A novel nonrepudiable threshold multi-proxy multi-signature scheme with shared verification

Tzeng et al. proposed a new threshold multi-proxy multi-signature scheme with threshold verification. In their scheme, a subset of original signers authenticates a designated proxy group to sign on behalf of the original group. A message m has to be signed by a subset of proxy signers who can represent the proxy group. Then, the proxy signature is sent to the verifier group. A subset of verifiers in the verifier group can also represent the group to authenticate the proxy signature. Subsequently, there are two improved schemes to eliminate the security leak of Tzeng et al.’s scheme. In this paper, we have pointed out the security leakage of the three schemes and further proposed a novel threshold multi-proxy multi-signature scheme with threshold verification.

Investigation of molecular mechanisms regulating biomineralization of pearl oyster Pinctada maxima

Biomineralization is a process encompassing all mineral containing tissues produced within an organism. The most dynamic example of this process is the formation of the mollusk shell, comprising a variety of crystal phases and microstructures. The organic component incorporated within the shell is said to dictate this remarkable architecture. Subsequently, for the past decade considerable research have been undertaken to identify and characterize the protein components involved in biomineralization. Despite these efforts the general understanding of the process remains ambiguous. This study employs a novel molecular approach to further the elucidation of the shell biomineralization. A microarray platform has been custom generated (PmaxArray 1.0) from the pearl oyster Pinctada maxima. PmaxArray 1.0 consists of 4992 expressed sequence tags (ESTs) originating from the mantle, an organ involved in shell formation. This microarray has been used as the primary tool for three separate investigations in an effort to associate transcriptional gene expression from P. maxima to the process of shell biomineralization. The first investigation analyzes the spatial expression of ESTs throughout the mantle organ. The mantle was dissected into five discrete regions and each analyzed for gene expression with PmaxArray 1.0. Over 2000 ESTs were differentially expressed among the tissue sections, identifying five major expression regions. Three of these regions have been proposed to have shell formation functions belonging to nacre, prismatic calcite and periostracum. The spatial gene expression map was confirmed by in situ hybridization, localizing a subset of ESTs from each expression region to the same mantle area. Comparative sequence analysis of ESTs expressed in the proposed shell formation regions with the BLAST tool, revealed a number of the transcripts were novel while others showed significant sequence similarities to previously characterized shell formation genes. The second investigation correlates temporal EST expression during P. maxima larval ontogeny with transitions in shell mineralization during the same period. A timeline documenting the morphologicat microstructural and mineralogical shell characteristics of P. maxima throughout larval ontogeny has been established. Three different shell types were noted based on the physical characters and termed, prodissoconch I, prodissoconch 11 and dissoconch. PmaxArray 1.0 analyzed ESTs expression of animals throughout the larval development of P. maxima, noting up-regulation of 359 ESTs in association with the shell transitions from prodissoconch 1 to prodissoconch 11 to dissoconch. Comparative sequence analysis of these ESTs indicates a number of the transcripts are novel as well as showing significant sequence similarities between ESTs and known shell matrix associated genes and proteins. These ESTs are discussed in relation to the shell characters associated with their temporal expression. The third investigation uses PmaxArray 1.0 to analyze gene expression in the mantle tissue of P. maxima specimens exposed to sub-lethal concentrations of a shell-deforming toxin, tributyltin (TBT). The shell specific effects of TBT are used in this investigation to interpret differential expression of ESTs with respect to shell formation functions. A lethal and sublethal TBT concentration range was established for P. maxima, noting a concentration of 50 ng L- 1 TBT as sub-lethal over a 21 day period. Mantle tissue from P. maxima animals treated with 50 ng L- 1 TBT was assessed for differential EST expression with untreated control animals. A total of 102 ESTs were identified as differentially expressed in association with TBT exposure, comparative sequence identities included an up-regulation of immunity and detoxification related genes and down-regulation of several shell matrix genes. A number of transcripts encoding novel peptides were additionally identified. The potential actions of these genes are discussed with reference to TBT toxicity and shell biomineralization. This thesis has used a microarray platform to analyze gene expression in spatial, temporal and toxicity investigations, revealing the involvement of numerous gene transcripts in specific shell formation functions. Investigation of thousands of transcripts simultaneously has provided a holistic interpretation of the organic components regulating shell biomineralization.

