Donde cristaliza la esperanza: lectura de Los ingenios

Mediaba la década de 1850, en plena edad de oro de la industria azucarera y de la economía cubana, cuando comenzó a publicarse el libro Los ingenios, sin duda el resultado más esmerado y bello que han parido las prensas de la Gran Antilla. En ese sentido la obra fue fruto de su tiempo, pero también un producto muy peculiar y, por ello, sumamente valioso. En un panorama dominado por el progreso técnico y el aumento de la oferta de dulce, describía, detallaba y analizaba esmerada y prolijamente los procesos que permitían extraer sacarosa de la caña con los más modernos adelantos. Además, ofrecía al lector excelsas panorámicas litografiadas de las fábricas, sus campos y entornos paisajísticos de la mano de un brillante pintor, el francés Eduardo Laplante, que en su curriculum juntaba el conocimiento de las técnicas de su arte y de la manufactura retratada, pues se ganaba la vida como representante de comercio de fabricantes de maquinaria. De hecho fue la venta de esos equipos lo que le llevó a la isla caribeña en 1848.Peer reviewe

Looking for Archetypes: Applying Game Data Mining to Hearthstone Decks

Digital Collectible Cards Games such as Hearthstone have become a very proli c test-bed for Arti cial Intelligence algorithms. The main researches have focused on the implementation of autonomous agents (bots) able to effectively play the game. However, this environment is also very attractive for the use of Data Mining (DM) and Machine Learning (ML) techniques, for analysing and extracting useful knowledge from game data. The objective of this work is to apply existing Game Mining techniques in order to study more than 600,000 real decks (groups of cards) created by players with many di erent skill levels. Data visualisation and analysis tools have been applied, namely, Graph representations and Clustering techniques. Then, an expert player has conducted a deep analysis of the results yielded by these methods, aiming to identify the use of standard - and well-known - archetypes de ned by the players. The used methods will also make it possible for the expert to discover hidden relationships between cards that could lead to nding better combinations of them, enhancing players' decks or, otherwise, identify unbalanced cards that could lead to a disappointing game experience. Moreover, although this work is mostly focused on data analysis and visualization, the obtained results can be applied to improve Hearthstone Bots' behaviour, e.g. predicting opponent's actions after identifying a speci c archetype in his/her deck.Spanish Government PID2020-113462RB-I00 PID2020-115570 GB-C22Junta de Andalucia B-TIC-402-UGR18 P18-RT-4830 A-TIC-608-UGR2

Colonos. Agricultores cañeros, ¿clase media rural en Cuba? 1880-1898

In the end of the 19th Century and coincideing with the slavery abolition process, Cuban sugar sector suffered a strong transformation to keep its efficiency, wich has resultat the separation between agrarian and industrial parts of the ingenio. The colonato arose as an effect of this separation, to take charge of the cane supply, Institution not very studied in spite of its socio-economic importance. Our paper analizes its origins in relation to the two factors wich determinated it: the aforementioned efficiency of the sugar production and the necessity of attracting white inmigrations to one island sacarely populated to countereact the influence of the black element.Al final del siglo XIX, y coincidiendo con el proceso de abolición de la esclavitud, el sector azucarero cubano experimentó una fuerte transformación para mantener su eficiencia, la cual tuvo como resultado la separación de las partes agraria e industrial del ingenio. Como efecto de esa separación y para hacerse cargo de la oferta de la caña, surgió el colonato, institución poco estudiada a pesar de su importancia socio-económica. Nuestro trabajo analiza su origen en relación a los dos factores que lo determinaron: la referida eficiencia de la producción azucarera y la necesidad de atraer inmigración blanca a una isla poco poblada para contrarrestar la influencia del elemento negro

Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

This work has been partially funded by projects PID2020-113462RB-I00 (ANIMALICOS), granted by Ministerio Espanol de Economia y Competitividad; projects P18-RT-4830 and A-TIC-608-UGR20 granted by Junta de Andalucia, and project B-TIC-402-UGR18 (FEDER and Junta de Andalucia).During the recent COVID-19 pandemic, people were forced to stay at home to protect their own and others’ lives. As a result, remote technology is being considered more in all aspects of life. One important example of this is online reviews, where the number of reviews increased promptly in the last two years according to Statista and Rize reports. People started to depend more on these reviews as a result of the mandatory physical distance employed in all countries. With no one speaking to about products and services feedback. Reading and posting online reviews becomes an important part of discussion and decision-making, especially for individuals and organizations. However, the growth of online reviews usage also provoked an increase in spam reviews. Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem. As part of this study, we outline the concepts and detection methods of spam reviews, along with their implications in the environment of online reviews. The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation. Then, highlight the differences between the works before and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine different detection approaches have been classified in order to investigate their specific advantages, limitations, and ways to improve their performance. Additionally, a literature analysis, discussion, and future directions were also presented.Spanish Government PID2020-113462RB-I00Junta de Andalucia P18-RT-4830 A-TIC-608-UGR20 B-TIC-402-UGR18European Commission B-TIC-402-UGR1

Comparing the Performance of Deep Learning Methods to Predict Companies' Financial Failure

