Artículos (Lenguajes y Sistemas Informáticos)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11392

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Mutation Testing in Practice: Insights from Open-Source Software Developers
(IEEE Computer Soc, 2024-03-18) Sánchez Jerez, Ana Belén; Parejo Maestre, José Antonio; Segura Rueda, Sergio; Durán Toro, Amador; Papadakis, Mike; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; VI Plan Propio de Investigación y Transferencia de la Universidad de Sevilla 2021
Mutation testing drives the creation and improvement of test cases by evaluating their ability to identify synthetic faults. Over the past decades, the technique has gained popularity in academic circles. In practice, however, little is known about its adoption and use. While there are some pilot studies applying mutation testing in industry, the overall usage of mutation testing among developers remains largely unexplored. To fill this gap, this paper presents the results of a qualitative study among open-source developers on the use of mutation testing. Specifically, we report the results of a survey of 104 contributors to open-source projects using a variety of mutation testing tools. The findings of our study provide helpful insights into the use of mutation testing in practice, including its main benefits and limitations. Overall, we observe a high degree of satisfaction with mutation testing across different programming languages and mutation testing tools. Developers find the technique helpful for improving the quality of test suites, detecting bugs, and improving code maintainability. Popularity, usability, and configurability emerge as key factors for the adoption of mutation tools, whereas performance stands overwhelmingly as their main limitation. These results lay the groundwork for new research contributions and tools that meet the needs of developers and boost the widespread adoption of mutation testing.
ginmappeR: an unified approach for integrating gene and protein identifiers across biological sequence databases
(Oxford Univ Press, 2024-08-29) Sola Espinosa, Fernando Luis; Ayala Hernández, Daniel; Pulido, Marina R.; Ayala, Rafael; López Cerero, Lorena; Hernández Salmerón, Inmaculada Concepción; Ruiz Cortés, David; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. Departamento de Microbiología; Ministerio de Ciencia e Innovación, España; Instituto de Carlos III; Ministerio de Universidades
The proliferation of biological sequence data, due to developments in molecular biology techniques, has led to the creation of numerous open access databases on gene and protein sequencing. However, the lack of direct equivalence between identifiers across these databases difficults data integration. To address this challenge, we introduce ginmappeR, an integrated R package facilitating the translation of gene and protein identifiers between databases. By providing a unified interface, ginmappeR streamlines the integration of diverse data sources into biological workflows, so it enhances efficiency and user experience.
AYNEXT - tools for streamlining the evaluation of link prediction techniques
(Elsevier, 2023) Sola Espinosa, Fernando Luis; Ayala Hernández, Daniel; Hernández Salmerón, Inmaculada Concepción; Rivero, Carlos R.; Ruiz Cortés, David; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio Español de Ciencia, Innovación y Universidades
AYNEXT is an open source Python suite aimed towards researchers in the field of link prediction in Knowledge Graphs. Link prediction consists of predicting missing edges in a Knowledge Graph, which usually involves the application of different techniques to generate negative examples (false triples) to fit a model, and splitting edges into training, testing and validation sets. Setting up a correct evaluation setup or testing new negatives-generation strategies becomes more challenging as more complex strategies and considerations (e.g., removal of inverse relations) develop. AYNEXT makes it easy to configure and customize the creation of evaluation datasets and the computation of evaluation metrics and statistical significance tests for each pair of link prediction techniques. AYNEXT has been designed to be simple to use, but modular enough to enable customization of the main steps in the evaluation process. AYNEXT-DataGen covers the pre-processing, splitting, and negatives generation steps of the evaluation process, while AYNEXT-ResTest covers the metrics computing and the statistical tests. AYNEXT offers a simple to use command line interface that takes as input either a Knowledge Graph in standard formats or the results of applying existing techniques, but can be used programmatically for in-depth customization
asteRisk - Integration and Analysis of Satellite Positional Data in R
(R Foundation Statiscal Computing, 2023-03) Ayala, Rafael; Ayala Hernández, Daniel; Sellés Vidal, Lara; Ruiz Cortés, David; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio Español de Ciencia y Educación; Gobierno Regional de Andalucía
Over the past few years, the amount of artificial satellites orbiting Earth has grown fast, with close to a thousand new launches per year. Reliable calculation of the evolution of the satellites’ position over time is required in order to efficiently plan the launch and operation of such satellites, as well as to avoid collisions that could lead to considerable losses and generation of harmful space debris. Here, we present asteRisk, the first R package for analysis of the trajectory of satellites. The package provides native implementations of different methods to calculate the orbit of satellites, as well as tools for importing standard file formats typically used to store satellite position data and to convert satellite coordinates between different frames of reference. Such functionalities provide the foundation for integrating orbital data and astrodynamics analysis in R.