Soft-biometrics : unconstrained authentication in a surveillance environment

Soft biometrics are characteristics that can be used to describe, but not uniquely identify an individual. These include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional biometrics (i.e. face, voice) which require cooperation from the subject, soft biometrics can be acquired by surveillance cameras at range without any user cooperation. Whilst these traits cannot provide robust authentication, they can be used to provide coarse authentication or identification at long range, locate a subject who has been previously seen or who matches a description, as well as aid in object tracking. In this paper we propose three part (head, torso, legs) height and colour soft biometric models, and demonstrate their verification performance on a subset of the PETS 2006 database. We show that these models, whilst not as accurate as traditional biometrics, can still achieve acceptable rates of accuracy in situations where traditional biometrics cannot be applied.

Clustering of ad-hoc microphone arrays for robust blind beamforming

This paper proposes a clustered approach for blind beamfoming from ad-hoc microphone arrays. In such arrangements, microphone placement is arbitrary and the speaker may be close to one, all or a subset of microphones at a given time. Practical issues with such a configuration mean that some microphones might be better discarded due to poor input signal to noise ratio (SNR) or undesirable spatial aliasing effects from large inter-element spacings when beamforming. Large inter-microphone spacings may also lead to inaccuracies in delay estimation during blind beamforming. In such situations, using a cluster of microphones (ie, a sub-array), closely located both to each other and to the desired speech source, may provide more robust enhancement than the full array. This paper proposes a method for blind clustering of microphones based on the magnitude square coherence function, and evaluates the method on a database recorded using various ad-hoc microphone arrangements.

Latent variable models in statistical genetics

Understanding the complexities that are involved in the genetics of multifactorial diseases is still a monumental task. In addition to environmental factors that can influence the risk of disease, there is also a number of other complicating factors. Genetic variants associated with age of disease onset may be different from those variants associated with overall risk of disease, and variants may be located in positions that are not consistent with the traditional protein coding genetic paradigm. Latent Variable Models are well suited for the analysis of genetic data. A latent variable is one that we do not directly observe, but which is believed to exist or is included for computational or analytic convenience in a model. This thesis presents a mixture of methodological developments utilising latent variables, and results from case studies in genetic epidemiology and comparative genomics. Epidemiological studies have identified a number of environmental risk factors for appendicitis, but the disease aetiology of this oft thought useless vestige remains largely a mystery. The effects of smoking on other gastrointestinal disorders are well documented, and in light of this, the thesis investigates the association between smoking and appendicitis through the use of latent variables. By utilising data from a large Australian twin study questionnaire as both cohort and case-control, evidence is found for the association between tobacco smoking and appendicitis. Twin and family studies have also found evidence for the role of heredity in the risk of appendicitis. Results from previous studies are extended here to estimate the heritability of age-at-onset and account for the eect of smoking. This thesis presents a novel approach for performing a genome-wide variance components linkage analysis on transformed residuals from a Cox regression. This method finds evidence for a dierent subset of genes responsible for variation in age at onset than those associated with overall risk of appendicitis. Motivated by increasing evidence of functional activity in regions of the genome once thought of as evolutionary graveyards, this thesis develops a generalisation to the Bayesian multiple changepoint model on aligned DNA sequences for more than two species. This sensitive technique is applied to evaluating the distributions of evolutionary rates, with the finding that they are much more complex than previously apparent. We show strong evidence for at least 9 well-resolved evolutionary rate classes in an alignment of four Drosophila species and at least 7 classes in an alignment of four mammals, including human. A pattern of enrichment and depletion of genic regions in the profiled segments suggests they are functionally significant, and most likely consist of various functional classes. Furthermore, a method of incorporating alignment characteristics representative of function such as GC content and type of mutation into the segmentation model is developed within this thesis. Evidence of fine-structured segmental variation is presented.

