User and Machine Authentication and Authorization Infrastructure for Distributed Wireless Sensor Network Testbeds

The intention of an authentication and authorization infrastructure (AAI) is to simplify and unify access to different web resources. With a single login, a user can access web applications at multiple organizations. The Shibboleth authentication and authorization infrastructure is a standards-based, open source software package for web single sign-on (SSO) across or within organizational boundaries. It allows service providers to make fine-grained authorization decisions for individual access of protected online resources. The Shibboleth system is a widely used AAI, but only supports protection of browser-based web resources. We have implemented a Shibboleth AAI extension to protect web services using Simple Object Access Protocol (SOAP). Besides user authentication for browser-based web resources, this extension also provides user and machine authentication for web service-based resources. Although implemented for a Shibboleth AAI, the architecture can be easily adapted to other AAIs.

Multi-modal glioblastoma segmentation: man versus machine

BACKGROUND AND PURPOSE Reproducible segmentation of brain tumors on magnetic resonance images is an important clinical need. This study was designed to evaluate the reliability of a novel fully automated segmentation tool for brain tumor image analysis in comparison to manually defined tumor segmentations. METHODS We prospectively evaluated preoperative MR Images from 25 glioblastoma patients. Two independent expert raters performed manual segmentations. Automatic segmentations were performed using the Brain Tumor Image Analysis software (BraTumIA). In order to study the different tumor compartments, the complete tumor volume TV (enhancing part plus non-enhancing part plus necrotic core of the tumor), the TV+ (TV plus edema) and the contrast enhancing tumor volume CETV were identified. We quantified the overlap between manual and automated segmentation by calculation of diameter measurements as well as the Dice coefficients, the positive predictive values, sensitivity, relative volume error and absolute volume error. RESULTS Comparison of automated versus manual extraction of 2-dimensional diameter measurements showed no significant difference (p = 0.29). Comparison of automated versus manual segmentation of volumetric segmentations showed significant differences for TV+ and TV (p<0.05) but no significant differences for CETV (p>0.05) with regard to the Dice overlap coefficients. Spearman's rank correlation coefficients (ρ) of TV+, TV and CETV showed highly significant correlations between automatic and manual segmentations. Tumor localization did not influence the accuracy of segmentation. CONCLUSIONS In summary, we demonstrated that BraTumIA supports radiologists and clinicians by providing accurate measures of cross-sectional diameter-based tumor extensions. The automated volume measurements were comparable to manual tumor delineation for CETV tumor volumes, and outperformed inter-rater variability for overlap and sensitivity.

Combining Wireless Sensor Networks and Machine Learning for Flash Flood Nowcasting

This paper addresses an investigation with machine learning (ML) classification techniques to assist in the problem of flash flood now casting. We have been attempting to build a Wireless Sensor Network (WSN) to collect measurements from a river located in an urban area. The machine learning classification methods were investigated with the aim of allowing flash flood now casting, which in turn allows the WSN to give alerts to the local population. We have evaluated several types of ML taking account of the different now casting stages (i.e. Number of future time steps to forecast). We have also evaluated different data representation to be used as input of the ML techniques. The results show that different data representation can lead to results significantly better for different stages of now casting.

Un modèle animal simple pour l'apprentissage des techniques de microanastomoses vasculaires de congruences diefférentes

BACKGROUND Since the pioneering work of Jacobson and Suarez, microsurgery has steadily progressed and is now used in all surgical specialities, particularly in plastic surgery. Before performing clinical procedures it is necessary to learn the basic techniques in the laboratory. OBJECTIVE To assess an animal model, thereby circumventing the following issues: ethical rules, cost, anesthesia and training time. METHODS Between July 2012 and September 2012, 182 earthworms were used for 150 microsurgical trainings to simulate discrepancy microanastomoses. Training was undertaken over 10 weekly periods. Each training session included 15 simulations of microanastomoses performed using the Harashina technique (earthworm diameters >1.5 mm [n=5], between 1.0 mm and 1.5 mm [n=5], and <1.0 mm [n=5]). The technique is presented and documented. A linear model with main variable as the number of the week (as a numeric covariate) and the size of the animal (as a factor) was used to determine the trend in time of anastomosis over subsequent weeks as well as differences between the different size groups. RESULTS The linear model showed a significant trend (P<0.001) in time of anastomosis in the course of the training, as well as significant differences (P<0.001) between the groups of animal of different sizes. For diameter >1.5 mm, mean anastomosis time decreased from 19.6±1.9 min to 12.6±0.7 min between the first and last week of training. For training involving smaller diameters, the results showed a reduction in execution time of 36.1% (P<0.01) (diameter between 1.0 mm and 1.5 mm) and 40.6% (P<0.01) (diameter <1.0 mm) between the first and last weeks. The study demonstrates an improvement in the dexterity and speed of nodes' execution. CONCLUSION The earthworm appears to be a reliable experimental model for microsurgical training of discrepancy microanastomoses. Its numerous advantages, as discussed in the present report, show that this model of training will significantly grow and develop in the near future.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.

Discourse-level Annotation over Europarl for Machine Translation: Connectives and Pronouns

This paper describes methods and results for the annotation of two discourse-level phenomena, connectives and pronouns, over a multilingual parallel corpus. Excerpts from Europarl in English and French have been annotated with disambiguation information for connectives and pronouns, for about 3600 tokens. This data is then used in several ways: for cross-linguistic studies, for training automatic disambiguation software, and ultimately for training and testing discourse-aware statistical machine translation systems. The paper presents the annotation procedures and their results in detail, and overviews the first systems trained on the annotated resources and their use for machine translation.

Shallow Dialogue Processing Using Machine Learning Algorithms (or not)

This paper presents a shallow dialogue analysis model, aimed at human-human dialogues in the context of staff or business meetings. Four components of the model are defined, and several machine learning techniques are used to extract features from dialogue transcripts: maximum entropy classifiers for dialogue acts, latent semantic analysis for topic segmentation, or decision tree classifiers for discourse markers. A rule-based approach is proposed for solving cross-modal references to meeting documents. The methods are trained and evaluated thanks to a common data set and annotation format. The integration of the components into an automated shallow dialogue parser opens the way to multimodal meeting processing and retrieval applications.

Les réseaux d'entreprises formatrices: une nouvelle forme d'apprentissage en Suisse

Les réseaux d'entreprises formatrices constituent un modèle du système de formation professionnelle en alternance Suisse. Petites et moyennes entreprises peuvent ainsi mutualiser la formation des apprentis. Quelles raisons poussent les entreprises à participer à ce nouveau type d’organisation ? Quels conflits et tensions naissent au sein de ces réseaux ? Les analyses s’appuient sur quatre cas de réseaux et sur la théorie de l'économie des conventions. Ces réseaux naissent d’une pluralité de motifs de participation, source d’insatisfaction dans les entreprises et de conflits dans les réseaux tout au long du parcours de formation.