Ponencias (Lenguajes y Sistemas Informáticos)
URI permanente para esta colecciónhttps://hdl.handle.net/11441/11394
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Ponencia Weaving AspectJ aspects by means of transformations(2005) Reina Quintero, Antonia María; Torres Valderrama, Jesús; Universidad de Sevilla. Departamento de Lenguajes y Sistemas InformáticosIn the last few years, new software paradigms, such as Aspect-Oriented Software Development (AOSD) or Model Driven Development (MDD), have been brought up in order to improve software adaptability to changes. MDA improves the adaption to different technologies by means of three different levels of modelling. This paper is focused on the platform specific level, and proposes the use of transformations to weave AspectJ aspects and the basic functionality at the modelling level before the code generation phase.Ponencia Hacia la separación de enlaces en sistemas web(2005-09-13) Reina Quintero, Antonia María; Torres Valderrama, Jesús; Universidad de Sevilla. Departamento de Lenguajes y Sistemas InformáticosEl volumen de negocio que se genera en la red es cada vez mayor. Las aplicaciones web tienen características diferenciadoras con respecto a las aplicaciones tradicionales. Por ello, ha surgido una nueva disciplina conocida como ingeniería web. Este artículo propone un enfoque basado en la separación de conceptos para mejorar la evolución de las aplicaciones web. Esta propuesta se basa en las ideas aplicadas al sistema de evolución de bases de datos SADES, centrándose sobre todo en las relaciones dinámicas, ya que este tipo de relaciones se pueden aplicar muy bien a los enlaces de navegación.Ponencia Towards Pricing4SaaS: A Framework for Pricing-Driven Feature Toggling in SaaS(Springer, 2024) García Fernández, Alejandro; Parejo Maestre, José Antonio; Ruiz Cortés, Antonio; Universidad de Sevilla. Departamento de Lenguajes y Sistemas InformáticosIn a rapidly evolving digital marketplace, the ability to enable features and services dynamically on SaaS products in alignment with market conditions and pricing strategies is essential for sustaining competitiveness and improving the user experience. This demo paper presents Pricing4SaaS, a reference architecture designed to enhance the integration and management of pricing-driven feature toggles in SaaS systems. It centralizes the configuration of the pricing structure, spreading its changes across the whole application every time it is modified, while securing state synchronization between both the client and server. Additionally, a case study integrating a reference implementation of Pricing4SaaS inside a Spring+React PetClinic project demonstrates how such approach can be leveraged to optimize developer productivity, reducing technical debt, and improving operational efficiencyPonencia Pricing4SaaS: Towards a Pricing Model to Drive the Operation of SaaS(Springer, 2024-09-18) García Fernández, Alejandro; Parejo Maestre, José Antonio; Ruiz Cortés, Antonio; Universidad de Sevilla. Departamento de Lenguajes y Sistemas InformáticosThe Software as a Service (SaaS) model is a distribution and licensing model that leverages pricing structures and subscriptions to profit. The utilization of such structures allows Information Systems (IS) to meet a diverse range of client needs, while offering improved flexibility and scalability. However, they increase the complexity of variability management, as pricings are influenced by business factors, like strategic decisions, market trends or technological advancements. In pursuit of realizing the vision of pricing-driven IS engineering, this paper introduces Pricing4SaaS as a first step, a generalized specification model for the pricing structures of systems that apply the Software as a Service (SaaS) licensing model. With its proven expressiveness, demonstrated through the representation of 16 distinct popular SaaS systems, Pricing4SaaS aims to become the cornerstone of pricing-driven IS engineering.Ponencia On the Impact and Lessons Learned from Mindfulness Practice in a Real-World Software Company(IEEE COMPUTER SOC, 2023) Bernárdez Jiménez, Beatriz; Parejo Maestre, José Antonio; Cruz Risco, Margarita; Muñoz, Salvador; Antonio Ruiz Cortés; Universidad de Sevilla. Departamento de Lenguajes y Sistemas InformáticosMindfulness is a meditation technique whose main goal is educating attention by focusing only on one thing at a time, usually breathing. Mindfulness practices improve concentration and attention, being particularly valuable in demanding and high-stress work settings, such as those found in software companies. A family of five controlled experiments on the impact of mindfulness on future and current software engineers’ performance has been carried out in six years, whose participants practiced mindfulness daily for several weeks. Aims. This work has a twofold purpose, to present the fifth experiment in the series and to summarize the lessons learned across the family of experiments. The fifth experiment was carried out at INPRO, a public software company in Seville (Spain), in order to evaluate whether software workers improve their performance and some psychological factors, i.e. attention awareness, techno stress and well–being, compared to a control group. Method. Employees of two departments (Development and Operation) were recruited to participate in the study. Mindfulness (the treatment) was applied to 24 subjects who attended mindfulness sessions daily for six weeks, while the