Ranking Of Components Research Articles

Attack resistant watermarking technique based on fast curvelet transform and Robust Principal Component Analysis

Watermarking is a technique which embeds the copyright information/identifier to provide authenticity in robust and imperceptible manner. In this paper, a hybrid watermarking technique is proposed based on combination of Fast Curvelet Tansform (FCT), Robust Principal Component Analysis (RPCA) and Singular Value Decomposition (SVD). The gray-scale watermark logo is scrambled using Generalized Arnold Transform (GAT) to enhance the robustness and security. The original image is decomposed to low rank and sparse components using RPCA; the curvelet coefficients are obtained using FCT via Unequally-Spaced Fast Fourier Transforms (USFFT) to embed the processed watermark using SVD into the color image. In curvelet transform, fewer coefficients contain the most energy, also giving optimally sparse representation of the significant image features and edges that helps in efficient recovery of the embedded watermark even after severe image degradation. The robustness and imperceptibility of the proposed technique is verified against a variety of processing operations (noise, filtering) and geometric attacks (crop, resize, projection etc.). The quantitative and visual results reveal that the watermarking technique proposed is more efficient and provides high tolerance against different geometric and image processing attacks.

On the measures for ranking software components

Large software systems consist of many components some of which are more significant than others. A component is deemed significant if it plays an important role and can have a significant impact on the rest of the system when modified. Identifying such core components is important since change is inevitable as a normal course of evolution in any system and core components must be designed to minimize their impact of change. Several different graph-based strategies exist for ranking software components that can be used to identify the core components within a software system. However, each ranking strategy behaves differently and most fail to pick up all of the significant core components among their top tier of highly ranked components. In this paper, we propose a component ranking approach that models the component graph of a system as a Discrete-Time Markov Chain and uses it as a basis for component ranking. Using this approach produces results that are superior to ranking strategies based on centrality measures such as closeness, betweenness and eigenvector centrality. We demonstrate the utility of the metric and compare it against existing graph-based measures, in the analysis of Kona and JUnit, two published systems with documented architectures.

Analysis of reliability and error sources of judges’ rating in rhythmic gymnastics

Purpose The purpose of this study was (1) to analyze judges’ evaluation on rhythmic gymnastics performance by applying generalizability theory and (2) to suggest recommendations to improve judges' rating. Methods Data were 34 players’ scores from Senior Part at 29th KGA President’s Cup National Rhythmic Gymnastics Championship in Korea. Difficulty and execution scores in ball, clubs, hoop and ribbon event were analyzed. Analysis models containing components of area and reputation rank were designed and multivariate generalizability theory were used for analysis. Results The G-study results showed (1) that the error source about players has more significant impact to evaluation than other error sources in analysis model containing components of only player and judge, (2) that the error source about players has more significant impact to evaluation than other error sources in analysis model adding components of area, but the error source about area has more significant impact to evaluation of clubs event than other error sources, (3) that the error source about players has more significant impact to evaluation than other error sources in analysis model adding components of reputation rank, but the error source about reputation rank has more significant impact to evaluation of hoop event than other error sources in analysis model adding components of area. The D-study results showed generalizability coefficient was stable in analysis model without components of area and reputation rank, but generalizability coefficient in analysis model containing components of area and reputation rank not stable in some event. Conclusion Recommendations for improving judging were discussed.

A Technique to Improved Page Rank Algorithm in perspective to Optimized Normalization Technique

Page Ranking is an essential segment for information retrieval system. It is utilized to gauge the significance and conduct of website pages. We audit two methodologies for ranking: HITS idea and Page Rank technique. Both methodologies concentrate on the link structure of the Web to discover the significance of the Web pages. The Page Rank algorithm figures the rank of individual website page and Hypertext Induced Topic Search (HITS) relies on the hubs and authority framework. A quick and efficient page ranking component for web retrieval stays as a test. This paper proposed another page rank algorithm which utilizes a normalization technique in view of mean value of page ranks. The proposed scheme reduces the time complexity of the traditional Page Rank algorithm by diminishing the number of iterations to reach a convergence point . Keywords: Ranking, Page Rank, HITS, Hyperlink, Normalization

