Abstract
In view of the growing importance of nanotechnologies, the detection of nanoparticles type in several contexts has been considered a relevant topic. Several organisms, including the National Institutes of Health, have been highlighting the urge of developing nanoparticles exposure risk assessment assays, since very little is known about their physiological responses. Although the identi fi cation/characterization of synthetically produced nanoparticles is considered a priority, there are many examples of \ naturally" generated nanostructures that provide useful information about food components or human physiology. In fact, several nanoscale extracellular vesicles are present in physiological fluids with high potential as cancer biomarkers. However, scientists have struggled to fi nd a simple and rapid method to accurately detect/identify nanoparticles, since their majority have diameters between 100-150 nm -far below the di ff raction limit. Currently, there is a lack of instruments for nanoparticles detection and the few instrumentation that is commonly used is costly, bulky, complex and time consuming. Thus, considering our recent studies on particles identi fi cation through back-scattering, we examined if the time/frequency-domain features of the back-scattered signal provided from a 100 nm polystyrene nanoparticles suspension are able to detect their presence only by dipping a polymeric lensed optical fi ber in the solution. This novel technique allowed the detection of synthetic nanoparticles in distilled water versus \ blank solutions" (only distilled water) through Multivariate Statistics and Arti fi cial Intelligence (AI)-based techniques. While the state-of-the-art methods do not o ff er a ff ordable and simple approaches for nanoparticles detection, our technique can contribute for the development of a device with innovative characteristics.

Abstract
Machine learning systems are becoming increasingly ubiquitous. These systems's adoption has been expanding, accelerating the shift towards a more algorithmic society, meaning that algorithmically informed decisions have greater potential for significant social impact. However, most of these accurate decision support systems remain complex black boxes, meaning their internal logic and inner workings are hidden to the user and even experts cannot fully understand the rationale behind their predictions. Moreover, new regulations and highly regulated domains have made the audit and verifiability of decisions mandatory, increasing the demand for the ability to question, understand, and trust machine learning systems, for which interpretability is indispensable. The research community has recognized this interpretability problem and focused on developing both interpretable models and explanation methods over the past few years. However, the emergence of these methods shows there is no consensus on how to assess the explanation quality. Which are the most suitable metrics to assess the quality of an explanation? The aim of this article is to provide a review of the current state of the research field on machine learning interpretability while focusing on the societal impact and on the developed methods and metrics. Furthermore, a complete literature review is presented in order to identify future directions of work on this field.

Abstract
In this paper we present SEED, a generative system capable of arbitrarily extending recorded environmental sounds while preserving their inherent structure. The system architecture is grounded in concepts from concatenative sound synthesis and includes three top-level modules for segmentation, analysis, and generation. An input audio signal is first temporally segmented into a collection of audio segments, which are then reduced into a dictionary of audio classes by means of an agglomerative clustering algorithm. This representation, together with a concatenation cost between audio segment boundaries, is finally used to generate sequences of audio segments with arbitrarily long duration. The system output can be varied in the generation process by the simple and yet effective parametric control over the creation of the natural, temporally coherent, and varied audio renderings of environmental sounds. Copyright: © 2016 First author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract
Cooperative diversity techniques are being used to improve the communication reliability in Wireless Sensor Networks (WSN). Typically, these techniques use relay nodes to retransmit messages that otherwise would not be heard by their destination nodes. Thus, the relay selection techniques are fundamental to improve WSN's communication behavior. However, to perform the adequate relay selection, it is necessary to identify which are the most relevant parameters for the operation of the network and analyze their impact when used in the relay selection, that is, it is necessary to define which are the best parameters to use as selection criteria. In this context, this paper performs an analysis of the impact of each of the parameters used to perform the relay selection in the Optimized Relay Selection Technique (ORST). This analysis was assessed by simulation using the OMNeT++ tool and the WSN framework Castalia. It was considered a set of parameters, aiming to identify their relevance and possibly optimize the objective function used in this technique. Simulation results show that the objective function can be optimized considering a small number of parameters to perform the relay selection.

Abstract
Social entrepreneurship is emerging as an innovative approach for dealing with complex social and environmental needs, and is an important lever for the development of a sustainable society. Social entrepreneurship and related concepts have had a growing attention in the academy, giving rise to dissimilar approaches in the United States of America and in Western Europe. Despite the importance of the Third Sector in Portugal, it has been difficult to set ideal definitions for social entrepreneurship, social entrepreneur and social enterprises. By means of a qualitative study involving four Portuguese social ventures, this chapter identifies contemporary socio-cultural and economic factors that foster social innovation and intervention in Portugal, and contributes to understand the role of social entrepreneur in this context.

Abstract
Electricity markets are complex environments, which have been suffering continuous transformations due to the increase of renewable based generation and the introduction of new players in the system. In this context, players are forced to re-think their behavior and learn how to act in this dynamic environment in order to get as much benefit as possible from market negotiations. This paper introduces a new learning model to enable players identifying the expected prices of future bilateral agreements, as a way to improve the decision-making process in deciding the opponent players to approach for actual negotiations. The proposed model introduces a con-textual dimension in the well-known Q-Learning algorithm, and includes a simulated annealing process to accelerate the convergence process. The proposed model is integrated in a multi-agent decision support system for electricity market players negotiations, enabling the experimentation of results using real data from the Iberian electricity market.