other 27 subjects were the control group. For all subjects, such psychological factors were measured using questionnaires, whereas performance was measured using INPRO’s task management systems. Results. Findings have shown significant differences in terms of attention awareness and techno–stress levels between the practitioners who practised mindfulness and those who did not. Benefits on the perceived well-being have also been reported by participants after the continued practise of mindfulness. Regarding the performance, the analysis depicts inconclusive results, probably due to the small size of the sample, a problem which was accentuated by significant variability in the kinds of tasks performed in both departments. Conclusions. Mindfulness practice has yielded significant benefits in the series of experiments, such as performance in the academy and psychological factors in the industry. Nevertheless, its impact on performance in software companies requires further research, since the limited data availability on subjects’ performance has led to a small sample size, ultimately posing challenges in drawing dependable conclusions.Ponencia Una herramienta para mejorar la experiencia de los alumnos en el aprendizaje de estrategias software(2021) Guerrero-Cuenca, C.; Olivero González, Miguel Ángel; Domínguez Mayo, Francisco José; Morales Trujillo, Leticia; Gutiérrez Rodríguez, Javier Jesús; Mejías Risoto, Manuel; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia, Innovación y Universidades del Gobierno de España; Universidad de Sevilla. TIC-021: Engineering and Science for Software SystemsTeaching in Software Engineering involves learning strategies to address so ft- ware discovery, development and operations. However, in the literature we can find strategies that are defined based on constructs that can even vary between similar strategies, which makes it difficult to learn these strategies. Here it is proposed a technique consisting of a canvas that organizes knowledge into a set of basic constructs that allow it to be applied to any soft- ware strategy to describe it. Post-its can be included on the canvas in an agile way to conceptually define any software strategy taught in class. It is intended to improve the experience ofstudents during learning, so that they are active participants instead ofpassive observers, thus facilitatingmeaningful learning that endows the student with the ability to acquire concepts and establish re- lationships on the knowledge acquired. To validate the proposal, an evaluation of the student's experience during learning with the UEQ (User Experience Questionnaire) technique has been carried out. The results show that the tech- nique is perceived by students as a novel, stimulating and attractive learning technique.Ponencia Comparación Técnica de Sistemas de Información desde la Prerspectiva del Usuario. Experiencia Práctica en Soluciones para Tratamientos de Reproducción Asistida(SEIS, Sociedad Española de Informática de la Salud, 2021-06) Ramírez de Verger, Cristina; Morales Trujillo, Leticia; Bautista Llamas, María José; Sánchez Gómez, Nicolás; Olivero González, Miguel Ángel; Meidan, A.; Navarro Pando, José Manuel; Universidad de Sevilla. Departamento de Física de la Materia Condensada; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Economía y Competitividad de España, Proyecto NICO; Consejería de Economía, Conocimiento, Empresa y Universidad de Andalucía, Proyecto SocietySoftEn los últimos años, los avances en las Tecnologías de la Información y la Comunicación (TIC) las han hecho imprescindibles en nuestra sociedad, especialmente en el funcionamiento de muchas organizaciones. Un ejemplo sería el impacto que han tenido y tienen en la medicina. Son fundamentales para mejorar la calidad, eficacia y eficiencia de los servicios sanitarios, siendo necesario apostar por la interoperabilidad de estos sistemas tecnológicos.Ponencia Reconstrucción Visual 3D para el Prediagnóstico de Heridas Post-Operatorias(2018) Muntaner Estrellas, N.; Morales Trujillo, Leticia; Cid de la Paz, Virginia; Bonin Font, Francisco; Jiménez Ramírez, Andrés; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Economía y Competitividad, Proyecto Pololas; Fondos FederActualmente, la sanidad pública está colapsada por la cantidad de gente que asiste a consultas médicas sin ser realmente necesario. En particular, los departamentos de cirugía reciben diariamente decenas de pacientes para el control y la curación de las heridas posquirúrgicas, donde la mayoría de ellos no presenta anomalías y se pueden tratar fácilmente de forma remota.Ponencia IMEDEA. Sistema Integral de Gestión Clínica para Unidades de Reproducción Asistida [Póster](SEIS, Sociedad Española de Informática de la Salud, 2021-06) Ramírez de Verger, Cristina; Sánchez Gómez, Nicolás; Morales Trujillo, Leticia; García García, Julián Alberto; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. TIC-021: Engineering and Science for Software SystemsEn los últimos años, los avances en las Tecnologías de la Información y la Comunicación (TIC) las han hecho imprescindibles en nuestra sociedad, especialmente en el funcionamiento de muchas organizaciones. Un ejemplo sería el impacto que han tenido y tienen en la medicina. Son fundamentales para mejorar la calidad, eficacia y eficiencia de los servicios sanitarios, siendo necesario apostar por la interoperabilidad de estos sistemas tecnológicos.Ponencia Una comparación colaborativa del rendimiento en proyectos de