Integration of analytic network process with service measurement index framework for cloud service provider selection

SummaryThe growing popularity of cloud services has attracted large number of business organizations to enter into the cloud market. The varied availability of cloud service providers, for various services, leaves the cloud customers into skepticism. Service measurement index (SMI) framework, which is based on the analytic hierarchical process, is developed for the ranking of cloud services. As there are multiple dependencies among the criteria for the selection of cloud service provider, analytic hierarchical process formulation is not efficient. In the proposed work, analytic network process that captures the cloud service‐provider selection problem in a more realistic way is integrated in the ranking component of SMI framework. In this method, interactions among the criteria are used for the ranking of the cloud services. Few new criterions are also added into the existing SMI framework to make the framework more effective. A case study is conducted to analyze the proposed technique for the cloud provider selection.

High Dimensional Low Rank Plus Sparse Matrix Decomposition

This paper is concerned with the problem of low rank plus sparse matrix decomposition for big data. Conventional algorithms for matrix decomposition use the entire data to extract the low-rank and sparse components, and are based on optimization problems with complexity that scales with the dimension of the data, which limits their scalability. Furthermore, existing randomized approaches mostly rely on uniform random sampling, which is quite inefficient for many real world data matrices that exhibit additional structures (e.g. clustering). In this paper, a scalable subspace-pursuit approach that transforms the decomposition problem to a subspace learning problem is proposed. The decomposition is carried out using a small data sketch formed from sampled columns/rows. Even when the data is sampled uniformly at random, it is shown that the sufficient number of sampled columns/rows is roughly O(r\mu), where \mu is the coherency parameter and r the rank of the low rank component. In addition, adaptive sampling algorithms are proposed to address the problem of column/row sampling from structured data. We provide an analysis of the proposed method with adaptive sampling and show that adaptive sampling makes the required number of sampled columns/rows invariant to the distribution of the data. The proposed approach is amenable to online implementation and an online scheme is proposed.

QBEES: query-by-example entity search in semantic knowledge graphs based on maximal aspects, diversity-awareness and relaxation

Structured knowledge bases are an increasingly important way for storing and retrieving information. Within such knowledge bases, an important search task is finding similar entities based on one or more example entities. We present QBEES, a novel framework for defining entity similarity based on structural features, so-called aspects and maximal aspects of the entities, that naturally model potential interest profiles of a user submitting an ambiguous query. Our approach based on maximal aspects provides natural diversity awareness and includes query-dependent and query-independent entity ranking components. We present evaluation results with a number of existing entity list completion benchmarks, comparing to several state-of-the-art baselines.

A Regular k-Shrinkage Thresholding Operator for the Removal of Mixed Gaussian-Impulse Noise

The removal of mixed Gaussian-impulse noise plays an important role in many areas, such as remote sensing. However, traditional methods may be unaware of promoting the degree of the sparsity adaptively after decomposing into low rank component and sparse component. In this paper, a new problem formulation with regular spectral k-support norm and regular k-support l1 norm is proposed. A unified framework is developed to capture the intrinsic sparsity structure of all two components. To address the resulting problem, an efficient minimization scheme within the framework of accelerated proximal gradient is proposed. This scheme is achieved by alternating regular k-shrinkage thresholding operator. Experimental comparison with the other state-of-the-art methods demonstrates the efficacy of the proposed method.