Improving automatic speaker verification using SVM techniques

Automatic recognition of people is an active field of research with important forensic and security applications. In these applications, it is not always possible for the subject to be in close proximity to the system. Voice represents a human behavioural trait which can be used to recognise people in such situations. Automatic Speaker Verification (ASV) is the process of verifying a persons identity through the analysis of their speech and enables recognition of a subject at a distance over a telephone channel { wired or wireless. A significant amount of research has focussed on the application of Gaussian mixture model (GMM) techniques to speaker verification systems providing state-of-the-art performance. GMM's are a type of generative classifier trained to model the probability distribution of the features used to represent a speaker. Recently introduced to the field of ASV research is the support vector machine (SVM). An SVM is a discriminative classifier requiring examples from both positive and negative classes to train a speaker model. The SVM is based on margin maximisation whereby a hyperplane attempts to separate classes in a high dimensional space. SVMs applied to the task of speaker verification have shown high potential, particularly when used to complement current GMM-based techniques in hybrid systems. This work aims to improve the performance of ASV systems using novel and innovative SVM-based techniques. Research was divided into three main themes: session variability compensation for SVMs; unsupervised model adaptation; and impostor dataset selection. The first theme investigated the differences between the GMM and SVM domains for the modelling of session variability | an aspect crucial for robust speaker verification. Techniques developed to improve the robustness of GMMbased classification were shown to bring about similar benefits to discriminative SVM classification through their integration in the hybrid GMM mean supervector SVM classifier. Further, the domains for the modelling of session variation were contrasted to find a number of common factors, however, the SVM-domain consistently provided marginally better session variation compensation. Minimal complementary information was found between the techniques due to the similarities in how they achieved their objectives. The second theme saw the proposal of a novel model for the purpose of session variation compensation in ASV systems. Continuous progressive model adaptation attempts to improve speaker models by retraining them after exploiting all encountered test utterances during normal use of the system. The introduction of the weight-based factor analysis model provided significant performance improvements of over 60% in an unsupervised scenario. SVM-based classification was then integrated into the progressive system providing further benefits in performance over the GMM counterpart. Analysis demonstrated that SVMs also hold several beneficial characteristics to the task of unsupervised model adaptation prompting further research in the area. In pursuing the final theme, an innovative background dataset selection technique was developed. This technique selects the most appropriate subset of examples from a large and diverse set of candidate impostor observations for use as the SVM background by exploiting the SVM training process. This selection was performed on a per-observation basis so as to overcome the shortcoming of the traditional heuristic-based approach to dataset selection. Results demonstrate the approach to provide performance improvements over both the use of the complete candidate dataset and the best heuristically-selected dataset whilst being only a fraction of the size. The refined dataset was also shown to generalise well to unseen corpora and be highly applicable to the selection of impostor cohorts required in alternate techniques for speaker verification.

Characteristics of sustainable entrepreneurship : some early explorations from the CAUSEE study