software libre(Sistedes, 2024) Sánchez Ruiz, José Manuel; Olivero González, Miguel Ángel; Domínguez Mayo, Francisco José; Benavides Cuevas, David Felipe; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Rodríguez Luaces, Miguel ÁngelEn los últimos años, los proyectos de software de código abierto (OSS) se han vuelto cada vez más importantes para muchas organizaciones. A medida que estos proyectos crecen en tamaño y complejidad, aumenta la necesidad de un desarrollo de software de alta calidad. Al mismo tiempo se ha comprobado que el uso de prácticas de DevOps mejora la calidad y el rendimiento organizacional. Sin embargo, es difícil medir el impacto real que supone aplicar estas prácticas porque los métodos de evaluación existentes, como los informes de DORA, se centran principalmente en la implementación continua y la entrega en producción. Esto se diferencia de las prioridades de los proyectos OSS, que enfatizan la liberación continua de código y su impacto en los usuarios en lugar de en sus implementaciones o entregas en producción. Para abordar esta situación se emplea un sistema colaborativo de evaluación, Performance-Tracker (PT), diseñado para evaluar y comparar el rendimiento de proyectos OSS usando varios factores. PT extrae información pública de proyectos OSS y ha permitido generar una base de conocimientos compartida que supone la primera base de conocimiento del marco de referencia. Esto se ha logrado evaluando el rendimiento de 50 proyectos OSS en su primera versión. Este enfoque permite que puedan evaluarse más proyectos y que comparen su rendimiento en base a los puntos de referencia de la base de conocimiento colaborativa. Usando PT y con las métricas propuestas, los proyectos OSS pueden analizar y comparar su rendimiento con respecto al resto de proyectos participantes. Esto, a su vez, permite que sus resultados alimenten la base de conocimiento y enriquezcan el marco de referencia. PT, junto a su base de conocimiento inicial, es una propuesta integral para la evaluación de proyectos OSS, abordando los desafíos propios de este tipo de proyectos. Además, el entorno de aprendizaje colaborativo persigue fomentar un proceso de desarrollo más eficiente en los proyectos OSS. Al permitir una comparación de métricas, los equipos de desarrollo pueden identificar claramente cómo mejorar su rendimiento.Ponencia Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection(SpringerLink, 2022) Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Sousa Brito, Isabel Sofía; Martínez Álvarez, Francisco; Asencio Cortés, Gualberto; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Junta de AndalucíaNeural networks have proven to be a good alternative in application fields such as healthcare, time-series forecasting and artificial vision, among others, for tasks like regression or classification. Their potential has been particularly remarkable in unstructured data, but recently developed architectures or their ensemble with other classical methods have produced competitive results in structured data. Feature selection has several beneficial properties: improve efficacy, performance, problem understanding and data recollection time. However, as new data sources become available and new features are generated using feature engineering techniques, more computational resources are required for feature selection methods. Feature selection takes an exorbitant amount of time in datasets with numerous features, making it impossible to use or achieving suboptimal selections that do not reflect the underlying behavior of the problem. We propose a nonparametric neural network layer which provides all the benefits of feature selection while requiring few changes to the architecture. Our method adds a novel layer at the beginning of the neural network, which removes the influence of features during training, adding inherent interpretability to the model without extra parameterization. In contrast to other feature selection methods, we propose an efficient and model-aware method to select the features with no need to train the model several times. We compared our method with a variety of popular feature selection strategies and datasets, showing remarkable results.Ponencia Explaining Learned Patterns in Deep Learning by Association Rules Mining(SpringerLink, 2023-08) Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Asencio Cortés, Gualberto; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Junta de AndalucíaThis paper proposes a novel approach that combines an association rule algorithm with a deep learning model to enhance the interpretability of prediction outcomes. The study aims to gain insights into the patterns that were learned correctly or incorrectly by the model. To identify these scenarios, an association rule algorithm is applied to extract the patterns learned by the deep learning model. The rules are then analyzed and classified based on specific metrics to draw conclusions about the behavior of the model. We applied this approach to a well-known dataset in various scenarios, such as underfitting and overfitting. The results demonstrate that the combination of the two techniques is highly effective in identifying the patterns learned by the model and analyzing its performance in different scenarios, through error analysis. We suggest that this methodology can enhance the transparency and interpretability of black-box models, thus improving their reliability for real-world applications.Ponencia Evolutionary computation to