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This paper describes the CIRANO platform, a cloud Integrated Development Environments (IDE) that substantially supports Model Driven Development (MDD) and team collaboration, in order to facilitate the development of cloud–based applications. The paper presents the state of the art in the field and discusses the technical details of CIRANO architecture and its modular implementation. The main features of the proposed platform are presented as a case study application concerning the update and porting of an existing collaboration system, called Comidor. The paper discusses the findings in comparison with existing tools and proposes extensions of the platform as future work. [ABSTRACT FROM AUTHOR]

The cloud computing paradigm encompasses several key differentiating elements and technologies, tackling a number of inefficiencies, limitations and problems that have been identified in the distributed and virtualized computing domain. Nonetheless, and as it is the case for all emerging technologies, their adoption led to the presentation of new challenges and new complexities. In this paper we present key application areas and capabilities of future scenarios, which are not tackled by current advancements and highlight specific requirements and goals for advancements in the cloud computing domain. We discuss these requirements and goals across different focus areas of cloud computing, ranging from cloud service and application integration, development environments and abstractions, to interoperability and relevant to it aspects such as legislation. The future application areas and their requirements are also mapped to the aforementioned areas in order to highlight their dependencies and potential for moving cloud technologies forward and contributing towards their wider adoption. [ABSTRACT FROM AUTHOR]

The interest in image dermoscopy has been significantly increased recently and skin lesion images are nowadays routinely acquired for a number of skin disorders. An important finding in the assessment of a skin lesion severity is the existence of dark dots and globules, which are hard to locate and count using existing image software tools. In this work we present a novel methodology for detecting/segmenting and count dark dots and globules from dermoscopy images. Segmentation is performed using a multi-resolution approach based on inverse non-linear diffusion. Subsequently, a number of features are extracted from the segmented dots/globules and their diagnostic value in automatic classification of dermoscopy images of skin lesions into melanoma and non-malignant nevus is evaluated. The proposed algorithm is applied to a number of images with skin lesions with known histo-pathology. Results show that the proposed algorithm is very effective in automatically segmenting dark dots and globules. Furthermore, it was found that the features extracted from the segmented dots/globules can enhance the performance of classification algorithms that discriminate between malignant and benign skin lesions, when they are combined with other region-based descriptors. [ABSTRACT FROM AUTHOR]

Abstract: Results of clinical studies suggest that there is a relationship between breathing-related sleep disorders and behavioral disorder and health effects. Apnea is considered one of the major sleep disorders with great accession in population and significant impact on patient''s health. Symptoms include disruption of oxygenation, snoring, choking sensations, apneic episodes, poor concentration, memory loss, and daytime somnolence. Diagnosis of apnea and breath disorders involves monitoring patient''s biosignals and breath during sleep in specialized clinics requiring expensive equipment and technical personnel. This paper discusses the design and technical details of an integrated low-cost system capable for preliminary detection of sleep breath disorders at patient''s home utilizing patient sound signals. The paper describes the proposed architecture and the corresponding HW and SW modules, along with a preliminary evaluation. [Copyright &y& Elsevier]

Abstract: In the digital city of the future there is the vision of seamless virtual and physical access for every home and between each home and the workplace, as well as critical city infrastructure such as the post office, the bank, hospitals, transportation systems, and other entities. This paper provides an overview of technical and other issues in extending at home (@home) assistive technologies for the elderly and the disabled. The paper starts by giving a vision of what this city is supposed to look like and how a human is to act, navigate and function in it. A framework for extending assistive technologies is proposed that considers individuals belonging to special groups of interest and locations other than their home. Technology has already reached the state of ubiquitous and pervasive sensor devices measuring everything, from temperature to human behavior. Implanting intelligence into and connecting such devices will be of immense use in preventive healthcare, security in industrial installations, greater energy efficiency, and numerous other applications. The paper reviews enabling technologies that exist and focuses on healthcare applications that support a longer and higher quality of life at home for the elderly and the disabled. It discusses intelligent platforms involving agents, context-aware and location-based services, and classification systems that enable advanced monitoring and interpretation of patient status and optimization of the environment to improve medical assessments. The paper concludes with a discussion of some of the challenges that exist in extending @home assistive technologies to @city assistive technologies. [Copyright &y& Elsevier]

