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4th International Conference on Sentiment analysis and Deep Learning, ICSADL 2025

作者： Youb, Ibtissam Hamlich, Mohamed Ventura, Sebastian Al-Obeidat, Feras University of Cordoba Department of Computer Science and Numerical Analysis Cordoba Spain University of Hassan Ii Ccps Laboratory Ensam Department of Electronic Casablanca Morocco College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331523923

Accurate sentiment analysis in financial contexts is crucial for market analysis and investment decisions. However, manually annotating large datasets for training models is time-consuming and costly. This study evaluates two automatic an-notation tools, VADER and TextBlob, for their effectiveness in generating labels for training large language models (LLMs) like BERT and GPT-2 in financial sentiment analysis. We used a large dataset of stock market-related tweets, including a manually annotated subset for benchmarking. The remaining tweets were automatically labeled using VADER and TextBlob. We trained BERT and GPT-2 models with these sentiment labels and compared their performance against the benchmark dataset. Our findings show that models trained with VADER annotations had a higher correlation with human-labeled data (62% accuracy) compared to those trained with TextBlob annotations (48% accuracy). These results suggest that VADER is more suitable for automatic annotation in financial sentiment analysis, providing more accurate and reliable sentiment labels, which can significantly improve the efficiency of training large language models for financial applications. © 2025 IEEE.

关键词： Tweets

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Sequential Solution Strategies for the Cahn-Hilliard-Biot Model

Sequential Solution Strategies for the Cahn-Hilliard-Biot ...

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European Conference on numerical Mathematics and Advanced Applications, ENUMATH 2023

作者： Storvik, Erlend Riethmüller, Cedric Both, Jakub W. Radu, Florin Adrian Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Institute of Applied Analysis and Numerical Simulation University of Stuttgart Stuttgart Germany Centre for Modeling of Coupled Subsurface Dynamics Department of Mathematics University of Bergen Bergen Norway

ISBN: (纸本)9783031861680

This paper presents a study of solution strategies for the Cahn-Hilliard-Biot equations, a complex mathematical model for understanding flow in deformable porous media with changing solid phases. Solving the Cahn-Hilliard-Biot system poses significant challenges due to its coupled, nonlinear and non-convex nature. We explore various solution algorithms, comparing monolithic and splitting strategies, focusing on both their computational efficiency and robustness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computational efficiency

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Evaluating Automatic Annotation Techniques for Fine-Tuning Large Language Models in Financial Sentiment analysis

Evaluating Automatic Annotation Techniques for Fine-Tuning L...

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Sentiment analysis and Deep Learning (ICSADL), International Conference on

作者： Ibtissam Youb Mohamed Hamlich Sebastian Ventura Feras Al-Obeidat Department of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain Department of Electronic CCPS Laboratory ENSAM University of Hassan II Casablanca Morocco College of Technological Innovation Zayed University Abu Dhabi UAE

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

关键词： Training Sentiment analysis Accuracy Annotations Social networking (online) Large language models Benchmark testing Data models Reliability Investment

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Survival of Listeria Monocytogenes Biofilms and Planktonic Cells on Cooked Ham in a Contamination-Storage-Digestion Process

SSRN

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SSRN 2025年

作者： Serrano-Heredia, Salud María Sánchez-Martín, Javier López Carmona, José Luis Sánchez-Gutiérrez, Mónica Valero Díaz, Antonio Jiménez, Elena Carrasco CeiA3 Universidad de Córdoba Campus Rabanales Córdoba14014 Spain KDISLab Department of Computer Science and Numerical Analysis University of Córdoba Spain Rabanales Campus Córdoba14071 Spain

In 2023, listeriosis experienced a significant increase in Europe, with 2,952 invasive cases and 19.70% mortality rate, being the ready-to-eat foods category identified as the mostly involved. This study focused on the survival of L. monocytogenes through a process of food contamination-storage-digestion. For this, it was assayed the transfer of biofilm and inoculation of planktonic cells to cooked ham, and the subsequent survival of the pathogen (planktonic or biofilm) after in vitro digestion with or without storage of cooked ham vacuum-packed and stored at 7°C for 7 days. Transfer results to cooked ham after 90 seconds of contact revealed inter-strain variability, with a global transfer rate of 14.07%. From biofilm, initial concentration of L. monocytogenes in the food, 3.53 log CFU/cm2 on average, grew ≈ 0.5 log after storage. Digestion, both immediate after contamination or after food storage, resulted in a reduction of ≈ 0.5 log. However, in the case of planktonic cells, although reduction after immediate digestion (≈ -2.1 log) and after storage (≈ - 0.6 log) were observed as expected (given their less resistance than biofilm cells), an unexpectedly high concentration of the pathogen was found after food storage and digestion (even with a slight increase of ≈ 0.25 log). It is plausible that a biofilm was formed during storage of the contaminated food with planktonic cells. In all cases, high variability in numbers was observed after storage and digestion, being L196 the most resistant strain. More research in omics sciences is needed to clarify L. monocytogenes behaviour. © 2025, The Authors. All rights reserved.

