The motivation and structure of corrupt police networks : theorising the dark side of the ‘thin blue line’

In recent times considerable research attention has been directed to understanding dark networks, especially criminal and terrorist networks. Dark networks are those in which member motivations are self rather than public interested, achievements come at the cost of other individuals, groups or societies and, in addition, their activities are both ‘covert and illegal’ (Raab & Milward, 2003: 415). This ‘darkness’ has implications for the way in which these networks are structured, the strategies adopted and their recruitment methods. Such entities exhibit distinctive operating characteristics including most notably the tension between creating an efficient network structure while retaining the ability to hide from public view while avoiding catastrophic collapse should one member cooperate with authorities (Bouchard 2007). While theoretical emphasis has been on criminal and terrorist networks, recent work has demonstrated that corrupt police networks exhibit some distinctive characteristics. In particular, these entities operate within the shadows of a host organisation - the Police Force and distort the functioning of the ‘Thin Blue Line’ as the interface between the law abiding citizenry and the criminal society. Drawing on data derived from the Queensland Fitzgerald Commission of Enquiry into Police Misconduct and related documents, this paper examines the motivations, structural properties and operational practices of corrupt police networks and compares and contrasts these with other dark networks with ‘bright’ public service networks. The paper confirms the structural differences between dark corrupt police networks and bright networks and suggests. However, structural embeddedness alone is found to be an insufficient theoretical explanation for member involvement in networks and that a set of elements combine to impact decision-making. Although offering important insights into network participation, the paper’s findings are especially pertinent in identifying additional points of intervention for police corruption networks.

Quantitative approaches to content analysis : identifying conceptual drift across publication outlets

Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25-year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text.

Communications and learning structures that facilitate transfer of knowledge at innovation transition points

Innovation processes are rarely smooth and disruptions often occur at transition points were one knowledge domain passes the technology on to another domain. At these transition points communication is a key component in assisting the smooth hand over of technologies. However for smooth transitions to occur we argue that appropriate structures have to be in place and boundary spanning activities need to be facilitated. This paper presents three case studies of innovation processes and the findings support the view that structures and boundary spanning are essential for smooth transitions. We have explained the need to pass primary responsibility between agents to successfully bring an innovation to market. We have also shown the need to combine knowledge through effective communication so that absorptive capacity is built in process throughout the organisation rather than in one or two key individuals.

Improving recall in appearance-based visual SLAM using visual expectation

In this paper, we present a new algorithm for boosting visual template recall performance through a process of visual expectation. Visual expectation dynamically modifies the recognition thresholds of learnt visual templates based on recently matched templates, improving the recall of sequences of familiar places while keeping precision high, without any feedback from a mapping backend. We demonstrate the performance benefits of visual expectation using two 17 kilometer datasets gathered in an outdoor environment at two times separated by three weeks. The visual expectation algorithm provides up to a 100% improvement in recall. We also combine the visual expectation algorithm with the RatSLAM SLAM system and show how the algorithm enables successful mapping

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

ArtConnexions series

The exhibition consists of a series of 9 large-scale cotton rag prints, printed from digital files, and a sound and picture animation on DVD composed of drawings, sound, analogue and digital photographs, and Super 8 footage. The exhibition represents the artist’s experience of Singapore during her residency. Source imagery was gathered from photographs taken at the Bukit Brown abandoned Chinese Cemetery in Singapore, and Australian native gardens in Parkville Melbourne. Historical sources include re-photographed Singapore 19th and early 20th century postcard images. The works use analogue, hand-drawn and digital imaging, still and animated, to explore the digital interface’s ability to combine mixed media. This practice stems from the digital imaging practice of layering, using various media editing software. The work is innovative in that it stretches the idea of the layer composition in a single image by setting each layer into motion using animation techniques. This creates a multitude of permutations and combinations as the two layers move in different rhythmic patterns. The work also represents an innovative collaboration between the photographic practitioner and a sound composer, Duncan King-Smith, who designed sound for the animation based on concepts of trance, repetition and abstraction. As part of the Art ConneXions program, the work travelled to numerous international venues including: Space 217 Singapore, RMIT Gallery Melbourne, National Museum Jakarta, Vietnam Fine Arts Museum Hanoi, and ifa (Institut fur Auslandsbeziehungen) Gallery in both Stuttgart and Berlin.