An Approach to Enhance Time Series Forecasting by Fast Fourier Transform
(Springer, 2023) Galán Sales, Francisco Javier; Reina Jiménez, Pablo; Carranza García, Manuel; Luna Romera, José María; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Feature engineering is a decisive step in time series forecasting, as it directly influences the performance of predictive models. In recent years, the Fast Fourier Transform (FFT) has gained popularity as an algorithm for extracting frequency-domain features from time series data. In this paper, we investigate the potential of using FFT as feature engineering to improve the accuracy and efficiency of time-series forecasting models. We performed a comparative analysis of the performance of models trained with FFT-based features versus traditional time domain features on two datasets. Our results demonstrate that FFT-based feature engineering outperforms traditional feature engineering methods in terms of forecast accuracy and computational efficiency. Additionally, we provide insights into the interpretability of the frequency domain features and their relationship with the underlying time series patterns. Overall, our study suggests that FFT-based feature engineering is a promising approach to enhance the performance of time-series forecasting models.
Advancing unsupervised anomaly detection with normalizing flow and multi-scale ensemble learning
(Pergamon Elsevier, 2024-08) Campos-Romero, Miguel; Carranza García, Manuel; Riquelme Santos, José Cristóbal; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Visual anomaly detection plays a crucial role in manufacturing to ensure product quality by identifying image patterns that deviate from the expected ones. Existing methods that rely on distribution estimation struggle with the complexity of real-world images, resulting in complex and inefficient procedures. This study leverages normalizing flow techniques to address the cold start anomaly detection problem, where no prior examples of anomalies are available during the training phase. In such scenarios, models must learn exclusively from defect-free images and still accurately identify anomalies. We propose a novel unsupervised multi-scale and multi-semantic normalizing flow model, enhanced with an ensemble of neural networks, to detect anomalies based on their feature distributions. Our model estimates the likelihood of non-defective features, identifying anomalies as out-of-distribution values. Extensive experiments on three state-of-the-art anomaly detection datasets demonstrate that our proposal achieves superior AUROC performance and improves computational efficiency compared to existing approaches. Furthermore, we validate the robustness and adaptability of our proposal through low-shot training experiments using only 20% of available training data, highlighting its potential as an efficient solution for cold start anomaly detection.
Impact of Aquaporin-4 and CD11c + Microglia in the Development of Ependymal Cells in the Aqueduct: Inferences to Hydrocephalus
(Springer Nature, 2024) Domínguez Mayo, Francisco José; González Vinceiro, Lourdes; Hiraldo González, Laura; Rodríguez Gómez, Francisco D.; Calle Castillejo, Claudia; Mayo León, Manuel; Netti, Vanina; Ramírez Lorca, Reposo; Echevarría Irusta, Miriam; Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear; Universidad de Sevilla. Departamento de Fisiología Médica y Biofísica; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Economía y Competitividad (MINECO). España; Junta de Andalucía
AQP4 is expressed in the endfeet membranes of subpial and perivascular astrocytes and in the ependymal cells that line the ventricular system. The sporadic appearance of obstructive congenital hydrocephalus (OCHC) has been observed in the offspring of AQP4−/− mice (KO) due to stenosis of Silvio’s aqueduct. Here, we explore whether the lack of AQP4 expression leads to abnormal development of ependymal cells in the aqueduct of mice. We compared periaqueductal samples from wild-type and KO mice. The microarray-based transcriptome analysis reflected a large number of genes with differential expression (809). Gene sets (GS) associated with ependymal development, ciliary function and the immune system were specially modified qPCR confirmed reduced