Improving data perturbation testing techniques for Web services

The widespread use of service-oriented architectures (SOAs) and Web services in commercial software requires the adoption of development techniques to ensure the quality of Web services. Testing techniques and tools concern quality and play a critical role in accomplishing quality of SOA based systems. Existing techniques and tools for traditional systems are not appropriate to these new systems, making the development of Web services testing techniques and tools required. This article presents new testing techniques to automatically generate a set of test cases and data for Web services. The techniques presented here explore data perturbation of Web services messages upon data types, integrity and consistency. To support these techniques, a tool (GenAutoWS) was developed and applied to real problems. (C) 2010 Elsevier Inc. All rights reserved.

A non-parametric vessel detection method for complex vascular structures

Modern medical imaging techniques enable the acquisition of in vivo high resolution images of the vascular system. Most common methods for the detection of vessels in these images, such as multiscale Hessian-based operators and matched filters, rely on the assumption that at each voxel there is a single cylinder. Such an assumption is clearly violated at the multitude of branching points that are easily observed in all, but the Most focused vascular image studies. In this paper, we propose a novel method for detecting vessels in medical images that relaxes this single cylinder assumption. We directly exploit local neighborhood intensities and extract characteristics of the local intensity profile (in a spherical polar coordinate system) which we term as the polar neighborhood intensity profile. We present a new method to capture the common properties shared by polar neighborhood intensity profiles for all the types of vascular points belonging to the vascular system. The new method enables us to detect vessels even near complex extreme points, including branching points. Our method demonstrates improved performance over standard methods on both 2D synthetic images and 3D animal and clinical vascular images, particularly close to vessel branching regions. (C) 2008 Elsevier B.V. All rights reserved.

Comparing time-varying autoregressive structures of locally stationary processes

In this paper, a novel statistical test is introduced to compare two locally stationary time series. The proposed approach is a Wald test considering time-varying autoregressive modeling and function projections in adequate spaces. The covariance structure of the innovations may be also time- varying. In order to obtain function estimators for the time- varying autoregressive parameters, we consider function expansions in splines and wavelet bases. Simulation studies provide evidence that the proposed test has a good performance. We also assess its usefulness when applied to a financial time series.

Missing data mechanisms and their implications on the analysis of categorical data

We review some issues related to the implications of different missing data mechanisms on statistical inference for contingency tables and consider simulation studies to compare the results obtained under such models to those where the units with missing data are disregarded. We confirm that although, in general, analyses under the correct missing at random and missing completely at random models are more efficient even for small sample sizes, there are exceptions where they may not improve the results obtained by ignoring the partially classified data. We show that under the missing not at random (MNAR) model, estimates on the boundary of the parameter space as well as lack of identifiability of the parameters of saturated models may be associated with undesirable asymptotic properties of maximum likelihood estimators and likelihood ratio tests; even in standard cases the bias of the estimators may be low only for very large samples. We also show that the probability of a boundary solution obtained under the correct MNAR model may be large even for large samples and that, consequently, we may not always conclude that a MNAR model is misspecified because the estimate is on the boundary of the parameter space.

A robust Bayesian approach to null intercept measurement error model with application to dental data

Measurement error models often arise in epidemiological and clinical research. Usually, in this set up it is assumed that the latent variable has a normal distribution. However, the normality assumption may not be always correct. Skew-normal/independent distribution is a class of asymmetric thick-tailed distributions which includes the Skew-normal distribution as a special case. In this paper, we explore the use of skew-normal/independent distribution as a robust alternative to null intercept measurement error model under a Bayesian paradigm. We assume that the random errors and the unobserved value of the covariate (latent variable) follows jointly a skew-normal/independent distribution, providing an appealing robust alternative to the routine use of symmetric normal distribution in this type of model. Specific distributions examined include univariate and multivariate versions of the skew-normal distribution, the skew-t distributions, the skew-slash distributions and the skew contaminated normal distributions. The methods developed is illustrated using a real data set from a dental clinical trial. (C) 2008 Elsevier B.V. All rights reserved.

Novel Application of 2D and 3D-Similarity Searches To Identify Substrates among Cytochrome P450 2C9, 2D6, and 3A4

Cytochrome P450 (CYP450) is a class of enzymes where the substrate identification is particularly important to know. It would help medicinal chemists to design drugs with lower side effects due to drug-drug interactions and to extensive genetic polymorphism. Herein, we discuss the application of the 2D and 3D-similarity searches in identifying reference Structures with higher capacity to retrieve Substrates of three important CYP enzymes (CYP2C9, CYP2D6, and CYP3A4). On the basis of the complementarities of multiple reference structures selected by different similarity search methods, we proposed the fusion of their individual Tanimoto scores into a consensus Tanimoto score (T(consensus)). Using this new score, true positive rates of 63% (CYP2C9) and 81% (CYP2D6) were achieved with false positive rates of 4% for the CYP2C9-CYP2D6 data Set. Extended similarity searches were carried out oil a validation data set, and the results showed that by using the T(consensus) score, not only the area of a ROC graph increased, but also more substrates were recovered at the beginning of a ranked list.

On the k-restricted structure ratio in planar and outerplanar graphs

A planar k-restricted structure is a simple graph whose blocks are planar and each has at most k vertices. Planar k-restricted structures are used by approximation algorithms for Maximum Weight Planar Subgraph, which motivates this work. The planar k-restricted ratio is the infimum, over simple planar graphs H, of the ratio of the number of edges in a maximum k-restricted structure subgraph of H to the number edges of H. We prove that, as k tends to infinity, the planar k-restricted ratio tends to 1/2. The same result holds for the weighted version. Our results are based on analyzing the analogous ratios for outerplanar and weighted outerplanar graphs. Here both ratios tend to 1 as k goes to infinity, and we provide good estimates of the rates of convergence, showing that they differ in the weighted from the unweighted case.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.