Principal Topic: For forward thinking companies, the environment may represent the ''biggest opportunity for enterprise and invention the industrial world has ever seen'' (Cairncross 1990). Increasing awareness of environmental and sustainability issues through media including the promotion of Al Gore's ''An Inconvenient Truth'' has seen increased awareness of environmental and sustainability issues and increased demand for business processes that reduce detrimental environmental impacts of global development (Dean & McMullen 2007). The increased demand for more environmentally sensitive products and services represents an opportunity for the development of ventures that seek to satisfy this demand through entrepreneurial action. As a consequence, increasing recent market developments in renewable energy, carbon emissions, fuel cells, green building, and other sectors suggest an increasing importance of opportunities for environmental entrepreneurship (Dean and McMullen 2007) and increasingly important area of business activity (Schaper 2005). In the last decade in particular, big business has sought to develop a more ''sustainability/ green friendly'' orientation as a response to public pressure and increased government legislation and policy to improve environmental performance (Cohen and Winn 2007). Whilst much of the literature and media is littered with examples of sustainability practices of large firms, nascent and young sustainability firms have only recently begun generating strong research and policy interest (Shepherd, Kuskova and Patzelt 2009): not only for their potential to generate above average financial performance and returns owing to a greater popularity and demand towards sustainability products and services offerings, but also for their intent to lessen environmental impacts, and to provide a more accurate reflection of the ''true cost'' of market offerings taking into account carbon and environmental impacts. More specifically, researchers have suggested that although the previous focus has been on large firms and their impact on the environment, the estimated collective impact of entries and exits of nascent and young firms in development is substantial and could outweigh the combined environmental impact of large companies (Hillary, 2000). Therefore, it may be argued that greater attention should be paid to nascent and young firms and researching sustainability practices, for both their impact in reducing environmental impacts and potential higher financial performance. Whilst acknowledging this research only uses the first wave of a four year longitudinal study of nascent and young firms, it can still begin to provide initial analysis on which to continue further research. The aim of this paper therefore is to provide an overview of the emerging literature in sustainable entrepreneurship and to present some selected preliminary results from the first wave of the data collection, with comparison, where appropriate, of sustainable and firms that do not fulfil this criteria. ''One of the key challenges in evaluating sustainability entrepreneurship is the lack of agreement in how it is defined'' (Schaper, 2005: 10). Some evaluate sustainable entrepreneurs simply as one category of entrepreneurs with little difference between them and traditional entrepreneurs (Dees, 1998). Other research recognises values-based sustainable enterprises requiring a unique perspective (Parrish, 2005). Some see the environmental or sustainable entrepreneurship is a subset of social entrepreneurship (Cohen & Winn, 2007; Dean & McMullen, 2007) whilst others see it as a separate, distinct theory (Archer 2009). Following one of the first definitions of sustainability developed by the Brundtland Commission (1987) we define sustainable entrepreneurship as firms which ''seek to meet the needs and aspirations of the present without compromising the ability to meet those of the future''. ---------- Methodology/Key Propositions: In this exploratory paper we investigate sustainable entrepreneurship using Cohen et al.'s (2008) framework to identify strategies of nascent and young entrepreneurial firms. We use data from The Comprehensive Australian Study of Entrepreneurial Emergence (CAUSEE). This study shares the general empirical approach with PSED studies in the US (Reynolds et al 1994; Reynolds & Curtin 2008). The overall study uses samples of 727 nascent (not yet operational) firms and another 674 young firms, the latter being in an operational stage but less than four years old. To generate the sub sample of sustainability firms, we used content analysis techniques on firm titles, descriptions and product descriptions provided by respondents. Two independent coders used a predefined codebook developed from our review of the sustainability entrepreneurship literature (Cohen et al. 2009) to evaluate the content based on terms such as ''sustainable'' ''eco-friendly'' ''renewable energy'' ''environment'' amongst others. The inter-rater reliability was checked and the Kappa's co-efficient was found to be within the acceptable range (0.746). 85 firms fulfilled the criteria given for inclusion in the sustainability cohort. ---------- Results and Implications: The results for this paper are based on Wave one of the CAUSEE survey which has been completed and the data is available for analysis. It is expected that the findings will assist in beginning to develop an understanding of nascent and young firms that are driven to contribute to a society which is sustainable, not just from an economic perspective (Cohen et al 2008), but from an environmental and social perspective as well. The CAUSEE study provides an opportunity to compare the characteristics of sustainability entrepreneurs with entrepreneurial firms without a stated environmental purpose, which constitutes the majority of the new firms created each year, using a large scale novel longitudinal dataset. The results have implications for Government in the design of better conditions for the creation of new business, firms who assist sustainability in developing better advice programs in line with a better understanding of their needs and requirements, individuals who may be considering becoming entrepreneurs in high potential arenas and existing entrepreneurs make better decisions.