expression in the KO mice genes: (i) coding for transcription factors for ependymal differentiation (Rfx4 and FoxJ1), (ii) involved in the constitution of the central apparatus of the axoneme (Spag16 and Hydin), (iii) associated with ciliary assembly (Cfap43, Cfap69 and Ccdc170), and (iv) involved in intercellular junction complexes of the ependyma (Cdhr4). By contrast, genes such as Spp1, Gpnmb, Itgax, and Cd68, associated with a Cd11c-positive microglial population, were overexpressed in the KO mice. Electron microscopy and Immunofluorescence of vimentin and γ-tubulin revealed a disorganized ependyma in the KO mice, with changes in the intercellular complex union, unevenly orientated cilia, and variations in the planar cell polarity of the apical membrane. These structural alterations translate into reduced cilia beat frequency, which might alter cerebrospinal fluid movement. The presence of CD11c + microglia cells in the periaqueductal zone of mice during the first postnatal week is a novel finding. In AQP4−/− mice, these cells remain present around the aqueduct for an extended period, showing peak expression at P11. We propose that these cells play an important role in the normal development of the ependyma and that their overexpression in KO mice is crucial to reduce ependyma abnormalities that could otherwise contribute to the development of obstructive hydrocephalus.
The innovation challenge in Spain: A Delphi study
(PERGAMON-ELSEVIER SCIENCE LTD, 2023) Giménez Medina, Manuel; Enríquez González, José; Domínguez Mayo, Francisco José; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Public funding for information and communication technology (ICT) innovation in Spain appears to be slow, bureaucratic, highly restrictive, and not agile. Therefore, the innovation process is negatively affected. These restrictions could be attributed to inadequate trust from public funders toward executors and ontological problems regarding the definitions of ICT innovation (i.e., the I+D+i formula), affecting all Quadruple Helix stakeholders. In this study, a Delphi study was proposed to reach a consensus among 81 experts (i.e., innovation managers, public funders, and consultants) to validate this hypothesis. The study included 41 statements and 59 questions organized into the following five objectives: (1) concept of innovation, (2) public funding and its restrictions, (3) theoretical model of innovation, (4) public funders’ trust and executors’ freedom, (5) assessment of capabilities and maturity for innovation. The experts discussed, evaluated, and reached a consensus, after two rounds, on 52 of the 59 questions. The results revealed wide dispersion of the proposed ICT innovation questions. They demonstrated that the innovation management ecosystem in Spain’s ICT context is immature and the I+D+i formula did not represent the innovation process. The study reached a consensus on requirements for an Agile Innovation Funding Framework (AIFF) oriented toward obtaining an improved competitive advantage for ICT products or services based on trust, transparency, inspection, and adaptation principles. The results revealed that a joint framework involving public funders and executors based on organizational capability and maturity positively affects the innovation process. The capabilities of the executors should be standardized and measured, and public funders must move from supervisors to mentors to acquire new capabilities. Furthermore, innovation regulation and the various types of calls for proposals should be analyzed globally to change their fiscal and controlling nature restricting innovation.
FIDES: A Proposal for Federated Accountability in the Compute Continuum
(IEEE COMPUTER SOC, 2023) Durán Toro, Amador; Fernández Montes, Pablo; García Rodríguez, José María; Dustdar, Schahram; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
In this visionary article, we present the concept of federated accountability, an innovative approach that distributes accountability-related computation and data across the computer continuum. To demonstrate the feasibility and versatility of our approach, we developed a prototype using blockchain technology that serves as a tangible illustration of how federated accountability can be applied across various domains.
