Social Robot Navigation is the skill that allows robots to move efficiently in human-populated environments while ensuring safety, comfort, and trust. Unlike other areas of research, the scientific community has not yet achieved an agreement on how Social Robot Navigation should be benchmarked. This is notably important, as the lack of a de facto standard to benchmark Social Robot Navigation can hinder the progress of the field and may lead to contradicting conclusions. Motivated by this gap, we contribute with a short review focused exclusively on benchmarking trends in the period from January 2020 to July 2025. Of the 130 papers identified by our search using IEEE Xplore, we analysed the 85 papers that met the criteria of the review. This review addresses the metrics used in the literature for benchmarking purposes, the algorithms employed in such benchmarks, the use of human surveys for benchmarking, and how conclusions are drawn from the benchmarking results, when applicable.

Biomedical decision making involves multiple signal processing, either from different sensors or from different channels. In both cases, information fusion plays a significant role. A deep learning based electroencephalogram channels' feature level fusion is carried out in this work for the electroencephalogram cyclic alternating pattern A phase classification. Channel selection, fusion, and classification procedures were optimized by two optimization algorithms, namely, Genetic Algorithm and Particle Swarm Optimization. The developed methodologies were evaluated by fusing the information from multiple electroencephalogram channels for patients with nocturnal frontal lobe epilepsy and patients without any neurological disorder, which was significantly more challenging when compared to other state of the art works. Results showed that both optimization algorithms selected a comparable structure with similar feature level fusion, consisting of three electroencephalogram channels, which is in line with the CAP protocol to ensure multiple channels' arousals for CAP detection. Moreover, the two optimized models reached an area under the receiver operating characteristic curve of 0.82, with average accuracy ranging from 77% to 79%, a result which is in the upper range of the specialist agreement. The proposed approach is still in the upper range of the best state of the art works despite a difficult dataset, and has the advantage of providing a fully automatic analysis without requiring any manual procedure. Ultimately, the models revealed to be noise resistant and resilient to multiple channel loss.

Longitudinal data is commonly utilised across various domains, such as health, biomedical, education and survey studies. This ubiquity has led to a rise in statistical, machine and deep learning-based methods for Longitudinal Data Classification (LDC). However, the intricate nature of the data, characterised by its multi-dimensionality, causes instance-level heterogeneity and temporal correlations that add to the complexity of longitudinal data analysis. Additionally, LDC accuracy is often hampered by the pervasiveness of missing values in longitudinal data. Despite ongoing research that draw on the generative power and utility of Generative Adversarial Networks (GANs) to address the missing data problem, critical considerations include statistical assumptions surrounding longitudinal data and missingness within it, as well as other data-level challenges like class imbalance and mixed data types that impact longitudinal data imputation (LDI) and the subsequent LDC process in GANs. This paper provides a comprehensive overview of how GANs have been applied in LDI, with a focus whether GANS have adequately addressed fundamental assumptions about the data from a LDC perspective. We propose a categorisation of main approaches to GAN-based LDI, highlight strengths and limitations of methods, identify key research trends, and provide promising future directions. Our findings indicate that while GANs show great potential for LDI to improve usability and quality of longitudinal data for tasks like LDC, there is need for more versatile approaches that can handle the wider spectrum of challenges presented by longitudinal data with missing values. By synthesising current knowledge and identifying critical research gaps, this survey aims to guide future research efforts in developing more effective GAN-based solutions to address LDC challenges.

Ensuring the safe and efficient operation of CAVs relies heavily on the software framework used. A software framework needs to ensure real-time properties, reliable communication, and efficient resource utilization. Furthermore, a software framework needs to enable seamless transition between testing stages, from simulation to small-scale to full-scale experiments. In this paper, we survey prominent software frameworks used for in-vehicle and inter-vehicle communication in CAVs. We analyze these frameworks regarding opportunities and challenges, such as their real-time properties and transitioning capabilities. Additionally, we delve into the tooling requirements necessary for addressing the associated challenges. We illustrate the practical implications of these challenges through case studies focusing on critical areas such as perception, motion planning, and control. Furthermore, we identify research gaps in the field, highlighting areas where further investigation is needed to advance the development and deployment of safe and efficient CAV systems.

This paper examines the saturation of pressure signals during various handwriting tasks, including drawings, cursive text, capital words text, and signature, under different levels of fatigue. Experimental results demonstrate a significant rise in the proportion of saturated samples following strenuous exercise in tasks performed without resting wrist. The analysis of saturation highlights significant differences when comparing the results to the baseline situation and strenuous fatigue.