关键词： Food storage

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Integrating Physics and Data-Driven Approaches: An Explainable and Uncertainty-Aware Hybrid Model for Wind Turbine Power Prediction

arXiv

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arXiv 2025年

作者： Gijón, Alfonso Eiraudo, Simone Manjavacas, Antonio Schiera, Daniele Salvatore Molina-Solana, Miguel Gómez-Romero, Juan Department of Computer Science and Artificial Intelligence Universidad de Granada Granada18071 Spain Energy Center Lab Politecnico di Torino Torino10138 Italy Deparment of Informatics and Numerical Analysis Universidad de Córdoba Córdoba14014 Spain

The rapid growth of the wind energy sector underscores the urgent need to optimize turbine operations and ensure effective maintenance through early fault detection systems. While traditional empirical and physics-based models offer approximate predictions of power generation based on wind speed, they often fail to capture the complex, non-linear relationships between other input variables and the resulting power output. Data-driven machine learning methods present a promising avenue for improving wind turbine modeling by leveraging large datasets, enhancing prediction accuracy but often at the cost of interpretability. In this study, we propose a hybrid semi-parametric model that combines the strengths of both approaches, applied to a dataset from a wind farm with four turbines. The model integrates a physics-inspired submodel, providing a reasonable approximation of power generation, with a non-parametric submodel that predicts the residuals. This non-parametric submodel is trained on a broader range of variables to account for phenomena not captured by the physics-based component. The hybrid model achieves a 37% improvement in prediction accuracy over the physics-based model. To enhance interpretability, SHAP values are used to analyze the influence of input features on the residual submodel’s output. Additionally, prediction uncertainties are quantified using a conformalized quantile regression method. The combination of these techniques, alongside the physics grounding of the parametric submodel, provides a flexible, accurate, and reliable framework. Ultimately, this study opens the door for evaluating the impact of unmodeled variables on wind turbine power generation, offering a basis for potential optimization. © 2025, CC BY.

关键词： Windmill

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Rational Gaussian wavelets and corresponding model driven neural networks

arXiv

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arXiv 2025年

作者： Ámon, Attila Miklós Fenech, Kristian Kovács, Péter Dózsa, Tamás Eötvös Loránd University Faculty of Informatics Department of Numerical Analysis Hungary Eötvös Loránd University Faculty of Informatics Department of Artificial Intelligence Hungary HUN-REN Institute for Computer Science and Control Systems and Control Laboratory Hungary University of Győr Hungary

In this paper we consider the continuous wavelet transform using Gaussian wavelets multiplied by an appropriate rational term. The zeros and poles of this rational modifier act as free parameters and their choice highly influences the shape of the mother wavelet. This allows the proposed construction to approximate signals with complex morphology using only a few wavelet coefficients. We show that the proposed rational Gaussian wavelets are admissible and provide numerical approximations of the wavelet coefficients using variable projection operators. In addition, we show how the proposed variable projection based rational Gaussian wavelet transform can be used in neural networks to obtain a highly interpretable feature learning layer. We demonstrate the effectiveness of the proposed scheme through a biomedical application, namely, the detection of ventricular ectopic beats (VEBs) in real ECG *** Codes 65D15 © 2025, CC BY.

关键词： Electrocardiography

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FIXED POINT RESULTS AND THE EKELAND VARIATIONAL PRINCIPLE IN VECTOR B-METRIC SPACES

arXiv

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arXiv 2025年

作者： Precup, Radu Stan, Andrei Faculty of Mathematics and Computer Science Institute of Advanced Studies in Science and Technology Babeş-Bolyai University Cluj-Napoca400084 Romania Tiberiu Popoviciu Institute of Numerical Analysis Romanian Academy P.O. Box 68-1 Cluj-Napoca400110 Romania Department of Mathematics Babeş-Bolyai University Cluj-Napoca400084 Romania

In this paper, we extend the concept of b-metric spaces to the vectorial case, where the distance is vector-valued, and the constant in the triangle inequality axiom is replaced by a matrix. For such spaces, we establish results analogous to those in the b-metric setting: fixed-point theorems, stability results, and a variant of Ekeland’s variational principle. As a consequence, we also derive a variant of Caristi’s fixed-point theorem. © 2025, CC BY.

