Binary image steganographic techniques classification based on multi-class steganalysis

In this paper, we propose a new multi-class steganalysis for binary image. The proposed method can identify the type of steganographic technique used by examining on the given binary image. In addition, our proposed method is also capable of differentiating an image with hidden message from the one without hidden message. In order to do that, we will extract some features from the binary image. The feature extraction method used is a combination of the method extended from our previous work and some new methods proposed in this paper. Based on the extracted feature sets, we construct our multi-class steganalysis from the SVM classifier. We also present the empirical works to demonstrate that the proposed method can effectively identify five different types of steganography.

Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology

Background Timely diagnosis and reporting of patient symptoms in hospital emergency departments (ED) is a critical component of health services delivery. However, due to dispersed information resources and a vast amount of manual processing of unstructured information, accurate point-of-care diagnosis is often difficult. Aims The aim of this research is to report initial experimental evaluation of a clinician-informed automated method for the issue of initial misdiagnoses associated with delayed receipt of unstructured radiology reports. Method A method was developed that resembles clinical reasoning for identifying limb abnormalities. The method consists of a gazetteer of keywords related to radiological findings; the method classifies an X-ray report as abnormal if it contains evidence contained in the gazetteer. A set of 99 narrative reports of radiological findings was sourced from a tertiary hospital. Reports were manually assessed by two clinicians and discrepancies were validated by a third expert ED clinician; the final manual classification generated by the expert ED clinician was used as ground truth to empirically evaluate the approach. Results The automated method that attempts to individuate limb abnormalities by searching for keywords expressed by clinicians achieved an F-measure of 0.80 and an accuracy of 0.80. Conclusion While the automated clinician-driven method achieved promising performances, a number of avenues for improvement were identified using advanced natural language processing (NLP) and machine learning techniques.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

Climate change and dengue: a critical and systematic review of quantitative modelling approaches

Background Many studies have found associations between climatic conditions and dengue transmission. However, there is a debate about the future impacts of climate change on dengue transmission. This paper reviewed epidemiological evidence on the relationship between climate and dengue with a focus on quantitative methods for assessing the potential impacts of climate change on global dengue transmission. Methods A literature search was conducted in October 2012, using the electronic databases PubMed, Scopus, ScienceDirect, ProQuest, and Web of Science. The search focused on peer-reviewed journal articles published in English from January 1991 through October 2012. Results Sixteen studies met the inclusion criteria and most studies showed that the transmission of dengue is highly sensitive to climatic conditions, especially temperature, rainfall and relative humidity. Studies on the potential impacts of climate change on dengue indicate increased climatic suitability for transmission and an expansion of the geographic regions at risk during this century. A variety of quantitative modelling approaches were used in the studies. Several key methodological issues and current knowledge gaps were identified through this review. Conclusions It is important to assemble spatio-temporal patterns of dengue transmission compatible with long-term data on climate and other socio-ecological changes and this would advance projections of dengue risks associated with climate change. Keywords: Climate; Dengue; Models; Projection; Scenarios

Safety climate : recent developments and future implications

Safety climate is a current interest to construction practitioners and researchers. The concept of safety climate has been actively explored in the field of Industrial and Organizational (I/O) psychology but yet in the construction industry. This paper aims to review the literature of safety climate in a systematic manner and highlight future directions for safety research and development of safety practices in the construction industry. The value of safety climate lies on its ability to predict safety behavior. Safety climate, as a mediator, unfolds the relationship between organizational variables and safety behavior. It, as a moderator, affects the effectiveness of any safety initiatives to improve safety performance. Future research directions would be likely to look at relationship between organizational factors and safety climate using multi-level analysis. To the construction industry, safety climate measurement is a good indicator to assess safety performance. Empirical studies show that frontline supervisor would be the best conduit to create a positive safety climate at workgroup level. It is believed that this paper is beneficial to researchers interested in behavioral aspect of construction safety and industry practitioners striving for safety on site.

