Capítulos (Lenguajes y Sistemas Informáticos)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11393

Examinar

Mostrando 1 - 20 de 164

A-MINT: An LLM Pipeline for Automated Modeling of iPricings from SaaS Pricing Pages
(Springer, 2026) Cavero López, Francisco Javier; Parejo Maestre, José Antonio; Gómez Alonso, Juan Carlos; Ruíz Cortés, Antonio; Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España
SaaS pricing needs intelligent pricings (iPricings)—machinereadable representations that enable automated validation and analysis. Yet today, pricing is published in heterogeneous, ad-hoc HTML with no widely accepted notation or standard, leaving crucial associations (e.g., plan ↔ add-on ↔ subscription) tied to positional or visual cues and thus ambiguous for machines. We introduce A-MINT (Automated Modeling of iPricings from Natural Text), an end-to-end LLM-powered engine that converts free-form pricing pages into valid iPricings. A-MINT combines HTML preprocessing, LLM-driven extraction, automated constraint validation, and an LLM-based correction loop to resolve structural and linkage errors. A-MINT achieves a mean F1-score of 70.51% on SPECTRA, a dataset containing 162 real-world iPricings from 30 SaaS providers over six years, showing that iPricings can be modeled at scale despite the absence of a standard representation for pricings on the web.
From Static to Intelligent: Evolving SaaS Pricing with LLMs
(Springer, 2026) Cavero López, Francisco Javier; Gómez Alonso, Juan Carlos; Ruíz Cortés, Antonio; Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España
The SaaS paradigm has revolutionized software distribution by offering flexible pricing options, but its rapid growth has introduced complexity for DevOps teams, who still manage and evolve pricing structures manually. This process is time-consuming and error-prone, lacking automated tools for efficient pricing analysis, optimization, and scaling. We propose leveraging intelligent pricing (iPricing)—dynamic, machinereadable pricing models—to address these challenges. iPricing enables competitive analysis, streamlines decision-making, and adapts pricing to market changes, enhancing efficiency and accuracy. Our LLM-driven approach automates the transformation of static HTML pricing into iPricing improving consistency and reducing human error. The implementation, AI4Pricing, includes an Information Extractor using web scraping and LLMs to identify and extract key pricing elements—plans, features, usage limits, and add-ons—from SaaS websites. Validation on a dataset of up to 30 distinct commercial SaaS shows effective extraction across all steps, though challenges with hallucinations, complex structures, and dynamic content persist. This work underscores the potential of automating iPricing transformation to streamline SaaS pricing management, improving consistency and scalability. Future research will focus on refining extraction and expanding adaptability to a wider range of SaaS websites.
LLM-Assisted Grading of Fundamental Python Programming Exercises via In-Context Learning
(Springer, 2026) Sánchez López, José Enrique; Reina Jiménez, Pablo; Carranza García, Manuel; Vega Márquez, Belén; Lenguajes y Sistemas Informáticos; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España; TIC134: Sistemas Informáticos
Grading programming exams is challenging when runtime errors prevent automated tests from executing, leading to scores that may not reflect students’ partial understanding. This paper introduces an LLM-assisted grading pipeline for first-year Python exams that combines traditional execution checks with rubric-based inspection to award fair partial credit. The pipeline leverages in-context learning to integrate the grading workflow, from code simulation to evidence-based scoring, into a unified prompt architecture. We evaluate three prompting strategies to identify a suitable balance between processing efficiency and grading precision. Our results show a strong correlation with official human-assigned grades and indicate that the pipeline behaves as a reliable, conservative estimator. Rather than replacing the instructor, the proposed approach is intended to complement conventional execution-based assessment by improving transparency, supporting auditability, and helping teachers review partial credit decisions under an explicit rubric.
