Automated species recognition in environmental recordings

Faunal vocalisations are vital indicators for environmental change and faunal vocalisation analysis can provide information for answering ecological questions. Therefore, automated species recognition in environmental recordings has become a critical research area. This thesis presents an automated species recognition approach named Timed and Probabilistic Automata. A small lexicon for describing animal calls is defined, six algorithms for acoustic component detection are developed, and a series of species recognisers are built and evaluated.The presented automated species recognition approach yields significant improvement on the analysis performance over a real world dataset, and may be transferred to commercial software in the future.

The sphynx's new riddle : how to relate the canonical formula of myth to quantum interaction

We introduce Claude Lévi Strauss' canonical formula (CF), an attempt to rigorously formalise the general narrative structure of myth. This formula utilises the Klein group as its basis, but a recent work draws attention to its natural quaternion form, which opens up the possibility that it may require a quantum inspired interpretation. We present the CF in a form that can be understood by a non-anthropological audience, using the formalisation of a key myth (that of Adonis) to draw attention to its mathematical structure. The future potential formalisation of mythological structure within a quantum inspired framework is proposed and discussed, with a probabilistic interpretation further generalising the formula

Conceptualising, evaluating and reporting social resilience in vulnerable regional and remote communities facing climate change in tropical Queensland : marine and tropical sciences research facility transition project final report