The 3D object detection capabilities in urban environments have been enormously improved by recent developments in Light Detection and Range (LiDAR) technology. This paper presents a novel framework that transforms the detection and analysis of 3D objects in traffic scenarios by utilizing the power of elevated LiDAR sensors. We are presenting our methodology's remarkable capacity to collect complex 3D point cloud data, which allows us to accurately and in detail capture the dynamics of urban traffic. Due to the limitation in obtaining real-world traffic datasets, we utilize the simulator to generate 3D point cloud for specific scenarios. To support our experimental analysis, we firstly simulate various 3D point cloud traffic-related objects. Then, we use this dataset as a basis for training and evaluating our 3D object detection models, in identifying and monitoring both vehicles and pedestrians in simulated urban traffic environments. Next, we fine tune the Point Voxel-Region-based Convolutional Neural Network (PV-RCNN) architecture, making it more suited to handle and understand the massive volumes of point cloud data generated by our urban traffic simulations. Our results show the effectiveness of the proposed solution in accurately detecting objects in traffic scenes and highlight the role of LiDAR in improving urban safety and advancing intelligent transportation systems.

Detecting latent confounders from proxy variables is an essential problem in causal effect estimation. Previous approaches are limited to low-dimensional proxies, sorted proxies, and binary treatments. We remove these assumptions and present a novel Proxy Confounder Factorization (PCF) framework for continuous treatment effect estimation when latent confounders manifest through high-dimensional, mixed proxy variables. For specific sample sizes, our two-step PCF implementation, using Independent Component Analysis (ICA-PCF), and the end-to-end implementation, using Gradient Descent (GD-PCF), achieve high correlation with the latent confounder and low absolute error in causal effect estimation with synthetic datasets in the high sample size regime. Even when faced with climate data, ICA-PCF recovers four components that explain $75.9\%$ of the variance in the North Atlantic Oscillation, a known confounder of precipitation patterns in Europe. Code for our PCF implementations and experiments can be found here: https://github.com/IPL-UV/confound_it. The proposed methodology constitutes a stepping stone towards discovering latent confounders and can be applied to many problems in disciplines dealing with high-dimensional observed proxies, e.g., spatiotemporal fields.

This paper presents the concepts of Artificial Intelligence, Multi-Agent-Systems, Coordination, Intelligent Robotics and Deep Reinforcement Learning. Emphasis is given on and how AI and DRL, may be efficiently used to create efficient robot skills and coordinated robotic teams, capable of performing very complex actions and tasks, such as playing a game of soccer. The paper also presents the concept of robotic soccer and the vision and structure of the RoboCup initiative with emphasis on the Humanoid Simulation 3D league and the new challenges this competition, poses. The final topics presented at the paper are based on the research developed/coordinated by the author throughout the last 22 years in the context of the FCPortugal project. The paper presents a short description of the coordination methodologies developed, such as: Strategy, Tactics, Formations, Setplays, and Coaching Languages and the use of Machine Learning to optimize the use of this concepts. The topics presented also include novel stochastic search algorithms for black box optimization and their use in the optimization of omnidirectional walking skills, robotic multi-agent learning and the creation of a humanoid kick with controlled distance. Finally, new applications using variations of the Proximal Policy Optimization algorithm and advanced modelling for robot and multi-robot learning are briefly explained with emphasis for our new humanoid sprinting and running skills and an amazing humanoid robot soccer dribbling skill. FCPortugal project enabled us to publish more than 100 papers and win several competitions in different leagues and many scientific awards at RoboCup. In total, our team won more than 40 awards in international competitions including a clear victory at the Simulation 3D League at RoboCup 2022 competition, scoring 84 goals and conceding only 2.

Photo-trapping cameras are widely employed for wildlife monitoring. Those cameras take photographs when motion is detected to capture images where animals appear. A significant portion of these images are empty - no wildlife appears in the image. Filtering out those images is not a trivial task since it requires hours of manual work from biologists. Therefore, there is a notable interest in automating this task. Automatic discarding of empty photo-trapping images is still an open field in the area of Machine Learning. Existing solutions often rely on state-of-the-art supervised convolutional neural networks that require the annotation of the images in the training phase. PARDINUS (Weakly suPervised discARDINg of photo-trapping empty images based on aUtoencoderS) is constructed on the foundation of weakly supervised learning and proves that this approach equals or even surpasses other fully supervised methods that require further labeling work.

Fishery analysis is critical in maintaining the long-term sustainability of species and the livelihoods of millions of people who depend on fishing for food and income. The fishing gear, or metier, is a key factor significantly impacting marine habitats, selectively targeting species and fish sizes. Analysis of commercial catches or landings by metier in fishery stock assessment and management is crucial, providing robust estimates of fishing efforts and their impact on marine ecosystems. In this paper, we focus on a unique data set from the Azores' fishing data collection programs between 2010 and 2017, where little information on metiers is available and sparse throughout our timeline. Our main objective is to tackle the task of data set reconstruction, leveraging domain knowledge and machine learning methods to retrieve or associate metier-related information to each fish landing. We empirically validate the feasibility of this task using a diverse set of modeling approaches and demonstrate how it provides new insights into different fisheries' behavior and the impact of metiers over time, which are essential for future fish population assessments, management, and conservation efforts.