Interpretable Multilevel Analysis of Wind Turbine Operation Data via a Recursive Unsupervised and LLM-Assisted Framework
(Springer, 2026) Sánchez López, José Enrique; Carranza García, Manuel; Rodríguez López, Ana; Riquelme, José. C; Lenguajes y Sistemas Informáticos; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España; TIC134: Sistemas Informáticos
Wind-turbine operational analysis is essential for improving energy yield, reliability, and maintenance planning in modern wind farms. In this work, a hierarchical unsupervised framework is proposed for discovering operating regimes from daily aggregated Supervisory Control and Data Acquisition (SCADA) data. The method combines Genetic Algorithm Unsupervised Feature Selection (GAUFS) with recursive clustering, so that at each node it jointly selects an informative feature subset and estimates the number of clusters before expanding admissible child nodes. This produces a multilevel tree that captures broad regimes at upper levels and finer operational subregimes at deeper levels. To support interpretation, each cluster is characterized with descriptive statistics and then analyzed through a Large Language Model (LLM)-assisted layer to generate plausible operational explanations. In a real wind-farm case study, the framework identifies dominant production regimes and isolates rare anomalous states and possible data issues. Results show that the combination of recursive unsupervised feature selection and language-based interpretation provides an interpretable and scalable approach for characterizing SCADA behavior without requiring labeled fault data.
Año 2100: una odisea hacia el dominio público
(Atrapasueños, 2012) Neira Ayuso, Pablo; Lenguajes y Sistemas Informáticos; Cruz Rojo, Concepción; Ibañez Espinosa, Cristina; Moreno Maestro, Susana; TIC276: Diverso Lab - International Computing
La propiedad intelectual regula las creaciones de la mente y combina derechos morales permanentes y patrimoniales el copyright, limitado en el tiempo. El texto critica que estos derechos, pensados para proteger a los autores, acaban beneficiando sobre todo a los intermediarios: “sería más apropiado… denominar derechos de editor” . Internet rompe el modelo tradicional al permitir publicar sin editores, lo que provoca presiones legales y tecnológicas como la Ley Sinde o el DRM. En la universidad, las editoriales imponen el ciclo de “pagar por investigar, pagar por publicar y pagar por acceder” . Frente a ello surgen alternativas como el copyleft y las licencias Creative Commons, que permiten compartir obras con distintas condiciones. Algunos movimientos proponen incluso reducir drásticamente o eliminar el copyright para favorecer la creatividad y el acceso al conocimiento.
Mesa Comercio y Turismo
(Tyrant Humanidades, 2025) Acuña Garrido, María Dolores de; Lenguajes y Sistemas Informáticos
¿Cómo puede la inteligencia artificial mejorar la experiencia del cliente en la industria del turismo, desde la planificación del viaje hasta la experiencia en el destino?
Matrix Representation of Virus Machines
(Springer Nature, 2024) Ramírez de Arellano Marrero, Antonio; Cabarle, Francis George C.; Orellana Martín, David; Pérez Jiménez, Mario de Jesús; Adorna, Henry N.; Lenguajes y Sistemas Informáticos; Ciencias de la Computación e Inteligencia Artificial
Virus machines are unconventional and bio-inspired models of computation based on the transmission of viruses among hosts. Virus machines are known to be computationally complete (they are algorithms), able to solve computationally hard problems. In this work we present a novel matrix representation for virus machines. Discrete structures such as vectors and matrices are useful in many technical domains, both in theory and practice. The hosts, number of viruses, and the instructions to control virus transmission are represented as vectors and matrices. In this way the computations of virus machines can be described by linear algebra operations. We also use our matrix representation to show invariants, helpful in formal verifications, of such machines.
WebSnapse Reloaded: The Next-Generation Spiking Neural P System Visual Simulator using Client-Server Architecture
(Atlantis Press, 2024) Gulapa, Mutya; Luzada, Jarred Sueño; Cabarle, Francis George C.; Adorna, Henry N.; Buño, Kelvin C.; Ko, Daryll; Lenguajes y Sistemas Informáticos
Spiking Neural P Systems (SN P Systems) replicate the brain’s information spiking mechanism through synapses. These systems are known for trading memory for time, making them useful in different areas both inside and outside computer science. While web-based simulators likeWebSnapse offer the potential for centralized GUIs to simulate SN P system variants, their architecture, codebase, and technologies limit their capabilities. This paper presents WebSnapse Reloaded, a recreated version designed for centralization, optimization, and improved user and developer experiences. With a client-server architecture, it enhances storage usage, scalability, and future development possibilities. The modular codebase simplifies extension and maintenance. Optimizations include algorithm refinement through matrix representation and enhanced user experience. WebSnapse Reloaded reduces task steps, improves software implementation, passes correctness benchmarks, and resolves previous issues. Future recommendations include integrating WebGL or alternative graphing libraries, and developing APIs for other SN P variants to streamline simulator extension.