Regional and remote communities in tropical Queensland are among Australia’s most vulnerable in the face of climate change. At the same time, these socially and economically vulnerable regions house some of Australia’s most significant biodiversity values. Past approaches to terrestrial biodiversity management have focused on tackling biophysical interventions through the use of biophysical knowledge. An equally important focus should be placed on building regional-scale community resilience if some of the worst biodiversity impacts of climate change are to be avoided or mitigated. Despite its critical need, more systemic or holistic approaches to natural resource management have been rarely trialed and tested in a structured way. Currently, most strategic interventions in improving regional community resilience are ad hoc, not theory-based and short term. Past planning approaches have not been durable, nor have they been well informed by clear indicators. Research into indicators for community resilience has been poorly integrated within adaptive planning and management cycles. This project has aimed to resolve this problem by: * Reviewing the community and social resilience and adaptive planning literature to reconceptualise an improved framework for applying community resilience concepts; * Harvesting and extending work undertaken in MTSRF Phase 1 to identifying the learnings emerging from past MTSRF research; * Distilling these findings to identify new theoretical and practical approaches to the application of community resilience in natural resource use and management; * Reconsidering the potential interplay between a region’s biophysical and social planning processes, with a focus on exploring spatial tools to communicate climate change risk and its consequent environmental, economic and social impacts, and; * Trialling new approaches to indicator development and adaptive planning to improve community resilience, using a sub-regional pilot in the Wet Tropics. In doing so, we also looked at ways to improve the use and application of relevant spatial information. Our theoretical review drew upon the community development, psychology and emergency management literature to better frame the concept of community resilience relative to aligned concepts of social resilience, vulnerability and adaptive capacity. Firstly, we consider community resilience as a concept that can be considered at a range of scales (e.g. regional, locality, communities of interest, etc.). We also consider that overall resilience at higher scales will be influenced by resilience levels at lesser scales (inclusive of the resilience of constituent institutions, families and individuals). We illustrate that, at any scale, resilience and vulnerability are not necessarily polar opposites, and that some understanding of vulnerability is important in determining resilience. We position social resilience (a concept focused on the social characteristics of communities and individuals) as an important attribute of community resilience, but one that needs to be considered alongside economic, natural resource, capacity-based and governance attributes. The findings from the review of theory and MTSRF Phase 1 projects were synthesized and refined by the wider project team. Five predominant themes were distilled from this literature, research review and an expert analysis. They include the findings that: 1. Indicators have most value within an integrated and adaptive planning context, requiring an active co-research relationship between community resilience planners, managers and researchers if real change is to be secured; 2. Indicators of community resilience form the basis for planning for social assets and the resilience of social assets is directly related the longer term resilience of natural assets. This encourages and indeed requires the explicit development and integration of social planning within a broader natural resource planning and management framework; 3. Past indicator research and application has not provided a broad picture of the key attributes of community resilience and there have been many attempts to elicit lists of “perfect” indicators that may never be useful within the time and resource limitations of real world regional planning and management. We consider that modeling resilience for proactive planning and prediction purposes requires the consideration of simple but integrated clusters of attributes; 4. Depending on time and resources available for planning and management, the combined use of well suited indicators and/or other lesser “lines of evidence” is more flexible than the pursuit of perfect indicators, and that; 5. Index-based, collaborative and participatory approaches need to be applied to the development, refinement and reporting of indicators over longer time frames. We trialed the practical application of these concepts via the establishment of a collaborative regional alliance of planners and managers involved in the development of climate change adaptation strategies across tropical Queensland (the Gulf, Wet Tropics, Cape York and Torres Strait sub-regions). A focus on the Wet Tropics as a pilot sub-region enabled other Far North Queensland sub-region’s to participate and explore the potential extension of this approach. The pilot activities included: * Further exploring ways to innovatively communicate the region’s likely climate change scenarios and possible environmental, economic and social impacts. We particularly looked at using spatial tools to overlay climate change risks to geographic communities and social vulnerabilities within those communities; * Developing a cohesive first pass of a State of the Region-style approach to reporting community resilience, inclusive of regional economic viability, community vitality, capacitybased and governance attributes. This framework integrated a literature review, expert (academic and community) and alliance-based contributions; and * Early consideration of critical strategies that need to be included in unfolding regional planning activities with Far North Queensland. The pilot assessment finds that rural, indigenous and some urban populations in the Wet Tropics are highly vulnerable and sensitive to climate change and may require substantial support to adapt and become more resilient. This assessment finds that under current conditions (i.e. if significant adaptation actions are not taken) the Wet Tropics as a whole may be seriously impacted by the most significant features of climate change and extreme climatic events. Without early and substantive action, this could result in declining social and economic wellbeing and natural resource health. Of the four attributes we consider important to understanding community resilience, the Wet Tropics region is particularly vulnerable in two areas; specifically its economic vitality and knowledge, aspirations and capacity. The third and fourth attributes, community vitality and institutional governance are relatively resilient but are vulnerable in some key respects. In regard to all four of these attributes, however, there is some emerging capacity to manage the possible shocks that may be associated with the impacts of climate change and extreme climatic events. This capacity needs to be carefully fostered and further developed to achieve broader community resilience outcomes. There is an immediate need to build individual, household, community and sectoral resilience across all four attribute groups to enable populations and communities in the Wet Tropics region to adapt in the face of climate change. Preliminary strategies of importance to improve regional community resilience have been identified. These emerging strategies also have been integrated into the emerging Regional Development Australia Roadmap, and this will ensure that effective implementation will be progressed and coordinated. They will also inform emerging strategy development to secure implementation of the FNQ 2031 Regional Plan. Of most significance in our view, this project has taken a co-research approach from the outset with explicit and direct importance and influence within the region’s formal planning and management arrangements. As such, the research: * Now forms the foundations of the first attempt at “Social Asset” planning within the Wet Tropics Regional NRM Plan review; * Is assisting Local government at regional scale to consider aspects of climate change adaptation in emerging planning scheme/community planning processes; * Has partnered the State government (via the Department of Infrastructure and Planning and Regional Managers Coordination Network Chair) in progressing the Climate Change adaptation agenda set down within the FNQ 2031 Regional Plan; * Is informing new approaches to report on community resilience within the GBRMPA Outlook reporting framework; and * Now forms the foundation for the region’s wider climate change adaptation priorities in the Regional Roadmap developed by Regional Development Australia. Through the auspices of Regional Development Australia, the outcomes of the research will now inform emerging negotiations concerning a wider package of climate change adaptation priorities with State and Federal governments. Next stage research priorities are also being developed to enable an ongoing alliance between researchers and the region’s climate change response.