Darjeeling, a feature-rich VM for the resource poor

The programming and retasking of sensor nodes could benefit greatly from the use of a virtual machine (VM) since byte code is compact, can be loaded on demand, and interpreted on a heterogeneous set of devices. The challenge is to ensure good programming tools and a small footprint for the virtual machine to meet the memory constraints of typical WSN platforms. To this end we propose Darjeeling, a virtual machine modelled after the Java VM and capable of executing a substantial subset of the Java language, but designed specifically to run on 8- and 16-bit microcontrollers with 2 - 10 KB of RAM. The Darjeeling VM uses a 16- rather than a 32-bit architecture, which is more efficient on the targeted platforms. Darjeeling features a novel memory organisation with strict separation of reference from non-reference types which eliminates the need for run-time type inspection in the underlying compacting garbage collector. Darjeeling uses a linked stack model that provides light-weight threads, and supports synchronisation. The VM has been implemented on three different platforms and was evaluated with micro benchmarks and a real-world application. The latter includes a pure Java implementation of the collection tree routing protocol conveniently programmed as a set of cooperating threads, and a reimplementation of an existing environmental monitoring application. The results show that Darjeeling is a viable solution for deploying large-scale heterogeneous sensor networks. Copyright 2009 ACM.

Morphologic and genomic characterisation of the koala Chlamydia pneumoniae strain