Enriching XML documents clustering by using concise structure and content

With the growing number of XML documents on theWeb it becomes essential to effectively organise these XML documents in order to retrieve useful information from them. A possible solution is to apply clustering on the XML documents to discover knowledge that promotes effective data management, information retrieval and query processing. However, many issues arise in discovering knowledge from these types of semi-structured documents due to their heterogeneity and structural irregularity. Most of the existing research on clustering techniques focuses only on one feature of the XML documents, this being either their structure or their content due to scalability and complexity problems. The knowledge gained in the form of clusters based on the structure or the content is not suitable for reallife datasets. It therefore becomes essential to include both the structure and content of XML documents in order to improve the accuracy and meaning of the clustering solution. However, the inclusion of both these kinds of information in the clustering process results in a huge overhead for the underlying clustering algorithm because of the high dimensionality of the data. The overall objective of this thesis is to address these issues by: (1) proposing methods to utilise frequent pattern mining techniques to reduce the dimension; (2) developing models to effectively combine the structure and content of XML documents; and (3) utilising the proposed models in clustering. This research first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. A clustering framework with two types of models, implicit and explicit, is developed. The implicit model uses a Vector Space Model (VSM) to combine the structure and the content information. The explicit model uses a higher order model, namely a 3- order Tensor Space Model (TSM), to explicitly combine the structure and the content information. This thesis also proposes a novel incremental technique to decompose largesized tensor models to utilise the decomposed solution for clustering the XML documents. The proposed framework and its components were extensively evaluated on several real-life datasets exhibiting extreme characteristics to understand the usefulness of the proposed framework in real-life situations. Additionally, this research evaluates the outcome of the clustering process on the collection selection problem in the information retrieval on the Wikipedia dataset. The experimental results demonstrate that the proposed frequent pattern mining and clustering methods outperform the related state-of-the-art approaches. In particular, the proposed framework of utilising frequent structures for constraining the content shows an improvement in accuracy over content-only and structure-only clustering results. The scalability evaluation experiments conducted on large scaled datasets clearly show the strengths of the proposed methods over state-of-the-art methods. In particular, this thesis work contributes to effectively combining the structure and the content of XML documents for clustering, in order to improve the accuracy of the clustering solution. In addition, it also contributes by addressing the research gaps in frequent pattern mining to generate efficient and concise frequent subtrees with various node relationships that could be used in clustering.

Strategic project evaluation for open pit mining ventures using real options and allied econometric techniques

