A Semantic Model for Enhancing Data-Driven Open Banking Services

In current Open Banking services, the European Payment Services Directive (PSD2) allows the secure collection of bank customer information, on their behalf and with their consent, to analyze their financial status and needs. The PSD2 directive has lead to a massive number of daily transactions between Fintech entities which require the automatic management of the data involved, generally coming from multiple and heterogeneous sources and formats. In this context, one of the main challenges lies in defining and implementing common data integration schemes to easily merge them into knowledge-base repositories, hence allowing data reconciliation and sophisticated analysis. In this sense, Semantic Web technologies constitute a suitable framework for the semantic integration of data that makes linking with external sources possible and enhances systematic querying. With this motivation, an ontology approach is proposed in this work to operate as a semantic data mediator in real-world open banking operations. According to semantic reconciliation mechanisms, the underpinning knowledge graph is populated with data involved in PSD2 open banking transactions, which are aligned with information from invoices. A series of semantic rules is defined in this work to show how the financial solvency classification of client entities and transaction concept suggestions can be inferred from the proposed semantic model.This research has been partially funded by the Spanish Ministry of Science and Innovation via the Aether Project with grant number PID2020-112540RB-C41 (AEI/FEDER, UE), the Ministry of Industry, Commerce and Tourism via the Helix initiative with grant number AEI-010500-2020-34, and the Andalusian PAIDI program with grant number P18-RT-2799. Partial funding for open access charge: Universidad de Málag

On the Use of Explainable Artificial Intelligence for the Differential Diagnosis of Pigmented Skin Lesions

En los últimos años, la Inteligencia Artificial Explicable (XAI) ha atraído la atención en la analítica de datos, ya que muestra un gran potencial en la interpretación de los resultados de complejos modelos de aprendizaje automático en la aplicación de problemas médicos. Se trata de que el resultado de las aplicaciones basadas en el aprendizaje automático deben ser comprendidos por los usuarios finales, especialmente en el contexto de los datos médicos, donde las decisiones deben tomarse cuidadosamente. decisiones. Como tal, se han realizado muchos esfuerzos para explicar el resultado de un modelo complejo de aprendizaje profundo en procesos de reconocimiento y clasificación de y clasificación de imágenes, como en el caso del cáncer de melanoma. Este representa un primer intento (hasta donde sabemos) de investigar experimental y técnicamente la explicabilidad de los métodos modernos de XAI modernos de XAI: explicaciones de modelos de diagnóstico interpretables locales (LIME) y Shapley Additive exPlanations (SHAP), en términos de reproducibilidad de resultados y el tiempo de ejecución en un conjunto de datos de clasificación de imágenes de melanoma. Este artículo muestra que los métodos XAI proporcionan ventajas en la interpretación de los resultados del modelo en la clasificación de imágenes de melanoma. interpretación de los resultados del modelo en la clasificación de imágenes de melanoma. Concretamente, LIME se comporta mejor que el explicador de gradiente SHAP en términos de reproducibilidad y tiempo de ejecución.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Chapter On the Optical Response of Nanoparticles: Directionality Effects and Optical Forces

Organic chemistr

Experimental study on the dynamic behaviour of drones designed for racing competitions

[EN] Drones designed for racing usually feature powerful miniaturised electronics embedded in fairly light and strong geometric composite structures. The main objective of this article is to analyse the behaviour of various models of racing drones and their geometrical structures (airframes). Two approaches have been made: (i) an analysis of the information collected by a set of speed and time sensors located on an indoor race track and using a statistical technique (box and whiskers diagram) and (ii) an analysis of the know-how (flight sensations) of a group of racing pilots using a series of technical interviews on the behaviour of their drones. By contrasting these approaches, it has been possible to validate numerically the effects of varying the arm angles, as well as lengths, on a test race track and relate the geometry of these structures to racing behaviourThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was partially supported by project RTI2018-096904-B-I00 from the Spanish Ministry of Economy, and by project AICO/2019/055 from Generalitat Valenciana.Castiblanco Quintero, JM.; Garcia-Nieto, S.; Simarro Fernández, R.; Salcedo-Romero-De-Ávila, J. (2021). Experimental study on the dynamic behaviour of drones designed for racing competitions. International Journal of Micro Air Vehicles. 13:1-22. https://doi.org/10.1177/175682932110057571221

