Healthy waterways: Healthy catchments - An integrated research/management program to understand and reduce impacts of sediments and nutrients on waterways in Queensland, Australia

The Moreton Bay Waterways and Catchments Partnership, now branded the Healthy Waterways Partnership, has built on the experience of the past 15 years here in South East Queensland (SEQ). It focuses on water quality and the ecosystem health of our freshwater, estuarine and marine systems through the implementation of actions by individual partners and the collective oversight of a regional work program that assists partners to prioritise their investments and address emerging issues. This regional program includes monitoring, reporting, marketing and communication, development of decision support tools, research that is directed to problem solving, and maintaining extensive consultative and engagement arrangements. The Partnership has produced information-based outcomes which have led to significant cost savings in the protection of water quality and ecosystem resources by its stakeholders. This has been achieved by: – providing a clear focus for management actions that has ownership of governments, industry and community; – targeted scientific research to address issues requiring appropriate management actions; – management actions based on a sound understanding of the waterways and rigorous public consultation; and, – development and implementation of a strategy that incorporates commitments from all levels of stakeholders. While focusing on our waterways, the Partnership’s approach includes addressing catchment management issues particularly relating to the management of diffuse pollution sources in both urban and rural landscapes as well as point source loads. We are now working with other stakeholders to develop a framework for integrated water management that will link water quality and water quantity goals and priorities.

Intelligent agent supported personalization for virtual learning environments

Virtual learning environments (VLEs) are computer-based online learning environments, which provide opportunities for online learners to learn at the time and location of their choosing, whilst allowing interactions and encounters with other online learners, as well as affording access to a wide range of resources. They have the capability of reaching learners in remote areas around the country or across country boundaries at very low cost. Personalized VLEs are those VLEs that provide a set of personalization functionalities, such as personalizing learning plans, learning materials, tests, and are capable of initializing the interaction with learners by providing advice, necessary instant messages, etc., to online learners. One of the major challenges involved in developing personalized VLEs is to achieve effective personalization functionalities, such as personalized content management, learner model, learner plan and adaptive instant interaction. Autonomous intelligent agents provide an important technology for accomplishing personalization in VLEs. A number of agents work collaboratively to enable personalization by recognizing an individual's eLeaming pace and reacting correspondingly. In this research, a personalization model has been developed that demonstrates dynamic eLearning processes; secondly, this study proposes an architecture for PVLE by using intelligent decision-making agents' autonomous, pre-active and proactive behaviors. A prototype system has been developed to demonstrate the implementation of this architecture. Furthemore, a field experiment has been conducted to investigate the performance of the prototype by comparing PVLE eLearning effectiveness with a non-personalized VLE. Data regarding participants' final exam scores were collected and analyzed. The results indicate that intelligent agent technology can be employed to achieve personalization in VLEs, and as a consequence to improve eLeaming effectiveness dramatically.

A software audit framework for spyware risk mitigation

Our research described in this paper identifies a three part premise relating to the spyware paradigm. Firstly the data suggests spyware is proliferating at an exponential rate. Secondly ongoing research confirms that spyware produces many security risks – including that of privacy/confidentiality breaches via illicit data collection and reporting. Thirdly, anti-spyware controls are improving but are still considered problematic for several reasons. Our research then concludes that control measures to counter this very significant challenge should merit compliance auditing – and this auditing may effectively target the vital message passing performed by all illicit data collection spyware. Our research then evolves into an experiment involving the design and implementation of a software audit tool to conduct the desired compliance auditing. The software audit tool is positioned at the protected network’s gateway. The software audit tool uses ‘phone-home’ IP addresses as spyware signatures to detect the presence of the offending software. The audit tool also has the capability to differentiate legitimate message passing software from that produced by spyware – and ‘learn’ both new spyware signatures and new legitimate message passing profiles. The testing stage of the software has proven successful – albeit using very limited levels of network message passing variety and frequency.