Open pit mine operations are complex businesses that demand a constant assessment of risk. This is because the value of a mine project is typically influenced by many underlying economic and physical uncertainties, such as metal prices, metal grades, costs, schedules, quantities, and environmental issues, among others, which are not known with much certainty at the beginning of the project. Hence, mining projects present a considerable challenge to those involved in associated investment decisions, such as the owners of the mine and other stakeholders. In general terms, when an option exists to acquire a new or operating mining project, , the owners and stock holders of the mine project need to know the value of the mining project, which is the fundamental criterion for making final decisions about going ahead with the venture capital. However, obtaining the mine project’s value is not an easy task. The reason for this is that sophisticated valuation and mine optimisation techniques, which combine advanced theories in geostatistics, statistics, engineering, economics and finance, among others, need to be used by the mine analyst or mine planner in order to assess and quantify the existing uncertainty and, consequently, the risk involved in the project investment. Furthermore, current valuation and mine optimisation techniques do not complement each other. That is valuation techniques based on real options (RO) analysis assume an expected (constant) metal grade and ore tonnage during a specified period, while mine optimisation (MO) techniques assume expected (constant) metal prices and mining costs. These assumptions are not totally correct since both sources of uncertainty—that of the orebody (metal grade and reserves of mineral), and that about the future behaviour of metal prices and mining costs—are the ones that have great impact on the value of any mining project. Consequently, the key objective of this thesis is twofold. The first objective consists of analysing and understanding the main sources of uncertainty in an open pit mining project, such as the orebody (in situ metal grade), mining costs and metal price uncertainties, and their effect on the final project value. The second objective consists of breaking down the wall of isolation between economic valuation and mine optimisation techniques in order to generate a novel open pit mine evaluation framework called the ―Integrated Valuation / Optimisation Framework (IVOF)‖. One important characteristic of this new framework is that it incorporates the RO and MO valuation techniques into a single integrated process that quantifies and describes uncertainty and risk in a mine project evaluation process, giving a more realistic estimate of the project’s value. To achieve this, novel and advanced engineering and econometric methods are used to integrate financial and geological uncertainty into dynamic risk forecasting measures. The proposed mine valuation/optimisation technique is then applied to a real gold disseminated open pit mine deposit to estimate its value in the face of orebody, mining costs and metal price uncertainties.

Entertainment industries : entertainment as a cultural system

Entertainment Industries is the first book to map entertainment as a cultural system. Including work from world-renowned analysts such as Henry Jenkins and Jonathan Gray, this innovative collection explains what entertainment is and how it works. Entertainment is audience-centred culture. The Entertainment Industries are a uniquely interdisciplinary collection of evolving businesses that openly monitor evolving cultural trends and work within them. The producers of entertainment – central to that practice– are the new artists. They understand audiences and combine creative, business and legal skills in order to produce cultural products that cater to them. Entertainment Industries describes the characteristics of entertainment, the systems that produce it, and the role of producers and audiences in its development, as well as explaining the importance of this area of study, and how it might be better integrated into Universities.

Oodgeroo : bloodline to country

In 2009, the researcher acted as director and dramaturg for the development of Sam Watson’s play, Oodgeroo: Bloodline to Country, culminating in a season at La Boite Theatre. This project represents the first time notions of Aboriginal politics were seriously questioned. It aimed to illuminate a key divide in the way Australian indigenous people, and the wider Australian community, deal with issues of grief and outrage – the way of resistance and revolution, or the way of reconciliation and education. The work sought to combine specific cultural artefacts belonging to the Noonuccal people and the family of Oodgeroo of the Noonuccal (Kath Walker) with traditional and contemporary ideas and performance forms.

Privacy oriented access control for electronic health records

Security and privacy in electronic health record systems have been hindering the growth of e-health systems since their emergence. The development of policies that satisfy the security and privacy requirements of different stakeholders in healthcare has proven to be difficult. But, these requirements have to be met if the systems developed are to succeed in achieving their intended goals. Access control is a fundamental security barrier for securing data in healthcare information systems. In this paper we present an access control model for electronic health records. We address patient privacy requirements, confidentiality of private information and the need for flexible access for health professionals for electronic health records. We carefully combine three existing access control models and present a novel access control model for EHRs which satisfies requirements of electronic health records.

The'dream'of multi-agency crime prevention : pitfalls in policy and practice

During the 1980s, terms such as interagency or multi-agency cooperation, collaboration, coordination, and interaction have became permanent features of both crime prevention rhetoric and government crime policy. The concept of having the government, local authorities, and the community working in partnership has characterized both left and right politics for over a decade. The U.S. National Advisory Commission on Criminal Justice Standards and Goals in the U.S.. Circulars 8/84 and 44/90 released by the U.K. Home Office, and the British Morgan Report-coupled with the launch of government strategies in France, the Netherlands, England and Wales, Australia, and, more recently, in Belgium, New Zealand, and Canada-have all emphasized the importance of agencies working together to prevent or reduce crime. This paper draws upon recent Australian research and critically analyzes multi-agency crime prevention. It suggests that agency conflicts and power struggles may be exacerbated by neo-liberal economic theory, by the politics of crime prevention management, and by policies that aim to combine situational and social prevention endeavors. Furthermore, it concludes that indigenous peoples are excluded by crime prevention strategies that fail to define and interpret crime and its prevention in culturally appropriate ways.

