A recommendation system for predicting risks across multiple business process instances

This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a business process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we suggest to the participant the action to perform which minimizes the predicted process risk. Risks are predicted by traversing decision trees generated from the logs of past process executions, which consider process data, involved resources, task durations and other information elements like task frequencies. When applied in the context of multiple process instances running concurrently, a second technique is employed that uses integer linear programming to compute the optimal assignment of resources to tasks to be performed, in order to deal with the interplay between risks relative to different instances. The recommendation system has been implemented as a set of components on top of the YAWL BPM system and its effectiveness has been evaluated using a real-life scenario, in collaboration with risk analysts of a large insurance company. The results, based on a simulation of the real-life scenario and its comparison with the event data provided by the company, show that the process instances executed concurrently complete with significantly fewer faults and with lower fault severities, when the recommendations provided by our recommendation system are taken into account.

Working the iceberg : a staffroom morality play

This play comes from a research project about how teachers understand and sustain their work in challenging secondary school classrooms. The research asked “How DO teachers work in these classrooms?” not “How SHOULD they?” In the play you meet three teachers who speak candidly about their principles, priorities and vulnerabilities to a pre-service teacher as they move between classes and staffroom. These are real people, real quotes and real feelings taken from real interview data, not idealised guidelines for ‘best practice’. Rather than templates for practice, the play offers a variety of models, issues and food for thought to discuss in teacher education programs. The project was interested in the moral dynamics of classrooms created under the Council of Australian Governments’ 2009 Compact with Young Australians, a policy move that required students to be ‘earning or learning till 17’ across all Australian states. By removing the unemployment benefit for this age group, and tying school attendance to family welfare entitlements, these policies effectively raised the minimum school-leaving age. The risk in this well-intended policy move is that a lack of suitable job opportunities will keep young people at school longer than they want to be there. The effects of this ‘earning or learning’ policy will impact some communities, schools and classrooms much harder than others. The title uses the metaphor of an iceberg to refer to the complex community-school relations that lie below classroom interactions. The idea of a morality play in the play’s title refers back to a medieval form of popular play that used characters to instruct the audience in virtues and values. In the same way, this play seeks to bring to the surface and embody the different moral principles that can inform teacher’s work. The research involved classroom ethnographies of classes for 16 to 17 years olds in non-academic pathways. Eight different teacher/ class combinations were sampled across 2 high schools, 2 TAFE colleges and I hybrid TAFE/school program in three towns experiencing chronic youth unemployment. Their timetabled lessons were observed across 3 to 4 weeks and the teachers and some students were interviewed in each site. The project was funded by an ARC Discovery Early Career Award, 2012-214.

Course evidence matrix : a collaborative evaluation using multiple lines of evidence

Welcome to the Evaluation of course matrix. This matrix is designed for highly qualified discipline experts to evaluate their course, major or unit in a systemic manner. The primary purpose of the Evaluation of course matrix is to provide a tool that a group of academic staff at universities can collaboratively review the assessment within a course, major or unit annually. The annual review will result in you being ready for an external curricula review at any point in time. This tool is designed for use in a workshop format with one, two or more academic staff, and will lead to an action plan for implementation. I hope you find this tool useful in your assessment review.

Virtual business role-play : leveraging familiar environments to prime stakeholder memory during process elicitation

Business process models have traditionally been an effective way of examining business practices to identify areas for improvement. While common information gathering approaches are generally efficacious, they can be quite time consuming and have the risk of developing inaccuracies when information is forgotten or incorrectly interpreted by analysts. In this study, the potential of a role-playing approach for process elicitation and specification has been examined. This method allows stakeholders to enter a virtual world and role-play actions as they would in reality. As actions are completed, a model is automatically developed, removing the need for stakeholders to learn and understand a modelling grammar. Empirical data obtained in this study suggests that this approach may not only improve both the number of individual process task steps remembered and the correctness of task ordering, but also provide a reduction in the time required for stakeholders to model a process view.

A framework for analysing creative activity within puzzle-game play experiences

This thesis is an analyzing creative processes that can be fostered through computer gaming. Outcomes from the research build on our knowledge of how computer games foster creative thinking. The research proposes guidelines that build upon our understanding of the relationship between the creative processes that players undertake during a game and the components of the game that allow these processes to occur. These guidelines may be used in the game design process to better facilitate creative gameplay activity. A significant research contribution is the ability to create games that facilitate creative thinking through engaging interactions with technology.

What roles do contemporaneous and cumulative incomes play in the income–child health gradient for young children? Evidence from an Australian Panel

The literature to date shows that children from poorer households tend to have worse health than their peers, and the gap between them grows with age. We investigate whether and how health shocks (as measured by the onset of chronic conditions) contribute to the income–child health gradient and whether the contemporaneous or cumulative effects of income play important mitigating roles. We exploit a rich panel dataset with three panel waves called the Longitudinal Study of Australian children. Given the availability of three waves of data, we are able to apply a range of econometric techniques (e.g. fixed and random effects) to control for unobserved heterogeneity. The paper makes several contributions to the extant literature. First, it shows that an apparent income gradient becomes relatively attenuated in our dataset when the cumulative and contemporaneous effects of household income are distinguished econometrically. Second, it demonstrates that the income–child health gradient becomes statistically insignificant when controlling for parental health and health-related behaviours or unobserved heterogeneity.

