Abstract
Purpose: Visual perception enables robots to perceive the environment. Visual data is processed using computer vision algorithms that are usually time-expensive and require powerful devices to process the visual data in real-time, which is unfeasible for open-field robots with limited energy. This work benchmarks the performance of different heterogeneous platforms for object detection in real-time. This research benchmarks three architectures: embedded GPU-Graphical Processing Units (such as NVIDIA Jetson Nano 2 GB and 4 GB, and NVIDIA Jetson TX2), TPU-Tensor Processing Unit (such as Coral Dev Board TPU), and DPU-Deep Learning Processor Unit (such as in AMD-Xilinx ZCU104 Development Board, and AMD-Xilinx Kria KV260 Starter Kit). Methods: The authors used the RetinaNet ResNet-50 fine-tuned using the natural VineSet dataset. After the trained model was converted and compiled for target-specific hardware formats to improve the execution efficiency.Conclusions and Results: The platforms were assessed in terms of performance of the evaluation metrics and efficiency (time of inference). Graphical Processing Units (GPUs) were the slowest devices, running at 3 FPS to 5 FPS, and Field Programmable Gate Arrays (FPGAs) were the fastest devices, running at 14 FPS to 25 FPS. The efficiency of the Tensor Processing Unit (TPU) is irrelevant and similar to NVIDIA Jetson TX2. TPU and GPU are the most power-efficient, consuming about 5 W. The performance differences, in the evaluation metrics, across devices are irrelevant and have an F1 of about 70 % and mean Average Precision (mAP) of about 60 %.

Abstract
In an automatic music playlist generator, such as an automated online radio channel, how should the system react when a user hits the skip button? Can we use this type of negative feedback to improve the list of songs we will playback for the user next? We propose SkipAwareRec, a next-item recommendation system based on reinforcement learning. SkipAwareRec recommends the best next music categories, considering positive feedback consisting of normal listening behaviour, and negative feedback in the form of song skips. Since SkipAwareRec recommends broad categories, it needs to be coupled with a model able to choose the best individual items. To do this, we propose Hybrid SkipAwareRec. This hybrid model combines the SkipAwareRec with an incremental Matrix Factorisation (MF) algorithm that selects specific songs within the recommended categories. Our experiments with Spotify's Sequential Skip Prediction Challenge dataset show that Hybrid SkipAwareRec has the potential to improve recommendations by a considerable amount with respect to the skip-agnostic MF algorithm. This strongly suggests that reformulating the next recommendations based on skips improves the quality of automatic playlists. Although in this work we focus on sequential music recommendation, our proposal can be applied to other sequential content recommendation domains, such as health for user engagement.

Abstract
Missing data is an issue that can negatively impact any task performed with the available data and it is often found in real-world domains such as healthcare. One of the most common strategies to address this issue is to perform imputation, where the missing values are replaced by estimates. Several approaches based on statistics and machine learning techniques have been proposed for this purpose, including deep learning architectures such as generative adversarial networks and autoencoders. In this work, we propose a novel siamese neural network suitable for missing data imputation, which we call Siamese Autoencoder-based Approach for Imputation (SAEI). Besides having a deep autoencoder architecture, SAEI also has a custom loss function and triplet mining strategy that are tailored for the missing data issue. The proposed SAEI approach is compared to seven state-of-the-art imputation methods in an experimental setup that comprises 14 heterogeneous datasets of the healthcare domain injected with Missing Not At Random values at a rate between 10% and 60%. The results show that SAEI significantly outperforms all the remaining imputation methods for all experimented settings, achieving an average improvement of 35%.

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
Nowadays, the information regarding cosmetic products available to the community is vast, although not always trustworthy. The Pharmaceutical Technology Laboratory of the Faculty of Pharmacy of the University of Porto (FFUP) launched the Portal infoCosméticos aiming to provide professionals involved in cosmetic advice with reliable information, supported by up-to-date scientific evidence, while empowering Portuguese-speaking consumers to make better informed choices. Pre and post-graduates of the master's degree in Pharmaceutical Sciences are responsible for developing contents, which are submitted to a linguistic review by students of the Faculty of Arts and Humanities. Firstly, a relevant question is identified, following a comprehensive search on the topic and the creation of an infographic. The scientific validation is carried out by national and international scholars, and the national regulatory authority, INFARMED. Since it was released in 2017, the website has hit more than 170,000 visualizations, covering topics related to regulatory affairs, safety and efficacy, cosmetic ingredients and cosmetic products. The most accessed topics by digital users were disclosed by monitoring the visualizations of each question with Google Analytics, considering the publication date. According to the records, consumers seem to be more concerned about the safety of cosmetics and interested to know more about their composition.

Abstract
The integration of explanation techniques promotes the comprehension of a model's output and contributes to its interpretation e.g. by generating heat maps highlighting the most decisive regions for that prediction. However, there are several drawbacks to the current heat map-generating methods. Probability by itself is not indicative of the model's conviction in a prediction, as it is influenced by multiple factors, such as class imbalance. Consequently, it is possible that a model yields two true positive predictions - one with an accurate explanation map, and the other with an inaccurate one. Current state-of-the-art explanations are not able to distinguish both scenarios and alert the user to dubious explanations. The goal of this work is to represent these maps more intuitively based on how confident the model is regarding the diagnosis, by adding an extra validation step over the state-of-the-art results that indicates whether the user should trust the initial explanation or not. The proposed method, Confident-CAM, facilitates the interpretation of the results by measuring the distance between the output probability and the corresponding class threshold, using a confidence score to generate nearly null maps when the initial explanations are most likely incorrect. This study implements and validates the proposed algorithm on a multi-label chest X-ray classification exercise, targeting 14 radiological findings in the ChestX-Ray14 dataset with significant class imbalance. Results indicate that confidence scores can distinguish likely accurate and inaccurate explanations. Code available via GitHub.