An integrated approach for precise road reconstruction from aerial imagery and LiDAR data

Accurate and detailed road models play an important role in a number of geospatial applications, such as infrastructure planning, traffic monitoring, and driver assistance systems. In this thesis, an integrated approach for the automatic extraction of precise road features from high resolution aerial images and LiDAR point clouds is presented. A framework of road information modeling has been proposed, for rural and urban scenarios respectively, and an integrated system has been developed to deal with road feature extraction using image and LiDAR analysis. For road extraction in rural regions, a hierarchical image analysis is first performed to maximize the exploitation of road characteristics in different resolutions. The rough locations and directions of roads are provided by the road centerlines detected in low resolution images, both of which can be further employed to facilitate the road information generation in high resolution images. The histogram thresholding method is then chosen to classify road details in high resolution images, where color space transformation is used for data preparation. After the road surface detection, anisotropic Gaussian and Gabor filters are employed to enhance road pavement markings while constraining other ground objects, such as vegetation and houses. Afterwards, pavement markings are obtained from the filtered image using the Otsu's clustering method. The final road model is generated by superimposing the lane markings on the road surfaces, where the digital terrain model (DTM) produced by LiDAR data can also be combined to obtain the 3D road model. As the extraction of roads in urban areas is greatly affected by buildings, shadows, vehicles, and parking lots, we combine high resolution aerial images and dense LiDAR data to fully exploit the precise spectral and horizontal spatial resolution of aerial images and the accurate vertical information provided by airborne LiDAR. Objectoriented image analysis methods are employed to process the feature classiffcation and road detection in aerial images. In this process, we first utilize an adaptive mean shift (MS) segmentation algorithm to segment the original images into meaningful object-oriented clusters. Then the support vector machine (SVM) algorithm is further applied on the MS segmented image to extract road objects. Road surface detected in LiDAR intensity images is taken as a mask to remove the effects of shadows and trees. In addition, normalized DSM (nDSM) obtained from LiDAR is employed to filter out other above-ground objects, such as buildings and vehicles. The proposed road extraction approaches are tested using rural and urban datasets respectively. The rural road extraction method is performed using pan-sharpened aerial images of the Bruce Highway, Gympie, Queensland. The road extraction algorithm for urban regions is tested using the datasets of Bundaberg, which combine aerial imagery and LiDAR data. Quantitative evaluation of the extracted road information for both datasets has been carried out. The experiments and the evaluation results using Gympie datasets show that more than 96% of the road surfaces and over 90% of the lane markings are accurately reconstructed, and the false alarm rates for road surfaces and lane markings are below 3% and 2% respectively. For the urban test sites of Bundaberg, more than 93% of the road surface is correctly reconstructed, and the mis-detection rate is below 10%.

Mind games : applying White’s principles of narrative therapy to the creation of a cabaret about depression and bipolar disorder

