Learning style drift : correlation between built environment students' learning styles and the learning styles of their teachers

This paper focuses on the results of a cross-curriculum learning style survey conducted in an Australian School of Architecture and Building as part of an ongoing project aimed at resolving the learning difficulties of students collaborating in multi-disciplinary and multicultural team assignments. The research was conducted to determine how learning style differences in heterogeneous design teams might be addressed through pedagogy. We will argue that the likelihood of and reasons for learning style fluidity in student design cohorts needs determining if learning style theory is to provide a workable model for informing the teaching of design.
In light of evidence in student cohorts of learning style changes as students progress through their studies (Tucker, 2007), this research discusses one explanation of what appears to belearning style fluidity in architecture student cohorts. If, as prior research has indicated, the learning styles of academics are quite different from practitioners, evidence of a learning style drift in built environment students towards the predominant learning styles of their design teachers might suggest that students are learning how to be academics rather than practitioners. This, of course, might have serious implications for built environment teaching and for practice.

Abjection and midwifery : towards a revision of Julia Kristeva's theory of the maternal

This paper will develop a specific reading of Julia Kristeva’s analysis of the Mother in psychoanalytic contexts and artistic production. I want to suggest a particular connection between the Mother and a second figure closely associated with her: the Midwife. Such a move opens up the possibility for a new understanding of Kristeva’s correlation of the Mother with the psychoanalytic concept of “abjection”. I wish to identify the Midwife as the crucial intersection of a masculine and feminine subjectivity. I will undertake this project via a historical study of Midwifery, which will include an exploration of the Midwife’s relationship to masculine ideologies of medical thought, as well as an account of the problematic rise of the “Man-Midwife”. My strategy will be to extend the submerged historical and material content of Kristeva’s own theories, with particular reference to Powers of Horror: An Essay on Abjection.

Selection of working correlation structure and best model in GEE analyses of longitudinal data

The Generalized Estimating Equations (GEE) method is one of the most commonly used statistical methods for the analysis of longitudinal data in epidemiological studies. A working correlation structure for the repeated measures of the outcome variable of a subject needs to be specified by this method. However, statistical criteria for selecting the best correlation structure and the best subset of explanatory variables in GEE are only available recently because the GEE method is developed on the basis of quasi-likelihood theory. Maximum likelihood based model selection methods, such as the widely used Akaike Information Criterion (AIC), are not applicable to GEE directly. Pan (2001) proposed a selection method called QIC which can be used to select the best correlation structure and the best subset of explanatory variables. Based on the QIC method, we developed a computing program to calculate the QIC value for a range of different distributions, link functions and correlation structures. This program was written in Stata software. In this article, we introduce this program and demonstrate how to use it to select the most parsimonious model in GEE analyses of longitudinal data through several representative examples.

Discriminating DDoS attacks from flash crowds using flow correlation coefficient

Distributed Denial of Service (DDoS) attack is a critical threat to the Internet, and botnets are usually the engines behind them. Sophisticated botmasters attempt to disable detectors by mimicking the traffic patterns of flash crowds. This poses a critical challenge to those who defend against DDoS attacks. In our deep study of the size and organization of current botnets, we found that the current attack flows are usually more similar to each other compared to the flows of flash crowds. Based on this, we proposed a discrimination algorithm using the flow correlation coefficient as a similarity metric among suspicious flows. We formulated the problem, and presented theoretical proofs for the feasibility of the proposed discrimination method in theory. Our extensive experiments confirmed the theoretical analysis and demonstrated the effectiveness of the proposed method in practice.