Abstract: Taking into account that heart auscultation remains the dominant method for heart examination in the small health centers of the rural areas and generally in primary healthcare set-ups, the enhancement of this technique would aid significantly in the diagnosis of heart diseases. In this context, the present paper initially surveys the research that has been conducted concerning the exploitation of heart sound signals for automated and semi-automated detection of pathological heart conditions. Then it proposes an automated diagnosis system for the identification of heart valve diseases based on the Support Vector Machines (SVM) classification of heart sounds. This system performs a highly difficult diagnostic task (even for experienced physicians), much more difficult than the basic diagnosis of the existence or not of a heart valve disease (i.e. the classification of a heart sound as ‘healthy’ or ‘having a heart valve disease’): it identifies the particular heart valve disease. The system was applied in a representative global dataset of 198 heart sound signals, which come both from healthy medical cases and from cases suffering from the four most usual heart valve diseases: aortic stenosis (AS), aortic regurgitation (AR), mitral stenosis (MS) and mitral regurgitation (MR). Initially the heart sounds were successfully categorized using a SVM classifier as normal or disease-related and then the corresponding murmurs in the unhealthy cases were classified as systolic or diastolic. For the heart sounds diagnosed as having systolic murmur we used a SVM classifier for performing a more detailed classification of them as having aortic stenosis or mitral regurgitation. Similarly for the heart sounds diagnosed as having diastolic murmur we used a SVM classifier for classifying them as having aortic regurgitation or mitral stenosis. Alternative classifiers have been applied to the same data for comparison (i.e. back-propagation neural networks, k-nearest-neighbour and naïve Bayes classifiers), however their performance for the same diagnostic problems was lower than the SVM classifiers proposed in this work. [Copyright &y& Elsevier]

Background Stratification of cardiovascular risk in patients with ischemic stroke is important as it may inform management strategies. We aimed to develop a machine-learning-derived prognostic model for the prediction of cardiovascular risk in ischemic stroke patients.Materials and Methods: Two prospective stroke registries with consecutive acute ischemic stroke patients were used as training/validation and test datasets. The outcome assessed was major adverse cardiovascular event, defined as non-fatal stroke, non-fatal myocardial infarction, and cardiovascular death during 2-year follow-up. The variables selection was performed with the LASSO technique. The algorithms XGBoost (Extreme Gradient Boosting), Random Forest and Support Vector Machines were selected according to their performance. The evaluation of the classifier was performed by bootstrapping the dataset 1000 times and performing cross-validation by splitting in 60% for the training samples and 40% for the validation samples.Results: The model included age, gender, atrial fibrillation, heart failure, peripheral artery disease, arterial hypertension, statin treatment before stroke onset, prior anticoagulant treatment (in case of atrial fibrillation), creatinine, cervical artery stenosis, anticoagulant treatment at discharge (in case of atrial fibrillation), and statin treatment at discharge. The best accuracy was measured by the XGBoost classifier. In the validation dataset, the area under the curve was 0.648 (95%CI:0.619-0.675) and the balanced accuracy was 0.58 ± 0.14. In the test dataset, the corresponding values were 0.59 and 0.576.Conclusions: We propose an externally validated machine-learning-derived model which includes readily available parameters and can be used for the estimation of cardiovascular risk in ischemic stroke patients. [ABSTRACT FROM AUTHOR]

In this work we propose a novel algorithm for foreground segmentation in video sequences that achieves better accuracy, while maintaining low complexity and allows real time execution. The proposed algorithm combines selected features from a number of well-established and state of the art algorithms, such as the Gaussian mixture models, the Self Organizing Maps and the illumination sensitive method. The presented methodology is capable of efficiently handling sudden light changes, both from natural and multiple artificial light sources, which may have caused erroneous segmentation for other algorithms. Comparative results are presented utilizing user-defined ground truth segmentation for two different types of indoor video sequences, one of which was obtained by a hemispheric omnidirectional dioptric (fish-eye) camera, with and without illumination changes and the second by a plain camera. The behavior of the algorithm with respect to its controlling parameters is investigated and its computational burden is studied. [ABSTRACT FROM AUTHOR]