This PhD represents my attempt to make sense of my personal experiences of depression through the form of cabaret. I first experienced depression in 2006. Previously, I had considered myself to be a happy and optimistic person. I found the experience of depression to be a shock: both in the experience itself, and also in the way it effected my own self image. These personal experiences, together with my professional history as a songwriter and cabaret performer, have been the motivating force behind the research project. This study has explored the question: What are the implications of applying principles of Michael White’s narrative therapy to the creation of a cabaret performance about depression and bipolar disorder? There is a 50 percent weighting on the creative work, the cabaret performance Mind Games, and a 50 percent weighting on the written exegesis. This research has focussed on the illustration of therapeutic principles in order to play games of truth within a cabaret performance. The research project investigates ways of telling my own story in relation to others’ stories through three re-authoring principles articulated in Michael White’s narrative therapy: externalisation, an autonomous ethic of living and rich descriptions. The personal stories presented in the cabaret were drawn from my own experiences and from interviews with individuals with depression or bipolar disorder. The cabaret focussed on the illustration of therapeutic principles, and was not focussed on therapeutic ends for myself or the interviewees. The research question has been approached through a methodology combining autoethnographic, practice-led and action research. Auto ethnographic research is characterised by close investigation of assumptions, attitudes, and beliefs. The combination of autoethnographic, practice-led, action research has allowed me to bring together personal experiences of mental illness, research into therapeutic techniques, social attitudes and public discourses about mental illness and forms of contemporary cabaret to facilitate the creation of a one-woman cabaret performance. The exegesis begins with a discussion of games of truth as informed by Michel Foucault and Michael White and self-stigma as informed by Michael White and Erving Goffman. These concepts form the basis for a discussion of my own personal experiences. White’s narrative therapy is focused on individuals re-authoring their stories, or telling their stories in different ways. White’s principles are influenced by Foucault’s notions of truth and power. Foucault’s term games of truth has been used to describe the effect of a ‘truth in flux’ that occurs through White’s re-authoring process. This study argues that cabaret is an appropriate form to represent this therapeutic process because it favours heightened performativity over realism, and showcases its ‘constructedness’ and artificiality. Thus cabaret is well suited to playing games of truth. A contextual review compares two major cabaret trends, personal cabaret and provocative cabaret, in reference to the performer’s relationship with the audience in terms of distance and intimacy. The study draws a parallel between principles of distance and intimacy in Michael White’s narrative therapy and relates these to performative terms of distance and intimacy. The creative component of this study, the cabaret Mind Games, used principles of narrative therapy to present the character ‘Jo’ playing games of truth through: externalising an aspect of her personality (externalisation); exploring different life values (an autonomous ethic of living); and enacting multiple versions of her identity (rich descriptions). This constant shifting between distance and intimacy within the cabaret created the effect of a truth in ‘constant flux’, to use one of White’s terms. There are three inter-related findings in the study. The first finding is that the application of principles of White’s narrative therapy was able to successfully combine provocative and empathetic elements within the cabaret. The second finding is that the personal agenda of addressing my own self-stigma within the project limited the effective portrayal of a ‘truth in flux’ within the cabaret. The third finding presents the view that the cabaret expressed ‘Jo’ playing games of truth in order to journey towards her own "preferred identity claim" (White 2004b) through an act of "self care" (Foucault 2005). The contribution to knowledge of this research project is the application of therapeutic principles to the creation of a cabaret performance. This process has focussed on creating a self-revelatory cabaret that questions notions of a ‘fixed truth’ through combining elements of existing cabaret forms in new ways. Two major forms in contemporary cabaret, the personal cabaret and the provocative cabaret use the performer-audience relationship in distinctive ways. Through combining elements of these two cabaret forms, I have explored ways to create a provocative cabaret focussed on the act of self-revelation.

Caring for women suffering depression in the postnatal period

The research and theoretical perspectives that underpin knowledge of depression in the postnatal period have tended to rely on standardized measures and 'objective' data which combine to disempower women's individual experiences. From a feminist perspective, contradictions exist between women's accounts of the early days and months following childbirth and the diagnosis of postnatal depression made by some health professionals. A framework for nursing care that uses the strengths of feminist and cognitive therapies is suggested. From this feminist-cognitive perspective, mental health nursing care will provide opportunities for women suffering depression to debrief the experience of birth and pregnancy, manage conflicts about identity, gain a sense of survival, and develop a new perspective.