Chlamydia pneumoniae is a common human and animal pathogen associated with a wide range of upper and lower respiratory tract infections. In more recent years there has been increasing evidence to suggest a link between C. pneumoniae and chronic diseases in humans, including atherosclerosis, stroke and Alzheimer’s disease. C. pneumoniae human strains show little genetic variation, indicating that the human-derived strain originated from a common ancestor in the recent past. Despite extensive information on the genetics and morphology processes of the human strain, knowledge concerning many other hosts (including marsupials, amphibians, reptiles and equines) remains virtually unexplored. The koala (Phascolarctos cinereus) is a native Australian marsupial under threat due to habitat loss, predation and disease. Koalas are very susceptible to chlamydial infections, most commonly affecting the conjunctiva, urogenital tract and/or respiratory tract. To address this gap in the literature, the present study (i) provides a detailed description of the morphologic and genomic architecture of the C. pneumoniae koala (and human) strain, and shows that the koala strain is microscopically, developmentally and genetically distinct from the C. pneumoniae human strain, and (ii) examines the genetic relationship of geographically diverse C. pneumoniae isolates from human, marsupial, amphibian, reptilian and equine hosts, and identifies two distinct lineages that have arisen from animal-to-human cross species transmissions. Chapter One of this thesis explores the scientific problem and aims of this study, while Chapter Two provides a detailed literature review of the background in this field of work. Chapter Three, the first results chapter, describes the morphology and developmental stages of C. pneumoniae koala isolate LPCoLN, as revealed by fluorescence and transmission electron microscopy. The profile of this isolate, when cultured in HEp-2 human epithelial cells, was quite different to the human AR39 isolate. Koala LPCoLN inclusions were larger; the elementary bodies did not have the characteristic pear-shaped appearance, and the developmental cycle was completed within a shorter period of time (as confirmed by quantitative real-time PCR). These in vitro findings might reflect biological differences between koala LPCoLN and human AR39 in vivo. Chapter Four describes the complete genome sequence of the koala respiratory pathogen, C. pneumoniae LPCoLN. This is the first animal isolate of C. pneumoniae to be fully-sequenced. The genome sequence provides new insights into genomic ‘plasticity’ (organisation), evolution and biology of koala LPCoLN, relative to four complete C. pneumoniae human genomes (AR39, CWL029, J138 and TW183). Koala LPCoLN contains a plasmid that is not shared with any of the human isolates, there is evidence of gene loss in nucleotide salvage pathways, and there are 10 hot spot genomic regions of variation that were previously not identified in the C. pneumoniae human genomes. Sequence (partial-length) from a second, independent, wild koala isolate (EBB) at several gene loci confirmed that the koala LPCoLN isolate was representative of a koala C. pneumoniae strain. The combined sequence data provides evidence that the C. pneumoniae animal (koala LPCoLN) genome is ancestral to the C. pneumoniae human genomes and that human infections may have originated from zoonotic infections. Chapter Five examines key genome components of the five C. pneumoniae genomes in more detail. This analysis reveals genomic features that are shared by and/or contribute to the broad ecological adaptability and evolution of C. pneumoniae. This analysis resulted in the identification of 65 gene sequences for further analysis of intraspecific variation, and revealed some interesting differences, including fragmentation, truncation and gene decay (loss of redundant ancestral traits). This study provides valuable insights into metabolic diversity, adaptation and evolution of C. pneumoniae. Chapter Six utilises a subset of 23 target genes identified from the previous genomic comparisons and makes a significant contribution to our understanding of genetic variability among C. pneumoniae human (11) and animal (6 amphibian, 5 reptilian, 1 equine and 7 marsupial hosts) isolates. It has been shown that the animal isolates are genetically diverse, unlike the human isolates that are virtually clonal. More convincing evidence that C. pneumoniae originated in animals and recently (in the last few hundred thousand years) crossed host species to infect humans is provided in this study. It is proposed that two animal-to-human cross species events have occurred in the context of the results, one evident by the nearly clonal human genotype circulating in the world today, and the other by a more animal-like genotype apparent in Indigenous Australians. Taken together, these data indicate that the C. pneumoniae koala LPCoLN isolate has morphologic and genomic characteristics that are distinct from the human isolates. These differences may affect the survival and activity of the C. pneumoniae koala pathogen in its natural host, in vivo. This study, by utilising the genetic diversity of C. pneumoniae, identified new genetic markers for distinguishing human and animal isolates. However, not all C. pneumoniae isolates were genetically diverse; in fact, several isolates were highly conserved, if not identical in sequence (i.e. Australian marsupials) emphasising that at some stage in the evolution of this pathogen, there has been an adaptation/s to a particular host, providing some stability in the genome. The outcomes of this study by experimental and bioinformatic approaches have significantly enhanced our knowledge of the biology of this pathogen and will advance opportunities for the investigation of novel vaccine targets, antimicrobial therapy, or blocking of pathogenic pathways.

The Machine Vision Toolbox : A MATLAB Toolbox for vision and vision-based control

The article described an open-source toolbox for machine vision called Machine Vision Toolbox (MVT). MVT includes more than 60 functions including image file reading and writing, acquisition, display, filtering, blob, point and line feature extraction, mathematical morphology, homographies, visual Jacobians, camera calibration, and color space conversion. MVT can be used for research into machine vision but is also versatile enough to be usable for real-time work and even control. MVT, combined with MATLAB and a model workstation computer, is a useful and convenient environment for the investigation of machine vision algorithms. The article illustrated the use of a subset of toolbox functions for some typical problems and described MVT operations including the simulation of a complete image-based visual servo system.