Enabling security risk assessment and management for business process models
(Elsevier, 2024) Rosado, David G.; Sánchez, Luis E.; Varela Vaca, Ángel Jesús; Santos Olmo, Antonio; Gómez López, María Teresa; Martínez Gasca, Rafael; Fernández Medina, Eduardo; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Business processes (BP) are considered the enterprise’s cornerstone but are increasingly in the spotlight of attacks. Therefore, the design of business processes must consider the security risks and be adequately integrated into the information and operational systems. However, security risk assessment and management are rarely considered at the level of business processes during design time, let alone considering a risk architecture that takes into account the connection and dependencies of risks at these levels of the organisation, business processes, and information systems. In general, most approaches deal with integrating new artefacts for business process models to support risk analysis, but sometimes, the notation can increase complexity, making it difficult to have a risk management tool to support the analysis. After analysing the current risk processes and frameworks, we have realised that they are often neglected when considering organisational and business process levels. In this paper, MARISMA-BP (MARISMA for Business Process) pattern is proposed, a security risk pattern to enable the assessment and management of risks for business process models. This approach is an artefact that has been validated in a real scenario following the design science methodology. Further, MARISMA-BP pattern is supported by eMARISMA, an automated infrastructure that allows the definition and reuse of each risk component, helping us to carry out the risk assessment and management process in an efficient and dynamic way. To demonstrate the applicability of the proposal, MARISMA BP pattern is applied to a real health-based business process scenario. The findings illustrate the efficacy of MARISMA-BP within eMARISMA for comprehensive risk assessment and management, underscoring its versatility and practical relevance in any business process environment.
Research artifacts for human-oriented experiments in software engineering: An ACM badges-driven structure proposal
(ELSEVIER SCIENCE INC, 2024) Guevara Vega, Cathy; Bernárdez Jiménez, Beatriz; Cruz Risco, Margarita; Durán Toro, Amador; Ruiz Cortés, Antonio; Solari, M.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Context: The Open Science (OS) movement promotes the value of making public the research artifacts (datasets, analysis scripts, guidelines, etc.) used during empirical studies. OS is widely known in areas such as Medicine or Biology, where the process of sharing research artifacts is subject to strict protocols. Unfortunately, in Software Engineering (SE), this process is carried out in a non-systematic way, resulting in incomplete or inaccurate material shared by researchers, which hinders the reproducibility and replicability of empirical studies. Nevertheless, in recent years, it seems that the Empirical Software Engineering (ESE) community is embracing some of the proposed OS initiatives, such as the one proposed by the Association for Computing Machinery (ACM), which provides a badge system to evaluate the quality of a research artifact. This badge system has been adopted by several SE conferences as a method of assessing research artifacts. Aims: Focusing on human-oriented experiments (HOEs) in SE, whose research artifacts are more complex than those for computational experiments, this work applies Design Science Research (DSR) with a twofold purpose: (i) review the current status of HOEs research artifacts publication through evaluation of this practice in the most relevant ESE journals , and (ii) propose a structured outline for HOEs research artifacts driven by the aforementioned ACM badging policy. Method: Regarding the first purpose, we carried out a survey to analyze the current status of the publication of research artifacts considering relevant peer review journals and the quality of 106 research artifacts published in these journals with respect to the ACM badging policy. For the second purpose, an iterative process was carried out to review the content of 106 research artifacts research and their concordance with ACM badges, obtaining a structured scheme for HOEs research artifacts that has been validated through a detailed review of 12 research artifacts obtained from some of those of ACM badges in relevant SE conferences. In addition, we validated the proposal in the research artifacts of 2 of our own experiments. Results: Our survey reveals issues such as the 39,70% of journal studies making completely accessible their research artifacts; most of the analyzed research artifacts are incomplete; the most common repositories used in the ESE community to share the research artifacts are GitHub, institutional repositories, and Zenodo. On the other hand, the validated and structured research artifact outline consists of a list of ordered sections containing a set of artifacts, which can be mandatory or not to achieve a particular ACM badge. For its internal validation, several improvement iterations on the first release of the outline have been carried out based on the conference guidelines, the ACM badging policy, and other relevant proposals. Conclusions: Although the ESE community is making great efforts in standardization, review, and digital publishing related to OS, the availability and completeness of research artifacts can be improved. Our proposal for the elaboration of structured research artifact outline meets the requirements of HOEs in SE. Nevertheless, further research is needed not only to improve and externally validate it but also to disseminate its use among the research community.
