Abstract
This work proposes a multi-criteria decision making approach to help assessing and selecting suppliers in the olive oil sector. Olive oil is a protected agricultural product, by region and origin certificate. Therefore to select a supplier, it is of utter importance to inspect and test (taste, colour, smell, density, among others) the olive oil in addition to the supplying company. The identification of possible suppliers was done in two stages: firstly, the region of origin from which to choose possible suppliers was identified and then potential suppliers were evaluated on a set of characteristics for which minimum threshold values were set. From this study, which is not part of the research reported here, we were able to identify the suppliers of interest. Due to the several characteristics and characteristic dimensions used to choose a supplier we resort to the Analytic Hierarchy Process to rank them, this way allowing for a better choice. The rank obtained is robust as the top ranked supplier remains the same for any reasonable change in the criteria weighs and in the evaluation of the suppliers on each criterion. The involved company found the results of value, as well as the lessons learned by addressing the supplier evaluation problem using a more systematic approach.

Abstract
IDEs are environments specialized in support during the development of programs. They contain several utilities to code, run, debug, and deploy programs quickly. However, they do not provide the automatic assessment of programming exercises, which is required in both learning and competitive programming environment. Therefore, IDEs are often underestimated in these contexts and replaced by basic code editors. Yet, IDEs have unique features which are essential for programmers, such as the debugger or the package explorer. This paper presents Raccode, a plugin for assessment of programming exercises in Eclipse. This plugin integrates with Mooshak to combine the diverse capabilities of an IDE, like Eclipse, with the automatic evaluation of exercises, clarification requests, printouts, balloons, and rankings. It can be used both in competitive and learning environments. The paper describes Raccode, its concept, architecture and design. © André Silva, José Paulo Leal, and José Carlos Paiva.

Abstract

Abstract
New virtual reality technologies allow the user to gain a greater sense of presence in virtual environments. One of the areas where these technologies can have a major impact is the area of games that allow users to explore these environments and interact with them by receiving feedback from their actions in real time. The present study aimed to evaluate if the use of physiological signals to update the virtual environment in real-time could be used to increase the feeling of presence. To perform this study, an experimental study was designed based on a game that allowed the use of physiological data to calculate the participant's arousal in real-time and, based on that, modify certain elements of the virtual environment where the participants were asked to fulfill a task. With the analysis of the data obtained, it was possible to verify that the use of biofeedback did not reveal statistically significant differences for the variables tested, however, it can be concluded that the use of biofeedback improves some subscales of presence, being the users with more experience in games and more computer knowledge susceptible to such changes.

Abstract

Abstract
Complex optimization scheduling problems frequently arise in the manufacturing and transport industries, where the goal is to find a schedule that minimizes the total amount of time (or cost) required to complete all the tasks. Since it is a critical factor in many industries, it has been, historically, a target of the scientific community. Mathematically, these problems are modelled with Job Shop scheduling approaches. Benchmark results to solve them are achieved with evolutionary algorithms. However, they still present some limitations, mostly related to execution times and the difficulty to generalize to other problems. Deep Reinforcement Learning is poised to revolutionise the field of artificial intelligence. Chosen as one of the MIT breakthrough technologies, recent developments suggest that it is a technology of unlimited potential which shall play a crucial role in achieving artificial general intelligence. This paper puts forward a state-of-the-art review on Job Shop Scheduling, Evolutionary Algorithms and Deep Reinforcement Learning. It also proposes a novel architecture capable of solving Job Shop Scheduling optimization problems using Deep Reinforcement Learning. © 2020, Springer Nature Switzerland AG.