explain deep learning models for time series forecasting(Association for Computing Machinery, 2023-06) Troncoso García, Ángela del Robledo; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Troncoso Lora, Alicia; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Junta de AndalucíaDeep learning has become one of the most useful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, deep learning is known as a black box approach and most experts experience difficulties to explain and interpret deep learning results. In this context, explainable artificial intelligence (XAI) is emerging with the aim of providing black box models with sufficient interpretability so that models can be easily understood by humans. The use of an evolutionary-based association rules extraction algorithm to explain deep learning models for multi-step time series forecasting is addressed in this work. This evolutionary application is proposed to be used with the predictions obtained by long-short term memory (LSTM) deep learning network. Data from Spanish electricity energy consumption has been used to assess the suitability of thePonencia Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning(Springer Link, 2023-10) Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Asencio Cortés, Gualberto; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Junta de AndalucíaTraditional time series forecasting models often use all available variables, including potentially irrelevant or noisy features, which can lead to overfitting and poor performance. Feature selection can help address this issue by selecting the most informative variables in the temporal and feature dimensions. However, selecting the right features can be challenging for time series models. Embedded feature selection has been a popular approach, but many techniques do not include it in their design, including deep learning methods, which can lead to less efficient and effective feature selection. This paper presents a deep learning-based method for time series forecasting that incorporates feature selection to improve model efficacy and interpretability. The proposed method uses a multidimensional layer to remove irrelevant features along the temporal dimension. The resulting model is compared to several feature selection methods and experimental results demonstrate that the proposed approach can improve forecasting accuracy while reducing model complexity.Ponencia Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals(Springer Link, 2023-09) Troncoso García, Ángela del Robledo; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Troncoso Lora, Alicia; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). EspañaThis paper explores the use of deep learning techniques for detecting sleep apnea. Sleep apnea is a common sleep disorder characterized by abnormal breathing pauses or infrequent breathing during sleep. The current standard for diagnosing sleep apnea involves overnight polysomnography, which is expensive and requires specialized equipment and personnel. The proposed method utilizes a neural network to analyze physiological signals, such as heart rate and respiratory patterns, that are recorded during sleep to authomatic sleep apnea detection. The neural network is trained on a dataset of polysomnography recordings to identify patterns that are indicative of sleep apnea. The results compare the use of different physiological signals to detect sleep apnea. Nasal airflow seems to have the most accurate results and higher specificity, whereas EEG and ECG have higher levels of sensitivity. The best model concerning accuracy is compared to bias models previously applied to sleep apnea detection in literature, achieving greater results. This approach has the potential to provide automatic sleep apnea detection, being an accessible solution for diagnosing sleep apnea.Ponencia Association Rule Analysis of Student Satisfaction Surveys for Teaching Quality Evaluation(Springer Link, 2023-08) Jiménez Navarro, Manuel Jesús; Vega Márquez, Belén; Luna Romera, José María; Carranza García, Manuel; Martínez Ballesteros, María del Mar; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Junta de AndalucíaThe quality of university teaching is essential for the success of students and the academic excellence of an educational institution. The purpose of this work is to provide a methodology based on the Association Rule technique using the Apriori algorithm to analyze the results obtained from the student evaluation process regarding their satisfaction with the teaching received. This methodology has been applied in programming courses of students of several courses both in the Computer Engineering and Health Engineering degrees at University of Seville, Spain. The proposed methodology can serve as a starting point for a self-improvement process that clearly identifies strengths and weaknesses.Ponencia A novel approach to discover numerical association based on the Coronavirus Optimization Algorithm(Association for Computing Machinery, 2022-04) Segarra Martín, C.; Martínez Ballesteros, María del Mar; Troncoso Lora, Alicia; Martínez Álvarez, Francisco; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Economía y Competitividad (MINECO). España; Junta de AndalucíaThe disease caused by the SARS-CoV-2 (COVID-19) has affected millions of people around the world since its detection in 2019. This pandemic inspired the development of the Coronavirus Optimization Algorithm (CVOA), a bio-inspired metaheuristic