Differences between genders in coping: different strategies or different stressors?

These study analysed gender specificity in coping behaviours by taking into account the types of problem faced by Spanish adolescents attending school. It was focused on the ten problems most frequently reported by participants (828 adolescents, 355 boys, and 473 girls; Mage = 14.07, SD = 1.34), which were classified using a multi-axial classification system. Coping was examined as a two separate measures of approach and avoidance coping, and as a combined measure indicating the predominant use of coping, and total coping effort. A MANCOVA and subsequent univariate tests were conducted to analyse the specificity of coping according to problem and gender, controlled by age. The results showed that the percentage of types of problems reported by adolescents differed according to gender. The influence of gender on coping was scarcely relevant when the type of problem was controlled for. There were no gender differences when the predominant type of coping was considered, but when a total coping effort measure was analysed girls showed more coping efforts than boys to face interpersonal relationship problems and personal illness. Keywords: adolescence, coping, gender differences, stressors.

Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy

Diabetes is a rapidly increasing worldwide problem which is characterised by defective metabolism of glucose that causes long-term dysfunction and failure of various organs. The most common complication of diabetes is diabetic retinopathy (DR), which is one of the primary causes of blindness and visual impairment in adults. The rapid increase of diabetes pushes the limits of the current DR screening capabilities for which the digital imaging of the eye fundus (retinal imaging), and automatic or semi-automatic image analysis algorithms provide a potential solution. In this work, the use of colour in the detection of diabetic retinopathy is statistically studied using a supervised algorithm based on one-class classification and Gaussian mixture model estimation. The presented algorithm distinguishes a certain diabetic lesion type from all other possible objects in eye fundus images by only estimating the probability density function of that certain lesion type. For the training and ground truth estimation, the algorithm combines manual annotations of several experts for which the best practices were experimentally selected. By assessing the algorithm’s performance while conducting experiments with the colour space selection, both illuminance and colour correction, and background class information, the use of colour in the detection of diabetic retinopathy was quantitatively evaluated. Another contribution of this work is the benchmarking framework for eye fundus image analysis algorithms needed for the development of the automatic DR detection algorithms. The benchmarking framework provides guidelines on how to construct a benchmarking database that comprises true patient images, ground truth, and an evaluation protocol. The evaluation is based on the standard receiver operating characteristics analysis and it follows the medical practice in the decision making providing protocols for image- and pixel-based evaluations. During the work, two public medical image databases with ground truth were published: DIARETDB0 and DIARETDB1. The framework, DR databases and the final algorithm, are made public in the web to set the baseline results for automatic detection of diabetic retinopathy. Although deviating from the general context of the thesis, a simple and effective optic disc localisation method is presented. The optic disc localisation is discussed, since normal eye fundus structures are fundamental in the characterisation of DR.

Finding Methods for Recognizing and Classifying Spare Part Criticality – Case Full Deposit Stripping Machines

The aim of this Master’s thesis is to find a method for classifying spare part criticality in the case company. Several approaches exist for criticality classification of spare parts. The practical problem in this thesis is the lack of a generic analysis method for classifying spare parts of proprietary equipment of the case company. In order to find a classification method, a literature review of various analysis methods is required. The requirements of the case company also have to be recognized. This is achieved by consulting professionals in the company. The literature review states that the analytic hierarchy process (AHP) combined with decision tree models is a common method for classifying spare parts in academic literature. Most of the literature discusses spare part criticality in stock holding perspective. This is relevant perspective also for a customer orientated original equipment manufacturer (OEM), as the case company. A decision tree model is developed for classifying spare parts. The decision tree classifies spare parts into five criticality classes according to five criteria. The criteria are: safety risk, availability risk, functional criticality, predictability of failure and probability of failure. The criticality classes describe the level of criticality from non-critical to highly critical. The method is verified for classifying spare parts of a full deposit stripping machine. The classification can be utilized as a generic model for recognizing critical spare parts of other similar equipment, according to which spare part recommendations can be created. Purchase price of an item and equipment criticality were found to have no effect on spare part criticality in this context. Decision tree is recognized as the most suitable method for classifying spare part criticality in the company.

