Current methods of evaluating the performance of a runner using Energy Storing and Returning (ESR) prosthesis rely heavily on metabolic and biological factors. This makes it difficult to differentiate between the cont...
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Combination of classifiers leads to a substantial reduction of classification errors in a wide range of applications. Among them SVM ensembles with bagging have shown better performance in classification than a single...
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Combination of classifiers leads to a substantial reduction of classification errors in a wide range of applications. Among them SVM ensembles with bagging have shown better performance in classification than a single SVM. However, the training process of SVM ensembles is notably computationally intensive especially when the number of replicated training datasets is large. This paper presents MRESVM, a MapReduce based distributed SVM ensemble algorithm for image annotation which re-samples the training dataset based on bootstrapping and trains SVM on each dataset in parallel using a cluster of computers. MRESVM is evaluated in a experimental environment and the results show that the MRESVM algorithm reduces the training time significantly while achieves high level of accuracy in classifications.
This article presents an evolution-based model for the US airport network. The topological properties and the volume of people travelling are both studied in detail, revealing high heterogeneity in space and time. A r...
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Modeling and analyzing software architectures are useful for helping to understand the system structures and facilitate proper implementation of user requirements. Despite its importance in the software engineering pr...
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Modeling and analyzing software architectures are useful for helping to understand the system structures and facilitate proper implementation of user requirements. Despite its importance in the software engineering practice, the lack of formal description and verification support hinders the development of quality architectural models. In this work, we develop an approach for modeling and verifying software architectures specified using Monterey Phoenix (MP) architecture description language. Firstly, we formalize the syntax and operational semantics for MP. This language is capable of modeling system and environment behaviors based on event traces, as well as supporting different architecture composition operations and views. Secondly, a dedicated model checker for MP is developed based on PAT verification framework. Finally, several case studies are presented to evaluate the usability and effectiveness of our approach.
We propose a predictive model of structural changes in elementary subgraphs of social network based on Mixture of Markov Chains. The model is trained and verified on a dataset from a large corporate social network ana...
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ISBN:
(纸本)9781467324977
We propose a predictive model of structural changes in elementary subgraphs of social network based on Mixture of Markov Chains. The model is trained and verified on a dataset from a large corporate social network analyzed in short, one day-long time windows, and reveals distinctive patterns of evolution of connections on the level of local network topology. We argue that the network investigated in such short timescales is highly dynamic and therefore immune, to classic methods of link prediction and structural analysis, and show that in the case of complex networks, the dynamic subgraph mining may lead to better prediction accuracy. The experiments were carried out on the logs from the Wroclaw University of Technology mail server.
As the number of research papers increases, the need for academic categorizer system becomes crucial. This is to help academicians organize their research papers into pre-defined categories based on the documents'...
As the number of research papers increases, the need for academic categorizer system becomes crucial. This is to help academicians organize their research papers into pre-defined categories based on the documents' content similarity. This paper presents the Document Categorizer Agent based on ACM CCS (Association for computing Machinery computing Classification System). First, we studied the ACM categories hierarchy. Next, based on these categories, we retrieved our corpus from ACM DL (ACM Digital Library) to train our Categorizer Agent using a popular machine learning technique called Naïve Bayes Classifier. We used two types of training data for the corpus namely, negative training data and positive training data. Next, these papers are categorized according to their content based on the same training data. We tested our Document Categorizer Agent on a number of academic papers to test its accuracy. The result we obtained showed promising results.
The insulator-metal transition (IMT) property of vanadium dioxide provides a large, abrupt change in refractive index, making it a good candidate active material for optical modulators. We show, in this paper, that pl...
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The insulator-metal transition (IMT) property of vanadium dioxide provides a large, abrupt change in refractive index, making it a good candidate active material for optical modulators. We show, in this paper, that plasmonic modulators can leverage the high modulation contrast of vanadium dioxide, while at the same time solve the problems of high insertion loss and high phase-transition electric-field threshold faced by vanadium dioxide photonic modulators. Our simulation results show that vanadium dioxide plasmonic slot and hybrid-slot waveguide modulators can achieve extinction ratios in excess of 10dB/μm with insertion losses as low as 2dB/μm. We also show that vanadium dioxide can be used to build plasmonic ring modulators. These high performance modulators are foundations to realizing plasmonic nanocircuits for next-generation chip technology.
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