The impact of client attitudes on the selection of contractors

This research is concerned with identifying prequalification criteria that both clients and contractors believe are good indicators of future construction performance. Criteria used in the past have been developed by clients in a largely idiosyncratic manner with little or no consultation with the contractors affected. The methodology chosen for the research was a survey which probed stakeholder attitudes to commonly used prequalification criteria. This was carried out via a postal questionnaire involving contactors and clients across Australia. The data was analysed using Discriminant Analysis, which is a multivariate statistical approach that determines the differences between groups. The research is structured around 39 criteria that were developed as part of a whole-of–government task force into best practice in procurement. The findings identified the most important criteria from both a client’s perspective, and a contractor’s perspective. The purpose was to discover if those differences reduce the effectiveness of the procurement process. This paper contributes to a more clarified understanding of the impact or contrasting views between the stakeholders involved in the prequalification process. This work is innovative because it is one of a few pieces of research that showed that clients and contractors do actually have divergent opinions on the importance of some criteria currently relied upon in the decision making process. The most important prequalification criteria are identified and possible reasons for these differences are discussed.

Exploiting side information in locality preserving projection

Even if the class label information is unknown, side information represents some equivalence constraints between pairs of patterns, indicating whether pairs originate from the same class. Exploiting side information, we develop algorithms to preserve both the intra-class and inter-class local structures. This new type of locality preserving projection (LPP), called LPP with side information (LPPSI), preserves the data's local structure in the sense that the close, similar training patterns will be kept close, whilst the close but dissimilar ones are separated. Our algorithms balance these conflicting requirements, and we further improve this technique using kernel methods. Experiments conducted on popular face databases demonstrate that the proposed algorithm significantly outperforms LPP. Further, we show that the performance of our algorithm with partial side information (that is, using only small amount of pair-wise similarity/dissimilarity information during training) is comparable with that when using full side information. We conclude that exploiting side information by preserving both similar and dissimilar local structures of the data significantly improves performance.

Robust learning of discriminative projection for multicategory classification on the Stiefel manifold

Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical learning theory that facilitates robust learning of a discriminative projection. Dimensionality reduction using the projection matrix is combined with a linear classifier in the regularized framework of lasso regression. The projection matrix in conjunction with the classifier parameters are then found by solving an optimization problem over the Stiefel manifold. The experimental results on standard face databases suggest that the proposed method outperforms some recent regularized techniques when the number of training samples is small.

Face recognition using kernel ridge regression

In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques for multivariate labels and apply the methods to the problem of face recognition. Motivated by the fact that the regular simplex vertices are separate points with highest degree of symmetry, we choose such vertices as the targets for the distinct individuals in recognition and apply RR or KRR to map the training face images into a face subspace where the training images from each individual will locate near their individual targets. We identify the new face image by mapping it into this face subspace and comparing its distance to all individual targets. An efficient cross-validation algorithm is also provided for selecting the regularization and kernel parameters. Experiments were conducted on two face databases and the results demonstrate that the proposed algorithm significantly outperforms the three popular linear face recognition techniques (Eigenfaces, Fisherfaces and Laplacianfaces) and also performs comparably with the recently developed Orthogonal Laplacianfaces with the advantage of computational speed. Experimental results also demonstrate that KRR outperforms RR as expected since KRR can utilize the nonlinear structure of the face images. Although we concentrate on face recognition in this paper, the proposed method is general and may be applied for general multi-category classification problems.

Reliability and validity of a reactive agility test for Australian football

Purpose: The aim of this study was to test the reliability and construct validity of a reactive agility test (RAT), designed for Australian Football (AF).

Methods: Study I tested the reliability of the RAT, with 20 elite junior AF players (17.44 ± 0.55 y) completing the test on two occasions separated by 1 wk. Study II tested its construct validity by comparing the performance of 60 participants (16.60 ± 0.50 y) spread over three aged-matched population groups: 20 athletes participating in a State Under-18 AF league who had represented their state at national competitions (elite), 20 athletes participating in the same league who had not represented their state (subelite), and 20 healthy males who did not play AF (controls).

Results: Test-retest reliability reported a strong correlation (0.91), with no significant difference (P = .22) between the mean results (1.74 ± 0.07 s and 1.76 ± 0.07 s) obtained (split 2+3). Nonparametric tests (Kruskal-Wallis and Mann-Whitney) revealed both AF groups performed significantly faster on all measures than the control group (ranging from P = .001 to .005), with significant differences also reported between the two AF groups (ranging from P = .001 to .046). Stepwise discriminant analyses found total time discriminated between the groups, correctly classifying 75% of the participants.

Conclusions: The RAT used within this study demonstrates evidence of reliability and construct validity. It further suggests the ability of a reactive component within agility test designs to discriminate among athletes of different competition levels, highlighting its importance within training activities.

IEF pattern classification-derived criteria for the identification of epoetin-delta in urine

Epoetin-δ (Dynepo™ Shire Pharmaceuticals, Basing stoke, UK) is a synthetic form of erythropoietin (EPO) whose resemblance with endogenous EPO makes it hard to identify using the classical identification criteria. Urine samples collected from six healthy volunteers treated with epoetin-δ injections and from a control population were immuno-purified and analyzed with the usual IEF method. On the basis of the EPO profiles integration, a linear multivariate model was computed for discriminant analysis. For each sample, a pattern classification algorithm returned a bands distribution and intensity score (bands intensity score) saying how representative this sample is of one of the two classes, positive or negative. Effort profiles were also integrated in the model. The method yielded a good sensitivity versus specificity relation and was used to determine the detection window of the molecule following multiple injections. The bands intensity score, which can be generalized to epoetin-α and epoetin-β, is proposed as an alternative criterion and a supplementary evidence for the identification of EPO abuse.

