Improving the Flexibility of Active Grids through Web Services

Active Grids are a form of grid infrastructure where the grid network is active and programmable. These grids directly support applications with value added services such as data migration, compression, adaptation and monitoring. Services such as these are particularly important for eResearch applications which by their very nature are performance critical and data intensive. We propose an architecture for improving the flexibility of Active Grids through web services. These enable Active Grid services to be easily and flexibly configured, monitored and deployed from practically any platform or application. The architecture is called WeSPNI ('Web Services based on Programmable Networks Infrastructure'). We present the architecture together with some early experimental results on using web services to monitor data movement in an active grid.

Can IT Security be improved with better IT Leadership in the 21st Century University?

Organizations generally are not responding effectively to rising IT security threats because people issues receive inadequate attention. The stark example of IT security is just the latest strategic IT priority demonstrating deficient IT leadership attention to the social dimension of IT. Universities in particular, with their devolved people organization, diverse adoption of IT, and split central/local federated approach to governance and leadership of IT, demand higher levels of interpersonal sophistication and strategic engagement from their IT leaders. An idealized model for IT leaders for the 21st century university is proposed to be developed as a framework for further investigation. The testing of this model in an action research study is proposed.

2D-3D Face Recognition Based on PCA and Feature Modelling

Hybrid face recognition, using image (2D) and structural (3D) information, has explored the fusion of Nearest Neighbour classifiers. This paper examines the effectiveness of feature modelling for each individual modality, 2D and 3D. Furthermore, it is demonstrated that the fusion of feature modelling techniques for the 2D and 3D modalities yields performance improvements over the individual classifiers. By fusing the feature modelling classifiers for each modality with equal weights the average Equal Error Rate improves from 12.60% for the 2D classifier and 12.10% for the 3D classifier to 7.38% for the Hybrid 2D+3D clasiffier.