Exploring Gender Bias In Remote Pair Programming Among Software Engineering Students: The twincode Original Study And First External Replication
(Springer, 2024) Durán Toro, Amador; Fernández Montes, Pablo; Bernárdez Jiménez, Beatriz; Weinman, Nathaniel; Akalin, A.; Fox, A.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Context Women have historically been underrepresented in Software Engineering, due in part to the stereotyped assumption that women are less technically competent than men. Pair programming is both widely used in industry and has been shown to increase student interest in Software Engineering, particularly among women; but if those same gender biases are also present in pair programming, its potential for attracting women to the field could be thwarted. Objective We aim to explore the effects of gender bias in pair programming. Specifically, in a remote setting in which students cannot directly observe the gender of their peers, we study whether the perception of the partner, the behavior during programming, or the style of communication of Software Engineering students differ depending on the perceived gender of their remote partner. To our knowledge, this is the first study specifically focusing on the impact of gender stereotypes and bias within pairs in pair programming g. Method We have developed an online pair-programming platform (twin code) that provides a collaborative editing window and a chat pane, both of which are heavily instrumented. Students in the control group had no information about their partner’s gender, whereas students in the treatment group could see a gendered avatar representing the other participant as a man or as a woman. The gender of the avatar was swapped between programming tasks to analyze 45 variables related to the collaborative coding behavior, chat utterances, and questionnaire responses of 46 pairs in the original study at the University of Seville, and 23 pairs in the external replication at the University of California, Berkeley. Results We did not observe any statistically significant effect of the gender bias treatment, nor any interaction between the perceived partner’s gender and subject’s gender, in any of the 45 response variables measured in the original study. In the external replication, we observed statistically significant effects with moderate to large sizes in four dependent variables within the experimental group, comparing how subjects acted when their partners were represented as a man or a woman. Conclusions The results in the original study do not show any clear effect of the treatment in remote pair programming among current Software Engineering students. In the external replication, it seems that students delete more source code characters when they have a woman partner, and communicate using more informal utterances, reflections and yes/no questions when they have a man partner, although these results must be considered inconclusive because of the small number of subjects in the replication, and because when multiple test corrections are applied, only the result about informal utterances remains significant. In any case, more mixed methods replications are needed in order to confirm or refute the results in the same and other Software Engineering students populations.
A model-based approach for specifying changes in replications of empirical studies in computer Science
(SPRINGER; SPRINGER WIEN, 2023) Cruz Risco, Margarita; Bernárdez Jiménez, Beatriz; Durán Toro, Amador; Guevara Vega, Cathy; Ruiz Cortés, Antonio; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
The need of replicating empirical studies in Computer Science is widely recognized among the research community. It is essential to report the changes of each replication to promote not only the comprehensibility of the evolution of the experimental validity across a family of studies, but also replicability itself. Unfortunately, the lack of proposals for systematic reporting of changes in replications undermines these desirable objectives. The main goal of the work presented in this article is to provide researchers in Computer Science with a systematic tool-supported approach for the specification and reporting of changes in the replications of their empirical studies. Applying Design Science Research, we have developed and validated a composite artifact consisting of (i) a metamodel that formalizes all the relevant concepts related to replications and their changes; (ii) templates and linguistic patterns that facilitate their reporting; and (iii) a proof-of-concept model-based software tool that supports the proposed approach. For its validation, we have carried out a multiple case study that includes 9 families of empirical studies not only from Computer Science, but also from an area as different as Agrobiology , to check the external validity of our approach. The 9 families encompass 23 replication studies and a total of 92 replication changes, for which we have analyzed the suitability of our proposal. The multiple case study revealed some initial limitations of our approach related to threats to experimental validity and context variables. After several improvement iterations on the artifact, all of the 92 replication changes could be properly specified, including also their qualitatively estimated effects on experimental validity and their corresponding visualization. Our proposal for the specification of replication changes seems to fit the needs not only of replications in Computer Science, but also in other research areas. Nevertheless, further research is needed to improve it and disseminate its use among the research community.
