applied perturbations. It is proposed that the Daisystat may afford insights into the evolution of increasingly complex systems such as the Earth system

    Effect Of Water Sprays On Airflow Movement And Methane Dilution At The Working Face

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    The National Institute for Occupational Safety and Health (NIOSH) has been conducting re-search to determine the influence of mining machine mounted water sprays on airflows and methane concentrations at the face when blowing ventilation systems are used. Tests were conducted in a full-scale ventilation gallery. Airflow speeds and directions were measured at several locations near the face with ultrasonic anemometers. Methane was released from the face and concentrations were measured in the entry at locations above the mining machine using fixed point methanometers. Changes in airflow speed, direction, and methane concentrations were correlated with water spray operations. The test results using different spray arrangements and water pressures showed that operation of the machine-mounted sprayers can improve face ventilation effectiveness by increasing the velocity of airflow moving toward and away from the face. The improved ventilation resulted in reduced methane levels near the face
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