Abstract
Imbalanced data are present in various business sectors and must be handled with the proper resampling methods and classification algorithms. To handle imbalanced data, there are numerous resampling and learning method combinations; nonetheless, their effective use necessitates specialised knowledge. In this paper, several approaches, ranging from more accessible to more advanced in the domain of data resampling techniques, will be considered to handle imbalanced data. The application developed delivers recommendations of the most suitable combinations of techniques for a specific dataset by extracting and comparing dataset meta-feature values recorded in a knowledge base. It facilitates effortless classification and automates part of the machine learning pipeline with comparable or better results than state-of-the-art solutions and with a much smaller execution time.

Abstract
GLITCH is a versatile tool designed for detecting code smells in Infrastructure as Code (IaC) scripts across multiple technologies. Developed by researchers from INESC-ID (Lisbon), INESC TEC (Porto), Instituto Superior T & eacute;cnico / University of Lisbon, and the Faculty of Engineering / University of Porto, GLITCH automates the detection of both security and design flaws in scripts written in Ansible, Chef, Docker, Puppet, and Terraform. By using a technology-agnostic framework, GLITCH aims to improve the consistency and efficiency of code smell detection, making it valuable resource for DevOps engineers and researchers focused on software quality.

Abstract
In online three-dimensional packing problems (3D-PPs), unlike offline problems, items arrive sequentially and require immediate packing decisions without any information about the quantities and sizes of the items to come. Heuristic methods are of great importance in solving online problems to find good solutions in a reasonable amount of time. However, the literature on heuristics for online problems is sparse. As our first contribution, we developed a pool of heuristics applicable to online 3D-PPs with complementary performance on different sets of instances. Computational results showed that in terms of the number of used bins, in all problem instances, at least one of our heuristics had a better or equal performance compared to existing heuristics in the literature. The developed heuristics are also fully applicable to an intermediate class between offline and online problems, referred to in this paper as a specific type of semi-online with full look-ahead, which has several practical applications. In this class, as in offline problems, complete information about all items is known in advance (i.e., full look-ahead); however, due to time or space constraints, as in online problems, items should be packed immediately in the order of their arrival. As our second contribution, we presented an algorithm selection framework, building on developed heuristics and utilizing prior information about items in this specific class of problems. We used supervised machine learning techniques to find the relationship between the features of problem instances and the performance of heuristics and to build a prediction model. The results indicate an 88% accuracy in predicting (identifying) the most promising heuristic(s) for solving any new instance from this class of problems.

Abstract
The excitation of two different electromagnetic surface waves-surface plasmon polaritons (SPPs) and Bloch surface waves (BSWs)-is demonstrated in a 1-D metal-dielectric photonic crystal with numerical and experimental studies. The discussed structure consists of an Ag-TiO2 thin-film stack forming a metal-insulator-metal-insulator device. The thickness of the TiO2 layer placed between the metals is tested for two different values (50 and 300 nm), which also allows the excitation of guided-mode resonances. It is observed that BSWs in this metal-dielectric structure behave similar to the case of all-dielectric photonic crystals, whereas the SPP modes display similar properties to those excited in metal-insulator-metal cavities. The sensitivity of these surface states to variations in the refractive index (RI) of the external dielectric is characterized. For the case of the plasmonic modes, a maximum sensitivity of (7.2 +/- 0.3) x 10(3) nm/RIU was measured, while for the BSW the maximum sensitivity was (1.20 +/- 0.05) x 10(2) nm/RIU. Due to the large field enhancement and penetration on external media, these surface states display exceptional properties for application in optical sensors, and the presented results provide interesting possibilities in the design of novel sensing structures with a flexible selection of surface states for interrogation.

Abstract
Composites of poly(vinyl alcohol) (PVA) in the shape of braids, in combination with crystals of hydroxyapatite (HAp), were analyzed to perceive the influence of this bioceramic on both the quasi-static and viscoelastic behavior under tensile loading. Analyses involving energy-dispersive X-ray spectroscopy (EDS) and scanning electron microscopy (SEM) allowed us to conclude that the production of a homogeneous layer of HAp on the braiding surface and the calcium/phosphate atomic ratio were comparable to those of natural bone. The maximum degradation temperature established by thermogravimetric analysis (TGA) showed a modest decrease with the addition of HAp. By adding HAp to PVA braids, an increase in the glass transition temperature (Tg) is noticed, as demonstrated by dynamic mechanical analysis (DMA) and differential thermal analysis (DTA). The PVA/HAp composite braids' peaks were validated by Fourier transform infrared (FTIR) spectroscopy to be in good agreement with common PVA and HAp patterns. PVA/HAp braids, a solution often used in the textile industry, showed superior overall mechanical characteristics in monotonic tensile tests. Creep and relaxation testing showed that adding HAp to the eight and six-braided yarn architectures was beneficial. By exhibiting good mechanical performance and most likely increased biological qualities that accompany conventional care for bone applications in the fracture healing field, particularly multifragmentary ones, these arrangements can be applied as a fibrous fixation system.

Abstract
Robot Learning is one of the most important areas in Robotics and its relevance has only been increasing. The Robot Operating System (ROS) has been one of the most used architectures in Robotics but learning it is not a simple task. Additionally, ROS 1 is reaching its end-of-life and a lot of users are yet to make the transition to ROS 2. Reinforcement Learning (RL) and Robotics are rarely taught together, creating greater demand for tools to teach all these components. This paper aims to develop a learning kit that can be used to teach Robot Learning to students with different levels of expertise in Robotics. This kit works with the Flatland simulator using open-source free software, namely the OpenAI Gym and Stable-Baselines3 packages, and contains tutorials that introduce the user to the simulation environment as well as how to use RL to train the robot to perform different tasks. User tests were conducted to better understand how the kit performs, showing very positive feedback, with most participants agreeing that the kit provided a productive learning experience.