Abstract
Hazard Analysis and Critical Control Point (HACCP) system is based on a preventive methodology to avoid potential hazards that can cause harm and to ensure that unsafe food is not made available to consumers. This system is recognized by the Economic and Food Safety Authority, a criminal police responsible for food safety and economic inspection in Portugal. Every day, Economic and Food Safety Authority generates a large and complex volume of data from inspections and complaints, also in its classification, registration and in monitoring until the end of the process analysis. This study focuses on the reported entities that are related to non-compliance with HACCP, and tries to understand the most common infractions. Results show values between 30% and 37% related to non-compliance to HACCP. As main conclusions, from 2014 to 2018, the number of these infractions maintained the same level and it will be important to understand if the relationship between these problems are related to legislation understanding or application.

Abstract
Stochastic search and optimization techniques are used in a vast number of areas, ranging from refining the design of vehicles, determining the effectiveness of new drugs, developing efficient strategies in games, or learning proper behaviors in robotics. However, they specialize for the specific problem they are solving, and if the problem's context slightly changes, they cannot adapt properly. In fact, they require complete re-leaning in order to perform correctly in new unseen scenarios, regardless of how similar they are to previous learned environments. Contextual algorithms have recently emerged as solutions to this problem. They learn the policy for a task that depends on a given context, such that widely different contexts belonging to the same task are learned simultaneously. That being said, the state-of-the-art proposals of this class of algorithms prematurely converge, and simply cannot compete with algorithms that learn a policy for a single context. We describe the Contextual Relative Entropy Policy Search (CREPS) algorithm, which belongs to the before-mentioned class of contextual algorithms. We extend it with a technique that allows the algorithm to severely increase its performance, and we call it Contextual Relative Entropy Policy Search with Covariance Matrix Adaptation (CREPS-CMA). We propose two variants, and demonstrate their behavior in a set of classic contextual optimization problems, and on complex simulator robot tasks.

Abstract

Abstract
Currently, there are more than 30 million amputees in the world and each year thousands of people suffer from amputation and, therefore, the development of lower limb prostheses is crucial to improve the quality of millions of people's lives by restoring their mobility. This contribution proposes a simulated amputee model capable of reproducing a transtibial amputee subject wearing a passive prosthesis. The passive prosthesis behavior is simulated using a spring-damper system between shin and foot. This contribution provides a tool capable of reproducing an amputee subject wearing a passive prosthesis, as well as an adaptive framework where researchers can deploy controllers in the simulated transtibial prosthesis, transforming it in a powered transtibial prosthesis. Results show that the amputee model is good for the simulation of transtibial amputees wearing a passive device or an active transtibial prosthesis.

Abstract
The severe or prolonged exposure to stress-inducing factors in occupational and academic settings is a growing concern. The literature describes several potentially stressful moments experienced by medical students throughout the course, affecting cognitive functioning and learning. In this paper, we introduce the EUSTRESS Solution, that aims to create an Information System to monitor and assess, continuously and in real-time, the stress levels of the individuals in order to predict chronic stress. The Information System will use a measuring instrument based on wearable devices and machine learning techniques to collect and process stress-related data from the individual without his/her explicit interaction. A big database has been built through physiological, psychological, and behavioral assessments of medical students. In this paper, we focus on heart rate and heart rate variability indices, by comparing baseline and stress condition. In order to develop a predictive model of stress, we performed different statistical tests. Preliminary results showed the neural network had the better model fit. As future work, we will integrate salivary samples and self-report questionnaires in order to develop a more complex and intelligent model. © Springer Nature Switzerland AG 2019.

Abstract
The field of automated learning has been steadily growing in robotic tasks. This phenomenon is supported by the evolution of computational resources and new reinforcement learning algorithms. Researchers have drawn their attentions to methods that are easy to implement and tune, while achieving state-of-the-art performance. This trend also affects the world of robotic soccer, where new papers delve systematically into the optimization of basic skills. However, when learning higher-level strategies, there is space for improvement on two fronts. First, the simulation environment should allow the agent to abstract from low-level details. Second, the existing methods to train this kind of behaviors are still scarce. This paper contributes with innovative problem-solving methods, specifically in the rewards field. To test alternative approaches, an extended version of the RoboCup's official Soccer Server simulator was used. The results have confirmed the importance of the proposed reward components and their relationship with the episodes' initial conditions.