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Abstract
Deep Learning (DL) has emerged as a powerful tool for vulnerability detection, often outperforming traditional solutions. However, developing effective DL models requires large amounts of real-world data, which can be difficult to obtain in sufficient quantities. To address this challenge, DiverseVul dataset has been curated as one of the largest datasets of vulnerable and non-vulnerable C/C++ functions extracted exclusively from real-world projects. Its goal is to provide high-quality, large-scale samples for training DL models. Nevertheless, during our study several inconsistencies were identified in the raw dataset while applying pre-processing techniques, highlighting the need for a refined version. In this work, we present a refined version of DiverseVul dataset, which is used to fine-tune a large language model, LLaMA 3.2, for vulnerability detection. Experimental results show that the use of pre-processing techniques led to an improvement in performance, with the model achieving an F1-Score of 66%, a competitive result when compared to our baseline, which achieved a 47% F1-Score in software vulnerability detection.

Abstract
This study explores the applicability of the Laffer Curve in the context of the European Union (EU) by analyzing the relationship between taxation and fiscal revenue across personal income tax (PIT), corporate income tax (CIT), and value-added tax (VAT). Utilizing a comprehensive panel data set spanning 1995 to 2022 across all 27 EU member states, the research also integrates the Bird Index to assess fiscal effort and employs advanced econometric techniques, including the Hausman Test and log-quadratic regression models, to capture the non-linear dynamics of the Laffer Curve. The findings reveal that excessively high tax rates, particularly in some larger member states, may lead to revenue losses due to reduced economic activity and tax evasion, highlighting the existence of optimal tax rates that maximize revenue while sustaining economic growth. By estimating threshold tax rates and incorporating the Bird Index, the study provides a nuanced perspective on tax efficiency and fiscal sustainability, offering evidence-based policy recommendations for optimizing tax systems in the European Union to balance revenue generation with economic competitiveness.

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The detection of Pulmonary Hypertension (PH) from the computer analysis of digitized heart sounds is a low-cost and non-invasive solution for early PH detection and screening. We present an extensive cross-domain evaluation methodology with varying animals (humans and porcine animals) and varying auscultation technologies (phonocardiography and seisomocardiography) evaluated across four methods. We introduce PH-ELM, a resource-efficient PH detection model based on the extreme learning machine that is smaller (300x fewer parameters), energy efficient (532x fewer watts of power), faster (36x faster to train, 44x faster at inference), and more accurate on out-of-distribution testing (improves median accuracy by 0.09 area under the ROC curve (auROC)) in comparison to a previously best performing deep network. We make four observations from our analysis: (a) digital auscultation is a promising technology for the detection of pulmonary hypertension; (b) seismocardiography (SCG) signals and phonocardiography (PCG) signals are interchangeable to train PH detectors; (c) porcine heart sounds in the training data can be used to evaluate PH from human heart sounds (the PH-ELM model preserves 88 to 95% of the best in-distribution baseline performance); (d) predictive performance of PH detection can be mostly preserved with as few as 10 heartbeats and capturing up to approximately 200 heartbeats per subject can improve performance.

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