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Kuter U.,University of Maryland College Park | Golbeck J.,Institute for Advanced Computer Studies
ACM Transactions on Internet Technology | Year: 2010

In this article, we describe a new approach that gives an explicit probabilistic interpretation for social networks. In particular, we focus on the observation that many existing Web-based trust-inference algorithms conflate the notions of "trust" and "confidence," and treat the amalgamation of the two concepts to compute the trust value associated with a social relationship. Unfortunately, the result of such an algorithm that merges trust and confidence is not a trust value, but rather a new variable in the inference process. Thus, it is hard to evaluate the outputs of such an algorithm in the context of trust inference. This article first describes a formal probabilistic network model for social networks that allows us to address that issue. Then we describe SUNNY, a new trust inference algorithm that uses probabilistic sampling to separately estimate trust information and our confidence in the trust estimate and use the two values in order to compute an estimate of trust based on only those information sources with the highest confidence estimates. We present an experimental evaluation of SUNNY. In our experiments, SUNNY produced more accurate trust estimates than the well-known trust inference algorithm TIDALTRUST, demonstrating its effectiveness. Finally, we discuss the implications these results will have on systems designed for personalizing content and making recommendations. © 2010 ACM. Source


Patel V.M.,Rutgers University | Chellappa R.,University of Maryland University College | Chellappa R.,Institute for Advanced Computer Studies | Chandra D.,Google | Barbello B.,Google
IEEE Signal Processing Magazine | Year: 2016

Recent developments in sensing and communication technologies have led to an explosion in the use of mobile devices such as smartphones and tablets. With the increase in the use of mobile devices, users must constantly worry about security and privacy, as the loss of a mobile device could compromise personal information. To deal with this problem, continuous authentication systems (also known as active authentication systems) have been proposed, in which users are continuously monitored after initial access to the mobile device. In this article, we provide an overview of different continuous authentication methods on mobile devices. We discuss the merits and drawbacks of the available approaches and identify promising avenues of research in this rapidly evolving field. © 2016 IEEE. Source


Taheri S.,Digital Signal | Qiu Q.,Duke University | Chellappa R.,Institute for Advanced Computer Studies
IEEE Transactions on Image Processing | Year: 2014

Although facial expressions can be decomposed in terms of action units (AUs) as suggested by the facial action coding system, there have been only a few attempts that recognize expression using AUs and their composition rules. In this paper, we propose a dictionary-based approach for facial expression analysis by decomposing expressions in terms of AUs. First, we construct an AU-dictionary using domain experts' knowledge of AUs. To incorporate the high-level knowledge regarding expression decomposition and AUs, we then perform structure-preserving sparse coding by imposing two layers of grouping over AU-dictionary atoms as well as over the test image matrix columns. We use the computed sparse code matrix for each expressive face to perform expression decomposition and recognition. Since domain experts' knowledge may not always be available for constructing an AU-dictionary, we also propose a structure-preserving dictionary learning algorithm, which we use to learn a structured dictionary as well as divide expressive faces into several semantic regions. Experimental results on publicly available expression data sets demonstrate the effectiveness of the proposed approach for facial expression analysis. © 1992-2012 IEEE. Source


Fermuller C.,Institute for Advanced Computer Studies | Ji H.,National University of Singapore | Kitaoka A.,Ritsumeikan University
Vision Research | Year: 2010

A new class of patterns, composed of repeating patches of asymmetric intensity profile, elicit strong perception of illusory motion. We propose that the main cause of this illusion is erroneous estimation of image motion induced by fixational eye movements. Image motion is estimated with spatial and temporal energy filters, which are symmetric in space, but asymmetric (causal) in time. That is, only the past, but not the future, is used to estimate the temporal energy. It is shown that such filters mis-estimate the motion of locally asymmetric intensity signals at certain spatial frequencies. In an experiment the perception of the different illusory signals was quantitatively compared by nulling the illusory motion with opposing real motion, and was found to be predicted well by the model. © 2009 Elsevier Ltd. All rights reserved. Source


Lieberman M.D.,Institute for Advanced Computer Studies | Samet H.,Institute for Advanced Computer Studies
SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval | Year: 2011

News sources on the Web generate constant streams of information, describing many aspects of the events that shape our world. In particular, geography plays a key role in the news, and enabling geographic retrieval of news articles involves recognizing the textual references to geographic locations (called toponyms) present in the articles, which can be difficult due to ambiguity in natural language. Toponym recognition in news is often accomplished with algorithms designed and tested around small corpora of news articles, but these static collections do not reflect the streaming nature of online news, as evidenced by poor performance in tests. In contrast, a method for toponym recognition is presented that is tuned for streaming news by leveraging a wide variety of recognition components, both rule-based and statistical. An evaluation of this method shows that it outperforms two prominent toponym recognition systems when tested on large datasets of streaming news, indicating its suitability for this domain. Source

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