Abstract
This paper reports on the development of a formal model for the Hybrid ERTMS/ETCS Level 3 concept in Electrum, a lightweight formal specification language that extends Alloy with mutable relations and temporal logic operators. We show how Electrum and its Analyzer can be used to perform scenario exploration to validate this model, namely to check that all the operational scenarios described in the reference document are admissible, and to reason about expected safety properties, which can be easily specified and model checked for arbitrary track configurations. We also show how the Analyzer can be used to depict scenarios (and counter-examples) in a graphical notation that is logic-agnostic, making them understandable by stakeholders without expertise in formal specification. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Abstract
Interictal Epileptiform Discharge (IED) detection in EEG signals is widely used in the diagnosis of epilepsy. Visual analysis of EEGs by experts remains the gold standard, outperforming current computer algorithms. Deep learning methods can be an automated way to perform this task. We trained a VGG network using 2-s EEG epochs from patients with focal and generalized epilepsy (39 and 40 patients, respectively, 1977 epochs total) and 53 normal controls (110770 epochs). Five-fold cross-validation was performed on the training set. Model performance was assessed on an independent set (734 IEDs from 20 patients with focal and generalized epilepsy and 23040 normal epochs from 14 controls). Network visualization techniques (filter visualization and occlusion) were applied. The VGG yielded an Area Under the ROC Curve (AUC) of 0.96 (95% Confidence Interval (CI) = 0.95 - 0.97). At 99% specificity, the sensitivity was 79% and only one sample was misclassified per two minutes of analyzed EEG. Filter visualization showed that filters from higher level layers display patches of activity indicative of IED detection. Occlusion showed that the model correctly identified IED shapes. We show that deep neural networks can reliably identify IEDs, which may lead to a fundamental shift in clinical EEG analysis.

Abstract
Recommendation systems are usually evaluated through accuracy and classification metrics. However, when these systems are supported by crowdsourced data, such metrics are unable to estimate data authenticity, leading to potential unreliability. Consequently, it is essential to ensure data authenticity and processing transparency in large crowdsourced recommendation systems. In this work, processing transparency is achieved by explaining recommendations and data authenticity is ensured via blockchain smart contracts. The proposed method models the pairwise trust and system-wide reputation of crowd contributors; stores the contributor models as smart contracts in a private Ethereum network; and implements a recommendation and explanation engine based on the stored contributor trust and reputation smart contracts. In terms of contributions, this paper explores trust and reputation smart contracts for explainable recommendations. The experiments, which were performed with a crowdsourced data set from Expedia, showed that the proposed method provides cost-free processing transparency and data authenticity at the cost of latency. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
The use of adequate relay selection techniques is crucial to improve the behavior of cooperation based approaches in Wireless Sensor Networks (WSN). The Optimized Relay Selection Technique (ORST) is a relay selection technique that may be reduced to the application on classic set-covering problem (SCP) to WSN. The SCP seeks to find a minimum number of sets that contain all elements of all data sets. The SCP can be solved with different types of algorithms. This paper assesses the performance and quality of three different algorithms to solve the SCP generated by the previously proposed ORST technique, considering performance metrics relevant within WSNs context. The analysis was performed by simulation using the OMNeT++ tool and the WSN framework Castalia. The simulation results show that the branch and bound algorithm excels when compared to other state-of-the-art approaches.

Abstract
Forecasting vineyard yield with accuracy is one of the most important trends of research in viticulture today. Conventional methods for yield forecasting are manual, require a lot of labour and resources and are often destructive. Recently, image-analysis approaches have been explored to address this issue. Many of these approaches encompass cameras deployed on ground platforms that collect images in proximal range, on-the-go. As the platform moves, yield components and other image-based indicators are detected and counted to perform yield estimations. However, in most situations, when image acquisition is done in non-disturbed canopies, a high fraction of yield components is occluded. The present work's goal is twofold. Firstly, to evaluate yield components' visibility in natural conditions throughout the grapevine's phenological stages. Secondly, to explore single bunch images taken in lab conditions to obtain the best visible bunch attributes to use as yield indicators. In three vineyard plots of red (Syrah) and white varieties (Arinto and Encruzado), several canopy 1 m segments were imaged using the robotic platform Vinbot. Images were collected from winter bud stage until harvest and yield components were counted in the images as well as in the field. At pea-sized berries, veraison and full maturation stages, a bunch sample was collected and brought to lab conditions for detailed assessments at a bunch scale. At early stages, all varieties showed good visibility of spurs and shoots, however, the number of shoots was only highly and significantly correlated with the yield for the variety Syrah. Inflorescence and bunch occlusion reached high percentages, above 50 %. In lab conditions, among the several bunch attributes studied, bunch volume and bunch projected area showed the highest correlation coefficients with yield. In field conditions, using non-defoliated vines, the bunch projected area of visible bunches presented high and significant correlation coefficients with yield, regardless of the fruit's occlusion. Our results show that counting yield components with image analysis in non-defoliated vines may be insufficient for accurate yield estimation. On the other hand, using bunch projected area as a predictor can be the best option to achieve that goal, even with high levels of occlusion.