Optimising errors in signaling corporate collapse using MCCCRA

Purpose – The purpose of this paper is to put forward an innovative approach for reducing the variation between Type I and Type II errors in the context of ratio-based modeling of corporate collapse, without compromising the accuracy of the predictive model. Its contribution to the literature lies in resolving the problematic trade-off between predictive accuracy and variations between the two types of errors.

Design/methodology/approach – The methodological approach in this paper – called MCCCRA – utilizes a novel multi-classification matrix based on a combination of correlation and regression analysis, with the former being subject to optimisation criteria. In order to ascertain its accuracy in signaling collapse, MCCCRA is empirically tested against multiple discriminant analysis (MDA).

Findings – Based on a data sample of 899 US publicly listed companies, the empirical results indicate that in addition to a high level of accuracy in signaling collapse, MCCCRA generates lower variability between Type I and Type II errors when compared to MDA.

Originality/value – Although correlation and regression analysis are long-standing statistical tools, the optimisation constraints that are applied to the correlations are unique. Moreover, the multi-classification matrix is a first in signaling collapse. By providing economic insight into more stable financial modeling, these innovations make an original contribution to the literature.

Modelling SME credit risk : a Thai empirical evidence

This paper develops and tests a model to predict small and medium enterprise (SME) financial distress based on empirical evidence from Thailand. A sample comprising 198 financial statements of non-financially distressed and 68 statements of financially distressed SMEs were used. A parametric t-test was conducted to establish differences between financial characteristics of the two groups of SMEs.

Results show statistically significant differences (t values significant at .001) between the two groups of SMEs in the financial ratios used for the study. Discriminant analysis was then conducted to develop a model for predicting the likelihood of an SME experiencing financial distress.

The model hits an accuracy level of 97%, which compares favourably with the probability of accurate classification by chance (i.e., 65% after adjusting for the unequal sample sizes of the two groups of SMEs). A test of the model with a new sample shows the validity of the model beyond the original sample, confirming that Thai SME financial distress is amenable to prediction to a statistically significant extent. The model is expected to serve SME managers and creditors in assessing financial health of SMEs before making important decisions. The results are also expected to inform policymakers in formulating economic policies concerning SMEs.

Reliance of external auditors on internal audit work : a corporate governance perspective

The literature suggests an increasing need for interactions among board of directors, management, internal audit and external audit as the four components of corporate governance and presents internal audit as a resource for the other components. External auditing standards that originated in the Western world, which are also being applied in developing countries, recommend external auditor’s reliance on internal audit to achieve audit efficiency. Nevertheless, whether this efficiency motive explains such reliance in corporate governance settings that differ from the West has not been sufficiently explored as yet. This study examines external auditor reliance on internal audit work using questionnaire survey of 119 external auditors in Ethiopia. Mann-Whitney U test results suggest that external auditors’ reliance on internal audit work is not significantly associated with the competitiveness of external audit sub-markets in Ethiopia. Results of multiple discriminant analysis indicate internal audit work performance is the most important factor that determines the extent of external auditors’ reliance on internal audit work. Overall, findings suggest that organizations can enhance corporate governance effectiveness by strengthening internal audit and fostering internal-external auditor coordination.

A novel aggregate gene selection method for microarray data classification

This paper introduces a novel method for gene selection based on a modification of analytic hierarchy process (AHP). The modified AHP (MAHP) is able to deal with quantitative factors that are statistics of five individual gene ranking methods: two-sample t-test, entropy test, receiver operating characteristic curve, Wilcoxon test, and signal to noise ratio. The most prominent discriminant genes serve as inputs to a range of classifiers including linear discriminant analysis, k-nearest neighbors, probabilistic neural network, support vector machine, and multilayer perceptron. Gene subsets selected by MAHP are compared with those of four competing approaches: information gain, symmetrical uncertainty, Bhattacharyya distance and ReliefF. Four benchmark microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, prostate and colon are utilized for experiments. As the number of samples in microarray data datasets are limited, the leave one out cross validation strategy is applied rather than the traditional cross validation. Experimental results demonstrate the significant dominance of the proposed MAHP against the competing methods in terms of both accuracy and stability. With a benefit of inexpensive computational cost, MAHP is useful for cancer diagnosis using DNA gene expression profiles in the real clinical practice.

An intelligent decision support system for leukaemia diagnosis using microscopic blood images

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.

Predictors of academic performance of nursing and paramedic students in first year bioscience

The expanding scope of practice of paramedics and nurses demands they possess a sophisticated knowledge of bioscience to enable them to think critically and make rational clinical decisions. It is well documented that nursing students struggle with bioscience but there are no studies examining the performance of paramedic students in this crucial subject. In this study, we compared the academic performance of first year nursing, paramedic and nursing/paramedic double degree students in a bioscience subject. Regression analyses were used to identify predictors of academic success. Data revealed a low success rate in bioscience for all three degree programs (63.2, 58.8, and 67.6% respectively) and a strong correlation between academic success in bioscience and non-bioscience subjects (r(2)=0.49). The best predictors of overall academic success were the University Admission Index score and mature entry into the course. Previous study of biology was associated with an increased bioscience and overall GPA but not with non-bioscience grades. Discriminant analysis was used to develop a model that could predict overall academic success with an accuracy of 78.5%. These criteria may be useful during the admission process and for the early identification of students at risk of failure.