A crisis of social democracy : organized labour and the NDP in an era of neoliberalism

The NDP was founded out of the ashes of the Co-Operative Commonwealth Federation to cooperate with the Canadian Labour Congress to become the 'political arm of organized labour' in Canada. The NDP has long claimed they are the party which represents the policy goals of organized labour in Canada: that the NDP alone will fight for trade union rights, and will fight for Canadian workers. Divergent Paths is an examination of the links between the labour movement and the ND P in an era ofneo-liberalism. Provincial NDP governments have become increasingly neoliberal in their ideological orientation, and have often proved to be no friend to the labour movement when they hold office. The Federal party has never held power, nor have they ever formed the Official Opposition. This thesis charts the progress of the federal NDP as they become more neoliberal from 1988 to 2006, and shows how this trend effects the links between the NDP and labour. Divergent Paths studies each federal election from 1988 to 2006, looking at the interactions between Labour and the NDP during these elections. Elections provide critical junctions to study discourse - party platforms, speeches, and other official documents can be used to examine discourse. Extensive newspaper searches were used to follow campaign events and policy speeches. Studying the party's discourse can be used to determine the ideological orientation of the party itself: the fact that the party's discourse has become neoliberal is a sure sign that the party itself is neoliberal. The NDP continues to drive towards the centre of the political spectrum in an attempt to gain multi-class support. The NDP seems more interested in gaining seats at any cost, rather then promoting the agenda of Labour. As the party attempts to open up to more multi-class support, Labour becomes increasingly marginalised in the party. A rift which arguably started well before the 1988 election was exacerbated during that election; labour encouraged the NDP to campaign solely on the issue of Free Trade, and the NDP did not. The 1993 election saw the rift between the two grow even further as the Federal NDP suffered major blowbacks from the actions of the Ontario NDP. The 1997 and 2000 elections saw the NDP make a deliberate move to the centre of the political spectrum which increasingly marginalised labour. In the 2004 election, Jack Layton made no attempt to move the party back to the left; and in 2006 the link between labour and the NDP was perhaps irreparably damaged when the CAW endorsed the Liberal party in a strategic voting strategy, and the CLC did not endorse the NDP. The NDP is no longer a reliable ally of organized labour. The Canadian labour movement must decide wether the NDP can be 'salvaged' or if the labour movement should end their alliance with the NDP and engage in a new political project.

Construction of tunable radial basis function networks using orthogonal forward selection

An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines an RBF node, namely, its center vector and diagonal covariance matrix, by minimizing the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO mean-square error, while the LOO misclassification rate is adopted in two-class classification application. This OFS-LOO algorithm is computationally efficient, and it is capable of constructing parsimonious RBF networks that generalize well. Moreover, the proposed algorithm is fully automatic, and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

Secondary structure prediction with support vector machines

Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods. Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.

HOW FAR do WE GET USING MACHINE LEARNING BLACK-BOXES?

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Analisando o desempenho do ClassAge: um sistema multiagentes para classificação de padrões

The use of multi-agent systems for classification tasks has been proposed in order to overcome some drawbacks of multi-classifier systems and, as a consequence, to improve performance of such systems. As a result, the NeurAge system was proposed. This system is composed by several neural agents which communicate and negotiate a common result for the testing patterns. In the NeurAge system, a negotiation method is very important to the overall performance of the system since the agents need to reach and agreement about a problem when there is a conflict among the agents. This thesis presents an extensive analysis of the NeurAge System where it is used all kind of classifiers. This systems is now named ClassAge System. It is aimed to analyze the reaction of this system to some modifications in its topology and configuration

Attribute charts for monitoring the mean vector of bivariate processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Evaluation of texture features in hepatic tissue characterization from non-enhanced CT images

