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31.
In recent years, cubic regularization algorithms for unconstrained optimization have been defined as alternatives to trust-region and line search schemes. These regularization techniques are based on the strategy of computing an (approximate) global minimizer of a cubic overestimator of the objective function. In this work we focus on the adaptive regularization algorithm using cubics (ARC) proposed in Cartis et al. [Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results, Mathematical Programming A 127 (2011), pp. 245–295]. Our purpose is to design a modified version of ARC in order to improve the computational efficiency preserving global convergence properties. The basic idea is to suitably combine a Goldstein-type line search and a nonmonotone accepting criterion with the aim of advantageously exploiting the possible good descent properties of the trial step computed as (approximate) minimizer of the cubic model. Global convergence properties of the proposed nonmonotone ARC algorithm are proved. Numerical experiments are performed and the obtained results clearly show satisfactory performance of the new algorithm when compared to the basic ARC algorithm. 相似文献
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Elena Baralis Andrea Bianco Tania Cerquitelli Luca Chiaraviglio Marco Mellia 《Computer Networks》2013,57(17):3300-3315
Internet measured data collected via passive measurement are analyzed to obtain localization information on nodes by clustering (i.e., grouping together) nodes that exhibit similar network path properties. Since traditional clustering algorithms fail to correctly identify clusters of homogeneous nodes, we propose the NetCluster novel framework, suited to analyze Internet measurement datasets. We show that the proposed framework correctly analyzes synthetically generated traces. Finally, we apply it to real traces collected at the access link of Politecnico di Torino campus LAN and discuss the network characteristics as seen at the vantage point. 相似文献
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Marco Riccardi Roberto Di Pietro Marta Palanques Jorge Aguilà Vila 《Computer Networks》2013,57(2):422-435
Malware is one of the main threats to the Internet security in general, and to commercial transactions in particular. However, given the high level of sophistication reached by malware (e.g. usage of encrypted payload and obfuscation techniques), malware detection tools and techniques still call for effective and efficient solutions. In this paper, we address a specific, dreadful, and widely diffused financial malware: Zeus.The contributions of this paper are manifold: first, we propose a technique to break the encrypted malware communications, extracting the keystream used to encrypt such communications; second, we provide a generalization of the proposed keystream extraction technique. Further, we propose Cronus, an IDS that specifically targets Zeus malware. The implementation of Cronus has been experimentally tested on a production network, and its high quality performance and effectiveness are discussed. Finally, we highlight some principles underlying malware—and Zeus in particular—that could pave the way for further investigation in this field. 相似文献
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Current advancements in pervasive technologies allow users to create and share an increasing amount of whereabouts data. Thus, some rich datasets on human mobility are becoming available on the web. In this paper we extracted approximately 790,000 mobility traces from a web-based repository of GPS tracks—the Nokia Sports Tracker Service. Using data mining mechanisms, we show that this data can be analyzed to uncover daily routines and interesting schemes in the use of public spaces. We first show that our approach supports large-scale analysis of people’s whereabouts by comparing behavioral patterns across cities. Then, using Kernel Density Estimation, we present a mechanism to identify popular sport areas in individual cities. This kind of analysis allows us to highlight human-centered geographies that can support a wide range of applications ranging from location-based services to urban planning. 相似文献
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Anja Le Blanc John Brooke Donal Fellows Marco Soldati David Pérez-Suárez Alessandro Marassi Andrej Santin 《Journal of Grid Computing》2013,11(3):481-503
In this paper we describe how we have introduced workflows into the working practices of a community for whom the concept of workflows is very new, namely the heliophysics community. Heliophysics is a branch of astrophysics which studies the Sun and the interactions between the Sun and the planets, by tracking solar events as they travel throughout the Solar system. Heliophysics produces two major challenges for workflow technology. Firstly it is a systems science where research is currently developed by many different communities who need reliable data models and metadata to be able to work together. Thus it has major challenges in the semantics of workflows. Secondly, the problem of time is critical in heliophysics; the workflows must take account of the propagation of events outwards from the sun. They have to address the four dimensional nature of space and time in terms of the indexing of data. We discuss how we have built an environment for Heliophysics workflows building on and extending the Taverna workflow system and utilising the myExperiment site for sharing workflows. We also describe how we have integrated the workflows into the existing practices of the communities involved in Heliophysics by developing a web portal which can hide the technical details from the users, who can concentrate on the data from their scientific point of view rather than on the methods used to integrate and process the data. This work has been developed in the EU Framework 7 project HELIO, and is being disseminated to the worldwide Heliophysics community, since Heliophysics requires integration of effort on a global scale. 相似文献
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Mathematical simulation has been widely used in biomedical and biological sciences. In the case of the surface electromyographic (SEMG) activity, some models have been proposed aiming to study muscle contraction strategies that are used during different tasks and conditions. Most of SEMG simulators are based on energy modulation of a Gaussian noise. This work proposes a novel simulator in which the user-defined parameters are associated with the motor units (MUs) recruitment and their firing rate. Comparison between the mean spectrum of real SEMG signals collected in isometric contraction of the muscle biceps brachii and the mean spectrum obtained from simulated SEMG signals showed a good agreement, pointing the proposed simulator seems to be capable to generate consistent electromyographic signals in time and frequency domains and that can be used in many studies, in particular in the evaluation of automatic methods aimed to detect muscular contraction. 相似文献
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Applications of Bayesian networks in systems biology are computationally demanding due to the large number of model parameters. Conventional MCMC schemes based on proposal moves in structure space tend to be too slow in mixing and convergence, and have recently been superseded by proposal moves in the space of node orders. A disadvantage of the latter approach is the intrinsic inability to specify the prior probability on network structures explicitly. The relative paucity of different experimental conditions in contemporary systems biology implies a strong influence of the prior probability on the posterior probability and, hence, the outcome of inference. Consequently, the paradigm of performing MCMC proposal moves in order rather than structure space is not entirely satisfactory. In the present article, we propose a new and more extensive edge reversal move in the original structure space, and we show that this significantly improves the convergence of the classical structure MCMC scheme. 相似文献