An Internet of Things and Fuzzy Markup Language Based Approach to Prevent the Risk of Falling Object Accidents in the Execution Phase of Construction Projects

The Internet of Things (IoT) paradigm is establishing itself as a technology to improve data acquisition and information management in the construction field. It is consolidating as an emerging technology in all phases of the life cycle of projects and specifically in the execution phase of a construction project. One of the fundamental tasks in this phase is related to Health and Safety Management since the accident rate in this sector is very high compared to other phases or even sectors. For example, one of the most critical risks is falling objects due to the peculiarities of the construction process. Therefore, the integration of both technology and safety expert knowledge in this task is a key issue including ubiquitous computing, real-time decision capacity and expert knowledge management from risks with imprecise data. Starting from this vision, the goal of this paper is to introduce an IoT infrastructure integrated with JFML, an open-source library for Fuzzy Logic Systems according to the IEEE Std 1855-2016, to support imprecise experts’ decision making in facing the risk of falling objects. The system advises the worker of the risk level of accidents in real-time employing a smart wristband. The proposed IoT infrastructure has been tested in three different scenarios involving habitual working situations and characterized by different levels of falling objects risk. As assessed by an expert panel, the proposed system shows suitable results.This research was funded by University of Naples Federico II through the Finanziamento della Ricerca di Ateneo (FRA) 2020 (CUP: E69C20000380005) and has been partially supported by the ”Programa de ayuda para Estancias Breves en Centros de Investigación de Calidad” of the University of Málaga and the research project BIA2016-79270-P, the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund-ERDF (Fondo Europeo de Desarrollo Regional-FEDER) under project PGC2018-096156-B-I00 Recuperación y Descripción de Imágenes mediante Lenguaje Natural usando Técnicas de Aprendizaje Profundo y Computación Flexible and the Andalusian Government under Grant P18-RT-2248

A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems

In the design of an interpretable fuzzy rule-based classification system (FRBCS) the precision as much as the simplicity of the extracted knowledge must be considered as objectives. In any inductive learning algorithm, when we deal with problems with a large number of features, the exponential growth of the fuzzy rule search space makes the learning process more difficult. Moreover it leads to an FRBCS with a rule base with a high cardinality. In this paper, we propose a genetic-programming-based method for the learning of an FRBCS, where disjunctive normal form (DNF) rules compete and cooperate among themselves in order to obtain an understandable and compact set of fuzzy rules, which presents a good classification performance with high dimensionality problems. This proposal uses a token competition mechanism to maintain the diversity of the population. The good results obtained with several classification problems support our proposal.Spanish Ministry of Science and Technology TIN-2005-08386-C05-03 and TIN-2005-08386-C05-0

Experimental Study on 164 Algorithms Available in Software Tools for Solving Standard Non-Linear Regression Problems

In the specialized literature, researchers can find a large number of proposals for solving regression problems that come from different research areas. However, researchers tend to use only proposals from the area in which they are experts. This paper analyses the performance of a large number of the available regression algorithms from some of the most known and widely used software tools in order to help non-expert users from other areas to properly solve their own regression problems and to help specialized researchers developing well-founded future proposals by properly comparing and identifying algorithms that will enable them to focus on significant further developments. To sum up, we have analyzed 164 algorithms that come from 14 main different families available in 6 software tools (Neural Networks, Support Vector Machines, Regression Trees, Rule-Based Methods, Stacking, Random Forests, Model trees, Generalized Linear Models, Nearest Neighbor methods, Partial Least Squares and Principal Component Regression, Multivariate Adaptive Regression Splines, Bagging, Boosting, and other methods) over 52 datasets. A new measure has also been proposed to show the goodness of each algorithm with respect to the others. Finally, a statistical analysis by non-parametric tests has been carried out over all the algorithms and on the best 30 algorithms, both with and without bagging. Results show that the algorithms from Random Forest, Model Tree and Support Vector Machine families get the best positions in the rankings obtained by the statistical tests when bagging is not considered. In addition, the use of bagging techniques significantly improves the performance of the algorithms without excessive increase in computational times.This work was supported in part by the University of Córdoba under the project PPG2019-UCOSOCIAL-03, and in part by the Spanish Ministry of Science, Innovation and Universities under Grant TIN2015- 68454-R and Grant TIN2017-89517-P

