Abstract
Saliency maps have been used as one possibility to interpret deep neural networks. This method estimates the relevance of each pixel in the image classification, with higher values representing pixels which contribute positively to classification. The goal of this study is to understand how the complexity of the network affects the interpretabilty of the saliency maps in classification tasks. To achieve that, we investigate how changes in the regularization affects the saliency maps produced, and their fidelity to the overall classification process of the network. The experimental setup consists in the calculation of the fidelity of five saliency map methods that were compare, applying them to models trained on the CIFAR-10 dataset, using different levels of weight decay on some or all the layers. Achieved results show that models with lower regularization are statistically (significance of 5%) more interpretable than the other models. Also, regularization applied only to the higher convolutional layers or fully-connected layers produce saliency maps with more fidelity.

Abstract
This research proposes a novel methodology for adaptive learning in electricity markets negotiations, based on the principles of the determinism theory. The determinism theory states that all events are predetermined due to the cause-effect rule. At the same time, it is unmanageable to consider all causes to a certain effect, making it impossible to predict future events. However, in a controlled simulation environment, it is possible to access and analyze all involved variables; thus, making the application of this theory promising in such environments. This research applies the principles of the determinism theory to a new learning methodology, which optimizes players' actions, considering the predicted behavior of all involved players, with the objective of maximizing market gains. A case-based reasoning approach is used, providing adaptive context-aware decision support. Results show that the proposed approach is able to achieve better market results than all reference market strategies.

Abstract
The objective is to investigate the tools and methodologies used in Higher Education (HE) of Electrical and Computer Engineering (ECE), identifying the curricular units that used them. The analysis was based on the perception of the course coordinators. Triangulation between multiple methodologies and different methods of analysis was used, combining lexical analysis, analysis of keywords and content analysis. Course coordinators were interviewed at three universities in Portugal and Brazil. In the content analysis, two categories emerged: the coordinators' perception of non-traditional methodologies, and the perception of software tools as didactic resources. The initiatives using non-traditional teaching processes and methodologies were reduced and punctual, with individual actions by teachers and not of educational standards discussed and institutionalized. The precariousness of laboratory infrastructure and software licenses was a common report.

Abstract
Designing appropriate strategies for the participation of generation companies (GenCos) in the electricity markets has always been a concern for researchers. Generally, a set of dispatchable and non-dispatchable units constitute GenCos. This chapter presents a coordinated offering structure for the participation of a GenCo consisting of thermal, photovoltaic (PV), and battery storage system (BSS) in the day-ahead (DA) electricity market. The proposed offering structure is formulated as a three-stage stochastic programming problem while a scenario-based technique is utilized to handle the uncertainty related to electricity prices and PV production. From another point of view, a compatible risk-measuring index with multi-stage stochastic programming problems, namely conditional value at risk (CVaR), is also considered in the proposed structure. The proposed offering model is not only able to derive the offering curves of GenCo but also is capable of applying various emission limitations pertaining to thermal units. © Springer Nature Switzerland AG 2020.

Abstract

Abstract
The aim of this article is to show how autoethnography is a useful and revealing research methodology that should be encouraged in academia, especially in higher education. With objectivity, autoethnography, which is a relatively new approach, may be a path toward deeper cultural discussions that are so important in everyday life. Moreover, autoethnography leads to important reflexive and critical observations made by students. Autoethnography is a readily accessible, low-cost methodology and thus very appealing to students and younger researchers. With this article, the author exemplifies autoethnographic accounts and narrates three different stories that occurred while trekking with three different trekking guides in Patagonia (El Chalten), Argentina. Argentinian culture, in South America, is the focus. Researchers need to be careful of misleading statements in the literature, such as that in Argentina modesty is apparently not tolerated. We found that two of our guides and leaders - Mariano and Liz - both had modest (and pleasant) demeanors. Hence, we conclude that it is important to maintain an open mind and resist categorizing people. This is a vital point of cultural studies that is often not taken seriously. Cultures are made up of individuals and thus many differences can be found in the midst of an attempted standardization, and the desire to put everyone in the same "basket".