Aim of this paper is to evaluate the diagnostic contribution of various types of texture features in discrimination of hepatic tissue in abdominal non-enhanced Computed Tomography (CT) images. Regions of Interest (ROIs) corresponding to the classes: normal liver, cyst, hemangioma, and hepatocellular carcinoma were drawn by an experienced radiologist. For each ROI, five distinct sets of texture features are extracted using First Order Statistics (FOS), Spatial Gray Level Dependence Matrix (SGLDM), Gray Level Difference Method (GLDM), Laws' Texture Energy Measures (TEM), and Fractal Dimension Measurements (FDM). In order to evaluate the ability of the texture features to discriminate the various types of hepatic tissue, each set of texture features, or its reduced version after genetic algorithm based feature selection, was fed to a feed-forward Neural Network (NN) classifier. For each NN, the area under Receiver Operating Characteristic (ROC) curves (Az) was calculated for all one-vs-all discriminations of hepatic tissue. Additionally, the total Az for the multi-class discrimination task was estimated. The results show that features derived from FOS perform better than other texture features (total Az: 0.802+/-0.083) in the discrimination of hepatic tissue.

Characteristics of soil profiles from the Ouémé Upper Catchment (HVO), Térou, Benin

Soil degradation threatens agricultural production and food security in Sub-Saharan Africa. In the coming decades, soil degradation, in particular soil erosion, will become worse through the expansion of agriculture into savannah and forest and changes in climate. This study aims to improve the understanding of how land use and climate change affect the hydrological cycle and soil erosion rates at the catchment scale. We used the semi-distributed, time-continuous erosion model SWAT (Soil Water Assessment Tool) to quantify runoff processes and sheet and rill erosion in the Upper Ouémé River catchment (14500 km**2, Central Benin) for the period 1998-2005. We could then evaluate a range of land use and climate change scenarios with the SWAT model for the period 2001-2050 using spatial data from the land use model CLUE-S and the regional climate model REMO. Field investigations were performed to parameterise a soil map, to measure suspended sediment concentrations for model calibration and validation and to characterise erosion forms, degraded agricultural fields and soil conservation practices. Modelling results reveal current "hotspots" of soil erosion in the north-western, eastern and north-eastern parts of the Upper Ouémé catchment. As a consequence of rapid expansion of agricultural areas triggered by high population growth (partially caused by migration) and resulting increases in surface runoff and topsoil erosion, the mean sediment yield in the Upper Ouémé River outlet is expected to increase by 42 to 95% by 2025, depending on the land use scenario. In contrast, changes in climate variables led to decreases in sediment yield of 5 to 14% in 2001-2025 and 17 to 24% in 2026-2050. Combined scenarios showed the dominance of land use change leading to changes in mean sediment yield of -2 to +31% in 2001-2025. Scenario results vary considerably within the catchment. Current "hotspots" of soil erosion will aggravate, and a new "hotspot" will appear in the southern part of the catchment. Although only small parts of the Upper Ouémé catchment belong to the most degraded zones in the country, sustainable soil and plant management practices should be promoted in the entire catchment. The results of this study can support planning of soil conservation activities in Benin.

Automatic building detection using the Dempster-Shafer algorithm

An approach and strategy for automatic detection of buildings from aerial images using combined image analysis and interpretation techniques is described in this paper. It is undertaken in several steps. A dense DSM is obtained by stereo image matching and then the results of multi-band classification, the DSM, and Normalized Difference Vegetation Index (NDVI) are used to reveal preliminary building interest areas. From these areas, a shape modeling algorithm has been used to precisely delineate their boundaries. The Dempster-Shafer data fusion technique is then applied to detect buildings from the combination of three data sources by a statistically-based classification. A number of test areas, which include buildings of different sizes, shape, and roof color have been investigated. The tests are encouraging and demonstrate that all processes in this system are important for effective building detection.

Probabilistic resource space model for managing resources in cyber-physical society

Classification is the most basic method for organizing resources in the physical space, cyber space, socio space and mental space. To create a unified model that can effectively manage resources in different spaces is a challenge. The Resource Space Model RSM is to manage versatile resources with a multi-dimensional classification space. It supports generalization and specialization on multi-dimensional classifications. This paper introduces the basic concepts of RSM, and proposes the Probabilistic Resource Space Model, P-RSM, to deal with uncertainty in managing various resources in different spaces of the cyber-physical society. P-RSM’s normal forms, operations and integrity constraints are developed to support effective management of the resource space. Characteristics of the P-RSM are analyzed through experiments. This model also enables various services to be described, discovered and composed from multiple dimensions and abstraction levels with normal form and integrity guarantees. Some extensions and applications of the P-RSM are introduced.