Enhancing soft computing techniques to actively address imbalanced regression problems

This paper has been supported in part by the ERDF A way of making Europe/Health Institute Carlos III/Spanish Ministry of Science, Innovation and Universities (grant number PI20/00711), by the ERDF A way of making Europe/Regional Government of Andalusia/Ministry of Economic Transformation, Industry, Knowledge and Universities (grant numbers P18-RT-2248 and B-CTS-536-UGR20) and by the MCIN/AEI/10.13039/50110001103 (grant numbers PID2019-107793GB-I00 and PID2020-119478GB-I00). Funding for open access charge: Universidad de Granada / CBUA

Transparent but Accurate Evolutionary Regression Combining New Linguistic Fuzzy Grammar and a Novel Interpretable Linear Extension

Scientists must understand what machines do (systems should not behave like a black box), because in many cases how they predict is more important than what they predict. In this work, we propose a new extension of the fuzzy linguistic grammar and a mainly novel interpretable linear extension for regression problems, together with an enhanced new linguistic tree-based evolutionary multiobjective learning approach. This allows the general behavior of the data covered, as well as their specific variability, to be expressed as a single rule. In order to ensure the highest transparency and accuracy values, this learning process maximizes two widely accepted semantic metrics and also minimizes both the number of rules and the model mean squared error. The results obtained in 23 regression datasets show the effectiveness of the proposed method by applying statistical tests to the said metrics, which cover the different aspects of the interpretability of linguistic fuzzy models. This learning process has obtained the preservation of high-level semantics and less than 5 rules on average, while it still clearly outperforms some of the previous state-of-the-art linguistic fuzzy regression methods for learning interpretable regression linguistic fuzzy systems, and even to a competitive, pure accuracyoriented linguistic learning approach. Finally, we analyze a case study in a real problem related to childhood obesity, and a real expert carries out the analysis shown.Andalusian Government P18-RT-2248Health Institute Carlos III/Spanish Ministry of Science, Innovation and Universities PI20/00711Spanish Government PID2019-107793GB-I00 PID2020-119478GB-I0

Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy

Nowadays, the call for transparency in Artificial Intelligence models is growing due to the need to understand how decisions derived from the methods are made when they ultimately affect human life and health. Fuzzy Rule-Based Classification Systems have been used successfully as they are models that are easily understood by models themselves. However, complex search spaces hinder the learning process, and in most cases, lead to problems of complexity (coverage and specificity). This problem directly affects the intention to use them to enable the user to analyze and understand the model. Because of this, we propose a fuzzy associative classification method to learn classifiers with an improved trade-off between accuracy and complexity. This method learns the most appropriate granularity of each variable to generate a set of simple fuzzy association rules with a reduced number of associations that consider positive and negative dependencies to be able to classify an instance depending on the presence or absence of certain items. The proposal also chooses the most interesting rules based on several interesting measures and finally performs a genetic rule selection and adjustment to reach the most suitable context of the selected rule set. The quality of our proposal has been analyzed using 23 real-world datasets, comparing them with other proposals by applying statistical analysis. Moreover, the study carried out on a real biomedical research problem of childhood obesity shows the improved trade-off between the accuracy and complexity of the models generated by our proposal.Funding for open access charge: Universidad de Granada / CBUA.ERDF and the Regional Government of Andalusia/Ministry of Economic Transformation, Industry, Knowledge and Universities (grant numbers P18-RT-2248 and B-CTS-536-UGR20)ERDF and Health Institute Carlos III/Spanish Ministry of Science, Innovation and Universities (grant number PI20/00711)Spanish Ministry of Science and Innovation (grant number PID2019-107793GB-I00

Shared gene expression signatures between visceral adipose and skeletal muscle tissues are associated with cardiometabolic traits in children with obesity

