The increasing prevalence of audio deepfakes has raised serious concerns due to their potential misuse in identity theft, disinformation, and the compromise of voice authentication systems. Detecting these manipulations requires models capable of handling a wide range of audio features and attack...
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The increasing prevalence of audio deepfakes has raised serious concerns due to their potential misuse in identity theft, disinformation, and the compromise of voice authentication systems. Detecting these manipulations requires models capable of handling a wide range of audio features and attack...
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In modern smart manufacturing environments, human involvement remains critical for addressing complex tasks that require adaptability and decision-making, despite the growing presence of automation and artificial intelligence. This paper introduces SAMBA - Service-Augmented Manufacturing-Based...
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Data preparation has an important role in data analysis, and it is time and resource-consuming, both in terms of human and computational resources. The "Discount quality for responsible data science" project aims to focus on data-quality-based data preparation, analyzing the main characteristics of...
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The Varroa destructor mite poses a critical threat to global honey bee populations, contributing to colony collapse through parasitic infestation. Current detection methods rely on labor-intensive visual inspections, which are prone to human error and impractical for large-scale monitoring. To...
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The PMDI project aims at radically improving the safety of urban mobility by extending STEP, an automotive data management and analytics platform, to support real-time and near-real-time use cases, particularly focusing on dangerous crossings at urban intersections. Such capabilities will be...
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We take into consideration generalization bounds for the problem of the estimation of the drift component for ergodic stochastic differential equations, when the estimator is a ReLU neural network and the estimation is non-parametric with respect to the statistical model. We show a practical way to...
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Electroencephalography (EEG) offers high temporal resolution but struggles to accurately localize subcortical activity, partly due to the ill-posed nature of the inverse problem and the weak signals from deep structures. Traditional regularized inverse methods are computationally efficient yet...
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This study investigates the potential of functional ultrasound imaging (fUSI) as a promising, non-invasive, and cost-effective tool for identifying brain states and detecting pathological changes. We simulated brain activity using a Wilson-Cowan mass model, converting electrophysiological signals...
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Functional ultrasound imaging (fUSI) has emerged as a promising non-invasive neuroimaging modality that leverages neurovascular coupling to capture hemodynamic changes associated with neuronal activity. This study investigates the feasibility of fUSI for brain state classification in both healthy...