Perspectives on Australian bankruptcy law through the prism of the World Bank report on the treatment of the insolvency of natural persons

The insolvency of natural persons raises questions not only for a nation’s economy but also for its concern for equity. The World Bank has recently released a Report on the Treatment of the Insolvency of Natural Persons to guide nations in addressing the issues raised by an individual debtor’s insolvency. A brief review of Australia’s personal insolvency laws shows that it addresses many of the issues raised by the Report. However two areas are identified as worthy of further investigation by policy-makers and scholars to better address a concern for equity.

Fine-grained plant classification using convolutional neural networks for feature extraction

We present an overview of the QUT plant classification system submitted to LifeCLEF 2014. This system uses generic features extracted from a convolutional neural network previously used to perform general object classification. We examine the effectiveness of these features to perform plant classification when used in combination with an extremely randomised forest. Using this system, with minimal tuning, we obtained relatively good results with a score of 0:249 on the test set of LifeCLEF 2014.

Citizens and social media in times of natural disaster: Exploring information experience

Introduction. Social media is becoming a vital source of information in disaster or emergency situations. While a growing number of studies have explored the use of social media in natural disasters by emergency staff, military personnel, medial and other professionals, very few studies have investigated the use of social media by members of the public. The purpose of this paper is to explore citizens’ information experiences in social media during times of natural disaster. Method. A qualitative research approach was applied. Data was collected via in-depth interviews. Twenty-five people who used social media during a natural disaster in Australia participated in the study. Analysis. Audio recordings of interviews and interview transcripts provided the empirical material for data analysis. Data was analysed using structural and focussed coding methods. Results. Eight key themes depicting various aspects of participants’ information experience during a natural disaster were uncovered by the study: connected; wellbeing; coping; help; brokerage; journalism; supplementary and characteristics. Conclusion. This study contributes insights into social media’s potential for developing community disaster resilience and promotes discussion about the value of civic participation in social media when such circumstances occur. These findings also contribute to our understanding of information experiences as a new informational research object.

The evolving landscape of natural resource planning and governance in Australia

Australia’s governance of land and natural resources involves multiple polycentric domains of decision-making from global through to local levels. Although certainly complex, these arrangements have not necessarily translated into better decision-making or better environmental outcomes as evidenced by the growing concerns over the health and future of the Great Barrier Reef, (GBR). However within this system, arrangements for natural resource management (NRM) and reef water quality, which both use Australia’s integrated regional NRM model, have showed signs of improving decision-making and environmental outcomes in the GBR. In this paper we describe the latest evolutions in the governance and planning for natural resource use and management in Australia. We begin by reviewing the experience with first generation NRM as published in major audits and evaluations. As our primary interest is the health and future of the GBR, we then consider the impact of changes of second generation planning and governance outcomes in Queensland. We find that first generation plans, although developed under a relatively cohesive governance context, faced substantial problems in target setting, implementation, monitoring and review. Despite this, they were able to progress improvements in water quality in the Great Barrier Reef Regions. Second generation plans, currently being developed, face an even greater risk of failure due to the lack of bilateralism and cross-sectoral cooperation across the NRM governance system. The findings highlight the critical need to re-build and enhance the regional NRM model for NRM planning to have a positive impact on environmental outcomes in the GBR.

Injury narrative text classification using factorization model

Narrative text is a useful way of identifying injury circumstances from the routine emergency department data collections. Automatically classifying narratives based on machine learning techniques is a promising technique, which can consequently reduce the tedious manual classification process. Existing works focus on using Naive Bayes which does not always offer the best performance. This paper proposes the Matrix Factorization approaches along with a learning enhancement process for this task. The results are compared with the performance of various other classification approaches. The impact on the classification results from the parameters setting during the classification of a medical text dataset is discussed. With the selection of right dimension k, Non Negative Matrix Factorization-model method achieves 10 CV accuracy of 0.93.

Decision boundary setting and classifier combination for text classification

This thesis presents a promising boundary setting method for solving challenging issues in text classification to produce an effective text classifier. A classifier must identify boundary between classes optimally. However, after the features are selected, the boundary is still unclear with regard to mixed positive and negative documents. A classifier combination method to boost effectiveness of the classification model is also presented. The experiments carried out in the study demonstrate that the proposed classifier is promising.

Exploring the usefulness of structural-functional approaches to systematically assess the functionality of governance arrangements for natural resource management planning in two Australian case studies

This research developed and applied an evaluative framework to analyse multiple scales of decision-making for environmental management planning. It is the first exploration of the sociological theory of structural-functionalism and its usefulness to support evidence based decision-making in a planning context. The framework was applied to analyse decision-making in Queensland's Cape York Peninsula and Wet Tropics regions.

Classification of activities of daily living of individual with limb loss

The monitoring of the actual activities of daily living of individuals with lower limb amputation is essential for an evidence-based fitting of the prosthesis, more particularly the choice of components (e.g., knees, ankles, feet)[1-4]. The purpose of this presentation was to give an overview of the categorization of the load regime data to assess the functional output and usage of the prosthesis of lower limb amputees has presented in several publications[5, 6]. The objectives were to present a categorization of load regime and to report the results for a case.

