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A randomized protocol for signing contracts
, 1990
"... Two parties, A and B, want to sign a contract C over a communication network. To do so, they must “simultaneously” exchange their commitments to C. Since simultaneous exchange is usually impossible in practice, protocols are needed to approximate simultaneity by exchanging partial commitments in pie ..."
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Cited by 600 (11 self)
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exchange probadilistic options for committing both parties to the contract; the protocol never terminates in an asymmetric situation where party A knows that party B is committed to the contract while he is not; the protocol makes use of a weak form of a third party (judge). If both A and B are honest
Random Key Predistribution Schemes for Sensor Networks”,
 IEEE Symposium on Security and Privacy,
, 2003
"... Abstract Efficient key distribution is the basis for providing secure communication, a necessary requirement for many emerging sensor network applications. Many applications require authentic and secret communication among neighboring sensor nodes. However, establishing keys for secure communicatio ..."
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Cited by 834 (12 self)
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keys for all pairs of nodes is not viable due to the large number of sensors and the limited memory of sensor nodes. A new key distribution approach was proposed by Eschenauer and Gligor [11] to achieve secrecy for nodetonode communication: sensor nodes receive a random subset of keys from a key pool
An experimental comparison of three methods for constructing ensembles of decision trees
 Bagging, boosting, and randomization. Machine Learning
, 2000
"... Abstract. Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base ” learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approac ..."
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Cited by 610 (6 self)
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of the decisiontree algorithm C4.5. The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting. In situations with substantial classification noise, bagging is much better than
The ratedistortion function for source coding with side information at the decoder
 IEEE Trans. Inform. Theory
, 1976
"... AbstractLet {(X,, Y,J}r = 1 be a sequence of independent drawings of a pair of dependent random variables X, Y. Let us say that X takes values in the finite set 6. It is desired to encode the sequence {X,} in blocks of length n into a binary stream*of rate R, which can in turn be decoded as a seque ..."
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Cited by 1059 (1 self)
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AbstractLet {(X,, Y,J}r = 1 be a sequence of independent drawings of a pair of dependent random variables X, Y. Let us say that X takes values in the finite set 6. It is desired to encode the sequence {X,} in blocks of length n into a binary stream*of rate R, which can in turn be decoded as a
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
, 1998
"... This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I err ..."
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Cited by 723 (8 self)
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error). Two widely used statistical tests are shown to have high probability of type I error in certain situations and should never be used: a test for the difference of two proportions and a paireddifferences t test based on taking several random traintest splits. A third test, a paired
Network Coding for Large Scale Content Distribution
"... We propose a new scheme for content distribution of large files that is based on network coding. With network coding, each node of the distribution network is able to generate and transmit encoded blocks of information. The randomization introduced by the coding process eases the scheduling of bloc ..."
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Cited by 494 (7 self)
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We propose a new scheme for content distribution of large files that is based on network coding. With network coding, each node of the distribution network is able to generate and transmit encoded blocks of information. The randomization introduced by the coding process eases the scheduling
Spectral Efficiency of CDMA with Random Spreading
 IEEE TRANS. INFORM. THEORY
, 1999
"... The CDMA channel with randomly and independently chosen spreading sequences accurately models the situation where pseudonoise sequences span many symbol periods. Furthermore, its analysis provides a comparison baseline for CDMA channels with deterministic signature waveforms spanning one symbol per ..."
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Cited by 313 (23 self)
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The CDMA channel with randomly and independently chosen spreading sequences accurately models the situation where pseudonoise sequences span many symbol periods. Furthermore, its analysis provides a comparison baseline for CDMA channels with deterministic signature waveforms spanning one symbol
How to Use Expert Advice
 JOURNAL OF THE ASSOCIATION FOR COMPUTING MACHINERY
, 1997
"... We analyze algorithms that predict a binary value by combining the predictions of several prediction strategies, called experts. Our analysis is for worstcase situations, i.e., we make no assumptions about the way the sequence of bits to be predicted is generated. We measure the performance of the ..."
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Cited by 377 (79 self)
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We analyze algorithms that predict a binary value by combining the predictions of several prediction strategies, called experts. Our analysis is for worstcase situations, i.e., we make no assumptions about the way the sequence of bits to be predicted is generated. We measure the performance
INTRODUCING THE STUDY OF RANDOM SITUATIONS AND THEIR MODEL BUILDING PROCESS TO 1415 YEARS OLD STUDENTS
"... This paper proposes a first approach with random situations by using a modeling process within the model of Bernoulli’s Urn. This way of learning is accessible to 1415 years old pupils. The software Cabrigéomètre II is used as an empirical computation environment for simulation of the game of “Fra ..."
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This paper proposes a first approach with random situations by using a modeling process within the model of Bernoulli’s Urn. This way of learning is accessible to 1415 years old pupils. The software Cabrigéomètre II is used as an empirical computation environment for simulation of the game
Short Communication Quantitative Spatial Analysis of Randomly Situated Surface Water Bodies Through Spectra
, 2000
"... Reprints available directly from the publisher ..."
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