关键词： Vector spaces

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Reviewing practices in collaborative writing

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computer Supported Cooperative Work 2001年第2期10卷 247-259页

作者： Kim, H.-C. Eklundh, Kerstin Severinson Department of Numerical Analysis and Computer Science Royal Institute of Technology Stockholm Sweden

This paper presents an interview study in which 11 academics as interviewees participated for the purpose of revealing common collaborative writing practices, with particular focus on reviewing documents. First, we present the findings obtained concerning the issues of co-operating strategies underlying the reviewing process, how people revise their documents and comment on them, what they use the previous revision history for, and to what extent current technology is used in the reviewing process. Second, we also discuss aspects of the design of collaborative writing tools.

关键词： computer supported cooperative work

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On two-fluid equations for dispersed incompressible two-phase flow

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Computing and Visualization in science 2001年第2期4卷 125-135页

作者： Yström, Jacob Department of Numerical Analysis and Computer Science KTH 100 44 Stockholm Sweden

This paper deals with the two-fluid formulation for dispersed two-phase flow. In one space dimension the equations with constant viscosity for both phases are shown to result in a locally in time well-posed periodic problem but without viscosity there are regions in phase space where the problem is non-hyperbolic. It is shown that the viscid problem linearized at constant states in the non-hyperbolic region is terribly unstable for small viscosity coefficients. By numerical experiments for the solution to a model problem it is demonstrated that for smooth initial data in the nonhyperbolic region, discontinuities seem to form. Second order artificial dissipation is added, in an effort to regularize the problem. numerical examples are given, for different types of initial data. For initial data that is smooth in the nonhyperbolic region, the formation of jumps, or viscid layers, is strongly dependent on the amount of artificial dissipation. No convergence is obtained as the amount of artificial dissipation is diminished. On the other hand, if the initial data is smooth only in the hyperbolic region and with jumps through the nonhyperbolic region, then the jumps or viscid layers that later form, can damp the onset of new layers. In this case convergence in the L2 sense seem to hold, the computed solutions to the regularized problem approach a weak solution as the artificial dissipation is diminished. © Springer-Verlag 2001.

关键词： Viscosity

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Global and Diverse Ensemble model for regression

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Neurocomputing 2025年 647卷

作者： Antonio M. Durán-Rosal Thomas Ashley Javier Pérez-Rodríguez Francisco Fernández-Navarro Department of Quantitative Methods Universidad Loyola Andalucía Spain Department of Computer Science and Numerical Analysis University of Córdoba Spain Department of Computer Languages and Computer Science University of Málaga Spain

Diversity is a fundamental component in ensemble methods, crucial for enhancing the overall performance and robustness of predictive models. In bagging and boosting, diversity is implicitly generated through the data sampling process. In stacking, diversity is introduced by incorporating heterogeneous machine learning models as base learners, and an error function is created to focus on minimizing the meta-learner’s overall errors. Some models promote diversity directly within the error function; however, they often prioritize generating competitive and diverse individual learners, neglecting the necessity of creating an ensemble that is collectively accurate, as seen in stacking, whilst ensuring diversity among its members. Motivated by this point, two ensemble models are proposed in this manuscript, named Global and Diverse Ensemble Methods. These models incorporate implicit diversity through data, diversity in the heterogeneity of base learners, and an error function designed to produce a competitive overall ensemble with diverse individuals. The two diversity proposals included in the models are negative correlation and squared Pearson correlation. In both cases, the error function incorporates the minimization of the ensemble’s overall error (global error measure) in addition to promoting diversity. The proposed methods have been rigorously tested on 45 publicly available regression datasets using shallow base learners, as well as on 7 additional datasets using deep base learners, yielding very promising results. These findings underscore the importance of integrating these diversity-promoting elements and minimizing the global errors of the ensemble, rather than focusing solely on the errors of individual base learners, in the design of ensemble methods.

关键词： Ensemble learning Bagging Stacking Diversity

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