Performance dispersion for evidence-based classification of stationary throwers

Background There is a need for better understanding of the dispersion of classification-related variable to develop an evidence-based classification of athletes with a disability participating in stationary throwing events. Objectives The purposes of this study are (A) to describe tools designed to comprehend and represent the dispersion of the performance between successive classes, and (B) to present this dispersion for the elite male and female stationary shot-putters who participated in Beijing 2008 Paralympic Games. Study design Retrospective study Methods This study analysed a total of 479 attempts performed by 114 male and female stationary shot-putters in three F30s (F32-F34) and six F50s (F52-F58) classes during the course of eight events during Beijing 2008 Paralympic Games. Results The average differences of best performance were 1.46±0.46 m for males between F54 and F58 classes as well as 1.06±1.18 m for females between F55 and F58 classes. The results demonstrated a linear relationship between best performance and classification while revealing two male Gold Medallists in F33 and F52 classes were outliers. Conclusions This study confirms the benefits of the comparative matrices, performance continuum and dispersion plots to comprehend classification-related variables. The work presented here represents a stepping stone into biomechanical analyses of stationary throwers, particularly on the eve of the London 2012 Paralympic Games where new evidences could be gathered.

Design and synthesis of a screening library using the natural product scaffold 3-chloro-4-hydroxyphenylacetic acid

The fungal metabolite 3-chloro-4-hydroxyphenylacetic acid (1) was utilized in the generation of a unique drug-like screening library using parallel solution-phase synthesis. A 20-membered amide library (3–22) was generated by first converting 1 to methyl (3-chloro-4-hydroxyphenyl)acetate (2), then reacting this scaffold with a diverse series of primary amines via a solvent-free aminolysis procedure. The structures of the synthetic analogues (3–22) were elucidated by spectroscopic data analysis. The structures of compounds 8, 12, and 22 were confirmed by single X-ray crystallographic analysis. All compounds were evaluated for cytotoxicity against a human prostate cancer cell line (LNCaP) and for antiparasitic activity toward Trypanosoma brucei brucei and Plasmodium falciparum and showed no significant activity at 10 μM. The library was also tested for effects on the lipid content of LNCaP and PC-3 prostate cancer cells, and it was demonstrated that the fluorobenzyl analogues (12–14) significantly reduced cellular phospholipid and neutral lipid levels.

Exit polls, turnout, and bandwagon voting: Evidence from a natural experiment

We exploit a voting reform in France to estimate the causal effect of exit poll information on turnout and bandwagon voting. Before the change in legislation, individuals in some French overseas territories voted after the election result had already been made public via exit poll information from mainland France. We estimate that knowing the exit poll information decreases voter turnout by about 11 percentage points. Our study is the first clean empirical design outside of the laboratory to demonstrate the effect of such knowledge on voter turnout. Furthermore, we find that exit poll information significantly increases bandwagon voting; that is, voters who choose to turn out are more likely to vote for the expected winner.

Centroid training to achieve effective text classification

Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification) aims to build an uncertainty boundary to absorb as many indeterminate objects as possible so as to elevate the certainty of the relevant and irrelevant groups through the centroid clustering and training process. The clustering starts from the two training subsets labelled as relevant or irrelevant respectively to create two principal centroid vectors by which all the training samples are further separated into three groups: POS, NEG and BND, with all the indeterminate objects absorbed into the uncertain decision boundary BND. Two pairs of centroid vectors are proposed to be trained and optimized through the subsequent iterative multi-learning process, all of which are proposed to collaboratively help predict the polarities of the incoming objects thereafter. For the assessment of the proposed model, F1 and Accuracy have been chosen as the key evaluation measures. We stress the F1 measure because it can display the overall performance improvement of the final classifier better than Accuracy. A large number of experiments have been completed using the proposed model on the Reuters Corpus Volume 1 (RCV1) which is important standard dataset in the field. The experiment results show that the proposed model has significantly improved the binary text classification performance in both F1 and Accuracy compared with three other influential baseline models.

