Abstract
The use of Robotics as a teaching tool to foster knowledge in Science, Technology, Engineering and Mathematics - STEM for high school students is growing. Within this context, the need for solutions that decreases the overall cost of hardware is mandatory. In this work, a 25 euros Android-based mobile robot that allows connecting a smartphone to different robot "bodies" (each composed by different hardware: Arduino, LEGO, etc.) is proposed, targeting mainly high school students. The mobile platform was built using 3D printed parts and uses an Arduino board to control the servos used for the locomotion. To communicate with the Android application, a Bluetooth module is also used. The Android application takes advantage of the smartphone camera and has some pre-implemented image processing modules, allowing actions such as coloured line following, colour proximity detection and coloured object tracking. Another import feature present in the Android application is its coding capability, thanks to an embedded code interpreter. This feature was implemented using BeanShell, a lightweight script interpreter for Java. It allows writing scripts in Java programming language, which are then executed by the robot. This interpreter also provides a way for the user to write a set of instructions that defines the behaviour of the robot, primarily by checking the current status of the sensors and the smartphone camera, and manipulating the velocity of the motors. All of the necessary components (3D source CAD + STLs) of the mobile platform presented here are available online, enabling the recreation of a replica of the robot body. A detailed list of electronic components and its connections are also provided, as well as the Arduino source code along with the installation file needed for the Android application. All the necessary information can be accessed at: https://github.com/ee09115/squirlrob. The high school students are then challenged to solve classical robotics tasks like line following, obstacle avoidance, and using the virtual sensors to create new challenges. By providing this set of tools (Android app, source code, 3D CAD models, documentation), a promotion of knowledge is expected, thus developing enthusiast students in robotics multidisciplinary fields, such as mechanical designing, electronics designing, mathematics, coding, etc. (in short, all the relevant STEM areas). Finally, a general analysis of the usability, interoperability and feature extension capacity is made, always taking into consideration the educational purposes of this work.

Abstract
This article is about seeking a good feasible solution in a reasonable amount of computation time to the three-dimensional Multiple Bin Size Bin Packing Problem (MBSBPP). The MBSBPP studied considers additional constraints encountered in real world air transportation situations, such as cargo stability and the particular shape of containers. This MBSBPP has already been formulated as a Mixed Integer linear Programming problem, but as yet only poor results have been achieved for even fairly small problem sizes. The goal of the work this paper describes is to develop heuristics that are able to quickly provide good initial feasible solutions for the MBSBPP. Three methodologies are considered, which are based on the decomposition of the original problem into easier subproblems: the matheuristics Relax-and-Fix, Insert-and-Fix and Fractional Relax-and-Fix. They have been parametrised on real data sets and then compared to each other. In particular, two of these techniques show promising results in reasonable computational times.

Abstract
Energy management is a key tool that will enable consumers to optimize their energy use according to different objectives. Allow users to insert their energy use preferences combined with the effective configuration and control of existing devices (loads and micro generation) is the basis, in this paper, to design adaptable energy optimization algorithms that are capable of outputting feasible, understandable and useful actions, automated and/or manual, for the activation of the existing portfolio of flexible devices. This paper presents an advanced energy management system as an innovative platform that intends to accomplish real energy optimization schemes to support demand response, promote the energy efficiency and contribute towards renewable integration.

Abstract
In a growing number of domains, the provisioning of end-to-end services to the users depends on the proper interoperation of multiple systems, forming a new distributed system, often subject to timing constraints. To ensure interoperability and integrity, it is important to conduct integration tests that verify the interactions with the environment and between the system components in key scenarios. To tackle test automation challenges, we propose algorithms for decentralized conformance checking and test input generation, and for checking and enforcing the conditions (local observability and controllability) that allow decentralized test execution.

Abstract
The Acute Kidney Injury (AKI), is a disease that affects the kidneys and is characterized by the rapid deterioration of these organs, usually associated with a pre-existing critical illness. Being an acute disease, time is a key element in the prevention. By anticipating a patient's state transition, we are preventing future complications in his health, such as the development of a chronic disease or loss of an organ, in addition to decreasing the amount of money spent on the patient's care. The main goal of this paper is to address the problem of correctly predicting the illness path in various patients by studying different methodologies to predict this disease and propose new distinct approaches based on this idea of improving the performance of the classification. Through the comparison of five different approaches (Markov Chain Model ICU Specialists, Markov Chain Model Features, Markov Chain Model Conditional Features, Markov Chain Model and Random Forest), we came to the conclusion that the application of conditional probabilities to this problem produces a more accurate prediction, based on common inputs.

Abstract
This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE dataset contains 35 scenes that correspond to indoor domestic environments, with objects with different colors and specularities. O-HAZE contains 45 different outdoor scenes depicting the same visual content recorded in hazefree and hazy conditions, under the same illumination parameters. The dehazing process was learnable through provided pairs of haze-free and hazy train images. Each track had similar to 120 registered participants and 21 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing.