Occasional paper : Towards the development of an online learning environment (OLE) quality management framework

The project is committed to understanding, recognising and developing various forms of institutionally relevant distributed leadership in developing and trialling various components of a quality management framework for online learning environments in Australian higher education. This paper provides an overview of issues related to the management and improvement of quality, including in the context of higher education. In response to the complex and multi-dimensional nature of both quality and online learning environments (OLEs), the concept of a framework for organising policies, procedures and actions relating to the good governance of OLEs can be found in the literature. Such frameworks vary in scope, format and title, and a (non-exhaustive) sample is presented in summary here. Key learnings that can be drawn from the exemplars frameworks and the related literature include:
- the processes for the design of such frameworks;
- the components of such frameworks;
- the measurement mechanisms and metrics employed in such frameworks; and
- the validation of such frameworks.

Topic model kernel : an empirical study towards probabilistically reduced features for classification

Probabilistic topic models have become a standard in modern machine learning with wide applications in organizing and summarizing ‘documents’ in high-dimensional data such as images, videos, texts, gene expression data, and so on. Representing data by dimensional reduction of mixture proportion extracted from topic models is not only richer in semantics than bag-of-word interpretation, but also more informative for classification tasks. This paper describes the Topic Model Kernel (TMK), a high dimensional mapping for Support Vector Machine classification of data generated from probabilistic topic models. The applicability of our proposed kernel is demonstrated in several classification tasks from real world datasets. We outperform existing kernels on the distributional features and give the comparative results on non-probabilistic data types.

Biomedical time series analysis based on bag-of-words model

This research proposes a number of new methods for biomedical time series classification and clustering based on a novel Bag-of-Words (BoW) representation. It is anticipated that the objective and automatic biomedical time series clustering and classification technologies developed in this work will potentially benefit a wide range of applications, such as biomedical data management, archiving, retrieving, and disease diagnosis and prognosis in the future.

New approaches for adding value to paper and other substrates

This paper describes research into three different but interrelated technologies that can add value to commodity printing substrates by taking advantage of developments in synthetic chemistry, materials science and plasma physics. These investigations have been conducted in a Cooperative Research Centre (CRC) in Australia, called CRC Smartprint. Research into ink receptive coatings based on pigments possessing a positive surface charge has led to coatings that display improved resolution and colour saturation compared with silica based formulations. Although silica exhibits a high level of liquid absorption, it has relatively poor affinity for dye molecules contained in ink-jet ink. The second development involves the use of plasma enhanced chemical vapour deposition at atmospheric pressure to change surface functionality with particular emphasis on absorptive and printing properties. Thirdly, the development of a prototype labelling system based on the application of electrochromic conductive polymer to a flexible substrate that responds to electrical stimuli is discussed. Taken together, these three developments illustrate how both impact and non-impact printing technologies can be judiciously used to apply not only improved visual imagery to paper and paperboard, but also have the potential to enable printing of micro-electronic circuitry directly onto packaging materials, or onto labels that will enable a wide range of improved tracking, security and marketing functions to be incorporated cost-effectively into packaged goods in future.