Dynamic MRI reconstruction from highly undersampled (k, t)-space data using weighted Schatten p-norm regularizer of tensor

Combination of the low-rankness and sparsity has been successfully used to reconstruct desired dynamic magnetic resonance image (MRI) from highly-undersampled (k, t)-space data. However, nuclear norm, as a convex relaxation of the rank function, can cause the solution deviating from the original solution of low-rank problem. Moreover, equally treating different rank component is not flexible to deal with real applications. In this paper, an efficient reconstruction model is proposed to efficiently reconstruct dynamic MRI. First, we treat dynamic MRI as a 3rd-order tensor, and formulate the low-rankness via non-convex Schatten p-norm of matrices unfolded from the tensor. Secondly, we assign different weight for each rank component in Schatten p-norm. Furthermore, we combine the proposed weighted Schatten p-norm of a tensor as low-rank regularizer, and spatiotemporal total variation as sparse regularizer to formulate the reconstruction model for dynamic MRI. Thirdly, to efficiently solve the formulated reconstruction model, we derive an algorithm based on Bregman iterations with alternating direction multiplier. Over two public data sets of dynamic MRI, experiments demonstrate that the proposed method achieves much better quality.

A reconfigurable fabric for accelerating large-scale datacenter services

Datacenter workloads demand high computational capabilities, flexibility, power efficiency, and low cost. It is challenging to improve all of these factors simultaneously. To advance datacenter capabilities beyond what commodity server designs can provide, we designed and built a composable, reconfigurable hardware fabric based on field programmable gate arrays (FPGA). Each server in the fabric contains one FPGA, and all FPGAs within a 48-server rack are interconnected over a low-latency, high-bandwidth network. We describe a medium-scale deployment of this fabric on a bed of 1632 servers, and measure its effectiveness in accelerating the ranking component of the Bing web search engine. We describe the requirements and architecture of the system, detail the critical engineering challenges and solutions needed to make the system robust in the presence of failures, and measure the performance, power, and resilience of the system. Under high load, the large-scale reconfigurable fabric improves the ranking throughput of each server by 95% at a desirable latency distribution or reduces tail latency by 29% at a fixed throughput. In other words, the reconfigurable fabric enables the same throughput using only half the number of servers.

Robust image restoration via random projection and partial sorted ℓp norm

Image restoration plays an important role in video technology. In this paper, a robust image and video denoising method, based on random projection and partial sorted ℓp-norm, is proposed. First, the input signal is decomposed into two components: a low rank component and a sparse component. The low rank component is approximated by random projection. Second, the sparse one is recovered by partial sorted ℓp-norm. A generalized iterative thresholding shrinkage solver is developed for the resulting problem. Some theoretical results about sparse random projection are provided. Numerical experiments for mixed Gaussian and random value impulsive noise demonstrated that the proposed method outperforms some state-of-art restoration methods, both quantitatively and visually.

Weighted Schatten <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math> </inline-formula>-Norm Minimization for Image Denoising and Background Subtraction

Low rank matrix approximation (LRMA), which aims to recover the underlying low rank matrix from its degraded observation, has a wide range of applications in computer vision. The latest LRMA methods resort to using the nuclear norm minimization (NNM) as a convex relaxation of the nonconvex rank minimization. However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications. We propose a more flexible model, namely the Weighted Schatten $p$-Norm Minimization (WSNM), to generalize the NNM to the Schatten $p$-norm minimization with weights assigned to different singular values. The proposed WSNM not only gives better approximation to the original low-rank assumption, but also considers the importance of different rank components. We analyze the solution of WSNM and prove that, under certain weights permutation, WSNM can be equivalently transformed into independent non-convex $l_p$-norm subproblems, whose global optimum can be efficiently solved by generalized iterated shrinkage algorithm. We apply WSNM to typical low-level vision problems, e.g., image denoising and background subtraction. Extensive experimental results show, both qualitatively and quantitatively, that the proposed WSNM can more effectively remove noise, and model complex and dynamic scenes compared with state-of-the-art methods.

Decoupling social status and status certainty effects on health in macaques: a network approach.