WebSnapse v3: Optimization of the Web-based Simulator of Spiking Neural P System using Matrix Representation, WebAssembly and Other Tools
(Atlantis Press, 2024) Gallos, Louie; Sotto, Jose Lorenzo; Cabarle, Francis George C.; Adorna, Henry N.; Lenguajes y Sistemas Informáticos
Spiking Neural P systems (SN P system) are a kind of distributed and parallel computational model under membrane computing inspired by how neurons typically communicate with each other through the sending of spikes between synapses. To further study SN P systems, various dedicated simulators have been developed. WebSnapse, a web-based simulator of SN P systems, has been used to serve as visual aid for creating, modifying, and simulating SN P systems. This was upgraded on WebSnapse v2.0 by improving existing functionalities and adding support for more variants of SN P system. However, WebSnapse suffered performance and stability issues, especially when simulating larger SN P systems. This research aims to optimize the performance and stability of WebSnapse using a new way of computing the next state of the system while achieving feature parity with WebSnapse v2.0 and adding LaTeX support for rule input and visualization.
Application of the Weighted Nearest Neighbor Method to Power System Forecasting Problems
(Wiley, 2017-06-19) Gómez Expósito, Antonio; Troncoso, Alicia; Riquelme Santos, Jesús Manuel; Gómez-Quiles, Catalina; Martínez Ramos, José Luis; Riquelme Santos, José Cristóbal; Lenguajes y Sistemas Informáticos; Ingeniería Eléctrica
A time-series database consists of sequences of values or events obtained over repeated measurements of time. Normally, the values are measured at equal time intervals. Time-series databases are popular in many applications, such as stock market analysis, economic and sales forecasting, process and quality control, and observation of natural phenomena (temperature, earthquake, pollution) or medical treatments. The main goal of time series analysis is not only to forecast (i.e., to predict the future values), but also to find correlation relationships within time series; find similar or regular patterns; trends, and outliers. These last goals can be considered as time series modeling (i.e., to gain insight into the mechanisms or underlying causes that generate time series).
Toward Explaining Competitive Success in League of Legends: A Machine Learning Analysis
(Springer Nature, 2024) Galán Sales, Francisco Javier; Linares Barrera, María Lourdes; Reina Jiménez, Pablo; Rodríguez López, Ana; Jiménez Navarro, Manuel Jesús; Lenguajes y Sistemas Informáticos
Machine learning techniques have recently transformed the way we analyze competitive games. However, accurately detecting the impact of different insights on match outcomes remains a challenge. This study focuses on League of Legends, a popular multiplayer online battle arena game known for its strategic depth and teamwork requirements. We aim to understand how various actions and strategies influence match results, using a dataset from professional tournaments. Factors like “building damage”, “total gold”, and “assists” are analyzed as predictors. We employ tree-based and linear models to predict outcomes, supplemented by SHapley Additive exPlanations for explaining both local and global model outcomes. Our article offers a generalizable match analysis approach, compares explainable methods, and delves into key determinants of victory. The results, showcasing a remarkable 98.8% accuracy with the top-performing model, provide strong support for our conclusions, underlining their reliability.
Cloud development and deployment
(Springer, 2023) Parejo Maestre, José Antonio; Sánchez Jerez, Ana Belén; Lenguajes y Sistemas Informáticos
This chapter describes the challenges related to the software deployment and configuration activities, which could be addressed through AI techniques. Additionally, we illustrate the application of such techniques with two specific problems: the QoS-aware binding of web services, and the selection of the optimal configuration in highly-configurable systems. The AI techniques applied to solve such problems are GRASP with Path Relinking and Multi-Objective Evolutionary Algorithms, respectively. These techniques will be applied to deployment scenarios based on empirically obtained data from real-world web services and highly-configurable software systems. These examples are implemented in Java and C, respectively, providing the source code, build scripts and data sets for the sake of reproducibility.
Experiencias en la aplicación de un Ciclo de Mejora en el Aula en la asignatura Proyectos en Informática Clínica
(Editorial Universidad de Sevilla, 2022) González Enríquez, José; Lenguajes y Sistemas Informáticos
En este capítulo se describe la puesta en práctica de los conocimientos adquiridos en los Cursos Generales de Docencia Universitaria de la Universidad de Sevilla. Concretamente, el ciclo de mejora en el aula (CIMA) desarrollado se ha llevado a cabo en la asignatura Proyectos en Informática Clínica, optativa del cuarto curso del Grado en Ingeniería de la Salud, impartido en la Escuela Técnica Superior de Ingeniería Informática de esta universidad. Además de definir de forma clara el modelo metodológico a seguir, así como el mapa de contenidos, se desarrolló un cuestionario cuyos resultados fueron usados para analizar la escalera de aprendizaje de los alumnos. Los resultados, demuestran que la aplicación de CIMA mejora significativamente los conocimientos adquiridos por los alumnos, pasando de concentrar a la mayoría de los alumnos en los escalones más bajos, a los más altos.
Human–computer interaction analysis for RPA support
(De Gruyter, 2021) Jiménez Ramírez, Andrés; Reijers, Hajo A.; González Enríquez, José; Lenguajes y Sistemas Informáticos
In the last decade, industry has wholeheartedly embraced RPA. A large number of RPA projects have been conducted, which all involve the mimicking of human behavior by software. Although vendor-specific frameworks exist to support all aspects of RPA solutions – from the analysis of the system until the enactment and control of the developed robots – most of them base their solution on workflow diagrams (e. g., BluePrism or UIPath). This chapter explores the benefits of a conceptual RPA framework that is based on log information related to the exact human–computer interaction. While the observation of employees’ computers (i. e., clicks and keystrokes) may lead to the desired data, it is by no means trivial to convert such events into a meaningful log. Our approach shows how to create a so-called user interface (UI) log based on image similarity techniques. This UI log can then be used to provide support for all stages of the RPA lifecycle. Specifically, we will describe a use case in which the UI log is used (1) to support the analysis of the underlying process and (2) to generate a test platform which checks whether or not the developed robots behave in accordance with the analyzed process. We will also discuss how other stages of the RPA lifecycle can leverage the existence of such a UI log, which leads to the identification of new research lines.
Enseñanza de los algoritmos de programación exhaustivos desde un modelo docente constructivista
(Editorial Universidad de Sevilla, 2022) Jiménez Ramírez, Andrés; Lenguajes y Sistemas Informáticos
Dentro del Grado de ingeniería informática – ingeniería de computadores se ha desarrollado un ciclo de mejora de 8 horas donde se enseña la programación con algoritmos exhaustivos dentro de la asignatura Análisis y Diseño de Datos y Algoritmos. Este ciclo de mejora contempla un proceso metodológico donde las actividades de ideación y debate toman especial relevancia. El artículo contiene el mapa de contenidos y los cuestionarios inicial y final que sirven para comprobar si se ha producido aprendizaje en los alumnos. Tanto las sensaciones del profesor como los resultados de las escaleras de aprendizaje fueron positivos, aunque se identificaron punto de mejora para futuros ciclos de mejora.
Ciclo de Mejora en el Aula (CIMA) en la asignatura de Análisis y Diseño de Datos y Algoritmos. Algoritmos de resolución de problemas sobre grafos virtuales
(Editorial Universidad de Sevilla, 2022) Jiménez Ramírez, Andrés; Lenguajes y Sistemas Informáticos
Dentro del Grado de ingeniería informática – ingeniería de computadores se ha desarrollado un Ciclo de mejora en el Aula (CIMA) de 12 horas de duración donde los alumnos aprenden a resolver problemas usando grafos virtuales. En este CIMA se ha seguido un proceso metodológico que potencia la realización de actividades de ideación por los alumnos para poder resolver estos problemas. El profesor comienza describiendo el contexto y el problema, haciéndolos cercanos a los alumnos para que éstos puedan debatir sobre su solución, a la vez que el profesor aporta ideas que contrastan con las de los alumnos y les ayuda a reflexionar. Este artículo contiene el mapa de contenidos utilizado por el profesor, así como los cuestionarios que permiten conocer el aprendizaje de los alumnos. Aunque las sensaciones del profesor fueron positivas en su desarrollo, la evaluación del aprendizaje de los alumnos no se pudo realizar por la ausencia de alumnos en la última sesión.