Disclosure of climate change-related corporate governance practices

Based on a survey of climate change experts in different stakeholder groups and interviews with corporate climate change managers, this study provides insights into the gap between what information stakeholders expect and what Australian corporations disclose. This paper focuses on annual reports and sustainability reports with specific reference to the disclosure of climate change-related corporate governance practices. The findings culminate in the refinement of a best practice index for the disclosure of climate-change-related corporate governance practises. Interview results indicate that the low levels of disclosures made by Australian companies may be due to a number of factors. These include a potential expectations gap, the absence of pressure from powerful stakeholders, a concern for stakeholder information overload, the cost of providing information, limited perceived accountability for climate change, and preferring other media for disclosure.

Team change climate : a group-level analysis of the relationships among change information and change participation, role stressors, and well-being

We explored whether teams develop shared perceptions regarding the quantity and quality of information and the extent of participation in decision making provided in an environment of continuous change. In addition, we examined whether change climate strength moderated relationships between change climate level and team outcomes. We examined relationships among aggregated change information and change participation and aggregated team outcomes, including two role stressors (i.e., role ambiguity and role overload) and two indicators of well-being (i.e., quality of worklife and distress). Questionnaires were distributed in an Australian law enforcement agency and data were used from 178 teams. Structural equation modelling analyses, controlling for a marker variable, were conducted to examine the main effects of aggregated change information and aggregated change participation on aggregated team outcomes. Results provided support for a model that included method effects due to a marker variable. In this model, change information climate was significantly negatively associated with role ambiguity, role overload, and distress, and significantly positively associated with quality of worklife. Change participation climate was significantly positively associated with quality of worklife. Change climate strength did not moderate relationships among change climate level and team outcomes.

Organizational factors impacting on patient satisfaction : a cross sectional examination of service climate and linkages to nurses' effort and performance

Background Patient satisfaction is influenced by the setting in which patients are treated and the employees providing care. However, to date, limited research has explained how health care organizations or nurses influence patient satisfaction. Objectives The purpose of this study was to test the model that service climate would increase the effort and performance of nursing groups and, in turn, increase patient satisfaction. Method This study incorporated data from 156 nurses, 28 supervisors, and 171 patients. A cross-sectional design was utilized to examine the relationship between service climate, nurse effort, nurse performance and patient satisfaction. Structural equation modeling was conducted to test the proposed relationships. Results Service climate was associated with the effort that nurses directed towards technical care and extra-role behaviors. In turn, the effort that nurses exerted predicted their performance, as rated by their supervisors. Finally, task performance was a significant predictor of patient satisfaction. Conclusions This study suggests that both hospital management and nurses play a role in promoting patient satisfaction. By focusing on creating a climate for service, health care managers can improve nursing performance and patient satisfaction with care.

Random projections on manifolds of symmetric positive definite matrices for image classification

Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance. Taking into account manifold geometry is typically done via (1) embedding the manifolds in tangent spaces, or (2) embedding into Reproducing Kernel Hilbert Spaces (RKHS). While embedding into tangent spaces allows the use of existing Euclidean-based learning algorithms, manifold shape is only approximated which can cause loss of discriminatory information. The RKHS approach retains more of the manifold structure, but may require non-trivial effort to kernelise Euclidean-based learning algorithms. In contrast to the above approaches, in this paper we offer a novel solution that allows SPD matrices to be used with unmodified Euclidean-based learning algorithms, with the true manifold shape well-preserved. Specifically, we propose to project SPD matrices using a set of random projection hyperplanes over RKHS into a random projection space, which leads to representing each matrix as a vector of projection coefficients. Experiments on face recognition, person re-identification and texture classification show that the proposed approach outperforms several recent methods, such as Tensor Sparse Coding, Histogram Plus Epitome, Riemannian Locality Preserving Projection and Relational Divergence Classification.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

Improved image set classification via joint sparse approximated nearest subspaces

