Results 1  10
of
23,172
An XMLbased lightweight C++ fact extractor
 in Proceedings of 11th IEEE International Workshop on Program Comprehension (IWPC'03
, 2003
"... A lightweight fact extractor is presented that utilizes XML tools, such as XPath and XSLT, to extract static information from C++ source code programs. The source code is first converted into an XML representation, srcML, to facilitate the use of a wide variety of XML tools. The method is deemed lig ..."
Abstract

Cited by 71 (29 self)
 Add to MetaCart
A lightweight fact extractor is presented that utilizes XML tools, such as XPath and XSLT, to extract static information from C++ source code programs. The source code is first converted into an XML representation, srcML, to facilitate the use of a wide variety of XML tools. The method is deemed
Extractor
"... Suppose we have access to a source of randomness that isn’t completely random, but does contain a lot of randomness. Is there a way to convert this source into a source of truly random bits? Ideally we’d like an algorithm (called an Extractor) that does something like this: weak source ..."
Abstract
 Add to MetaCart
Suppose we have access to a source of randomness that isn’t completely random, but does contain a lot of randomness. Is there a way to convert this source into a source of truly random bits? Ideally we’d like an algorithm (called an Extractor) that does something like this: weak source
Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing McC and Mc#: Unified C++ and C # Design Facts Extractors Tools
"... In the last years, as objectoriented software systems have become more and more complex, the need of performing automatically reverse engineering upon such systems has significantly increased. It is well known that one step toward a research infrastructure accelerating the progress of reverse engin ..."
Abstract
 Add to MetaCart
In the last years, as objectoriented software systems have become more and more complex, the need of performing automatically reverse engineering upon such systems has significantly increased. It is well known that one step toward a research infrastructure accelerating the progress of reverse engineering is the creation of an intermediate representation of software systems. In the current demonstration we present an unified structure for representing objectoriented systems written in C++ and C#, together with the corresponding model capturing tools. As a result, we can uniformly analyze C++ and C # systems. Moreover, we have integrated the tools in the iPlasma reengineering infrastructure which permits us to obtain easily valuable information for a reverse engineering process. 1.
• Extractors • Extractors vs Codes
, 2004
"... Suppose we have access to a source of randomness that isn’t completely random, but does contain a lot of randomness. Is there a way to convert this source into a source of truly random bits? Ideally we’d like an algorithm (called an Extractor) that does something like this: weak source ..."
Abstract
 Add to MetaCart
Suppose we have access to a source of randomness that isn’t completely random, but does contain a lot of randomness. Is there a way to convert this source into a source of truly random bits? Ideally we’d like an algorithm (called an Extractor) that does something like this: weak source
Dimension extractors
, 2006
"... ddoty at iastate dot edu A dimension extractor is an algorithm designed to increase the effective dimension – i.e., the computational information density – of an infinite sequence. A constructive dimension extractor is exhibited by showing that every sequence of positive constructive dimension is Tu ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
ddoty at iastate dot edu A dimension extractor is an algorithm designed to increase the effective dimension – i.e., the computational information density – of an infinite sequence. A constructive dimension extractor is exhibited by showing that every sequence of positive constructive dimension
Fuzzy extractors
 In Security with Noisy Data
, 2007
"... This chapter presents a general approach for handling secret biometric data in cryptographic applications. The generality manifests itself in two ways: we attempt to minimize the assumptions we make about the data, and to present techniques that are broadly applicable wherever biometric inputs are u ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
This chapter presents a general approach for handling secret biometric data in cryptographic applications. The generality manifests itself in two ways: we attempt to minimize the assumptions we make about the data, and to present techniques that are broadly applicable wherever biometric inputs are used. Because biometric data comes from a variety of sources that are mostly outside of anyone’s control, it is prudent to assume as little as possible about how they are distributed; in particular, an adversary may know more about a distribution than a system’s designers and users. Of course, one may attempt to measure some properties of a biometric distribution, but relying on such measurements in the security analysis is dangerous, because the adversary may have even more accurate measurements available to it. For instance, even assuming that some property of a biometric behaves according to a binomial distribution (or some similar discretization of the normal distribution), one could determine the mean of this distribution only to within ≈ 1 √ after taking n n samples; a wellmotivated adversary can take more measurements, and thus determine the mean more accurately.
Feature Extractor
"... The traditional model of pattern recognition (since the late 50's) Fixed/engineered features (or fixed kernel) + trainable classifier Endtoend learning / Feature learning / Deep learning Trainable features (or kernel) + trainable classifier “Simple ” Trainable ..."
Abstract
 Add to MetaCart
The traditional model of pattern recognition (since the late 50's) Fixed/engineered features (or fixed kernel) + trainable classifier Endtoend learning / Feature learning / Deep learning Trainable features (or kernel) + trainable classifier “Simple ” Trainable
On using a benchmark to evaluate C++ extractors
 in Proceedings of 10th International Workshop on Program Comprehension
, 2002
"... In this paper, we take the concept of benchmarking as used extensively in computing and apply it to evaluating C++ fact extractors. We demonstrated the efficacy of this approach by developing a prototype benchmark, CppETS 1.0 (C++ Extractor Test Suite, pronounced seepets) and collecting feedback in ..."
Abstract

Cited by 24 (2 self)
 Add to MetaCart
In this paper, we take the concept of benchmarking as used extensively in computing and apply it to evaluating C++ fact extractors. We demonstrated the efficacy of this approach by developing a prototype benchmark, CppETS 1.0 (C++ Extractor Test Suite, pronounced seepets) and collecting feedback
Error Reduction for Extractors
, 1999
"... We present a general method to reduce the error of any extractor. Our method works particularly well in the case that the original extractor extracts up to a constant fraction of the source minentropy and achieves a polynomially small error. In that case, we are able to reduce the error to (almost) ..."
Abstract

Cited by 22 (6 self)
 Add to MetaCart
We present a general method to reduce the error of any extractor. Our method works particularly well in the case that the original extractor extracts up to a constant fraction of the source minentropy and achieves a polynomially small error. In that case, we are able to reduce the error to (almost
Extractors: Optimal up to Constant Factors
 STOC'03
, 2003
"... This paper provides the first explicit construction of extractors which are simultaneously optimal up to constant factors in both seed length and output length. More precisely, for every n, k, our extractor uses a random seed of length O(log n) to transform any random source on n bits with (min)ent ..."
Abstract

Cited by 50 (12 self)
 Add to MetaCart
This paper provides the first explicit construction of extractors which are simultaneously optimal up to constant factors in both seed length and output length. More precisely, for every n, k, our extractor uses a random seed of length O(log n) to transform any random source on n bits with (min
Results 1  10
of
23,172