A cognitive processing framework for learning analytics

Incorporating a learner’s level of cognitive processing into Learning Analytics presents opportunities for obtaining rich data on the learning process. We propose a framework called COPA that provides a basis for mapping levels of cognitive operation into a learning analytics system. We utilise Bloom’s taxonomy, a theoretically respected conceptualisation of cognitive processing, and apply it in a flexible structure that can be implemented incrementally and with varying degree of complexity within an educational organisation. We outline how the framework is applied, and its key benefits and limitations. Finally, we apply COPA to a University undergraduate unit, and demonstrate its utility in identifying key missing elements in the structure of the course.

Normative requirements for regulatory compliance: An abstract formal framework

By definition, regulatory rules (in legal context called norms) intend to achieve specific behaviour from business processes, and might be relevant to the whole or part of a business process. They can impose conditions on different aspects of process models, e.g., control-flow, data and resources etc. Based on the rules sets, norms can be classified into various classes and sub-classes according to their effects. This paper presents an abstract framework consisting of a list of norms and a generic compliance checking approach on the idea of (possible) execution of processes. The proposed framework is independent of any existing formalism, and provides a conceptually rich and exhaustive ontology and semantics of norms needed for business process compliance checking. The possible uses of the proposed framework include to compare different compliance management frameworks (CMFs).

"Re-thinking" the influence of regulatory capture in the development of government regulation

On 30 March 2015 the Australian Federal Government launched its "Re-Think" initiative with the objective of achieving a better tax system which delivers taxes that are lower, simpler and fairer. The discussion paper released as part of the "Re:think" initiative is designed to start a national conversation on tax reform. However, inquiries into Australia's future tax system, subsequent reforms and the introduction of new taxes are nothing new. Unfortunately, recent history also demonstrates that reform initiatives arising from reviews of the Australian tax system are often deemed a failure. The most prominent of these failures in recent times is the Minerals Resource Rent Tax (MRRT), which lasted a mere 16 months before its announced repeal. Using the established theoretic framework of regulatory capture to interpret publically observable data, the purpose of this article is to explain the failure of this arguably sound tax. It concludes that the MRRT legislation itself, through the capture by the mining companies, provided internal subsidization in the form of reduced tax and minimal or no rents. In doing so, it offers an opportunity to understand and learn from past experiences to ensure that recommendations coming out of the Re:think initiative do not suffer the same fate.

Modelling Denial of Service Attacks on JFK with Meadow's Cost-Based Framework

We present the first detailed application of Meadows’s cost-based modelling framework to the analysis of JFK, an Internet key agreement protocol. The analysis identifies two denial of service attacks against the protocol that are possible when an attacker is willing to reveal the source IP address. The first attack was identified through direct application of a cost-based modelling framework, while the second was only identified after considering coordinated attackers. Finally, we demonstrate how the inclusion of client puzzles in the protocol can improve denial of service resistance against both identified attacks.

A framework for fully decentralised cycle stealing

Ordinary desktop computers continue to obtain ever more resources – in-creased processing power, memory, network speed and bandwidth – yet these resources spend much of their time underutilised. Cycle stealing frameworks harness these resources so they can be used for high-performance computing. Traditionally cycle stealing systems have used client-server based architectures which place significant limits on their ability to scale and the range of applica-tions they can support. By applying a fully decentralised network model to cycle stealing the limits of centralised models can be overcome. Using decentralised networks in this manner presents some difficulties which have not been encountered in their previous uses. Generally decentralised ap-plications do not require any significant fault tolerance guarantees. High-performance computing on the other hand requires very stringent guarantees to ensure correct results are obtained. Unfortunately mechanisms developed for traditional high-performance computing cannot be simply translated because of their reliance on a reliable storage mechanism. In the highly dynamic world of P2P computing this reliable storage is not available. As part of this research a fault tolerance system has been created which provides considerable reliability without the need for a persistent storage. As well as increased scalability, fully decentralised networks offer the ability for volunteers to communicate directly. This ability provides the possibility of supporting applications whose tasks require direct, message passing style communication. Previous cycle stealing systems have only supported embarrassingly parallel applications and applications with limited forms of communication so a new programming model has been developed which can support this style of communication within a cycle stealing context. In this thesis I present a fully decentralised cycle stealing framework. The framework addresses the problems of providing a reliable fault tolerance sys-tem and supporting direct communication between parallel tasks. The thesis includes a programming model for developing cycle stealing applications with direct inter-process communication and methods for optimising object locality on decentralised networks.