Brief report : The defense mechanisms of homophobic adolescent males : a descriptive discriminant analysis

The study examined the role of defense mechanisms in homophobic attitudes of older male adolescents aged 17e18 years. A cross-sectional survey collected data from final year high school students (N ¼ 86) attending an all male school in a regional centre in Victoria, Australia. The school was identified by teachers as having a problematic culture of homophobic intolerance. Participants were divided into homophobic and non-homophobic groups based on their scores on the Homophobia Scale Questionnaire. Discriminant analysis was conducted to identify the predictors that would best categorise students into those two groups on the basis of defense styles derived from the Defense Style Questionnaire-40 (DSQ-40). The strongest predictors of homophobia amongst defense styles were idealisation, denial, somatisation and devaluation accounting for 18.31%, 17.64%, 13.10% and 11.35% of the variance, respectively. Results generally supported the larger contribution of more immature defenses to higher levels of homophobia.

Boosting performance for 2D linear discriminant analysis via regression

Two Dimensional Linear Discriminant Analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called Regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an extension of 2DLDA with the introduction of a regularization parameter to deal with the small sample size problem. RR-2DLDA integrates ridge regression into Regularized 2DLDA to balance the distances among different classes after the transformation. These proposed algorithms overcome the limitations of 2DLDA and boost recognition accuracy. The experimental results on the Yale, PIE and FERET databases showed that RR-2DLDA is superior not only to 2DLDA but also other state-of-the-art algorithms.

DDoS discrimination by linear discriminant analysis (LDA)

In this paper, we propose an effective approach with a supervised learning system based on Linear Discriminant Analysis (LDA) to discriminate legitimate traffic from DDoS attack traffic. Currently there is a wide outbreak of DDoS attacks that remain risky for the entire Internet. Different attack methods and strategies are trying to challenge defence systems. Among the behaviours of attack sources, repeatable and predictable features differ from source of legitimate traffic. In addition, the DDoS defence systems lack the learning ability to fine-tune their accuracy. This paper analyses real trace traffic from publicly available datasets. Pearson's correlation coefficient and Shannon's entropy are deployed for extracting dependency and predictability of traffic data respectively. Then, LDA is used to train and classify legitimate and attack traffic flows. From the results of our experiment, we can confirm that the proposed discrimination system can differentiate DDoS attacks from legitimate traffic with a high rate of accuracy.

Funding special education provision by means of discriminant function analysis

This thesis is concerned with the development of a funding mechanism, the Student Resource Index, which has been designed to resolve a number of difficulties which emerged following the introduction of integration or inclusion as an alternative means of providing educational support to students with disabilities in the Australian State of Victoria. Prior to 1984, the year in which the major integration or inclusion initiatives were introduced, the great majority of students with disabilities were educated in segregated special schools, however, by 1992 the integration initiatives had been successful in including within regular classes approximately half of the students in receipt of additional educational assistance on the basis of disability. The success of the integration program brought with it a number of administrative and financial problems which were the subject of three government enquiries. Central to these difficulties was the development of a dual system of special education provision. On one hand, additional resources were provided for the students attending segregated special schools by means of weighted student ratios, with one teacher being provided for each six students attending a special school. On the other hand, the requirements of individual students integrated into regular schools were assessed by school-based committees on the basis of their perceived extra educational needs. The major criticism of this dual system of special education funding was that it created inequities in the distribution of resources both between the systems and also within the systems. For example, three students with equivalent needs, one of whom attended a special school and two of whom attended different regular schools could each be funded at substantially differing levels. The solution to these inequities of funding was seen to be in the development of a needs based funding device which encompassed all students in receipt of additional disability related educational support. The Student Resource Index developed in this thesis is a set of behavioural descriptors designed to assess degree of additional educational need across a number of disability domains. These domains include hearing, vision, communication, health, co-ordination (manual and mobility), intellectual capacity and behaviour. The completed Student Resource Index provides a profile of the students’ needs across all of these domains and as such addresses the multiple nature of many disabling conditions. The Student Resource Index was validated in terms of its capacity to predict the ‘known’ membership or the type of special school which some 1200 students in the sample currently attended. The decision to use the existing special school populations as the criterion against which the Student Resource Index was validated was based on the premise that the differing resource levels of these schools had been historically determined by expert opinion, industrial negotiation and reference to other special education systems as the most reliable estimate of the enrolled students’ needs. When discriminant function analysis was applied to some 178 students attending one school for students with mild intellectual disability and one facility for students with moderate to severe intellectual disability the Student Resource Index was successful in predicting the student's known school in 92 percent of cases. An analysis of those students (8 percent) which the Student Resource Index had failed to predict their known school enrolment revealed that 13 students had, for a variety of reasons, been inappropriately placed in these settings. When these students were removed from the sample the predictive accuracy of the Student Resource Index was raised to 96 percent of the sample. By comparison the domains of the Vineland Adaptive Behaviour Scale accurately predicted known enrolments of 76 percent of the sample. By way of replication discriminant function analysis was then applied to the Student Resource Index profiles of 518 students attending Day Special Schools (Mild Intellectual Disability) and 287 students attending Special Developmental Schools (Moderate to Severe Intellectual Disability). In this case, the Student Resource Index profiles were successful in predicting the known enrolments of 85 percent of students. When a third group was added, 147 students attending Day Special Schools for students with physical disabilities, the Student Resource Index predicted known enrolments in 80 percent of cases. The addition of a fourth group of 116 students attending Day Special Schools (Hearing Impaired) to the discriminant analysis led to a small reduction in predictive accuracy from 80 percent to 78 percent of the sample. A final analysis which included students attending a School for the Deaf-Blind, a Hospital School and a Social and Behavioural Unit was successful in predicting known enrolments in 71 percent of the 1114 students in the sample. For reasons which are expanded upon within the thesis it was concluded that the Student Resource Index when used in conjunction with discriminant function analysis was capable of isolating four distinct groups on the basis of their additional educational needs. If the historically determined and varied funding levels provided to these groups, inherent in the cash equivalent of the staffing ratios of Day Special Schools (Mild Intellectual Disability), Special Development Schools (Moderate to Severe Intellectual Disability), Day Special Schools (Physical Disability) and Day Special Schools (Hearing Impairment) are accepted as reasonable reflections of these students’ needs these funding levels can be translated into funding bands. These funding bands can then be applied to students in segregated or inclusive placements. The thesis demonstrates that a new applicant for funding can be introduced into the existing data base and by the use of discriminant function analysis be allocated to one of the four groups. The analysis is in effect saying that this new student’s profile of educational needs has more in common with Group A than with the members of Groups B, C, or D. The student would then be funded at Group A level. It is immaterial from a funding point of view whether the student decides to attend a segregated or inclusive setting. The thesis then examines the impact of the introduction of Student Resource Index based funding upon the current funding of the special schools in one of the major metropolitan regions. Overall, such an initiative would lead to a reduction of 1.54 percent of the total funding accruing to the region’s special schools.

