Abstract
A fundamental question that often occurs in statistical tests is the normality of distributions. Countless distributions exist in science and life, but one distribution that is obtained via permutations, usually referred to as permutation distribution, is interesting. Although a permutation distribution should behave in accord with the central limit theorem, if both the independence condition and the identical distribution condition are fulfilled, no studies have corroborated this concurrence in functional magnetic resonance imaging data. In this work, we used Anderson-Darling test to evaluate the accordance level of permutation distributions of classification accuracies to normality expected under central limit theorem. A simulation study has been carried out using functional magnetic resonance imaging data collected, while human subjects responded to visual stimulation paradigms. Two scrambling schemes are evaluated: the first based on permuting both the training and the testing sets and the second on permuting only the testing set. The results showed that, while a normal distribution does not adequately fit to permutation distributions most of the times, it tends to be quite well acceptable when mean classification accuracies averaged over a set of different classifiers is considered. The results also showed that permutation distributions can be probabilistically affected by performing motion correction to functional magnetic resonance imaging data, and thus may weaken the approximation of permutation distributions to a normal law. Such findings, however, have no relation to univariate/univoxel analysis of functional magnetic resonance imaging data. Overall, the results revealed a strong dependence across the folds of cross-validation and across functional magnetic resonance imaging runs and that may hinder the reliability of using cross-validation. The obtained p-values and the drawn confidence level intervals exhibited beyond doubt that different permutation schemes may beget different permutation distributions as well as different levels of accord with central limit theorem. We also found that different permutation schemes can lead to different permutation distributions and that may lead to different assessment of the statistical significance of classification accuracy.

Abstract
OBJECTIVE: To evaluate endothelial dysfunction and microvascular damage in secondary Raynaud Phenomenon (SRP) and Systemic sclerosis (SSc)-associated patients as possible predictors of ischemic fingertip digital ulcers (DU) in a 3-year clinical follow-up. METHODS: Flow-mediated dilatation (FMD), nailfold videocapillaroscopy (NVC), endothelin-1 (ET-1) and asymmetric dimethylarginine (ADMA) were analysed in a 3-year observational cohort study of 77 SRP patients with systemic sclerosis. The primary outcome was the occurrence of a new DU. RESULTS: Risk factors for DU at baseline were low FMD% (p < 0.001), NVC pattern (p < 0.001), high microangiopathy evolution score (MES) (p < 0.001), increased ET-1 (p < 0.001) and increased ADMA serum levels (p = 0.001). Median time to the occurrence of a new DU was 4.50 (1.25-16.25) months. The risk factors for the occurrence of at least one new DU episode in follow-up included a history of at least one DU before enrolment (p < 0.001), autoantibody anti-scleroderma-70 (p = 0.012), NVC late pattern (p < 0.001), high MES score (p < 0.001), low FMD% (p < 0.001) and increased ET-1 serum levels (p < 0.001). We used univariate Cox regression analysis to show that FMD > 9.41% (HR: 0.37 95% CI: 0.14-0.99) and ET-1 > 11.85 pmol/L (HR: 3.81 95% CI: 1.41-10.26) and NVC (HR: 2.29 95% CI: 0.97-5.38) were predictors of DU recurrence. In terms of first DU event in naive DU patients at baseline, late NVC pattern (HR: 12.66 95% CI: 2.06-77.89) and MES score (HR: 1.693 95% CI: 1.257-2.279) were independent predictors. CONCLUSION: This study identified endothelium dysfunction biomarkers (FMD and ET-1) and severe microvascular damage in NVC as strong predictors of new DU in SSc patients.

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Abstract
In recent years we witnessed the arrival of new trends, such as server virtualization and cloud services, an increasing number of mobile devices and online contents, leading the networking industry to deliberate about how traditional network architectures can be adapted or even deciding if a new perspective for them should be taken. SDN (Software-Defined Networking) emerged under this framing, opening a road for new developments due to the centralized logic control and view of the network, the decoupling of data and control planes, and the abstraction of the underlying network infrastructure from the applications. Although firstly oriented to packet switching, network measurements have also emerged as one promising field for SDN, as its flexibility enables programmable measurements, allowing a SDN controller to manage measurement tasks concurrently at multiple spatial and temporal scales. In this context, this paper is focused on exploring the SDN architecture and components for supporting the flexible selection and configuration of network monitoring tasks that rely on the use of traffic sampling. The aim is to take advantage of the integrated view of SDN controllers to apply and configure appropriate sampling techniques in network measurement points according to the requirements of specific measurement tasks. Through SDN, flexible and service-oriented configuration of network monitoring can be achieved, allowing also to improve the trade-off between accuracy and overhead of the monitoring process. In this way, this study, examining relevant SDN elements and solutions for deploying this monitoring paradigm, provides useful insights to enhance the programmability and efficiency of sampling-based network monitoring. © 2017, Springer International Publishing AG.

Abstract
The problem of Label Ranking is receiving increasing attention from several research communities. The algorithms that have been developed/adapted to treat rankings of a fixed set of labels as the target object, including several different types of decision trees (DT). One DT-based algorithm, which has been very successful in other tasks but which has not been adapted for label ranking is the Random Forests (RF) algorithm. RFs are an ensemble learning method that combines different trees obtained using different randomization techniques. In this work, we propose an ensemble of decision trees for Label Ranking, based on Random Forests, which we refer to as Label Ranking Forests (LRF). Two different algorithms that learn DT for label ranking are used to obtain the trees. We then compare and discuss the results of LRF with standalone decision tree approaches. The results indicate that the method is highly competitive.