Clustered blind beamforming from ad-hoc microphone arrays

Microphone arrays have been used in various applications to capture conversations, such as in meetings and teleconferences. In many cases, the microphone and likely source locations are known \emph{a priori}, and calculating beamforming filters is therefore straightforward. In ad-hoc situations, however, when the microphones have not been systematically positioned, this information is not available and beamforming must be achieved blindly. In achieving this, a commonly neglected issue is whether it is optimal to use all of the available microphones, or only an advantageous subset of these. This paper commences by reviewing different approaches to blind beamforming, characterising them by the way they estimate the signal propagation vector and the spatial coherence of noise in the absence of prior knowledge of microphone and speaker locations. Following this, a novel clustered approach to blind beamforming is motivated and developed. Without using any prior geometrical information, microphones are first grouped into localised clusters, which are then ranked according to their relative distance from a speaker. Beamforming is then performed using either the closest microphone cluster, or a weighted combination of clusters. The clustered algorithms are compared to the full set of microphones in experiments on a database recorded on different ad-hoc array geometries. These experiments evaluate the methods in terms of signal enhancement as well as performance on a large vocabulary speech recognition task.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Towards a game theoretic authorisation model

Authorised users (insiders) are behind the majority of security incidents with high financial impacts. Because authorisation is the process of controlling users’ access to resources, improving authorisation techniques may mitigate the insider threat. Current approaches to authorisation suffer from the assumption that users will (can) not depart from the expected behaviour implicit in the authorisation policy. In reality however, users can and do depart from the canonical behaviour. This paper argues that the conflict of interest between insiders and authorisation mechanisms is analogous to the subset of problems formally studied in the field of game theory. It proposes a game theoretic authorisation model that can ensure users’ potential misuse of a resource is explicitly considered while making an authorisation decision. The resulting authorisation model is dynamic in the sense that its access decisions vary according to the changes in explicit factors that influence the cost of misuse for both the authorisation mechanism and the insider.

Molecular epidemiology of respiratory viruses in a paediatric cohort from Indonesia

Acute lower respiratory tract infections (ALRTIs) are a common cause of morbidity and mortality among children under 5 years of age and are found worldwide, with pneumonia as the most severe manifestation. Although the incidence of severe disease varies both between individuals and countries, there is still no clear understanding of what causes this variation. Studies of community-acquired pneumonia (CAP) have traditionally not focused on viral causes of disease due to a paucity of diagnostic tools. However, with the emergence of molecular techniques, it is now known that viruses outnumber bacteria as the etiological agents of childhood CAP, especially in children under 2 years of age. The main objective of this study was to investigate viruses contributing to disease severity in cases of childhood ALRTI, using a two year cohort study following 2014 infants and children enrolled in Bandung, Indonesia. A total of 352 nasopharyngeal washes collected from 256 paediatric ALRTI patients were used for analysis. A subset of samples was screened using a novel microarray pathogen detection method that identified respiratory syncytial virus (RSV), human metapneumovirus (hMPV) and human rhinovirus (HRV) in the samples. Real-time RT-PCR was used both for confirming and quantifying viruses found in the nasopharyngeal samples. Viral copy numbers were determined and normalised to the numbers of human cells collected with the use of 18S rRNA. Molecular epidemiology was performed for RSV A and hMPV using sequences to the glycoprotein gene and nucleoprotein gene respectively, to determine genotypes circulating in this Indonesian paediatric cohort. This study found that HRV (119/352; 33.8%) was the most common virus detected as the cause of respiratory tract infections in this cohort, followed by the viral pathogens RSV A (73/352; 20.7%), hMPV (30/352; 8.5%) and RSV B (12/352; 3.4%). Co-infections of more than two viruses were detected in 31 episodes (defined as an infection which occurred more than two weeks apart), accounting for 8.8% of the 352 samples tested or 15.4% of the 201 episodes with at least one virus detected. RSV A genotypes circulating in this population were predominantly GA2, GA5 and GA7, while hMPV genotypes circulating were mainly A2a (27/30; 90.0%), B2 (2/30; 6.7%) and A1 (1/30; 3.3%). This study found no evidence of disease severity associated either with a specific virus or viral strain, or with viral load. However, this study did find a significant association with co-infection of RSV A and HRV with severe disease (P = 0.006), suggesting that this may be a novel cause of severe disease.