Acquisition of labeled data for supervised Hyperspectral Image (HSI) classification is expensive in terms of both time and costs. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. Active learning (AL) can be a suitable approach for HSI classification as it integrates data acquisition to the classifier design by ranking the unlabeled data to provide advice for the next query that has the highest training utility. However, multiclass AL techniques tend to include redundant samples into the classifier to some extent. This paper addresses such a problem by introducing an AL pipeline which preserves the most representative and spatially heterogeneous samples. The adopted strategy for sample selection utilizes fuzziness to assess the mapping between actual output and the approximated a-posteriori probabilities, computed by a marginal probability distribution based on discriminative random fields. The samples selected in each iteration are then provided to the spectral angle mapper-based objective function to reduce the inter-class redundancy. Experiments on five HSI benchmark datasets confirmed that the proposed Fuzziness and Spectral Angle Mapper (FSAM)-AL pipeline presents competitive results compared to the state-of-the-art sample selection techniques, leading to lower computational requirements.

Dependability evaluation is an important often-mandatory step in designing and analyzing (critical) systems. Introducing control and/or computing devices to automate processes increases the system complexity, with an impact on the overall dependability. This occurs as a consequence of interferences, dependencies, and other similar effects that cannot be adequately managed through formalisms such as reliability block diagrams (RBDs), fault trees (FTs), and reliability graphs (RGs), since the statistical independence assumption is not satisfied. In addition, more enhanced notations such as dynamic FTs (DFTs) might not be adequate to represent all the behavioral aspects of dynamic systems. To overcome these problems, we developed a new formalism derived from RBD: the dynamic RBD (DRBD). DRBD exploits the concept of dependence as the building block to represent dynamic behaviors, allowing us to compose the dependencies and adequately manage the arising conflicts by means of a priority algorithm. In this paper, we explain how we can use the DRBD notation by specifying a practical methodology. Starting from the system knowledge, the proposed methodology drives to the overall system reliability evaluation through the entire phases of modeling and analysis. Such a technique is applied to an example taken from the literature, consisting of a distributed computing system.

This paper intends to contribute to the design of a pervasive infrastructure where new generation services interact with the surrounding environment, collecting data and applying management strategies. According to such perspective computing, storage and sensing become complementary aspects to be coordinated. In this way innovative and value-added services can be implemented by bridging Clouds with the Internet of Things. Heterogeneous resources can be aggregated and abstracted according to tailored thing-like semantics, thus enabling a Things as a Service paradigm, a way to build a "Cloud of Things" (CoT). This paper mainly focuses on the implementation of the underlying infrastructure at the basis of the CoT. An ad-hoc architecture and some preliminary background of this challenging view are provided and discussed, identifying guidelines and future directions.