Abstract
Providing gerontological cares represents an increased challenge when applied to rural scenarios. This paper discusses the role of technology in gerontology and specifically how technology-based solutions can be developed to assist the elderly population in rural areas. We also characterize the Northeast Portuguese region exposing its rural characteristics and presented some demographic numbers. Finally, a conceptual model and a prototype supported by mobile devices are presented to assist and monitor the elderly and enhance business opportunities. The developed prototype allows not only to assist the elderly in a set of typical elderly population routines - such as those related to health - but also to improve the interaction between the elderly and their relatives and / or caregivers. This work is part of a more extensive effort that has been made in the search for effective solutions to assist the elderly population in rural areas, typically distant from the main health and / or support services; contributing to relieve these deficits. Copyright © 2020. Carlos R. CUNHA, Vítor MENDONÇA, Elisabete Paulo MORAIS, Joana FERNANDES And Isaías LETRA. Distributed under Creative Commons Attribution 4.0 International CC-BY 4.0

Abstract
Services are the backbone of modern economies and are increasingly supported by technology. Meanwhile, there is an accelerated growth of new technologies that are able to learn from themselves, providing more and more relevant results, i.e. Artificial Intelligence (AI). While there have been significant advances on the capabilities of AI, the impacts of this technology on service provision are still unknown. Conceptual research claims that AI offers a way to augment human capabilities or position it as a threat to human jobs. The objective of this study is to better understand the impact of AI on service, namely by understanding current trends in AI, and how they are, and will, impact service provision. To achieve this, a qualitative study, following Grounded Theory methodology was performed, with ten Artificial Intelligence experts selected from industry and academia.

Abstract
Typical analogue-to-digital conversion (ADC) architectures, at Nyquist rate, tend to occupy a big portion of the integrated circuit die area and to consume more power than desired. Recently, with the rise of Interet-of-Things (IoT), there is a high demand for architectures that can have both reduced area and power consumption. Time encoding machines (TEM) might be a promising alternative. These types of encoders result in very simple and low-power analogue circuits, shifting most of its complexity to the decoding stage, typically stationed in a place with access to more resources. This paper focuses on a particular TEM, the integrate-and-fire neuron (IFN). The IFN modulation is based on a simplified first-order model of neural operation and it encodes the signal in a very power efficient manner. In the end, a novel hardware architecture for the reconstruction of the IFN encoded signal based on a spiking model will be presented. The method is demonstrated and implemented on FPGA, reaching an ENOB as high as 8.23.

Abstract
Concept drift detection is a crucial task in data stream evolving environments. Most of the state of the art approaches designed to tackle this problem monitor the loss of predictive models. Accordingly, an alarm is launched when the loss increases significantly, which triggers some adaptation mechanism (e.g. retrain the model). However, this modus operandi falls short in many real-world scenarios, where the true labels are not readily available to compute the loss. These often take up to several weeks to be available. In this context, there is increasing attention to approaches that perform concept drift detection in an unsupervised manner, i.e., without access to the true labels. We propose a novel approach to unsupervised concept drift detection, which is based on a student-teacher learning paradigm. Essentially, we create an auxiliary model (student) to mimic the behaviour of the main model (teacher). At run-time, our approach is to use the teacher for predicting new instances and monitoring the mimicking loss of the student for concept drift detection. In a set of controlled experiments, we discovered that the proposed approach detects concept drift effectively. Relative to the gold standard, in which the labels are immediately available after prediction, our approach is more conservative: it signals less false alarms, but it requires more time to detect changes. We also show the competitiveness of our approach relative to other unsupervised methods. © 2020, Springer Nature Switzerland AG.

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.