Objective grading of fabric pilling with wavelet texture analysis

A new objective fabric pilling grading method based on wavelet texture analysis was developed. The new method created a complex texture feature vector based on the wavelet detail coefficients from all decomposition levels and horizontal, vertical and diagonal orientations, permitting a much richer and more complete representation of pilling texture in the image to be used as a basis for classification. Standard multi-factor classification techniques of principal components analysis and discriminant analysis were then used to classify the pilling samples into five pilling degrees. The preliminary investigation of the method was performed using standard pilling image sets of knitted, woven and non-woven fabrics. The results showed that this method could successfully evaluate the pilling intensity of knitted, woven and non-woven fabrics by selecting the suitable wavelet and associated analysis scale.

Synchrotron-FTIR microspectroscopy enables the distinction of lipid accumulation in thraustochytrid strains through analysis of individual live cells

The superior characteristics of high photon flux and diffraction-limited spatial resolution achieved by synchrotron-FTIR microspectroscopy allowed molecular characterization of individual live thraustochytrids. Principal component analysis revealed distinct separation of the single live cell spectra into their corresponding strains, comprised of new Australasian thraustochytrids (AMCQS5-5 and S7) and standard cultures (AH-2 and S31). Unsupervised hierarchical cluster analysis (UHCA) indicated close similarities between S7 and AH-7 strains, with AMCQS5-5 being distinctly different. UHCA correlation conformed well to the fatty acid profiles, indicating the type of fatty acids as a critical factor in chemotaxonomic discrimination of these thraustochytrids and also revealing the distinctively high polyunsaturated fatty acid content as key identity of AMCQS5-5. Partial least squares discriminant analysis using cross-validation approach between two replicate datasets was demonstrated to be a powerful classification method leading to models of high robustness and 100% predictive accuracy for strain identification. The results emphasized the exceptional S-FTIR capability to perform real-time in vivo measurement of single live cells directly within their original medium, providing unique information on cell variability among the population of each isolate and evidence of spontaneous lipid peroxidation that could lead to deeper understanding of lipid production and oxidation in thraustochytrids for single-cell oil development.

