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8,649
Fuzzy extractors: How to generate strong keys from biometrics and other noisy data
, 2008
"... We provide formal definitions and efficient secure techniques for • turning noisy information into keys usable for any cryptographic application, and, in particular, • reliably and securely authenticating biometric data. Our techniques apply not just to biometric information, but to any keying mater ..."
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Cited by 535 (38 self)
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, it can be used to reliably reproduce errorprone biometric inputs without incurring the security risk inherent in storing them. We define the primitives to be both formally secure and versatile, generalizing much prior work. In addition, we provide nearly optimal constructions of both primitives
SMOTE: Synthetic Minority Oversampling Technique
 Journal of Artificial Intelligence Research
, 2002
"... An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often realworld data sets are predominately composed of ``normal'' examples with only a small percentag ..."
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Cited by 634 (27 self)
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An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often realworld data sets are predominately composed of ``normal'' examples with only a small
Prior distributions for variance parameters in hierarchical models
 Bayesian Analysis
, 2006
"... Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new foldednoncentralt family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider noninformative and weakly informative priors i ..."
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Cited by 430 (15 self)
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in this family. We use an example to illustrate serious problems with the inversegamma family of “noninformative ” prior distributions. We suggest instead to use a uniform prior on the hierarchical standard deviation, using the halft family when the number of groups is small and in other settings where a
Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm
 IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic limi ..."
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Cited by 639 (15 self)
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methods are limited to using MRF as a general prior in an FM modelbased approach. To fit the HMRF model, an EM algorithm is used. We show that by incorporating both the HMRF model and the EM algorithm into a HMRFEM framework, an accurate and robust segmentation can be achieved. More importantly
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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oscillations in the toyQMR case? To answer this question we repeated the ex periments reported in the previous section but rather than having the prior probability of each node be ran domly selected in the range [0, 1] we selected the prior uniformly in the range [0, U] and varied U. Unlike the previous
Cluster Analysis, Model Selection, and Prior Distributions on Models
, 2011
"... Clustering is an important and challenging statistical problem for which there is an extensive literature. Modeling approaches include mixture models and product partition models. Here we develop a product partition model and a model selection procedure based on Bayes factors from intrinsic priors. ..."
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Cited by 1 (1 self)
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. We also find that the choice of the prior on model space is of utmost importance, almost overshadowing the other parts of the clustering problem, and we examine the behavior of posterior odds based on different model space priors. We find, somewhat surprisingly, that procedures based on the oftenused
Shared memory consistency models: A tutorial
 IEEE Computer
, 1996
"... Parallel systems that support the shared memory abstraction are becoming widely accepted in many areas of computing. Writing correct and efficient programs for such systems requires a formal specification of memory semantics, called a memory consistency model. The most intuitive model—sequential con ..."
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Cited by 441 (10 self)
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optimizations they allow. We retain the systemcentric emphasis, but use uniform and simple terminology to describe the different models. We also briefly discuss an alternate programmercentric view that describes the models in terms of program behavior rather than specific system optimizations. 1
The iSLIP Scheduling Algorithm for InputQueued Switches
, 1999
"... An increasing number of high performance internetworking protocol routers, LAN and asynchronous transfer mode (ATM) switches use a switched backplane based on a crossbar switch. Most often, these systems use input queues to hold packets waiting to traverse the switching fabric. It is well known th ..."
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Cited by 425 (8 self)
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An increasing number of high performance internetworking protocol routers, LAN and asynchronous transfer mode (ATM) switches use a switched backplane based on a crossbar switch. Most often, these systems use input queues to hold packets waiting to traverse the switching fabric. It is well known
Localization from Mere Connectivity
 In Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
, 2003
"... It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. We present an algorithm that uses connectivity information— who is within communications range of whom—to derive the locat ..."
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Cited by 367 (9 self)
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It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. We present an algorithm that uses connectivity information— who is within communications range of whom—to derive
Removing camera shake from a single photograph
 ACM Trans. Graph
, 2006
"... Camera shake during exposure leads to objectionable image blur and ruins many photographs. Conventional blind deconvolution methods typically assume frequencydomain constraints on images, or overly simplified parametric forms for the motion path during camera shake. Real camera motions can follow c ..."
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Cited by 325 (16 self)
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convoluted paths, and a spatial domain prior can better maintain visually salient image characteristics. We introduce a method to remove the effects of camera shake from seriously blurred images. The method assumes a uniform camera blur over the image and negligible inplane camera rotation. In order
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