Intelligent Document Processing in End-to-End RPA Contexts: A Systematic Literature Review
(Springer Nature, 2023-03) Martínez Rojas, Antonio; López Carnicer, José Manuel; González Enríquez, José; Jiménez Ramírez, Andrés; Sánchez-Oliva, J. M.; Lenguajes y Sistemas Informáticos; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
Automating organizational processes typically involves document processing techniques for a large document set. For that purpose, the Intelligent Document Processing (IDP) paradigm has been studied for decades. With the fast emergence of Robotic Process Automation (RPA) in the process automation landscape, the industrial solution of IDP with RPA integration has risen significantly in the last few years. However, there is no up-to-date overview of the available knowledge in this area. Therefore, this chapter studies the current scientific knowledge about IDP and its integration into RPA through a systematic literature review that analyzed 77 primary studies. In addition, an industry review was performed, analyzing and characterizing 37 industrial tools. Although the results confirm the growth in the research interest in IDP in different dimensions, they also identify a lack of proposals that integrate IDP and RPA paradigms in confrontation with the industrial solutions that have increasingly led to its integration.
Revealing the Importance of Setting Parameters in Declarative Discovery Algorithms: An Evolutionary-Based Methodology
(Springer, 2023) Pradras Fernández, Vanessa; Varela Vaca, Ángel Jesús; Gómez López, María Teresa
Through constraints, declarative process models represent the permitted behaviour associated with a business process, by limiting the potential correct traces. These models can be discovered by analysing an event log. However, various declarative business models can be extracted from a single event log, depending on the desirable level of metrics, such as fitness and generalisation. Existing discovery algorithms enable the type of discovered declarative process model to be customised through a set of configuration parameters. Depending on the values of these parameters, the discovered process can be of high or low quality. Unfortunately, the high number of combinatorial parameters and the high time consumption of process discovery make it impractical to conduct an exhaustive analysis of the configuration parameters to determine the most suitable declarative process model discovered. As a solution, we propose a methodology supported by an implemented framework that uses evolutionary algorithms to reduce computational complexity and to select the highest quality declarative business processes. An experiment is included to show the feasibility of our proposal.
Multi-Objective Lagged Feature Selection Based on Dependence Coefficient for Time-Series Forecasting
(Springer, 2024) Linares Barrera, María Lourdes; Jiménez Navarro, Manuel Jesús; Riquelme Santos, José Cristóbal; Martínez Ballesteros, María del Mar; Lenguajes y Sistemas Informáticos; MICIU/AEI/10.13039/501100011033
In the fast-evolving field of machine learning, the process of feature selection is essential for reducing model complexity and enhancing interpretability. Within this context, filter methods have gained recognition for their effectiveness in assessing features through statistical metrics. A recently introduced metric, the Conditional Dependence Coefficient, aims to assess the dependence between subsets of features and a target variable, enhancing our understanding of feature relevance. This paper presents a novel feature selection approach that integrates this statistical metric with a multi-objective evolutionary algorithm. This strategy leverages the flexibility of evolutionary algorithms to efficiently explore the feature space and employs an intuitive metric for identifying pertinent features. Unlike many filter-based approaches, our method does not require thresholds or percentiles related to the number of selected features and evaluates the collective merit of feature subsets instead of the significance of individual features. To address the forecasting challenge of identifying the appropriate time lags and features, we performed experiments on eight distinct datasets containing multivariate time-series data. Comparing our method against a baseline with no feature selection, our results show solid performance in efficacy and a notable reduction in model complexity.
Ciberseguridad cognitiva aplicada al phishing
(Universidad de Vigo, 2023-06) Mariano Velo, José; Varela Vaca, Ángel Jesús; Martínez Gasca, Rafael; Lenguajes y Sistemas Informáticos; Proyecto COPERNICA; Proyecto METAMORFOSIS; Proyecto AETHER-US
Los Sistemas cognitivos son sistemas informáticos que emplean las teorías, métodos y herramientas de diversas disciplinas de computación para modelar tareas o procesos cognitivos emulando a la cognición humana. Una de las aplicaciones de éstos sistemas es en el campo de la Ciberseguridad es la detección de técnicas de phishing. Se reciben diariamente miles o millones de correos electrónicos, que son procesados por los distintos filtros de seguridad para catalogarlos en diferentes clases, correos legítimos, con malware, o phishing. A pesar de todas las medidas que se toman, siempre hay un porcentaje de correos electrónicos maliciosos que por diversos motivos pasan dichos filtros. Por ello vamos a definir una propuesta para mejorar la detección de dichos ataques, basada en una aproximación cognitiva. La propuesta se centra en definir el proceso, las potenciales técnicas y métricas usando motores para toma de decisiones ampliada basada en modelos de decisión.

Examinar

Envíos recientes