Obesity in children is related to the development of cardiometabolic complications later in life, where molecular changes of visceral adipose tissue (VAT) and skeletal muscle tissue (SMT) have been proven to be fundamental. The aim of this study is to unveil the gene expression architecture of both tissues in a cohort of Spanish boys with obesity, using a clustering method known as weighted gene co-expression network analysis. For this purpose, we have followed a multi-objective analytic pipeline consisting of three main approaches; identification of gene co-expression clusters associated with childhood obesity, individually in VAT and SMT (intra-tissue, approach I); identification of gene co-expression clusters associated with obesitymetabolic alterations, individually in VAT and SMT (intra-tissue, approach II); and identification of gene co-expression clusters associated with obesity-metabolic alterations simultaneously in VAT and SMT (intertissue, approach III). In both tissues, we identified independent and inter-tissue gene co-expression signatures associated with obesity and cardiovascular risk, some of which exceeded multiple-test correction filters. In these signatures, we could identify some central hub genes (e.g., NDUFB8, GUCY1B1, KCNMA1, NPR2, PPP3CC) participating in relevant metabolic pathways exceeding multiple-testing correction filters. We identified the central hub genes PIK3R2, PPP3C and PTPN5 associated with MAPK signaling and insulin resistance terms. This is the first time that these genes have been associated with childhood obesity in both tissues. Therefore, they could be potential novel molecular targets for drugs and health interventions, opening new lines of research on the personalized care in this pathology. This work generates interesting hypotheses about the transcriptomics alterations underlying metabolic health alterations in obesity in the pediatric populationERDF/Health Institute Carlos III (grant numbers PI20/00711 and PI20/00563)ERDF/Regional Government of Andalusia/Ministry of Economic Transformation, Industry, Knowledge and Universities (grant numbers P18- RT-2248 and B-CTS-536-UGR20

La respuesta de Quevedo al padre Pineda: una obra posiblemente censurada

Entre 1626 y 1635 circulan numerosas invectivas contra las obras de Quevedo, en coincidencia con la difusión impresa de algunos de sus textos más polémicos: Política de Dios, el Buscón y los Sueños. Una de las primeras es una diatriba del jesuita Juan de Pineda contra el tratado político, manuscrita y hoy perdida, a la que replica Quevedo en el propio año 1626. De su respuesta se conservan dos fuentes manuscritas del siglo XVII, una de ellas con significativas omisiones nunca señaladas por la crítica. El propósito de este artículo es dar a conocer una veintena de pasajes que, incluidos en la versión presumiblemente más próxima a la voluntad del autor, podrían haber sido censurados en la otra, texto base de los editores modernos. Dichas lagunas parecen obedecer a una posible censura: desaparecen pasajes críticos y hasta insultantes contra Pineda, así como elogios y citas de un controvertido jesuita, Gabriel Vázquez, acusado por la heterodoxia de sus ideas y encarcelado por la InquisiciónNumerous invectives against Quevedo’s works were disseminated from 1626 to 1635, coinciding with the publication of his most polemical texts: Política de Dios, Buscón and Sueños. Among the earliest ones, there is a diatribe against the political treatise by the Jesuit priest Juan de Pineda, handwritten and now lost. Quevedo replied to it quickly, in 1626. His response is preserved in two manuscript sources dated in the 17th century, one of them with relevant omissions never mentioned by scholars. The aim of this paper is to provide information about more than twenty excerpts that were included in the version that could be presumably closer to the author’s will; the other one, which was precisely the base text of modern editors, might have censored them. The above omissions seem to be due to a possible censure: some insulting passages against Pineda dissapear, as well as praises and quotes from a controversial Jesuit, Gabriel Vázquez, who was accused for his heterodox ideas and even imprisoned by the InquisitionEste artículo es resultado de los proyectos de investigación “Edición crítica y anotada de la obra en prosa de Quevedo, IX” (MINECO, Excelencia 2015, FFI2015-64389-P; AEI/FEDER, UE) y “Edición crítica y anotada de la poesía completa de Quevedo, 1: Las silvas” (Ministerio de Ciencia, Innovación y Universidades, Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+d+i, PGC2018-093413-B-I00; AEI/FEDER, UE), ambos con financiación del Plan Nacional; así como de la ayuda del Programa de Consolidación y Estructuración de Unidades de Investigación Competitivas de la Xunta de Galicia para el año 2018, Grupo GI-1373, "Edición crítica y anotada de las obras completas de Quevedo" (EDIQUE), con referencia ED431B 2018/11"S

Optimización evolutiva multi-objetivo de medidas de complejidad e interpretabilidad semántica para sistemas basados en reglas lingüísticas

Tesis Univ. Granada. Departamento de Ciencias de la Computación e Inteligencia Artificia