Abstract
This paper sets the basis for a compositional and structured approach to the specification of paraconsistent transitions systems, framed as an institution. The latter and theirs logics were previously introduced in [CMB22] to deal with scenarios of inconsistency in which several requirements are on stake, either reinforcing or contradicting each other. © 2023, IFIP International Federation for Information Processing.

Abstract

Abstract
In the last decades most efforts to catalog and characterize the built environment for multi-hazard risk assessment have focused on the exploration of census data, cadastral datasets, and local surveys. The first approach is only updated every 10 years and does not provide building locations, the second type of data is only available for restricted urban centers, and the third approach requires surveyors with an engineering background, which is cost-prohibitive for large-scale risk studies. It is thus clear that methods to characterize the built environment for large-scale risk analysis at the asset level are currently missing, which hampers the assessment of the impact of natural hazards for the purposes of risk management. Some recent efforts have demonstrated how deep learning algorithms can be trained to recognize specific architectural and structural features of buildings, which is needed for earthquake risk analysis. In this paper we describe how convolutional neural networks can be combined with data from OpenStreetMap and Google Street View to help develop exposure models for multi-hazard risk analysis. This project produced an original comprehensively annotated (15 characteristics) dataset of approximately 5000 images of buildings from the parish of Alvalade (Lisbon, Portugal). The dataset was used to train and test different deep learning networks for building exposure models. The best results were obtained with ResNet50V2, InceptionV3 and DenseNet201, all with accuracies above 82%. These results will support future developments for assessing exposure models for seismic risk analysis. The novelty of our work consists in the number of characteristics of the images in the dataset, the number of deep learning models trained and the number of classes that can be used for building exposure models.

Abstract
Bone marrow edema (BME) is the term given to the abnormal fluid signal seen within the bone marrow on magnetic resonance imaging (MRI). It usually indicates the presence of underlying pathology and is associated with a myriad of conditions/causes. However, it can be misleading, as in some cases, it may be associated with normal changes in the bone, especially during the growth period of childhood, and objective methods for assessment are lacking. In this work, learning models for BME detection were developed. Transfer learning was used to overcome the size limitations of the dataset, and two different regions of interest (ROI) were defined and compared to evaluate their impact on the performance of the model: bone segmention and intensity mask. The best model was obtained for the high intensity masking technique, which achieved a balanced accuracy of 0.792 +/- 0.034. This study represents a comparison of different models and data regularization techniques for BME detection and showed promising results, even in the most difficult range of ages: children and adolescents. The application of machine learning methods will help to decrease the dependence on the clinicians, providing an initial stratification of the patients based on the probability of edema presence and supporting their decisions on the diagnosis.

Abstract
This position paper builds on the authors' previous work on paraconsistent transition systems to propose a modelling framework for quantum circuits with explicit representation of decoherence.

Abstract
Information and communication technologies, such as serious games, have contributed to addressing the gaps in cognitive rehabilitation for individuals with acquired brain injury (ABI), particularly in the context of the COVID-19 pandemic. Although there are effective software programs and games available for cognitive rehabilitation, they have certain limitations. Most current programs have difficulties to adapt to individual performance, a critical factor in promoting neuroplasticity. Additionally, these programs typically only offer single-player modes. However, patients experience difficulties in social interactions leading to social isolation. To overcome these limitations, we propose a novel platform called CogniChallenge. It introduces multiplayer serious games designed for cognitive and psychosocial rehabilitation, offering competitive and cooperative game modes. This platform facilitates engagement with other patients, family members, caregivers, and virtual agents that simulate human interaction. CogniChallenge consists of three games based on activities of daily life and incorporates a multi-agent game balance system. Future research endeavors will focus on evaluating the usability and gameplay experience of CogniChallenge among healthcare professionals and individuals with ABI. By proposing this innovative platform, we intend to contribute to expanding the application of serious games and their potential to solve problems and limitations in the specific field of cognitive rehabilitation. © 2023, Serious Games Society. All rights reserved.