A Tool for Incorporating Eye Tracking Data in RPA: Enhancing User Behavior Logs
(2024-09-07) García Romero, Manuel; Martínez Rojas, Antonio; González Enríquez, José; Jiménez Ramírez, Andrés; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; EQUAVEL; Ministerio de Economía y Transformación Digital; Universidad de Sevilla. TIC-021:Engineering and Science for Software Systems
This paper presents UBGI, an innovative tool designed to enhance Robotic Process Automation (RPA) by integrating eye tracking data with user interface (UI) logs. UBGI processes and combines gaze logs with UI logs to create enriched User Behaviour (UB) logs, enabling more precise identification of user focus areas. By applying filtering masks to screenshots, UBGI highlights relevant data, facilitating analysis of user interactions. This tool enables further analysis of user behaviour through an external source, specifically eye tracking data.
Decision support system to detect hidden pathologies of stroke: the CIPHER project
(INST ENGINEERING TECH-IET, 2019) González Enríquez, José; Morales Trujillo, Leticia; Moreno Leonardo, Sara; Domínguez Mayo, Francisco José; García García, Julián Alberto; Mejías Risoto, Manuel; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Economía y Competitividad, proyecto CIPHER; Universidad de Sevilla; Universidad de Sevilla. TIC-021: Engineering and Science for Software Systems
Currently, it is difficult to find platforms connected to health systems that exploit data in a coherent way and that allow, on the one hand, to send sanitary warnings and on the other, to validate the performance of medical specialists according to the models set by the best practices of the specialty. This chapter aims to explain the CIPHER project, a decision support system (DSS), based on machine-learning (ML) and big data technologies, capable of alerting a clinician when a situation of risk is detected in a patient suffering from a certain pathology, so that could be able to carry out the appropriate measures. CIPHER, is a project born from scratch. For its development, different methodologies, such as design sprint (for product prototyping), navigational development techniques (for product analysis and testing) or SCRUM (for product development), have been applied. In addition, this product has been defined in direct contact with medical specialists and under the umbrella of international standards and models such as ISO 13606, SNOMED, REGICOR or CHADS2. As a result of the development of this product, we have obtained a DSS, which offers health professionals the possibility of receiving alerts from patientswhomay be at risk of suffering from a specific pathology, based on a series of criteria defined by international standards. Moreover, health professionals would be able to find hidden symptomatology of the pathology mentioned above, which, a priori, are not known.
A Practical Medical Experience of successfully mixing Model-Driven Paradigm and Business Process Management principles
(SCITEPRESS, 2019-02-20) Cid de la Paz, Virginia; Morales Trujillo, Leticia; Ramos, J.M.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; POLOLAS; IDE4ICDS; Universidad de Sevilla
The Model-Driven Paradigm has been successfully used in several different software contexts and there are a lot of literature offering approaches, techniques and tools to guarantee its application in different areas, such as software design, software testing, and so on. But, this paradigm can be also used in other contexts offering very good results. In this paper, we illustrate the power of using models and transformations to make an effective and efficient management of clinical guides in medical environments. The paper shows how using business process management to represent clinical guidelines, principles of Model-Driven paradigm can be successfully used. The paper presents the experiences in the IDE4ICDS, which is framed into the medical context to provide a solution to manage the life cycle of clinical guidelines. This project presents a methodology that allows the management of clinical guidelines to be automated, as well as a software platform to support it. This platform has been validated with health professionals from the Hospital Virgen del Rocio (Seville), obtaining promising results. Nowadays, this platform is been validated by healthcare professionals of Primary Care with patients suffering from Diabetes Mellitus Type 2.
A new deep learning architecture with inductive bias balance for transformer oil temperature forecasting
(Springer Nature, 2023) Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Martínez-Álvarez, Francisco; Asencio-Cortés, Gualberto; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Univesidad de Sevilla. TIC-134: Sistemas Informáticos
Ensuring the optimal performance of power transformers is a laborious task in which the insulation system plays a vital role in decreasing their deterioration. The insulation system uses insulating oil to control temperature, as high temperatures can reduce the lifetime of the transformers and lead to expensive maintenance. Deep learning architectures have been demonstrated remarkable results in various fields. However, this improvement often comes at the cost of increased computing resources, which, in turn, increases the carbon footprint and hinders the optimization of architectures. In this study, we introduce a novel deep learning architecture that achieves a comparable efficacy to the best existing architectures in transformer oil temperature forecasting while improving efficiency. Effective forecasting can help prevent high temperatures and monitor the future condition of power transformers, thereby reducing unnecessary waste. To balance the inductive bias in our architecture, we propose the Smooth Residual Block, which divides the original problem into multiple subproblems to obtain different representations of the time series, collaboratively achieving the final forecasting. We applied our architecture to the Electricity Transformer datasets, which obtain transformer insulating oil temperature measures from two transformers in China. The results showed a 13% improvement in MSE and a 57% improvement in performance compared to the best current architectures, to the best of our knowledge. Moreover, we analyzed the architecture behavior to gain an intuitive understanding of the achieved solution.