Semantic modelling of Earth Observation remote sensing

Earth Observation (EO) based on Remote Sensing (RS) is gaining importance nowadays, since it offers a well-grounded technological framework for the development of advanced applications in multiple domains, such as climate change, precision agriculture, smart urbanism, safety, and many others. This promotes the continuous generation of data-driven software facilities oriented to advanced processing, analysis and visualization, which often offer enhanced computing capabilities. Nevertheless, the development of knowledge-driven approaches is still an open challenge in remote sensing, besides they provide human experts with domain knowledge representation, support for data standardization and semantic integration of sources, which indeed enhance the construction of advanced on-top applications. To this end, the use of ontologies and web semantic technologies have shown high success in knowledge representation in many fields, in which the Earth Observation is not an exception. However, as argued by the research community, there is large room for improvement in the specific case of remote sensing, where ontologies that consider the special nature and structure of different satellital and airborne data products are demanded. This article addresses, in first instance, part of this need by proposing a semantic model for the consolidation, integration, reasoning and linking of data (and meta-data), in the context of satellital remote sensing products for EO. With this objective, an OWL ontology has been developed and an RDF repository has been generated to allow advanced SPARQL querying. Although the proposal has been designed to consider remote sensing data products in general, the current study is mainly focused on the Sentinel 2 satellite mission from the Copernicus Programme of the European Space Agency (ESA). (...)Funding for open access charge: Universidad de Málaga / CBUA. This work has been partially funded by FEDER Grants TIN2017-86049-R and PID2020-112540RB-C41 (AEI/FEDER, UE) (Spanish Ministry of Education and Science), Andalusian PAIDI program with grant P18-RT-2799, and Green-Senti 2019 & 2021 PP Smart Campus UMA. It is also granted by the LifeWatch-ERIC initiative Smartfood Lifewatch (FEDER & Junta de Andalucia)

TITAN: A knowledge-based platform for Big Data workflow management

Modern applications of Big Data are transcending from being scalable solutions of data processing and analysis, to now provide advanced functionalities with the ability to exploit and understand the underpinning knowledge. This change is promoting the development of tools in the intersection of data processing, data analysis, knowledge extraction and management. In this paper, we propose TITAN, a software platform for managing all the life cycle of science workflows from deployment to execution in the context of Big Data applications. This platform is characterised by a design and operation mode driven by semantics at different levels: data sources, problem domain and workflow components. The proposed platform is developed upon an ontological framework of meta-data consistently managing processes and models and taking advantage of domain knowledge. TITAN comprises a well-grounded stack of Big Data technologies including Apache Kafka for inter-component communication, Apache Avro for data serialisation and Apache Spark for data analytics. A series of use cases are conducted for validation, which comprises workflow composition and semantic meta-data management in academic and real-world fields of human activity recognition and land use monitoring from satellite images.This work has been partially funded by the Spanish Ministry of Science and Innovation via Grant PID2020 112540RB-C41 (AEI/FEDER, UE) and Andalusian PAIDI program with grant P18-RT-2799. Funding for open access charge: Universidad de Málaga / CBUA

Improving Cr (VI) Extraction through Electrodialysis

A laboratory study has been carried out to determine the feasibility of in situ remediation of chromium (VI) contaminated soil using electrodialysis. In a classic setup, this technique implies the application of a low intensity direct current to the soil, which is separated from the electrode compartments by ion-exchange membranes. If the pollutants are ionic compounds, they can be forced to migrate to the oppositely charged electrode by electro-migration. Membranes selectively impede the flow of ions in the electrode compartments back to the soil. If a metal species is naturally present as an anion, mobilization from the soil at alkaline pH can be realized and, at the same time, the mobilization of other metal cations that occur at low pH can be minimized. Experiments have been carried out with clayey soils (kaolinite clay and soil clay mixtures) that have been characterized and then contaminated by mixing with a potassium dichromate solution for several days. Initial Cr (VI) content ranges from 500 to 4000 mg/kg. Treatment tests were carried out in an acrylic laboratory cells consisting of a central soil compartment and two electrode compartments located at both ends of the column. Dimensionally stable titanium electrodes coated with mixed metal oxides were placed in the electrode compartments. 0.01M Na2SO4 electrolytes were recirculated through them from two 1-liter deposits using a peristaltic pump. Two commercial ion exchange membranes separated the anolyte and catholyte compartments from the soil in the standard configuration. A programmable DC: power supply was connected to the electrodes and a computer for data acquisition.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. The authors acknowledge the financial support from the "Plan Propio de Investigación de la Universidad de Málaga" with project numbers PPIT.UMA.D1; PPIT.UMA.B1.2017/20 and PPIT.UMA.B5.2018/17. This work has also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 778045

TITAN: A knowledge-based platform for Big Data workflow management

Modern applications of Big Data are transcending from being scalable solutions of data processing and analysis, to now provide advanced functionalities with the ability to exploit and understand the underpinning knowledge. This change is promoting the development of tools in the intersection of data processing, data analysis, knowledge extraction and management. In this paper, we propose TITAN, a software platform for managing all the life cycle of science workflows from deployment to execution in the context of Big Data applications. This platform is characterised by a design and operation mode driven by semantics at different levels: data sources, problem domain and workflow components. The proposed platform is developed upon an ontological framework of meta-data consistently managing processes and models and taking advantage of domain knowledge. TITAN comprises a well-grounded stack of Big Data technologies including Apache Kafka for inter-component communication, Apache Avro for data serialisation and Apache Spark for data analytics. A series of use cases are conducted for validation, which comprises workflow composition and semantic meta-data management in academic and real-world fields of human activity recognition and land use monitoring from satellite images.Universidad de Málaga. Andalucía TECH

Un lugar concreto desde el que vivir, mirar y pensar el mundo (educativo)

Editorial de presentación del Volumen 2 número 1 de Márgenes, Revista de Educación de la Universidad de Málaga. Quinto número de la revista, compuesto por un total de 17 textos, distribuidos en las diferentes secciones. Se trata de un número de carácter ecléctico en el que continuamos ofreciendo contenidos fieles a las premisas y los pilares educativos con los que partíamos