Obesity screening for young Japanese males and females using skin fold measurements : the classification revisited

Anthropometric assessment is a simple, safe, and cost-efficient method to examine the health status of individu-als. The Japanese obesity classification based on the sum of two skin folds (Σ2SF) was proposed nearly 40 years ago therefore its applicability to Japanese living today is unknown. The current study aimed to determine Σ2SF cut-off values that correspond to percent body fat (%BF) and BMI values using two datasets from young Japa-nese adults (233 males and 139 females). Using regression analysis, Σ2SF and height-corrected Σ2SF (HtΣ2SF) values that correspond to %BF of 20, 25, and 30% for males and 30, 35, and 40% for females were determined. In addition, cut-off values of both Σ2SF and HtΣ2SF that correspond to BMI values of 23 kg/m2, 25 kg/m2 and 30 kg/m2 were determined. In comparison with the original Σ2SF values, the proposed values are smaller by about 10 mm at maximum. The proposed values show an improvement in sensitivity from about 25% to above 90% to identify individuals with ≥20% body fat in males and ≥30% body fat in females with high specificity of about 95% in both genders. The results indicate that the original Σ2SF cut-off values to screen obese individuals cannot be applied to young Japanese adults living today and modification is required. Application of the pro-posed values may assist screening in the clinical setting.

Adaptive Bayesian compound designs for dose finding studies

We consider the problem of how to efficiently and safely design dose finding studies. Both current and novel utility functions are explored using Bayesian adaptive design methodology for the estimation of a maximum tolerated dose (MTD). In particular, we explore widely adopted approaches such as the continual reassessment method and minimizing the variance of the estimate of an MTD. New utility functions are constructed in the Bayesian framework and are evaluated against current approaches. To reduce computing time, importance sampling is implemented to re-weight posterior samples thus avoiding the need to draw samples using Markov chain Monte Carlo techniques. Further, as such studies are generally first-in-man, the safety of patients is paramount. We therefore explore methods for the incorporation of safety considerations into utility functions to ensure that only safe and well-predicted doses are administered. The amalgamation of Bayesian methodology, adaptive design and compound utility functions is termed adaptive Bayesian compound design (ABCD). The performance of this amalgamation of methodology is investigated via the simulation of dose finding studies. The paper concludes with a discussion of results and extensions that could be included into our approach.

Robust designs for Poisson regression models

We consider the problem of how to construct robust designs for Poisson regression models. An analytical expression is derived for robust designs for first-order Poisson regression models where uncertainty exists in the prior parameter estimates. Given certain constraints in the methodology, it may be necessary to extend the robust designs for implementation in practical experiments. With these extensions, our methodology constructs designs which perform similarly, in terms of estimation, to current techniques, and offers the solution in a more timely manner. We further apply this analytic result to cases where uncertainty exists in the linear predictor. The application of this methodology to practical design problems such as screening experiments is explored. Given the minimal prior knowledge that is usually available when conducting such experiments, it is recommended to derive designs robust across a variety of systems. However, incorporating such uncertainty into the design process can be a computationally intense exercise. Hence, our analytic approach is explored as an alternative.

Robust designs for Poisson regression models

We consider the problem of how to construct robust designs for Poisson regression models. An analytical expression is derived for robust designs for first-order Poisson regression models where uncertainty exists in the prior parameter estimates. Given certain constraints in the methodology, it may be necessary to extend the robust designs for implementation in practical experiments. With these extensions, our methodology constructs designs which perform similarly, in terms of estimation, to current techniques, and offers the solution in a more timely manner. We further apply this analytic result to cases where uncertainty exists in the linear predictor. The application of this methodology to practical design problems such as screening experiments is explored. Given the minimal prior knowledge that is usually available when conducting such experiments, it is recommended to derive designs robust across a variety of systems. However, incorporating such uncertainty into the design process can be a computationally intense exercise. Hence, our analytic approach is explored as an alternative.

Transferring knowledge through peer-reviewed assessment : the creation of a community of practice and the threats to its survival

While it is generally accepted in the learning and teaching literature that assessment is the single biggest influence on how students approach their learning, assessment methods within higher education are generally conservative and inflexible. Constrained by policy and accreditation requirements and the need for the explicit articulation of assessment standards for public accountability purposes, assessment tasks can fail to engage students or reflect the tasks students will face in the world of practice. Innovative assessment design can simultaneously deliver program objectives and active learning through a knowledge transfer process which increases student participation. This social constructivist view highlights that acquiring an understanding of assessment processes, criteria and standards needs active student participation. Within this context, a peer-assessed, weekly, assessment task was introduced in the first “serious” accounting subject offered as part of an undergraduate degree. The positive outcomes of this assessment innovation was that student failure rates declined 15%, tutorial participation increased fourfold, tutorial engagement increased six-fold and there was a 100% approval rating for the retention of the assessment task. In contributing to the core conference theme of “seismic” shifts within higher education, in stark contrast to the positive student response, staff-related issues of assessment conservatism and the necessity of meeting increasing research commitments, threatened the assessment task’s survival. These opposing forces to change have the potential to weaken the ability of higher education assessment arrangements to adequately serve either a new generation of students or the sector's community stakeholders.

Transferring knowledge through peer-reviewed assessment : the creation of a community of practice and the threats to its survival

While it is generally accepted in the learning and teaching literature that assessment is the single biggest influence on how students approach their learning, assessment methods within higher education are generally conservative and inflexible. Constrained by policy and accreditation requirements and the need for the explicit articulation of assessment standards for public accountability purposes, assessment tasks can fail to engage students or reflect the tasks students will face in the world of practice. Innovative assessment design can simultaneously deliver program objectives and active learning through a knowledge transfer process which increases student participation. This social constructivist view highlights that acquiring an understanding of assessment processes, criteria and standards needs active student participation. Within this context, a peer-assessed, weekly, assessment task was introduced in the first “serious” accounting subject offered as part of an undergraduate degree. The positive outcomes of this assessment innovation was that student failure rates declined 15%, tutorial participation increased fourfold, tutorial engagement increased six-fold and there was a 100% approval rating for the retention of the assessment task. In contributing to the core conference theme of “seismic” shifts within higher education, in stark contrast to the positive student response, staff-related issues of assessment conservatism and the necessity of meeting increasing research commitments, threatened the assessment task’s survival. These opposing forces to change have the potential to weaken the ability of higher education assessment arrangements to adequately serve either a new generation of students or the sector's community stakeholders.

An automated tool for assessing security-critical designs and programs

This paper describes in detail our Security-Critical Program Analyser (SCPA). SCPA is used to assess the security of a given program based on its design or source code with regard to data flow-based metrics. Furthermore, it allows software developers to generate a UML-like class diagram of their program and annotate its confidential classes, methods and attributes. SCPA is also capable of producing Java source code for the generated design of a given program. This source code can then be compiled and the resulting Java bytecode program can be used by the tool to assess the program's overall security based on our security metrics.

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.