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The logsupport encoding of CSP into SAT
 In Proceedings of CP2007
, 2007
"... Abstract. It is known that Constraint Satisfaction Problems (CSP) can be converted into Boolean Satisfiability problems (SAT); however how to encode a CSP into a SAT problem such that a SAT solver will efficiently find a solution is still an open question. Various encodings have been proposed in the ..."
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Cited by 12 (0 self)
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in the literature. Some of them use a logical variable for each element in each domain: among these very successful are the direct and the support encodings. It is known that a SAT solver based on the DPLL procedure obtains a propagation similar to Forward Checking on a directencoded CSP, and to Maintaining Arc
Practical network support for IP traceback
, 2000
"... This paper describes a technique for tracing anonymous packet flooding attacks in the Internet back towards their source. This work is motivated by the increased frequency and sophistication of denialofservice attacks and by the difficulty in tracing packets with incorrect, or “spoofed”, source ad ..."
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Cited by 666 (14 self)
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addresses. In this paper we describe a general purpose traceback mechanism based on probabilistic packet marking in the network. Our approach allows a victim to identify the network path(s) traversed by attack traffic without requiring interactive operational support from Internet Service Providers (ISPs
Computer support for knowledgebuilding communities
 The Journal of the Learning Sciences
, 1994
"... Nobody wants to use technology to recreate education as it is, yet there is not much to distinguish what goes on in most computersupported classrooms versus traditional classrooms. Kay (1991) has suggested that the phenomenon of reframing innovations to recreate the familiar is itself commonplace. ..."
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Cited by 593 (4 self)
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Nobody wants to use technology to recreate education as it is, yet there is not much to distinguish what goes on in most computersupported classrooms versus traditional classrooms. Kay (1991) has suggested that the phenomenon of reframing innovations to recreate the familiar is itself commonplace
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
, 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
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Cited by 1513 (20 self)
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Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear measurements do we need to recover objects from this class to within accuracy ɛ? This paper shows that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal f ∈ F decay like a powerlaw (or if the coefficient sequence of f in a fixed basis decays like a powerlaw), then it is possible to reconstruct f to within very high accuracy from a small number of random measurements. typical result is as follows: we rearrange the entries of f (or its coefficients in a fixed basis) in decreasing order of magnitude f  (1) ≥ f  (2) ≥... ≥ f  (N), and define the weakℓp ball as the class F of those elements whose entries obey the power decay law f  (n) ≤ C · n −1/p. We take measurements 〈f, Xk〉, k = 1,..., K, where the Xk are Ndimensional Gaussian
Training Support Vector Machines: an Application to Face Detection
, 1997
"... We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision sur ..."
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Cited by 728 (1 self)
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We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 766 (29 self)
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algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled d...
Supporting RealTime Applications in an Integrated Services Packet Network: Architecture and Mechanism
, 1992
"... This paper considers the support of realtime applications in an ..."
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Cited by 575 (25 self)
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This paper considers the support of realtime applications in an
Transductive Inference for Text Classification using Support Vector Machines
, 1999
"... This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try to minimiz ..."
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Cited by 887 (4 self)
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This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try
Knowledgebased Analysis of Microarray Gene Expression Data By Using Support Vector Machines
, 2000
"... We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of ..."
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Cited by 514 (8 self)
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We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge
A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of ContextAware Applications
, 2001
"... Computing devices and applications are now used beyond the desktop, in diverse environments, and this trend toward ubiquitous computing is accelerating. One challenge that remains in this emerging research field is the ability to enhance the behavior of any application by informing it of the context ..."
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Cited by 891 (28 self)
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application. We have built a toolkit, the Context Toolkit, that instantiates this conceptual framework and supports the rapid development of a rich space of contextaware applications. We illustrate the usefulness of the conceptual framework by describing a number of contextaware applications that h...
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871,952