A Secure Grid Medical Data Manager Interfaced to the gLite Middleware

The medical community is producing and manipulating a tremendous volume of digital data for which computerized archiving, processing and analysis is needed. Grid infrastructures are promising for dealing with challenges arising in computerized medicine but the manipulation of medical data on such infrastructures faces both the problem of interconnecting medical information systems to Grid middlewares and of preserving patients’ privacy in a wide and distributed multi-user system. These constraints are often limiting the use of Grids for manipulating sensitive medical data. This paper describes our design of a medical data management system taking advantage of the advanced gLite data management services, developed in the context of the EGEE project, to fulfill the stringent needs of the medical community. It ensures medical data protection through strict data access control, anonymization and encryption. The multi-level access control provides the flexibility needed for implementing complex medical use-cases. Data anonymization prevents the exposure of most sensitive data to unauthorized users, and data encryption guarantees data protection even when it is stored at remote sites. Moreover, the developed prototype provides a Grid storage resource manager (SRM) interface to standard medical DICOM servers thereby enabling transparent access to medical data without interfering with medical practice.     

An Industrial Case Study on Distributed Prioritisation in Market-Driven Requirements Engineering for Packaged Software

When developing packaged software, which is sold ‘off-the-shelf’ on a worldwide marketplace, it is essential to collect needs and opportunities from different market segments and use this information in the prioritisation of requirements for the next software release. This paper presents an industrial case study where a distributed prioritisation process is proposed, observed and evaluated. The stakeholders in the requirements prioritisation process include marketing offices distributed around the world. A major objective of the distributed prioritisation is to gather and highlight the differences and similarities in the requirement priorities of the different market segments. The evaluation through questionnaires shows that the stakeholders found the process useful. The paper also presents novel approaches to visualise the priority distribution among stakeholders, together with measures on disagreement and satisfaction. Product management found the proposed charts valuable as decision support when selecting requirements for the next release, as they revealed unforeseen differences among stakeholder priorities. Conclusions on stakeholder tactics are provided and issues of further research are identified, including ways of addressing identified challenges.     

Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers

In the past few years, we have witnessed the proliferation of a heterogeneous ecosystem of cloud providers, each one with a different infrastructure offer and pricing policy. We explore this heterogeneity in a novel cloud brokering approach that optimizes placement of virtual infrastructures across multiple clouds and also abstracts the deployment and management of infrastructure components in these clouds. The feasibility of our approach is evaluated in a high throughput computing cluster case study. Experimental results confirm that multi-cloud deployment provides better performance and lower costs compared to the usage of a single cloud only.     

Estimating spine shape in lateral sleep positions using silhouette-derived body shape models

This study aims at evaluating spinal alignment during sleep by combining personalized human models with mattress indentation measurements. A generic surface model has been developed that can be personalized based on anthropometric parameters derived from silhouette extraction. Shape assessment of the personalized surface models, performed by comparison with 3-D surface scans of the trunk, showed a mean unsigned distance of 9.77 mm between modeled and scanned surface meshes. The surface model is combined with an inner skeleton model, allowing the model to simulate distinct sleep postures. An automatic fitting algorithm sets the appropriate degrees of freedom to position the model on the measured indentation according to the adopted sleep posture. Validation on lateral sleep positions showed good intraclass correlations (0.73–0.88) between estimated and measured angular spinal deformations, indicating that silhouette-derived body shape models provide a valuable tool for the unobtrusive assessment of spinal alignment during sleep.Relevance to industry: A common drawback of the available techniques to assess spinal deformation on bedding systems is that they interfere with the actual sleep process. The current study presents a novel method based on silhouette-derived body shape models in order to estimate spine shape during sleep unobtrusively.     

Logic and the Dynamics of Information

We discuss how issues of information and computation interact with logic today, and what might be a natural extended agenda of investigation.     

Benchmarking Least Squares Support Vector Machine Classifiers

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.     

Future train traffic control: control by re-planning

Improving train traffic control can be a cost-efficient way to improve train traffic punctuality and increase utilization of existing and future railway infrastructure. However, performance in train traffic control tasks currently involves working on a technical level in order to regulate the traffic flow. Working in a preventive manner is poorly supported and train traffic controllers are usually restricted to just solving problems as they occur. This often results in unnecessarily long delays and decreased timeliness of train traffic. The main objective of this paper is to describe a proposed control strategy and a case study, which evaluates the control strategy and the prototype tool derived from the research. By shifting the control paradigm to a high-level control strategy, many of today’s problems may be avoided, with benefits of the reduction in delays, improved timeliness and better utilization of the infrastructure. Twenty-one train traffic controllers participated in a case study, with a simulated prototype environment. The majority of the participating train traffic controllers were positive to the new concepts and ideas. Many of the important aspects of the proposed control strategy can be investigated with the simulation, but due to the complexity of train traffic some issues must be evaluated in an operative environment.     

Bis(Benzofuran–1,3-N,N-heterocycle)s as Symmetric and Synthetic Drug Leads against Yellow Fever Virus

The yellow fever virus (YFV) is an emerging RNA virus and has caused large outbreaks in Africa and Central and South America. The virus is often transmitted through infected mosquitoes and spreads from area to area because of international travel. Being an acute viral hemorrhagic disease, yellow fever can be prevented by an effective, safe, and reliable vaccine, but not be eliminated. Currently, there is no antiviral drug available for its cure. Thus, two series of novel bis(benzofuran–1,3-imidazolidin-4-one)s and bis(benzofuran–1,3-benzimidazole)s were designed and synthesized for the development of anti-YFV lead candidates. Among 23 new bis-conjugated compounds, 4 of them inhibited YFV strain 17D (Stamaril) on Huh-7 cells in the cytopathic effect reduction assays. These conjugates exhibited the most compelling efficacy and selectivity with an EC₅₀ of <3.54 μM and SI of >15.3. The results are valuable for the development of novel antiviral drug leads against emerging diseases.