Discovering quantitative association rules: A novel approach based on evolutionary algorithms
(IOS Press, 2014) Martínez Ballesteros, María del Mar; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
This work proposes a novel methodology to improve the discovery of quantitative association rules in continuous datasets. This methodology comprises several evolutionary algorithms able to deal with real-valued variables without performing a static discretization process. Additionally, several quality measures are analysed to select the set of measures to be optimized with the aim of finding high-quality rules.
Sparse Spiking Neural-Like Membrane Systems on Graphics Processing Units
(World Scientific, 2024) Hernandez Tello, Javier; Martínez del Amor, Miguel Ángel; Orellana Martín, David; Cabarle, Francis George C.; Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia, Innovación y Universidades (MICINN). España; Junta de Andalucía; Grupo de Investigación en Computación Natural TIC193; I3US; SCORE Lab
The parallel simulation of Spiking Neural P systems is mainly based on a matrix representation, where the graph inherent to the neural model is encoded in an adjacency matrix. The simulation algorithm is based on a matrix-vector multiplication, which is an operation efficiently implemented on parallel devices. However, when the graph of a Spiking Neural P system is not fully connected, the adjacency matrix is sparse and hence, lots of computing resources are wasted in both time and memory domains. For this reason, two compression methods for the matrix representation were proposed in a previous work, but they were not implemented nor parallelized on a simulator. In this paper, they are implemented and parallelized on GPUs as part of a new Spiking Neural P system with delays simulator. Extensive experiments are conducted on high-end GPUs (RTX2080 and A100 80GB), and it is concluded that they outperform other solutions based on state-of-the-art GPU libraries when simulating Spiking Neural P systems.
Forecasting solar energy production in Spain: A comparison of univariate and multivariate models at the national level
(Elsevier, 2023-08-10) Cabello-López, Tomás; Carranza García, Manuel; Riquelme Santos, José Cristóbal; García Gutiérrez, Jorge; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Junta de Andalucía; TIC134: Sistemas Informáticos
Renewable energies, such as solar power, offer a clean and cost-effective energy source. However, their integration into national electricity grids poses challenges due to their dependence on climate and geography. While numerous studies have focused on solar energy time series, few have specifically addressed the critical task of forecasting solar energy production at the national level. Accurate national-level forecasting is crucial for optimizing energy management, informing policy development, and promoting environmental sustainability. This study aims to address the challenges associated with the significant variability in renewable energy production and its impact on grid stability by improving the accuracy of existing forecasting approaches.To achieve this goal, we evaluate the effectiveness of univariate and multivariate approaches for time series forecasting of national solar energy production data from ESIOS (the Spanish System Operator). Our primary focus is on leveraging external solar variables, such as solar irradiance data. To this end, we propose a methodology to integrate solar irradiance forecasts with historical data from solar power plants in Spain to improve the performance of multivariate models. Subsequently, we compare the performance of classical regression techniques and state-of-the-art deep learning algorithms, presenting univariate and multivariate models for three forecast horizons (1 h, 24 h, and 48 h). Finally, we assess the performance of our best univariate and multivariate models by comparing them with the official forecast of ESIOS. Our findings indicate that the best-performing models are deep-learning multivariate approaches, which benefit from incorporating solar irradiance forecasts, particularly for longer forecast horizons (24 h and 48 h), and avoid the detrimental effects of the Hughes Phenomenon, which seems to hamper non-deep-learning forecasters. The top-performing models, based on Convolutional Networks and Convolutional + Recurrent Neural Networks, outperform ESIOS by reducing mean absolute error by 41% and 47.58%, respectively.

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