Methodological shifts in ratio-based modelling of corporate collapse

The year 1968 saw a major shift from univariate to multivariate methodological approaches to ratio-based modelling of corporate collapse. This was facilitated by the introduction of a new statistical tool called Multiple Discriminant Analysis (MDA). However, it did not take long before other statistical tools were developed. The primary objective for developing these tools was to enable deriving models that would at least do as good a job asMDA, but rely on fewer assumptions. With the introduction of new statistical tools, researchers became pre-occupied with testing them in signalling collapse. lLTUong the ratio-based approaches were Logit analysis, Neural Network analysis, Probit analysis, ID3, Recursive Partitioning Algorithm, Rough Sets analysis, Decomposition analysis, Going Concern Advisor, Koundinya and Purl judgmental approach, Tabu Search and Mixed Logit analysis. Regardless of which methodological approach was chosen, most were compared to MDA. This paper reviews these various approaches. Emphasis is placed on how they fared against MDA in signalling corporate collapse.

A dynamic ratio-based model for signalling corporate collapse

The recognition of behavioural elements in finance has caused major shifts in the analytic framework pertaining to ratio-based modeling of corporate collapse. The modeling approach so far has been based on the classical rational theory in behavioural economics, which assumes that the financial ratios (i.e., the predictors of collapse) are static over time. The paper argues that, in the absence of rational economic theory, a static model is flawed, and that a suitable model instead is one that reflects the heuristic behavioural framework, which is what characterises behavioural attributes of company directors and in turn influences the accounting numbers used in calculating the financial ratios. This calls for a dynamic model: dynamic in the sense that it does not rely on a coherent assortment of financial ratios for signaling corporate collapse over multiple time periods. This paper provides empirical evidence, using a data set of Australian publicly listed companies, to demonstrate that a dynamic model consistently outperforms its static counterpart in signaling the event of collapse. On average, the overall predictive power of the dynamic model is 86.83% compared to an average overall predictive power of 69.35% for the static model.

Discrimination of origin of farmed trout by means of biometrical parameters, fillet chemical composition and flavour volatile compounds

To date it is well known that the quality of farmed trout is affected by diet composition, by feeding regime, by husbandry practices and by rearing conditions and environment. The trout processing industry and the large-scale retail trade, in consideration of the wide variability of trout quality and characteristics, have imposed, or will soon impose, quality criteria for the end product. Moreover, recent food scares and the malpractices of some food producers have increased public requests
for traceability. The aim of the present study was to evaluate the main chemical quality and the biometrical characteristics of rainbow trout produced in three different farms in Italy (two intensive farms, located one on mountain and one on plain, and an extensive farm in which fish fed only on naturally available nutrients) and to establish whether farmed trout
origins could be differentiated by these parameters. Trout farmed in the intensive mountain farm (IMF) showed the highest crude lipid content in the fillets and the fatty acids of their fillets were characterized by the highest percentage of MUFA. Trout farmed in the intensive plain farm (IPF) were characterized by low dressing percentage, and the lipid of their fillets
was rich in n-6 fatty acids. Trout stocked for the last year of their life in the extensive farm (EF) were leaner both in the carcass and in the fillets. The analysis of flavor volatile compounds showed some differences in the bouquet design, particularly differences in the amounts of n-3 and n-6 derivates volatile aldehydes and alcohols. All data significantly different
(P<0.05) were subjected to Linear Discriminant Analysis (LDA) and 8 variables were chosen to create two discriminant equations generating a strong prediction model for classification of farmed trout respective to their origins.

Wavelet packets for lighting-effects determination

This paper presents a system to determine lighting effiects within face images. The theories of multivariate discriminant analysis and wavelet packets transform are utilised to develop the proposed system. An extensive set of face images of different poses, illuminated from different angles, are used to train the system. The performance of the proposed system is evaluated by conducting experiments on different test sets, and by comparing its results against those of some existing counterparts.

Pricing segmentation and MBA course choice

The consensus from studies of the price-demand relationship for higher education is that this relationship is negative but small. This paper investigates the circumstances in which demand for an MBA is positive to price increases. A survey of currently enrolled MBA students, and prospective MBA students, found that most students displayed the expected price elasticity in a conjoint analysis of hypothetical MBA course ratings. However, 12 per cent of respondents exhibited “reversal” behaviour regarding price. Profiling these respondents using discriminant analysis suggested that “reversals” seemed prepared to pay more for a course at a high prestige university, if they could study off-campus using print-based materials.

Benchmarking new statistical techniques in ration-based modelling of corporate collapse

Up until 1979, Multiple Discriminant Analysis (MDA) was the primary multivariate methodological approaches to ratio-based modelling of corporate collapse. However, as new statistical tools became available, researchers started testing them with the primary objective of deriving models that would at least do as good a job as MDA, but that rely on fewer assumptions. Regardless of which methodological approach was chosen, most were compared to MDA. This paper analyses 84 studies on ratio based modelling of corporate collapse over the period 1968 to 2004. The results indicate that when MDA was not the primary methodology it was the benchmark of choice for comparison; thereby, demonstrating its importance as a foundation multivariate methodological approach in signalling corporate collapse.