Cryptanalysis of block ciphers with overdefined systems of equations

Several recently proposed ciphers, for example Rijndael and Serpent, are built with layers of small S-boxes interconnected by linear key-dependent layers. Their security relies on the fact, that the classical methods of cryptanalysis (e.g. linear or differential attacks) are based on probabilistic characteristics, which makes their security grow exponentially with the number of rounds N r r. In this paper we study the security of such ciphers under an additional hypothesis: the S-box can be described by an overdefined system of algebraic equations (true with probability 1). We show that this is true for both Serpent (due to a small size of S-boxes) and Rijndael (due to unexpected algebraic properties). We study general methods known for solving overdefined systems of equations, such as XL from Eurocrypt’00, and show their inefficiency. Then we introduce a new method called XSL that uses the sparsity of the equations and their specific structure. The XSL attack uses only relations true with probability 1, and thus the security does not have to grow exponentially in the number of rounds. XSL has a parameter P, and from our estimations is seems that P should be a constant or grow very slowly with the number of rounds. The XSL attack would then be polynomial (or subexponential) in N r> , with a huge constant that is double-exponential in the size of the S-box. The exact complexity of such attacks is not known due to the redundant equations. Though the presented version of the XSL attack always gives always more than the exhaustive search for Rijndael, it seems to (marginally) break 256-bit Serpent. We suggest a new criterion for design of S-boxes in block ciphers: they should not be describable by a system of polynomial equations that is too small or too overdefined.

On secure multi-party computation in black-box groups

We study the natural problem of secure n-party computation (in the passive, computationally unbounded attack model) of the n-product function f G (x 1,...,x n ) = x 1 ·x 2 ⋯ x n in an arbitrary finite group (G,·), where the input of party P i is x i  ∈ G for i = 1,...,n. For flexibility, we are interested in protocols for f G which require only black-box access to the group G (i.e. the only computations performed by players in the protocol are a group operation, a group inverse, or sampling a uniformly random group element). Our results are as follows. First, on the negative side, we show that if (G,·) is non-abelian and n ≥ 4, then no ⌈n/2⌉-private protocol for computing f G exists. Second, on the positive side, we initiate an approach for construction of black-box protocols for f G based on k-of-k threshold secret sharing schemes, which are efficiently implementable over any black-box group G. We reduce the problem of constructing such protocols to a combinatorial colouring problem in planar graphs. We then give two constructions for such graph colourings. Our first colouring construction gives a protocol with optimal collusion resistance t < n/2, but has exponential communication complexity O(n*2t+1^2/t) group elements (this construction easily extends to general adversary structures). Our second probabilistic colouring construction gives a protocol with (close to optimal) collusion resistance t < n/μ for a graph-related constant μ ≤ 2.948, and has efficient communication complexity O(n*t^2) group elements. Furthermore, we believe that our results can be improved by further study of the associated combinatorial problems.

A statistical approach to quantify the impact on voltage quality caused by PV generators

A probabilistic method is proposed to evaluate voltage quality of grid-connected photovoltaic (PV) power systems. The random behavior of solar irradiation is described in statistical terms and the resulting voltage fluctuation probability distribution is then derived. Reactive power capabilities of the PV generators are then analyzed and their operation under constant power factor mode is examined. By utilizing the reactive power capability of the PV-generators to the full, it is shown that network voltage quality can be greatly enhanced.

miRPlant : an integrated tool for identification of plant miRNA from RNA sequencing data

Background Small RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeep’s probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction. Result We have developed a user-friendly plant miRNA prediction tool called miRPlant. We show using 16 plant miRNA datasets from four different plant species that miRPlant has at least a 10% improvement in accuracy compared to miRDeep-P, which is the most popular plant miRNA prediction tool. Furthermore, miRPlant uses a Graphical User Interface for data input and output, and identified miRNA are shown with all RNAseq reads in a hairpin diagram. Conclusions We have developed miRPlant which extends miRDeep* to various plant species by adopting suitable strategies to identify hairpin excision regions and hairpin structure filtering for plants. miRPlant does not require any third party tools such as mapping or RNA secondary structure prediction tools. miRPlant is also the first plant miRNA prediction tool that dynamically plots miRNA hairpin structure with small reads for identified novel miRNAs. This feature will enable biologists to visualize novel pre-miRNA structure and the location of small RNA reads relative to the hairpin. Moreover, miRPlant can be easily used by biologists with limited bioinformatics skills.

Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.

