Abstract
Human-Robot Collaboration is a critical component of Industry 4.0, contributing to a transition towards more flexible production systems that are quickly adjustable to changing production requirements. This paper aims to increase the natural collaboration level of a robotic engine assembly station by proposing a cognitive system powered by computer vision and deep learning to interpret implicit communication cues of the operator. The proposed system, which is based on a residual convolutional neural network with 34 layers and a long -short term memory recurrent neural network (ResNet-34 + LSTM), obtains assembly context through action recognition of the tasks performed by the operator. The assembly context was then integrated in a collaborative assembly plan capable of autonomously commanding the robot tasks. The proposed model showed a great performance, achieving an accuracy of 96.65% and a temporal mean intersection over union (mIoU) of 94.11% for the action recognition of the considered assembly. Moreover, a task-oriented evaluation showed that the proposed cognitive system was able to leverage the performed human action recognition to command the adequate robot actions with near-perfect accuracy. As such, the proposed system was considered as successful at increasing the natural collaboration level of the considered assembly station.

Abstract
Concerns with the interpretability of ML models are growing as the technology is used in increasingly sensitive domains (e.g., health and public administration). Synthetic data can be used to understand models better, for instance, if the examples are generated close to the frontier between classes. However, data augmentation techniques, such as Generative Adversarial Networks (GAN), have been mostly used to generate training data that leads to better models. We propose a variation of GANs that, given a model, generates realistic data that is classified with low confidence by a given classifier. The generated examples can be used in order to gain insights on the frontier between classes. We empirically evaluate our approach on two well-known image classification benchmark datasets, MNIST and Fashion MNIST. Results show that the approach is able to generate images that are closer to the frontier when compared to the original ones, but still realistic. Manual inspection confirms that some of those images are confusing even for humans.

Abstract
An autonomous vehicle relying on LiDAR data should be able to assess its limitations in real time without depending on external information or additional sensors. The point cloud generated by the sensor is subjected to significant degradation under adverse weather conditions (rain, fog, and snow), which limits the vehicle's visibility and performance. With this in mind, we show that point cloud data contains sufficient information to estimate the weather accurately and present MobileWeatherNet, a LiDAR-only convolutional neural network that uses the bird's-eye view 2D projection to extract point clouds' weather condition and improves state-of-the-art performance by 15% in terms of the balanced accuracy while reducing inference time by 63%. Moreover, this paper demonstrates that among common architectures, the use of the bird's eye view significantly enhances their performance without an increase in complexity. To the extent of our knowledge, this is the first approach that uses deep learning for weather estimation using point cloud data in the form of a bird's-eye-view projection.

Abstract
Early detection of melanoma is crucial for preventing severe complications and increasing the chances of successful treatment. Existing deep learning approaches for melanoma skin lesion diagnosis are deemed black-box models, as they omit the rationale behind the model prediction, compromising the trustworthiness and acceptability of these diagnostic methods. Attempts to provide concept-based explanations are based on post-hoc approaches, which depend on an additional model to derive interpretations. In this paper, we propose an inherently interpretable framework to improve the interpretability of concept-based models by incorporating a hard attention mechanism and a coherence loss term to assure the visual coherence of concept activations by the concept encoder, without requiring the supervision of additional annotations. The proposed framework explains its decision in terms of human-interpretable concepts and their respective contribution to the final prediction, as well as a visual interpretation of the locations where the concept is present in the image. Experiments on skin image datasets demonstrate that our method outperforms existing black-box and concept-based models for skin lesion classification. © 2023 IEEE.

Abstract
This paper presents the main contributions of the VCMI Team to the ImageCLEFmedical GANs 2023 task. This task aims to evaluate whether synthetic medical images generated using Generative Adversarial Networks (GANs) contain identifiable characteristics of the training data. We propose various approaches to classify a set of real images as having been used or not used in the training of the model that generated a set of synthetic images. We use similarity-based approaches to classify the real images based on their similarity to the generated ones. We develop autoencoders to classify the images through outlier detection techniques. Finally, we develop patch-based methods that operate on patches extracted from real and generated images to measure their similarity. On the development dataset, we attained an F1-score of 0.846 and an accuracy of 0.850 using an autoencoder-based method. On the test dataset, a similarity-based approach achieved the best results, with an F1-score of 0.801 and an accuracy of 0.810. The empirical results support the hypothesis that medical data generated using deep generative models trained without privacy constraints threatens the privacy of patients in the training data. © 2023 Copyright for this paper by its authors.

Abstract
Deep Learning models are tremendously valuable in several prediction tasks, and their use in the medical field is spreading abruptly, especially in computer vision tasks, evaluating the content in X-rays, CTs or MRIs. These methods can save a significant amount of time for doctors in patient diagnostics and help in treatment planning. However, these models are significantly sensitive to confounders in the training data and generally suffer a performance hit when dealing with out-of-distribution data, affecting their reliability and scalability in different medical institutions. Deep Learning research on Medical datasets may overlook essential details regarding the image acquisition procedure and the preprocessing steps. This work proposes a data-centric approach, exploring the potential of attention maps as a regularisation technique to improve robustness and generalisation. We use image metadata and explore self-attention maps and contrastive learning to promote feature space invariance to image disturbance. Experiments were conducted using Chest X-ray datasets that are publicly available. Some datasets contained information about the windowing settings applied by the radiologist, acting as a source of variability. The proposed model was tested and outperformed the baseline in out-of-distribution data, serving as a proof of concept. © 2023 IEEE.