BackgroundAlthough a wealth of literature points to the importance of social factors on health, a detailed understanding of the complex interplay between social and biological systems is lacking. Social status is one aspect of social life that is made up of multiple structural (humans: income, education; animals: mating system, dominance rank) and relational components (perceived social status, dominance interactions). In a nonhuman primate model we use novel network techniques to decouple two components of social status, dominance rank (a commonly used measure of social status in animal models) and dominance certainty (the relative certainty vs. ambiguity of an individual’s status), allowing for a more complex examination of how social status impacts health.MethodsBehavioral observations were conducted on three outdoor captive groups of rhesus macaques (N = 252 subjects). Subjects’ general physical health (diarrhea) was assessed twice weekly, and blood was drawn once to assess biomarkers of inflammation (interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and C-reactive protein (CRP)).ResultsDominance rank alone did not fully account for the complex way that social status exerted its effect on health. Instead, dominance certainty modified the impact of rank on biomarkers of inflammation. Specifically, high-ranked animals with more ambiguous status relationships had higher levels of inflammation than low-ranked animals, whereas little effect of rank was seen for animals with more certain status relationships. The impact of status on physical health was more straightforward: individuals with more ambiguous status relationships had more frequent diarrhea; there was marginal evidence that high-ranked animals had less frequent diarrhea.DiscussionSocial status has a complex and multi-faceted impact on individual health. Our work suggests an important role of uncertainty in one’s social status in status-health research. This work also suggests that in order to fully explore the mechanisms for how social life influences health, more complex metrics of social systems and their dynamics are needed.

Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten <inline-formula> <tex-math notation="LaTeX">$p$</tex-math> </inline-formula>-Norm Minimization

Hyperspectral images (HSIs) are inevitably corrupted by mixture noise during their acquisition process, in which various kinds of noise, e.g., Gaussian noise, impulse noise, dead lines, and stripes, may exist concurrently. In this paper, mixture noise removal is well illustrated by the task of recovering the low-rank and sparse components of a given matrix, which is constructed by stacking vectorized HSI patches from all the bands at the same position. Instead of applying a traditional nuclear norm, a nonconvex low-rank regularizer, i.e., weighted Schatten p -norm (WSN), is introduced to not only give better approximation to the original low-rank assumption but also to consider the importance of different rank components. The resulted nonconvex low-rank matrix approximation (LRMA) model falls into the applicable scope of an augmented Lagrangian method, and its WSN minimization subproblem can be efficiently solved by generalized iterated shrinkage algorithm. Moreover, the proposed model is integrated into an iterative regularization schema to produce final results, leading to a completed HSI restoration framework. Extensive experimental testing on simulated and real data shows, both qualitatively and quantitatively, that the proposed method has achieved highly competent objective performance compared with several state-of-the-art HSI restoration methods.

Signal-Level Information Fusion for Less Constrained Iris Recognition Using Sparse-Error Low Rank Matrix Factorization

Iris recognition systems working in less constrained environments with the subject at-a-distance and on-the-move suffer from the noise and degradations in the iris captures. These noise and degradations significantly deteriorate iris recognition performance. In this paper, we propose a novel signal-level information fusion method to mitigate the influence of noise and degradations for less constrained iris recognition systems. The proposed method is based on low rank approximation (LRA). Given multiple noisy captures of the same eye, we assume that: 1) the potential noiseless images lie in a low rank subspace and 2) the noise is spatially sparse. Based on these assumptions, we seek an LRA of noisy captures to separate the noiseless images and noise for information fusion. Specifically, we propose a sparse-error low rank matrix factorization model to perform LRA, decomposing the noisy captures into a low rank component and a sparse error component. The low rank component estimates the potential noiseless images, while the error component models the noise. Then, the low rank and error components are utilized to perform signal-level fusion separately, producing two individually fused images. Finally, we combine the two fused images at the code level to produce one iris code as the final fusion result. Experiments on benchmark data sets demonstrate that the proposed signal-level fusion method is able to achieve a generally improved iris recognition performance in less constrained environment, in comparison with the existing iris recognition algorithms, especially for the iris captures with heavy noise and low quality.

