Abstract
Photoplethysmography (PPG) sensors are an inexpensive yet cost-effective way to track data correlated to the heart pulsation. The information is acquired with light signals generated by means of a photodiode and by detecting the amount of reflected or transmitted light through the tissue. In this paper, a novel methodology that enables a systematic approach for evaluating high compact signal processing designs, which are now a trend among several semiconductor manufacturers, is proposed and discussed. In this context, an integrated pulse oximeter sensor is embedded in a custom board and used to test the methodology concept proposed by the authors. Conclusions are duly drawn.

Abstract
Auscultation is a routine exam and the first line of screening in heart pathologies. The objective of this study was to assess if using a new data collection system, the DigiScope Collector, with a guided and automatic annotation of heart auscultation, different levels of expertise/experience users could collect similar digital auscultations. Data were collected within the Heart Caravan Initiative (Paraiba, Brasil). Patients were divided into two study groups: Group 1 evaluated by a third year medical student (User 1), and an experienced nurse (User 2); Group 2 evaluated by User 2 and an Information Technology professional (User 3). Patients were auscultated sequentially by the two users, according to the randomization. Features extracted from each data set included the length (HR) of the audio files, the number of repetitions per auscultation area, heart rate, first (S1) and second (S2) heart sound amplitudes, S2/S1, and aortic (A2) and pulmonary (P2) components of the second heart sound and relative amplitudes (P2/A2). Features extracted were compared between users using paired-sample test Wilcoxon test, and Spearman correlations (P < 0.05 considered significant). Twenty-seven patients were included in the study (13 Group 1, and 14 Group 2). No statistical significant differences were found between groups, except in the time of auscultation (User 2 consistently presented longer auscultation time). Correlation analysis showed significant correlations between extracted features from both groups: S2/S1 in Group 1, and S1, S2, A2, P2, P2/A2 amplitudes, and HR in Group 2. Using the DigiScope Collector, we were able to collect similar digital auscultations, according to the features evaluated. This may indicate that in sites with limited access to specialized clinical care, auscultation files may be acquired and used in telemedicine for an expert evaluation.

Abstract
We demonstrated the possibility to inscribe long-period fiber gratings (LPFGs) in a B/Ge codoped fiber by using grating periods shorter than 150 mu m. We also have arc-induced in the SMF 28 fiber an LPFG in the dispersion turning points by using a grating period of 197 mu m. In previous works, the shortest periods were, respectively, of the order of 190 and 320 mu m for the same fibers. To achieve such a considerable reduction in the grating periods which enables access to the higher order cladding modes (higher sensitivity), we have developed a high-voltage power supply that allows for a constant and stable electric current ranging from 10.5 up to 21 mA. Computer simulations were used to identify the cladding mode resonances for each grating inscribed in the different fibers. The fabricated LPFGs were characterized as a function of the external refractive index from 1.33 up to 1.42, and an average refractive index sensitivity of -720 nm/RIU in the 1.33-1.41 range was obtained for a 192-mu m LPFG without further optimization, such as the use of etching or thin films deposition.

Abstract
Matrix Decomposition methods are applied to a wide range of tasks, such as data denoising, dimensionality reduction, co-clustering and community detection. However, in the presence of boolean inputs, common methods either do not scale or do not provide a boolean reconstruction, which results in high reconstruction error and low interpretability of the decomposition. We propose a novel step decomposition of boolean matrices in non-negative factors with boolean reconstruction. By formulating the problem using threshold operators and through suitable relaxation of this problem, we provide a scalable algorithm that can be applied to boolean matrices with millions of non-zero entries. We show that our method achieves significantly lower reconstruction error when compared to standard state of the art algorithms. We also show that the decomposition keeps its interpretability by analyzing communities in a flights dataset (where the matrix is interpreted as a graph in which nodes are airports) and in a movie-ratings dataset with 10 million non-zeros.

Abstract
Focus group interactions led to a set of strategic recommendations with regards to how to improve e-Commerce adoption levels in Iberian enterprises, essential in times of crises. Suggestions include: (1) Create actions to influence the governments of the Iberian Peninsula to re-evaluate the legislation that regulates e-Commerce; (2) Encourage venture capitalists, banks and business angels to create financing lines with better access; (3) Encourage European institutions of higher education to create partnerships with Iberian enterprises in a way that the technical know-how in these enterprises could be mixed with the scientific knowledge of those institutions; (4) Create, alongside with training organizations and universities, a set of new training courses directed at Iberian enterprises focusing on concepts such as Web 2.0 capabilities and the maintaining of a coherent online organizational identity. Increases in e-Commerce transactions may bring changes in mentality and greater prosperity to nations such as Portugal and Spain.

Abstract
Decision rules are one of the most expressive and interpretable models for machine learning. In this article, we present Adaptive Model Rules (AMRules), the first stream rule learning algorithm for regression problems. In AMRules, the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of the attributes. In order to maintain a regression model compatible with the most recent state of the process generating data, each rule uses a Page-Hinkley test to detect changes in this process and react to changes by pruning the rule set. Online learning might be strongly affected by outliers. AMRules is also equipped with outliers detection mechanisms to avoid model adaption using anomalous examples. In the experimental section, we report the results of AMRules on benchmark regression problems, and compare the performance of our system with other streaming regression algorithms.