Decision support system for age-related macular degeneration using discrete wavelet transform

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, k -nearest neighbor ( k -NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

Improving voltage profile of residential distribution systems using rooftop PVs and Battery Energy Storage systems

Large number of rooftop Photovoltaics (PVs) have turned traditional passive networks into active networks with intermittent and bidirectional power flow. A community based distribution network grid reinforcement process is proposed to address technical challenges associated with large integration of rooftop PVs. Probabilistic estimation of intermittent PV generation is considered. Depending on the network parameters such as the R/X ratio of distribution feeder, either reactive control from PVs or coordinated control of PVs and Battery Energy Storage (BES) has been proposed. Determination of BES capacity is one of the significant outcomes from the proposed method and several factors such as variation in PV installed capacity as well as participation from community members are analyzed. The proposed approach is convenient for the community members providing them flexibility of managing their integrated PV and BES systems

Comparison of eight published static finite element models of the intact lumbar spine : predictive power of models improves when combined together

Finite element (FE) model studies have made important contributions to our understanding of functional biomechanics of the lumbar spine. However, if a model is used to answer clinical and biomechanical questions over a certain population, their inherently large inter-subject variability has to be considered. Current FE model studies, however, generally account only for a single distinct spinal geometry with one set of material properties. This raises questions concerning their predictive power, their range of results and on their agreement with in vitro and in vivo values. Eight well-established FE models of the lumbar spine (L1-5) of different research centres around the globe were subjected to pure and combined loading modes and compared to in vitro and in vivo measurements for intervertebral rotations, disc pressures and facet joint forces. Under pure moment loading, the predicted L1-5 rotations of almost all models fell within the reported in vitro ranges, and their median values differed on average by only 2° for flexion-extension, 1° for lateral bending and 5° for axial rotation. Predicted median facet joint forces and disc pressures were also in good agreement with published median in vitro values. However, the ranges of predictions were larger and exceeded those reported in vitro, especially for the facet joint forces. For all combined loading modes, except for flexion, predicted median segmental intervertebral rotations and disc pressures were in good agreement with measured in vivo values. In light of high inter-subject variability, the generalization of results of a single model to a population remains a concern. This study demonstrated that the pooled median of individual model results, similar to a probabilistic approach, can be used as an improved predictive tool in order to estimate the response of the lumbar spine.

Data fusion and tracking with multiple UAVs

This chapter describes decentralized data fusion algorithms for a team of multiple autonomous platforms. Decentralized data fusion (DDF) provides a useful basis with which to build upon for cooperative information gathering tasks for robotic teams operating in outdoor environments. Through the DDF algorithms, each platform can maintain a consistent global solution from which decisions may then be made. Comparisons will be made between the implementation of DDF using two probabilistic representations. The first, Gaussian estimates and the second Gaussian mixtures are compared using a common data set. The overall system design is detailed, providing insight into the overall complexity of implementing a robust DDF system for use in information gathering tasks in outdoor UAV applications.

A cost-effectiveness analysis of a telephone-linked care intervention for individuals with Type 2 diabetes

AIM: To assess the cost-effectiveness of an automated telephone-linked care intervention, Australian TLC Diabetes, delivered over 6 months to patients with established Type 2 diabetes mellitus and high glycated haemoglobin level, compared to usual care. METHODS: A Markov model was designed to synthesize data from a randomized controlled trial of TLC Diabetes (n=120) and other published evidence. The 5-year model consisted of three health states related to glycaemic control: 'sub-optimal' HbA1c ≥58mmol/mol (7.5%); 'average' ≥48-57mmol/mol (6.5-7.4%) and 'optimal' <48mmol/mol (6.5%) and a fourth state 'all-cause death'. Key outcomes of the model include discounted health system costs and quality-adjusted life years (QALYS) using SF-6D utility weights. Univariate and probabilistic sensitivity analyses were undertaken. RESULTS: Annual medication costs for the intervention group were lower than usual care [Intervention: £1076 (95%CI: £947, £1206) versus usual care £1271 (95%CI: £1115, £1428) p=0.052]. The estimated mean cost for intervention group participants over five years, including the intervention cost, was £17,152 versus £17,835 for the usual care group. The corresponding mean QALYs were 3.381 (SD 0.40) for the intervention group and 3.377 (SD 0.41) for the usual care group. Results were sensitive to the model duration, utility values and medication costs. CONCLUSION: The Australian TLC Diabetes intervention was a low-cost investment for individuals with established diabetes and may result in medication cost-savings to the health system. Although QALYs were similar between groups, other benefits arising from the intervention should also be considered when determining the overall value of this strategy.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.