A learning community approach to doctoral education in the social sciences

The paper reports on the findings of a community learning approach to doctoral education involving scholarly writing groups (SWGs) which was developed and implemented in the context of a higher degree research programme within the social sciences in an Australian university. The research evaluated the impact of the teaching intervention on students' perceptions of the community learning experience, their knowledge of scholarly writing and their attitudes towards writing. The findings are suggestive of the advantages of community approaches to learning in higher degree research education as a supplement to independent supervision. The SWGs were associated with improvements in both participants' knowledge of scholarly writing and their attitudes towards writing. However, a variety of characteristics of doctoral education are potential impediments to the creation of ongoing and regular interactions in learning communities such as SWGs. The paper concludes that a flexible approach to the recognition and enhancement of community approaches to learning is required to acknowledge the complex and diverse context of contemporary doctoral education.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.