Autonomic computing is emerging as a significant new approach to the design of computing systems. Its goal is the development of systems that are self-configuring, self-healing, self-protecting and self-optimizing. De...
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Autonomic computing is emerging as a significant new approach to the design of computing systems. Its goal is the development of systems that are self-configuring, self-healing, self-protecting and self-optimizing. Dependability is a long-standing desirable property of all computer-based systems. The purpose of the paper is to consider how autonomic computing can provide a framework for dependability.
In this article we thoroughly discuss conceptual and navigational modeling and query issues for repositories of metrics and their cataloging system by exploiting as well the power of the semantic web approach. This en...
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We apply the replica method of Statistical Physics combined with a variational method to the approximate analytical computation of bootstrap averages for estimating the generalization error. We demonstrate our approac...
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ISBN:
(纸本)0262025507
We apply the replica method of Statistical Physics combined with a variational method to the approximate analytical computation of bootstrap averages for estimating the generalization error. We demonstrate our approach on regression with Gaussian processes and compare our results with averages obtained by Monte-Carlo sampling.
This paper classifies and reviews the available algorithms to blind signal separation (BSS) problem. Based on the separation criteria, we broadly divide all the reviewed algorithms into four categories, namely: classi...
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This paper classifies and reviews the available algorithms to blind signal separation (BSS) problem. Based on the separation criteria, we broadly divide all the reviewed algorithms into four categories, namely: classical adaptive, higher-order statistics based, information theory based algorithms and others. For algorithms which might fall into more than one category, categorizing is made according to their main features. Most of the algorithms reviewed in this paper are benchmarks in BSS area. Many BSS algorithms use neural networks to perform the learning rules, probably because neural networks are powerful in nonlinear mapping and learning ability.
A general linear response method for deriving improved estimates of correlations in the variational Bayes framework is presented. Three applications are given and it is discussed how to use linear response as a genera...
A general linear response method for deriving improved estimates of correlations in the variational Bayes framework is presented. Three applications are given and it is discussed how to use linear response as a general principle for improving mean field approximations.
In this paper, new structures that implement RSA cryptographic algorithm are presented. These structures are built using a modified Montgomery modular multiplier, where the operations of multiplication and modular red...
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In this paper, new structures that implement RSA cryptographic algorithm are presented. These structures are built using a modified Montgomery modular multiplier, where the operations of multiplication and modular reductions are carried out in parallel rather than interleaved as in the traditional Montgomery multiplier. The global broadcast data lines are avoided by interleaving two operations into the same structure, thus making the implementation systolic. The results of implementation in FPGA have shown that the proposed RSA structures outperformed those structures built around a traditional Montgomery multiplier in terms of speed. In terms of area usage, an area-efficient architecture is shown in this paper that has the merit of having a high speed and a reduced area usage when compared with other architectures.
We compute approximate analytical bootstrap averages for support vector classification using a combination of the replica method of statistical physics and the TAP approach for approximate inference. We test our metho...
We compute approximate analytical bootstrap averages for support vector classification using a combination of the replica method of statistical physics and the TAP approach for approximate inference. We test our method on a few datasets and compare it with exact averages obtained by extensive Monte-Carlo sampling.
This paper presents a novel and efficient analytic framework for the performance analysis and capacity-assignment optimisation of a wireless GSM cell employing the Re-Use Partitioning (RUP) policy. RUP splits hierarch...
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