Inferring phylogenies of evolving sequences without multiple sequence alignment

Alignment-free methods, in which shared properties of sub-sequences (e.g. identity or match length) are extracted and used to compute a distance matrix, have recently been explored for phylogenetic inference. However, the scalability and robustness of these methods to key evolutionary processes remain to be investigated. Here, using simulated sequence sets of various sizes in both nucleotides and amino acids, we systematically assess the accuracy of phylogenetic inference using an alignment-free approach, based on D2 statistics, under different evolutionary scenarios. We find that compared to a multiple sequence alignment approach, D2 methods are more robust against among-site rate heterogeneity, compositional biases, genetic rearrangements and insertions/deletions, but are more sensitive to recent sequence divergence and sequence truncation. Across diverse empirical datasets, the alignment-free methods perform well for sequences sharing low divergence, at greater computation speed. Our findings provide strong evidence for the scalability and the potential use of alignment-free methods in large-scale phylogenomics.

The effect of autonomy transparency in human-robot interactions : a preliminary study on operator cognitive workload and situation awareness in multiple heterogeneous UAV management

The autonomous capabilities in collaborative unmanned aircraft systems are growing rapidly. Without appropriate transparency, the effectiveness of the future multiple Unmanned Aerial Vehicle (UAV) management paradigm will be significantly limited by the human agent’s cognitive abilities; where the operator’s CognitiveWorkload (CW) and Situation Awareness (SA) will present as disproportionate. This proposes a challenge in evaluating the impact of robot autonomous capability feedback, allowing the human agent greater transparency into the robot’s autonomous status - in a supervisory role. This paper presents; the motivation, aim, related works, experiment theory, methodology, results and discussions, and the future work succeeding this preliminary study. The results in this paper illustrates that, with a greater transparency of a UAV’s autonomous capability, an overall improvement in the subjects’ cognitive abilities was evident, that is, with a confidence of 95%, the test subjects’ mean CW was demonstrated to have a statistically significant reduction, while their mean SA was demonstrated to have a significant increase.

Managing and learning with multiple models: Objectives and optimization algorithms

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. © 2010 Elsevier Ltd.

Reconceptualizing green infrastructure for climate change adaptation: Barriers to adoption and drivers for uptake by spatial planners

Urban green infrastructure can help cities adapt to climate change. Spatial planning can play an important role in utilizing green infrastructure for adaptation. Yet climate change risks represent a different sort of challenge for planning institutions. This paper aims to address two issues arising from this challenge. First, it defines the concept of green infrastructure within the context of climate adaptation. Second, it identifies and puts into perspective institutional barriers to adopting green infrastructure for climate adaptation, including path dependence. We begin by arguing that there is growing confusion among planners and policy makers about what constitutes green infrastructure. Definitional ambiguity may contribute to inaction on climate change adaptation, because it muddies existing programs and initiatives that are to do with green-space more broadly, which in turn feeds path dependency. We then report empirical findings about how planners perceive the institutional challenge arising from climate change and the adoption of green infrastructure as an adaptive response. The paper concludes that spatial planners generally recognize multiple rationales associated with green infrastructure. However they are not particularly keen on institutional innovation and there is a tendency for path dependence. We propose a conceptual model that explicitly recognizes such institutional factors. This paper contributes to the literature by showing that agency and institutional dimensions are a limiting factor in advancing the concept of green infrastructure within the context of climate change adaptation.

First Literacies: Art, Creativity, Play, Constructive Meaning-Making

In this chapter art and play are considered children’s ‘first languages’, and therefore are placed at the centre of a curriculum for young children. Through art and play, children represent thought and action, which underpins their later understanding of the ‘second languages’ of reading, writing and numbering. Key issues such as image-making, graphic action, imagination, narrative, empathetic engagement and internalised thought are analysed as evidence of children’s construction of knowledge through art and play. Symbol making is the essence of being human. In children’s art and play, their symbol use captures their sensory modes in emotional and embodied ways, as children know their worlds and their place. The chapter addresses how children’s creation, manipulation and meaning making through engaged interaction with art materials are precursors to learning to read and write and, as first languages, should not be discarded nor replaced. The notion of creativity is explored in relation to pedagogical approaches. In a climate of testing regimes that emphasise ‘academic’ achievements, teachers are encouraged to not lose sight of imagination, pretence, constructive meaning making, holistic teaching and being a co-player and co-artist.

The ‘state of play’ in Australia: Early childhood educators and play-based learning

This article provides an overview of the Education Meets Play study that will investigate early childhood educators’ use of play-based learning, now mandatory under the National Quality Standard. By building on what can be gleaned about educators’ approaches to play-based learning prior to the implementation of the Early Years Learning Framework, the study will contribute to the evidence base concerning the implementation and effects of Australia’s early childhood education and care policy reform initiatives.

A framework for identification of similarities between multiple algorithms

This thesis in software engineering presents a novel automated framework to identify similar operations utilized by multiple algorithms for solving related computing problems. It provides a new effective solution to perform multi-application based algorithm analysis, employing fundamentally light-weight static analysis techniques compared to the state-of-art approaches. Significant performance improvements are achieved across the objective algorithms through enhancing the efficiency of the identified similar operations, targeting discrete application domains.