that was originally used to adjust deep learning models for time series forecasting, by means of a binary codification. In this paper, a integer codification for the CVOA individual is introduced and used for optimizing a novel approach for numerical association rules mining. As an application case, the prediction of earthquakes of large magnitude has been addressed. This kind of events are rare and, therefore, they can be characterized by rules with very high interest or lift and low support. Thus, the algorithm has been applied to the extraction of rules meeting specific criteria in an earthquake data set, provided by the National Geographic Institute of Spain. The results show CVOA as a promising tool for numerical association rules mining, obtaining rules with useful and meaningful information for predicting the occurrence of large earthquakes.Ponencia A New Hybrid CNN-LSTM for Wind Power Forecasting in Ethiopia(Springer Link, 2023-08) Tefera Habtemariam, Ejigu; Martínez Ballesteros, María del Mar; Troncoso Lora, Alicia; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Junta de AndalucíaRenewable energies are currently experiencing promising growth as an alternative solution to minimize the emission of pollutant gases from the use of fossil fuels, which contribute to global warming. To integrate these renewable energies safely with the grid system and make the electric grid system more stable, it is vitally important to accurately forecast the amount of wind power generated at specific wind power generation sites and the timing of this generation. Deep learning approaches have shown good forecasting performance for complex and nonlinear problems, such as time series wind power data. However, further study is needed to optimize deep learning models by integrating multiple models with hyperparameter optimization, to attain optimal performance from these individual models. In this paper, we propose a hybrid CNN-LSTM model for wind power forecasting in Ethiopia. Bayesian optimization is applied to tune the hyperparameters of the individual learners, including 1D-CNN and LSTM models, before building the hybrid CNN-LSTM model. The proposed model is tested on three case study wind power datasets obtained from the Ethiopian Electric Power Corporation. According to the MAE, RMSE, and MAPE evaluation metrics, the hybrid model performs significantly better than benchmark models, including ANN, RNN, BiLSTM, CNN, and LSTM models, for all case study data.Ponencia A Feature Selection and Association Rule Approach to Identify Genes Associated with Metastasis and Low Survival in Sarcoma(SpringerLink, 2023-08) Linares Barrera, María Lourdes; Martínez Ballesteros, María del Mar; García Heredia, José Manuel; Riquelme Santos, José Cristóbal; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Junta de AndalucíaSarcomas are rare mesodermal tumors of heterogeneous nature and have a higher incidence in children. The relative 5-year survival rate for patients with metastatic sarcoma is usually low. Standard treatment for sarcomas involves surgical resection, and investigating the genetic basis of these tumors through genome-wide analysis is crucial due to their rarity and late diagnosis. This work proposes a methodology that combines preprocessing, feature selection and association rule mining to identify relevant genes and significant relationships in biological data from sarcoma patients. Our study aims to identify the relationships between metastasis-associated genes and patient survival of less than 5 years. The proposed approach was applied to a sarcoma dataset containing data on gene expression, metastasis occurrence, and survival time, revealing a set of biologically relevant gene interactions associated with sarcoma metastasis and low survival rates. The combined use of these techniques can facilitate the identification of biomarkers or gene signatures associated with the disease and provide insight into the underlying biological mechanisms involved in sarcomas.Ponencia A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal(Association for Computing Machinery, 2023-03) Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Sofia Brito, Isabel; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Junta de AndalucíaThe year 2022 was the driest year in Portugal since 1931 with 97% of territory in severe drought. Water is especially important for the agricultural sector in Portugal, as it represents 78% total consumption according to theWater Footprint report published in 2010. Reference evapotranspiration is essential due to its importance in optimal irrigation planning that reduces water consumption. This study analyzes and proposes a framework to forecast daily reference evapotranspiration at eight stations in Portugal from 2012 to 2022 without relying on public meteorological forecasts. The data include meteorological data obtained from sensors included in the stations. The goal is to perform a multi-horizon forecasting of reference evapotranspiration using the multiple related covariates. The framework combines the data processing and the analysis of several state-of-the-art forecasting methods including classical, linear, tree-based, artificial neural network and ensembles. Then, an ensemble of all trained models is proposed using a recent bioinspired metaheuristic named Coronavirus Optimization Algorithm to weight the predictions. The results in terms of MAE and MSE are reported, indicating that our approach achieved a MAE of 0.658.