Role of rainfall and catchment characteristics on urban stormwater quality

Urbanisation significantly changes the characteristics of a catchment as natural areas are transformed to impervious surfaces such as roads, roofs and parking lots. The increased fraction of impervious surfaces leads to changes to the stormwater runoff characteristics, whilst a variety of anthropogenic activities common to urban areas generate a range of pollutants such as nutrients, solids and organic matter. These pollutants accumulate on catchment surfaces and are removed and trans- ported by stormwater runoff and thereby contribute pollutant loads to receiving waters. In summary, urbanisation influences the stormwater characteristics of a catchment, including hydrology and water quality. Due to the growing recognition that stormwater pollution is a significant environmental problem, the implementation of mitigation strategies to improve the quality of stormwater runoff is becoming increasingly common in urban areas. A scientifically robust stormwater quality treatment strategy is an essential requirement for effective urban stormwater management. The efficient design of treatment systems is closely dependent on the state of knowledge in relation to the primary factors influencing stormwater quality. In this regard, stormwater modelling outcomes provide designers with important guidance and datasets which significantly underpin the design of effective stormwater treatment systems. Therefore, the accuracy of modelling approaches and the reliability modelling outcomes are of particular concern. This book discusses the inherent complexity and key characteristics in the areas of urban hydrology and stormwater quality, based on the influence exerted by a range of rainfall and catchment characteristics. A comprehensive field sampling and testing programme in relation to pollutant build-up, an urban catchment monitoring programme in relation to stormwater quality and the outcomes from advanced statistical analyses provided the platform for the knowledge creation. Two case studies and two real-world applications are discussed to illustrate the translation of the knowledge created to practical use in relation to the role of rainfall and catchment characteristics on urban stormwater quality. An innovative rainfall classification based on stormwater quality was developed to support the effective and scientifically robust design of stormwater treatment systems. Underpinned by the rainfall classification methodology, a reliable approach for design rainfall selection is proposed in order to optimise stormwater treatment based on both, stormwater quality and quantity. This is a paradigm shift from the common approach where stormwater treatment systems are designed based solely on stormwater quantity data. Additionally, how pollutant build-up and stormwater runoff quality vary with a range of catchment characteristics was also investigated. Based on the study out- comes, it can be concluded that the use of only a limited number of catchment parameters such as land use and impervious surface percentage, as it is the case in current modelling approaches, could result in appreciable error in water quality estimation. Influential factors which should be incorporated into modelling in relation to catchment characteristics, should also include urban form and impervious surface area distribution. The knowledge created through the research investigations discussed in this monograph is expected to make a significant contribution to engineering practice such as hydrologic and stormwater quality modelling, stormwater treatment design and urban planning, as the study outcomes provide practical approaches and recommendations for urban stormwater quality enhancement. Furthermore, this monograph also demonstrates how fundamental knowledge of stormwater quality processes can be translated to provide guidance on engineering practice, the comprehensive application of multivariate data analyses techniques and a paradigm on integrative use of computer models and mathematical models to derive practical outcomes.

Effective emergency messaging during natural disasters: An application of message compliance theories

Study/Objective This research examines the types of emergency messages used in Australia during the response and early recovery phases of a natural disaster. The aim of the research is to develop theory-driven emergency messages that increase individual behavioural compliance during a disaster. Background There is growing evidence of non-compliant behaviour in Australia, such as refusing to evacuate and travelling through hazardous areas. This can result in personal injury, loss of life, and damage to (or loss of) property. Moreover, non-compliance can place emergency services personnel in life-threatening situations when trying to save non-compliant individuals. Drawing on message compliance research in psychology and sociology, a taxonomy of message types was developed to ascertain how emergency messaging can be improved to produce compliant behaviour. Method A review of message compliance literature was conducted to develop the taxonomy of message types previously found to achieve compliance. Seven categories were identified: direct-rational, manipulation, negative phrasing, positive phrasing, exchange appeals, normative appeals, and appeals to self. A content analysis was then conducted to assess the emergency messages evident in the Australian emergency management context. The existing messages were aligned with the literature to identify opportunities to improve emergency messaging. Results & Conclusion The results suggest there is an opportunity to improve the effectiveness of emergency messaging to increase compliance during the response and early recovery phases of a natural disaster. While some message types cannot legally or ethically be used in emergency communication (e.g. manipulative messaging), there is an opportunity to create more persuasive messages (e.g. appeals to self) that personalise the individual’s perception of risk, triggering them to comply with the message.

Towards robust automatic affective classification of images using facial expressions for practical applications

Affect is an important feature of multimedia content and conveys valuable information for multimedia indexing and retrieval. Most existing studies for affective content analysis are limited to low-level features or mid-level representations, and are generally criticized for their incapacity to address the gap between low-level features and high-level human affective perception. The facial expressions of subjects in images carry important semantic information that can substantially influence human affective perception, but have been seldom investigated for affective classification of facial images towards practical applications. This paper presents an automatic image emotion detector (IED) for affective classification of practical (or non-laboratory) data using facial expressions, where a lot of “real-world” challenges are present, including pose, illumination, and size variations etc. The proposed method is novel, with its framework designed specifically to overcome these challenges using multi-view versions of face and fiducial point detectors, and a combination of point-based texture and geometry. Performance comparisons of several key parameters of relevant algorithms are conducted to explore the optimum parameters for high accuracy and fast computation speed. A comprehensive set of experiments with existing and new datasets, shows that the method is effective despite pose variations, fast, and appropriate for large-scale data, and as accurate as the method with state-of-the-art performance on laboratory-based data. The proposed method was also applied to affective classification of images from the British Broadcast Corporation (BBC) in a task typical for a practical application providing some valuable insights.