A new indicator for international visibility: exploring Brazilian scientific community

Brazilian science has increased fast during the last decades. An example is the increasing in the country`s share in the world`s scientific publication within the main international databases. But what is the actual weight of international publications to the whole Brazilian productivity? In order to respond this question, we have elaborated a new indicator, the International Publication Ratio (IPR). The data source was Lattes Database, a database organized by one of the main Brazilian S&T funding agency, which encompasses publication data from 1997 to 2004 of about 51,000 Brazilian researchers. Influences of distinct parameters, such as sectors, fields, career age and gender, are analyzed. We hope the data presented may help S&T managers and other S&T interests to better understand the complexity under the concept scientific productivity, especially in peripheral countries in science, such as Brazil.

Fuzzy Control System for Voltage Regulation In Power Transformers

A fuzzy control strategy for voltage regulation in electric power distribution systems is introduced in this article. This real-time controller would act on power transformers equipped with under-load tap changers. The fuzzy system was employed to turn the voltage-control relays into adaptive devices. The scope of the present study has been limited to the power distribution substation, and both the voltage measurements and control actions are carried out on the secondary bus. The capacity of fuzzy systems to handle approximate data, together with their unique ability to interpret qualitative information, make it possible to design voltage control strategies that satisfy both the requirements of the Brazilian regulatory bodies and the real concerns of the electric power distribution companies. A prototype based on the fuzzy control strategy proposed in this paper has also been implemented for validation purposes and its experimental results were highly satisfactory.

Robot measuring form errors

This work presents an automated system for the measurement of form errors of mechanical components using an industrial robot. A three-probe error separation technique was employed to allow decoupling between the measured form error and errors introduced by the robotic system. A mathematical model of the measuring system was developed to provide inspection results by means of the solution of a system of linear equations. A new self-calibration procedure, which employs redundant data from several runs, minimizes the influence of probes zero-adjustment on the final result. Experimental tests applied to the measurement of straightness errors of mechanical components were accomplished and demonstrated the effectiveness of the employed methodology. (C) 2007 Elsevier Ltd. All rights reserved.

Refined sandwich model for the vibration of beams with embedded shear piezoelectric actuators and sensors

This work extends a previously presented refined sandwich beam finite element (FE) model to vibration analysis, including dynamic piezoelectric actuation and sensing. The mechanical model is a refinement of the classical sandwich theory (CST), for which the core is modelled with a third-order shear deformation theory (TSDT). The FE model is developed considering, through the beam length, electrically: constant voltage for piezoelectric layers and quadratic third-order variable of the electric potential in the core, while meclianically: linear axial displacement, quadratic bending rotation of the core and cubic transverse displacement of the sandwich beam. Despite the refinement of mechanical and electric behaviours of the piezoelectric core, the model leads to the same number of degrees of freedom as the previous CST one due to a two-step static condensation of the internal dof (bending rotation and core electric potential third-order variable). The results obtained with the proposed FE model are compared to available numerical, analytical and experimental ones. Results confirm that the TSDT and the induced cubic electric potential yield an extra stiffness to the sandwich beam. (C) 2007 Elsevier Ltd. All rights reserved.

Logistics information systems adoption: an empirical investigation in Brazil

Purpose - The purpose of this paper is to examine whether the level of logistics information systems (LIS) adoption in manufacturing companies is influenced by organizational profile variables, such as the company`s size, the nature of its operations and their subsectors. Design/methodology/approach - A review of the mainstream literature on US was carried out to identify the factors influencing the adoption of such information systems and also some research gaps. The empirical study`s strategy is based on a survey research in Brazilian manufacturing firms from the capital goods industry. Data collected were analyzed through Kruskall-Wallis and Mann Whitney`s non-parametric tests. Findings - The analysis indicates that characteristics such as the size of companies and the nature of their operations influence the levels of LIS adoption, whilst comparisons regarding the subsectors appeared to be of little influence. Originality/value - This is the first known study to examine the influence of organizational profiles such as size, nature of operations and subsector on the level of US adoption in manufacturing companies. Moreover, it is unique in portraying the Brazilian scenario on this topic and addressing the adoption of seven types of LIS in a single study.

A Constructive Genetic Algorithm for permutation flowshop scheduling

The general flowshop scheduling problem is a production problem where a set of n jobs have to be processed with identical flow pattern on in machines. In permutation flowshops the sequence of jobs is the same on all machines. A significant research effort has been devoted for sequencing jobs in a flowshop minimizing the makespan. This paper describes the application of a Constructive Genetic Algorithm (CGA) to makespan minimization on flowshop scheduling. The CGA was proposed recently as an alternative to traditional GA approaches, particularly, for evaluating schemata directly. The population initially formed only by schemata, evolves controlled by recombination to a population of well-adapted structures (schemata instantiation). The CGA implemented is based on the NEH classic heuristic and a local search heuristic used to define the fitness functions. The parameters of the CGA are calibrated using a Design of Experiments (DOE) approach. The computational results are compared against some other successful algorithms from the literature on Taillard`s well-known standard benchmark. The computational experience shows that this innovative CGA approach provides competitive results for flowshop scheduling; problems. (C) 2007 Elsevier Ltd. All rights reserved.