Existing multi-model approaches for image set classification extract local models by clustering each image set individually only once, with fixed clusters used for matching with other image sets. However, this may result in the two closest clusters to represent different characteristics of an object, due to different undesirable environmental conditions (such as variations in illumination and pose). To address this problem, we propose to constrain the clustering of each query image set by forcing the clusters to have resemblance to the clusters in the gallery image sets. We first define a Frobenius norm distance between subspaces over Grassmann manifolds based on reconstruction error. We then extract local linear subspaces from a gallery image set via sparse representation. For each local linear subspace, we adaptively construct the corresponding closest subspace from the samples of a probe image set by joint sparse representation. We show that by minimising the sparse representation reconstruction error, we approach the nearest point on a Grassmann manifold. Experiments on Honda, ETH-80 and Cambridge-Gesture datasets show that the proposed method consistently outperforms several other recent techniques, such as Affine Hull based Image Set Distance (AHISD), Sparse Approximated Nearest Points (SANP) and Manifold Discriminant Analysis (MDA).

The lag effects and vulnerabilities of temperature effects on cardiovascular disease mortality in a subtropical climate zone in China

This research quantifies the lag effects and vulnerabilities of temperature effects on cardiovascular disease in Changsha—a subtropical climate zone of China. A Poisson regression model within a distributed lag nonlinear models framework was used to examine the lag effects of cold- and heat-related CVD mortality. The lag effect for heat-related CVD mortality was just 0–3 days. In contrast, we observed a statistically significant association with 10–25 lag days for cold-related CVD mortality. Low temperatures with 0–2 lag days increased the mortality risk for those ≥65 years and females. For all ages, the cumulative effects of cold-related CVD mortality was 6.6% (95% CI: 5.2%–8.2%) for 30 lag days while that of heat-related CVD mortality was 4.9% (95% CI: 2.0%–7.9%) for 3 lag days. We found that in Changsha city, the lag effect of hot temperatures is short while the lag effect of cold temperatures is long. Females and older people were more sensitive to extreme hot and cold temperatures than males and younger people.

Assessing resilience of water supply systems under the impacts of climate change

This project was a step forward in developing the scientific basis for a methodology to assess the resilience of water supply systems under the impacts of climate change. The improved measure of resilience developed in this project provides an approach to assess the ability of water supply systems to absorb the pressure due changing climate while sustaining supply, and their speed of recovery in case of failure. The approach developed can be applied to any generic water supply system.

A study of the emotional climate of a science education class for pre-service teachers in Bhutan

This thesis studied the emotional climate (EC) of a pre-service science teachers' class in Bhutan. It examined the types of activities students engaged in and the relationship between the tutor and students whose interactions produced both positive and negative EC in the class. The major finding was that the activities involving students' presentations using video clips and models, group activity, and coteaching valenced the class EC positively. Negative EC was identified when the tutor ignored students' responses, during formal lectures, and when the tutor was uncertain of the subject knowledge. The replication of activities that produce positive EC by other Bhutanese tutors may improve the standard of science education in the country.

Pleistocene climate fluctuations influence phylogeographical patterns in Melomys cervinipes across the mesic forests of eastern Australia

Aim Our aim was to clarify the lineage-level relationships for Melomys cervinipes and its close relatives and investigate whether the patterns of divergence observed for these wet-forest-restricted mammals may be associated with recognized biogeographical barriers. Location Mesic closed forest along the east coast of Australia, from north Queensland to mid-eastern New South Wales. Methods To enable rigorous phylogenetic reconstruction, divergence-date estimation and phylogeographical inference, we analysed DNA sequence and microsatellite data from 307 specimens across the complete distribution of M. cervinipes (45 localities). Results Three divergent genetic lineages were found within M. cervinipes, corresponding to geographically delineated northern, central and southern clades. Additionally, a fourth lineage, comprising M. rubicola and M. capensis, was identified and was most closely related to the northern M. cervinipes lineage. Secondary contact of the northern and central lineages was identified at one locality to the north of the Burdekin Gap. Main conclusions Contemporary processes of repeated habitat fragmentation and contraction, local extinction events and subsequent re-expansion across both small and large areas, coupled with the historical influence of the Brisbane Valley Barrier, the St Lawrence Gap and the Burdekin Gap, have contributed to the present phylogeographical structure within M. cervinipes. Our study highlights the need to sample close to the periphery of putative biogeographical barriers or risk missing vital phylogeographical information that may significantly alter the interpretation of biogeographical hypotheses.