Identifying Broad and Narrow Financial Risk Factors with Convex Optimization

Factor analysis of security returns aims to decompose a return covariance matrix into systematic and specific risk components. To date, most commercially successful factor analysis has been based on fundamental models, although there is a large academic literature on statistical models. While successful in many respects, traditional statistical approaches like principal component analysis and maximum likelihood suffer from several drawbacks. These include a lack of robustness, strict assumptions on the underlying model of returns, and insensitivity to narrow factors such as industries and currencies, which affect only a small number of securities, but in an important way.We apply convex optimization methods to decompose a security return covariance matrix into low rank and sparse parts. The low rank component includes the market and other broad factors that affect most securities. The sparse component includes narrow factors and security specific effects.We measure the variance forecasting accuracy of a low rank plus sparse covariance matrix estimator on an equally weighted portfolio of 100 securities simulated from a model with two broad factors and 21 narrow factors. We find that the low rank plus sparse estimators are more accurate than estimates made with classical principal component analysis, in particular, at forecasting risk due to narrow factors. Finally, we illustrate a low rank plus sparse decomposition of an empirical covariance matrix of 100 equities drawn from 21 countries.

Resilience-Based Component Importance Measures for Critical Infrastructure Network Systems

In this paper, we propose two metrics, i.e., the optimal repair time and the resilience reduction worth, to measure the criticality of the components of a network system from the perspective of their contribution to system resilience. Specifically, the two metrics quantify: 1) the priority with which a failed component should be repaired and re-installed into the network and 2) the potential loss in the optimal system resilience due to a time delay in the recovery of a failed component, respectively. Given the stochastic nature of disruptive events on infrastructure networks, a Monte Carlo-based method is proposed to generate probability distributions of the two metrics for all of the components of the network; then, a stochastic ranking approach based on the Copeland's pairwise aggregation is used to rank components importance. Numerical results are obtained for the IEEE 30-bus test network and a comparison is made with three classical centrality measures.

The Analysis of Administrators and Staffs Attitude from the Obstacles of Delegation of Authority

<p>This study was conducted to analyze the managers’ and employees’ attitude towards obstacles to devolutionin Mazandaran Province Gas Company (Iran). According to the exploratory studies, devolutionobstacles was explored and identified at two dimensions including managers’ unwillingness to devolutionand subordinates’ reluctance to accept authority. To analyze the data and to confirm or reject the research questions, first,<strong> </strong>the <strong><em>Kolmogorov</em></strong><em>-<strong>Smirnov test </strong></em>was used to determine the normality of data. Given the non-normality of data, non-parametric tests such as one-sample one-tailed Wilcoxon test, Friedman test for ranking components, and Mann–Whitney test were used to identify the employees’ and managers’ different perception of obstacles to delegation of authority. Findings of the study indicated that Wilcoxon test was not significant at o.o5; that is, lack of trust and confidence in subordinates, inability of managers in guiding subordinates, lack of controlling processes, managers’ sense of insecurity, and unwillingness of managers to delegate authority were not the main obstacles to devolutionby managers in Mazandran Province Gas Company, and their effects were not significant. In line with examining the obstacles effective to the devolutionby subordinates, it was found that the fear of criticism, blame, and dismissal along with the lack of adequate motivation in subordinates were recognized as the most important obstacles to adoption of authority by subordinates in Mazandran Province Gas Company.</p>

Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features

Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DWT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening.

Beyond Low Rank + Sparse: Multiscale Low Rank Matrix Decomposition

We present a natural generalization of the recent low rank + sparse matrix decomposition and consider the decomposition of matrices into components of multiple scales. Such decomposition is well motivated in practice as data matrices often exhibit local correlations in multiple scales. Concretely, we propose a multi-scale low rank modeling that represents a data matrix as a sum of block-wise low rank matrices with increasing scales of block sizes. We then consider the inverse problem of decomposing the data matrix into its multi-scale low rank components and approach the problem via a convex formulation. Theoretically, we show that under various incoherence conditions, the convex program recovers the multi-scale low rank components either exactly or approximately. Practically, we provide guidance on selecting the regularization parameters and incorporate cycle spinning to reduce blocking artifacts. Experimentally, we show that the multi-scale low rank decomposition provides a more intuitive decomposition than conventional low rank methods and demonstrate its effectiveness in four applications, including illumination normalization for face images, motion separation for surveillance videos, multi-scale modeling of the dynamic contrast enhanced magnetic resonance imaging and collaborative filtering exploiting age information.