This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades under Project RTI2018-102002-A-I00, in part by the Ministerio de Economia y Competitividad under Project TIN2017-85727-C4-2-P and Project PID2020-115570GB-C22, in part by the Fondo Europeo de Desarrollo Regional (FEDER) and Junta de Andalucia under Project B-TIC-402-UGR18, and in part by the Junta de Andalucia under Project P18-RT-4830.One of the most crucial problems in the eld of business is nancial forecasting. Many companies are interested in forecasting their incoming nancial status in order to adapt to the current nancial and business environment to avoid bankruptcy. In this work, due to the effectiveness of Deep Learning methods with respect to classi cation tasks, we compare the performance of three well-known Deep Learning methods (Long-Short Term Memory, Deep Belief Network and Multilayer Perceptron model of 6 layers) with three bagging ensemble classi ers (Random Forest, Support Vector Machine and K-Nearest Neighbor) and two boosting ensemble classi ers (Adaptive Boosting and Extreme Gradient Boosting) in companies' nancial failure prediction. Because of the inherent nature of the problem addressed, three extremely imbalanced datasets of Spanish, Taiwanese and Polish companies' data have been considered in this study. Thus, ve oversampling balancing techniques, two hybrid balancing techniques (oversamplingundersampling) and one clustering-based balancing technique have been applied to avoid data inconsistency problem. Considering the real nancial data complexity level and type, the results show that the Multilayer Perceptron model of 6 layers, in conjunction with SMOTE-ENN balancing method, yielded the best performance according to the accuracy, recall and type II error metrics. In addition, Long-Short Term Memory and ensemble methods obtained also very good results, outperforming several classi ers used in previous studies with the same datasets.Ministerio de Ciencia, Innovacion y Universidades RTI2018-102002-A-I00Spanish Government TIN2017-85727-C4-2-P PID2020-115570GB-C22European Commission B-TIC-402-UGR18Junta de Andalucia B-TIC-402-UGR18 P18-RT-483

Meteorological conditions for the triggering of landslides in Asturias (NW Spain): a preliminary analysis of synoptic patterns

Póster presentado en: EGU General Assembly celebrada del 23 al 28 de abril de 2017 en Viena, Austria

Experiencias en la asignatura Diseño y evaluación de configuraciones

En este trabajo se exponen las experiencias docentes que han tenido durante los últimos cinco años los profesores de la asignatura Diseño y Evaluación de Configuraciones, asignatura optativa de las Ingenierías Técnicas en Informática en la Universidad de Granada. Igualmente, también se comentan las herramientas utilizadas para gestionar dicha asignatura, intentando facilitar, tanto a los alumnos, como al profesor, todas las tareas propias de la misma. El principal objetivo de esta asignatura es dar una metodología para la evaluación de prestaciones (o rendimiento) de un ordenador. Se divide, a grosso modo, en tres partes: una parte dedicada a los monitores, o herramientas encargadas de medir la carga de un ordenador, otra parte dedicada a la mejora de prestaciones, y otra parte dedicada a la reproducción de la carga de un ordenador, los llamados benchmarks.Financiado por el proyecto EvOrq (TIC-3903) y la Beca FPU AP2009-294

Meteorological patterns linked to landslide triggering in asturias (NW Spain): A preliminary analysis

Asturias is one of the most landslide prone areas in the north of Spain. Most landslides are linked to intense and continue rainfall events, especially between October and May. This fact points out precipitation as the main triggering factor in the study area. Thirteen rainfall episodes that caused 1064 landslides between 2008 and 2016 have been selected for its study. Landslide records come from the Principality of Asturias Landslide Database (BAPA) and meteorological data from the Spanish Meteorological Agency (AEMET). Meteorological conditions which took place during each period have been characterized by using NCEP/NCAR Reanalysis data. Four main landslide-triggering meteorological patterns have been identified for the Asturian territory: Strong Atlantic Anticyclone pattern (SAA), Atlantic Depression pattern (AD), Anticyclonic ridge pattern (AR) and Cut-off Low pattern (CL).This research is funded by the Department of Employment, Industry and Tourism of the Government of Asturias, Spain, and the European Regional Development Fund FEDER, within the framework of the research grant “GEOCANTABRICA: Procesos geológicos modeladores del relieve de la Cordillera Cantábrica” (FC-15-GRUPIN14-044), and supported on the cooperation between the Department of Geology at the University of Oviedo and the AEMET

Applying Data Mining and Machine Learning Techniques to Predict Powerlifting Results

This work was partially funded by projects PID2020-113462RB-I00 (ANIMALICOS), PID2020-115570GB-C22, and PID2020-115570GB-C21, granted by the Ministerio Español de Economía y Competitividad; project TED2021-129938B-I00, granted by the Ministerio Español de Ciencia e Innovación; projects P18-RT-4830 and A-TIC-608-UGR20, granted by Junta de Andalucía; and project B-TIC-402-UGR18 (FEDER and Junta de Andalucía).This paper presents a study on the creation of a tool to help powerlifting athletes and coaches, as well as bodybuilders and other amateur gym athletes, to analyse their data and obtain useful information regarding the athlete’s performance. The tool should also predict future personal records in lifting for both raw (non-equipped) and non-raw (equipped) attempts, and their various exercises. In order to achieve this, a dataset with entries of around 500 k lifters and more than 20 k official powerlifting competitions was used. Among those entries, biometric variables of the lifters and the weights they lift in each of the three movements of this sport discipline were included: squat, bench press, and deadlift. We applied data preprocessing and visualising as well as data splitting and scaling techniques in order to train the machine learning models that are used to make the predictions. Lastly, the best predictive models were used in the implemented tool.Ministerio Español de Economía y Competitividad ID2020-113462RB-I00, PID2020-115570GB-C22, PID2020-115570GB-C2Ministerio Español de Ciencia e Innovación TED2021-129938B-I00Junta de Andalucía P18-RT-4830, A-TIC-608-UGR20FEDER and Junta de Andalucía B-TIC-402-UGR1