Abstract
Due to the technological evolution on wearable devices, biosignals, such as inter-cardiac beat interval (RR) time series, are being captured in a non-controlled environment. These RR signals, derived from photoplethysmography (PPG), enable health status assessment in a more continuous, non-invasive, non-obstructive way, and fully integrated into the individual’s daily activity. However, PPG is vulnerable to motion artefacts, which can affect the accuracy of the estimated neurophysiological markers. This paper introduces a method for motion artefact characterization in terms of location and relative variation parameters obtained in different common daily activities. The approach takes into consideration interindividual variability. Data was analyzed throughout related-samples Friedman’s test, followed by pairwise comparison with Wilcoxon signed-rank tests with a Bonferroni correction. Results showed that movement, involving only arms, presents more variability in terms of the two analyzed parameters. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The preoperative localisation of non-palpable lesions guided by breast imaging is an important and required procedure for breast-conserving surgery. We conducted a systematic review and meta-analysis of the literature on the comparative impact of different techniques for guided surgical excision of non-palpable breast lesions from reports of clinical or patient-reported outcomes and costs. A literature search of PubMed, ISI, SCOPUS and Cochrane databases was conducted for relevant publications and their references, along with public documents, national and international guidelines, conference proceedings and presentations. From 5720 retrieved articles screened through title and abstract, 5346 were excluded and 374 assessed for full-text eligibility. For data extraction and quality assessment, 49 studies were included. Results of this review demonstrate that Radioactive Seed Localisation (RSL) and Radioactive Occult Lesion Localisation (ROLL) outperform Wire in terms of involved margins and reoperations. Between RSL and ROLL, there is a tendency to favour RSL. Similarly, Clip-guided localisation seems preferred when compared to ROLL, however further studies are needed. In summary, there seems to exist evidence that RSL and ROLL are better than Wire, representing potential alternatives, with a quick learning curve, better scheduling and management issues. Although, for recent techniques, more research is needed in order to achieve the same level of evidence.

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.