Abstract
O 6.º Encontro Internacional sobre Jogos e Mobile Learning (EJML) é organizado na Faculdade de Psicologia e de Ciências da Educação, no âmbito das atividades do Laboratório de Tecnologia Educativa (LabTE) da Universidade de Coimbra e do Centro de Estudos Interdisciplinares, em coorganização com a UNISINOS, a Universidad de La Laguna e o Instituto Politécnico de Tomar. Os autores partilham as suas investigações nas áreas de jogos educativos (serious games), Mobile Learning e Formação de Professores e as múltiplas literacias. As comunicações reportam estudos com diferentes públicos etários, desde os mais jovens até aos séniores. As temáticas abarcam desenvolvimento de jogos, aprendizagem baseada em jogos, avaliação da aprendizagem com dispositivos móveis, educação inclusiva, cyberbullying, gamificação, ambientes imersivos de aprendizagem, realidade virtual, realidade aumentada e inteligência artificial no ensino. Todas as comunicações foram submetidas para avaliação, sendo analisadas por três membros da Comissão Científica, através de um processo de blind review. A Comissão Científica é constituída por investigadores de Portugal, Brasil, Espanha, Moçambique e Reino Unido. O evento integra comunicações longas e breves, que estão publicadas nestas atas, relatos de experiências numa outra publicação e onze workshops, cujos tutoriais constituem uma terceira publicação do evento.

Abstract
In the present paper, we propose a control approach to perform docking of an autonomous surface vehicle (ASV) while avoiding surrounding obstacles. This control architecture is composed of two sequential controllers. The first outputs a feasible trajectory between the vessel's initial and target state while avoiding obstacles. This trajectory also minimizes the vehicle velocity while performing the maneuvers to increase the safety of onboard passengers. The second controller performs trajectory tracking while accounting for the actuator's physical limits (extreme actuation values and the rate of change). The method's performance is tested on simulation, as it enables a reliable ground truth method to validate the control architecture proposed.

Abstract
Ego -motion estimation plays a critical role in autonomous driving systems by providing accurate and timely information about the vehicle's position and orientation. To achieve high levels of accuracy and robustness, it is essential to leverage a range of sensor modalities to account for highly dynamic and diverse scenes, and consequent sensor limitations. In this work, we introduce TEFu-Net, a Deep -Learning -based late fusion architecture that combines multiple ego -motion estimates from diverse data modalities, including stereo RGB, LiDAR point clouds and GNSS/IMU measurements. Our approach is non -parametric and scalable, making it adaptable to different sensor set configurations. By leveraging a Long Short -Term Memory (LSTM), TEFu-Net produces reliable and robust spatiotemporal ego -motion estimates. This capability allows it to filter out erroneous input measurements, ensuring the accuracy of the car's motion calculations over time. Extensive experiments show an average accuracy increase of 63% over TEFu-Net's input estimators and on par results with the state-of-the-art in real -world driving scenarios. We also demonstrate that our solution can achieve accurate estimates under sensor or input failure. Therefore, TEFu-Net enhances the accuracy and robustness of ego -motion estimation in real -world driving scenarios, particularly in challenging conditions such as cluttered environments, tunnels, dense vegetation, and unstructured scenes. As a result of these enhancements, it bolsters the reliability of autonomous driving functions.

Abstract
Point source detection algorithms play a pivotal role across diverse applications, influencing fields such as astronomy, biomedical imaging, environmental monitoring, and beyond. This article reviews the algorithms used for space imaging applications from ground and space telescopes. The main difficulties in detection arise from the incomplete knowledge of the impulse function of the imaging system, which depends on the aperture, atmospheric turbulence (for ground-based telescopes), and other factors, some of which are time-dependent. Incomplete knowledge of the impulse function decreases the effectiveness of the algorithms. In recent years, deep learning techniques have been employed to mitigate this problem and have the potential to outperform more traditional approaches. The success of deep learning techniques in object detection has been observed in many fields, and recent developments can further improve the accuracy. However, deep learning methods are still in the early stages of adoption and are used less frequently than traditional approaches. In this review, we discuss the main challenges of point source detection, as well as the latest developments, covering both traditional and current deep learning methods. In addition, we present a comparison between the two approaches to better demonstrate the advantages of each methodology.

Abstract
Exploiting stronger winds at offshore farms leads to a cyclical need for maintenance due to the harsh maritime conditions. While autonomous vehicles are the prone solution for O&M procedures, sub-sea phenomena induce severe data degradation that hinders the vessel's 3D perception. This article demonstrates a hybrid underwater imaging system that is capable of retrieving tri-dimensional information: dense and textured Photogrammetric Stereo (PS) point clouds and multiple accurate sets of points through Light Stripe Ranging (LSR), that are combined into a single dense and accurate representation. Two novel fusion algorithms are introduced in this manuscript. A Joint Masked Regression (JMR) methodology propagates sparse LSR information towards the PS point cloud, exploiting homogeneous regions around each beam projection. Regression curves then correlate depth readings from both inputs to correct the stereo-based information. On the other hand, the learning-based solution (RHEA) follows an early-fusion approach where features are conjointly learned from a coupled representation of both 3D inputs. A synthetic-to-real training scheme is employed to bypass domain-adaptation stages, enabling direct deployment in underwater contexts. Evaluation is conducted through extensive trials in simulation, controlled underwater environments, and within a real application at the ATLANTIS Coastal Testbed. Both methods estimate improved output point clouds, with RHEA achieving an average RMSE of 0.0097 m -a 52.45% improvement when compared to the PS input. Performance with real underwater information proves that RHEA is robust in dealing with degraded input information; JMR is more affected by missing information, excelling when the LSR data provides a complete representation of the scenario, and struggling otherwise.

Abstract
Arbitrary, inconsistent, or faulty decision-making raises serious concerns, and preventing unfair models is an increasingly important challenge in Machine Learning. Data often reflect past discriminatory behavior, and models trained on such data may reflect bias on sensitive attributes, such as gender, race, or age. One approach to developing fair models is to preprocess the training data to remove the underlying biases while preserving the relevant information, for example, by correcting biased labels. While multiple label noise correction methods are available, the information about their behavior in identifying discrimination is very limited. In this work, we develop an empirical methodology to systematically evaluate the effectiveness of label noise correction techniques in ensuring the fairness of models trained on biased datasets. Our methodology involves manipulating the amount of label noise and can be used with fairness benchmarks but also with standard ML datasets. We apply the methodology to analyze six label noise correction methods according to several fairness metrics on standard OpenML datasets. Our results suggest that the Hybrid Label Noise Correction [20] method achieves the best trade-off between predictive performance and fairness. Clustering-Based Correction [14] can reduce discrimination the most, however, at the cost of lower predictive performance.