Abstract
Two-dimensional echocardiography is the most widely used non-invasive imaging modality due to its fast acquisition time, low cost, and high temporal resolution. Accurate segmentation of the left ventricle in echocardiography is vital for ensuring the accuracy of subsequent diagnosis. Currently, numerous efforts have been made to automatize this task and various public datasets have been released in recent decades to further develop present research. However, medical datasets acquired at different institutions have inherent bias caused by various confounding factors, such as operation policies, machine protocols, treatment preference, etc. As a result, models trained on one dataset, regardless of volume, cannot be confidently utilized for the others. In this study, we investigated model robustness to dataset bias using two publicly available echocardiographic datasets. This work validates the efficacy of a supervised deep learning model for left ventricle segmentation and ejection fraction prediction, outside the dataset on which it was trained. The exposure of this model to unseen, but related samples without additional training maintained a good performance. However, a performance decrease from the original results can be observed, while the impact of quality is also noteworthy with lower quality data leading to decreased performance.

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
To design and deploy their supply chains, companies must naturally take quite different decisions, some being strategic or tactical, and others of an operational nature. This work resulted in a decision support system for optimising a biomass supply chain in Portugal, allowing a more efficient operations management, and enhancing the design process. Uncertainty and variability in the biomass supply chain is a critical issue that needs to be considered in the production planning of bioenergy plants. A simulation/optimisation framework was developed to support decision-making, by combining plans generated by a resource allocation optimisation model with the simulation of disruptive wildfire scenarios in the forest biomass supply chain. Different scenarios have been generated to address uncertainty and variability in the quantity and quality of raw materials in the different supply nodes. Computational results show that this simulation/optimisation approach can have a significant impact in the operations efficiency, particularly when disruptions occur closer to the end of the planning horizon. The approach seems to be easily scalable and easy to extend to other sectors.

Abstract
The gastrointestinal tract is responsible for the entire digestive process. Several diseases, including colorectal cancer, can affect this pathway. Among the deadliest cancers, colorectal cancer is the second most common. It arises from benign tumors in the colon, rectum, and anus. These benign tumors, known as colorectal polyps, can be diagnosed and removed during colonoscopy. Early detection is essential to reduce the risk of cancer. However, approximately 28% of polyps are lost during this examination, mainly because of limitations in diagnostic techniques and image analysis methods. In recent years, computer-aided detection techniques for these lesions have been developed to improve detection quality during periodic examinations. We proposed an automatic method for polyp detection using colonoscopy images. This study presents a two-stage polyp detection method for colonoscopy images using transformers. In the first stage, a saliency map extraction model is supported by the extracted depth maps to identify possible polyp areas. The second stage of the method consists of detecting polyps in the extracted images resulting from the first stage, combined with the green and blue channels. Several experiments were performed using four public colonoscopy datasets. The best results obtained for the polyp detection task were satisfactory, reaching 91% Average Precision in the CVC-ClinicDB dataset, 92% Average Precision in the Kvasir-SEG dataset, and 84% Average Precision in the CVC-ColonDB dataset. This study demonstrates that polyp detection in colonoscopy images can be efficiently performed using a combination of depth maps, salient object-extracted maps, and transformers.

Abstract
Cryptographic ransomware attacks are constantly evolving by obfuscating their distinctive features (e.g., I/O patterns) to bypass detection mechanisms and to run unnoticed at infected servers. Thus, efficiently exploring the I/O behavior of ransomware families is crucial so that security analysts and engineers can better understand these and, with such knowledge, enhance existing detection methods. In this paper, we propose CRIBA, an open-source framework that simplifies the exploration, analysis, and comparison of I/O patterns for Linux cryptographic ransomware. Our solution combines the collection of comprehensive information about system calls issued by ransomware samples, with a customizable and automated analysis and visualization pipeline, including tailored correlation algorithms and visualizations. Our study, including 5 Linux ransomware families, shows that CRIBA provides comprehensive insights about the I/O patterns of these attacks while aiding in exploring common and differentiating traits across families.

Abstract
Variational quantum circuits are being used as versatile quantum machine learning models. Some empirical results exhibit an advantage in supervised and generative learning tasks. However, when applied to reinforcement learning, less is known. In this work, we considered a variational quantum circuit composed of a low-depth hardware-efficient ansatz as the parameterized policy of a reinforcement learning agent. We show that an epsilon-approximation of the policy gradient can be obtained using a logarithmic number of samples concerning the total number of parameters. We empirically verify that such quantum models behave similarly to typical classical neural networks used in standard benchmarking environments and quantum control, using only a fraction of the parameters. Moreover, we study the barren plateau phenomenon in quantum policy gradients using the Fisher information matrix spectrum.