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The paper deals with production performance evaluation of the EU selected regions in the period 2000 – 2011. We estimate a translog stochastic production frontier using the true fixed-effects methods. We detect statistically significant savings with respect to the technical progress in capital input and consumption with respect to the technical progress in labor input. We evaluate the performance...
We explore the dependence structure in the sampled sequence of complex networks. We consider randomized algorithms to sample the nodes and study external properties in any associated stationary sequence of characteristics of interest like node degrees, number of followers or income of the nodes in Online Social Networks etc, which satisfy two mixing conditions. Several useful extremes of the sampled...
This study is concerned with text clustering and evaluating the clusters. Fuzzy neighborhood, a method of text mining, is used for this purpose. Five different clustering algorithms are used, they are kernel affinity propagation, kernel hard c-means, kernel fuzzy c-means, kernel hard k-means++ and kernel variable size hard c-means. These algorithms are applied to the clustering of nouns and adjectives...
Clustering is a method to group given data into clusters. In this research, we focus on data sets with nominal attributes. For such nominal data sets, it is important to pursue clusters having simple logical representations (patterns) as well as gathering similar objects and separate dissimilar ones. However, conventional clustering methods do not explicitly deal with patterns of clusters. In this...
Stream processing has emerged as an important model of computation in the context of multimedia and communication sub-systems of embedded System-on-Chip (SoC) architectures. The dataflow nature of streaming applications allows them to be most naturally expressed as a set of kernels iteratively operating on continuous streams of data. The kernels are computationally intensive and exhibit large amounts...
Intelligent Kernel K-Means is a fully unsupervised clustering technique. This technique is developed by combining Intelligent K-Means and Kernel K-Means. Intelligent Kernel K-Means used to cluster kernel matrix without any information about the number of clusters. The goal of this research is to evaluate the performance of Intelligent Kernel K-Means for clustering nonlinearly separable data. Various...
A texture descriptor based on a set of indices of degrees of local approximating polynomials is proposed in this paper. An image is split into non-overlapping patches, reshaped into one-dimensional source vectors and convolved with the polynomial approximation kernels of various degrees p. As a result, a set of approximations is obtained. For each element of the source vector, these approximations...
Imperfection of remote sensing data greatly affects the performance of information fusion algorithm. To solve this problem, a Gaussian kernel-based Fuzzy Rough Set fusion algorithm is proposed, since Fuzzy Rough Set theory is an effect tool to model uncertainties of data. For feature reduction a novel index is proposed to evaluate the significance of features, considering both the relevance between...
Aiming at the issue that fault prediction of power electronic circuits is not accurate enough, a method based on multi-scale relevance vector machine (MSRVM) is proposed. Then the kernel parameters are optimized by Genetic Algorithm (GA) to avoid the negative affect on the performance of MSRVM by the non-ideal ones. The feasibility and advantages of MSRVM are proved by the fault prediction of a Buck...
In this paper the alternative method for determination of all possible lower reachability indices has been proposed. Method is based on the extension of parallel digraphs creation algorithm presented previously. As a solution of problem of determination of lower reachability index, method finds all possible finite paths in the digraphs — each of them representing one of the indices. By performing...
Due to the diversity of processor architectures and application memory access patterns, the performance impact of using local memory in OpenCL kernels has become unpredictable. For example, enabling the use of local memory for an OpenCL kernel can be beneficial for the execution on a GPU, but can lead to performance losses when running on a CPU. To address this unpredictability, we propose an empirical...
In this paper, a segmentation-free keyword spotting method is proposed for Bangla handwritten documents. In order to tolerate large variations in handwritten scenarios, we extracted key points based on SIFT key point detector, and the end and intersection points found by morphological operations. Heat Kernel signature (HKS) is used to present the local characteristics of detected key points. Instead...
Variety in Big Data means we have a wide range of data types and sources: e.g. File systems and database systems co-exist for decades as two popular data-accessing interfaces. This work is to unify these two interfaces by presenting a Data Interface All-iN-A-place (DIANA). The first challenge lies in distinguishing structured and un-structured data and diverting them to different underlying platforms...
We propose a new Kernel-based Atlas Image Selection computed in the Embedding Representation space (termed KAISER) aiming to support labeling of brain tissue on 3D magnetic resonance (MR) images. KAISER approach provides efficient feature extraction from MR volumes based on an introduced inter-slice kernel (ISK). Thus, using the ISK matrix eigendecomposition, the inherent structure of data distribution...
We propose a novel approach for measuring the stationarity level of multichannel time-series. This measure is based on stationarity definition over time-varying spectra and aims to quantify the relationship between local (single-channel dynamics) and global (multichannel dynamics) stationarity. With the purpose of separate among several motor/imagery tasks, we asssume that movement imagination implies...
Motion blur is usually generated when people captured a picture in the daily life. This kind of blur is often non-liner motion and may cause the blurred contents seriously in this image. Hence, how to remove the blurred image into a clear image becomes a very important scheme. In this paper, the primary aim is to propose an efficient blurred image restoration method based on fast blur-kernel estimation,...
This paper proposed a low-complexity algorithm and of analysis and synthesis quadrature mirror filter banks (AQMF, SQMF) on the spectral band replication (SBR) for digital radio mondiale (DRM). Based on recent Lai et al.'s concept, an extended issue is addressed form the view point of recursively computing the AQMF and SQMF coefficients. The proposed recursively computational method not only combines...
When the stock market has become more and more competitive, the stock market prediction has been a hot research topic. Traditional methods are based on historical stock data, which ignore the latest market information. Later although financial news is proposed to access market information, there are some disadvantages for news to predict the stock market. Recently when the micro-blogging service has...
Considering the problems of slow convergence and easily getting into local optimum of intelligent optimization algorithms in finding the optimal solution to complex high-dimensional functions, we have proposed an improved invasive weed optimization (IIWO). Concrete adjustments include setting the newborn seeds per plant to a fixed number, changing the initial step and final step to adaptive one, and...
The software maintainability which is an important part of software quality and trustworthiness has always been a hot topic in software engineering domain. The measurements of the software are multi-source and heterogeneous data, which result in big challenges on data aggregation for software maintainability evaluation. In this paper, an Intuitionistic Fuzzy Set-based Data Aggregation (IFSDA) approach...
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