Measuring satisfaction in societies with opinion leaders and mediators

An opinion leader-follower model (OLF) is a two-action collective decision-making model for societies, in which three kinds of actors are considered:Preprin

Cooperation through social influence

We consider a simple and altruistic multiagent system in which the agents are eager to perform a collective task but where their real engagement depends on the willingness to perform the task of other influential agents. We model this scenario by an influence game, a cooperative simple game in which a team (or coalition) of players succeeds if it is able to convince enough agents to participate in the task (to vote in favor of a decision). We take the linear threshold model as the influence model. We show first the expressiveness of influence games showing that they capture the class of simple games. Then we characterize the computational complexity of various problems on influence games, including measures (length and width), values (Shapley-Shubik and Banzhaf) and properties (of teams and players). Finally, we analyze those problems for some particular extremal cases, with respect to the propagation of influence, showing tighter complexity characterizations.Peer ReviewedPostprint (author’s final draft

Agua y Suero Fisiológico para Prevenir la Formación de Paracloroanilina

Indexación: Web of Science; Scielo.ABSTRACT: This study determined if p-chloroaniline (PCA) can be minimized by using distilled water and physiological saline solution following sodium hypochlorite but before chlorhexidine. Hypochlorite 5%, 0.5%, 0.05%, 0.005% and 0.0005% dissolved in 0.9% NaCl and in distilled water were mixed with 2% chlorhexidine for the formation of PCA. The PCA was determined using UV-VISIBLE spectrometry, with spectral curve was determined the wavelength of maximum absorption of PCA. Formed PCA absorbance was measured between 0.025%, 0.02%, 0.015%, 0.01%, 0.005% and 0.0025% hypochlorite and 2% chlorhexidine. 2% chlorhexidine and hypochlorite with physiological saline form a white precipitate which prevents the measurement of PCA. Colored PCA is formed with 0.05%, 0.005% hypochlorite aqueous dilutions and 2% chlorhexidine. The lwavelength of maximum absorption obtained was 470 nm and absorbance of PCA showed a linear decrease. 0.005% NaClO produces the least amount of PCA. The best solvent to prevent the formation of PCA during the interaction of sodium hypochlorite with chlorhexidine is distilled water.Este estudio determinó si la p-cloroanilina (PCA) puede ser minimizada mediante el uso de agua destilada y solución salina fisiológica seguido de la aplicación de hipoclorito de sodio, previo a la aplicación de clorhexidina. Hipoclorito al 5%, 0,5%, 0,05%, 0,005% y 0,0005% fue disuelto en 0,9% de NaCl y en agua destilada se mezcló con 2% de clorhexidina para la formación de PCA. El PCA se determinó mediante espectrometría UV-Visible, y con curva espectral se determinó la longitud de onda máxima del PCA. La absorbancia del PCA formado se midió con 0,025%, 0,02%, 0,015%, 0,01%, 0,005% y 0,0025% de hipoclorito y 2% de clorhexidina. La combinación de 2% de clorhexidina e hipoclorito en solución salina fisiológica forman un precipitado blanco que impide la medición del PCA. El PCA coloreado es formado con 0,05%, 0,005% diluciones acuosas de hipoclorito y 2% de clorhexidina. La longitud de onda máxima obtenida fue de 470 nm y la absorbancia del PCA mostró una disminución lineal. NaClO al 0,005% produce menor cantidad de PCA. El mejor disolvente para evitar la formación de PCA durante la interacción de hipoclorito de sodio con clorhexidina es agua destilada.http://ref.scielo.org/2kpw6

Analysis of the evolution of the Spanish labour market through unsupervised learning

Unemployment in Spain is one of the biggest concerns of its inhabitants. Its unemployment rate is the second highest in the European Union, and in the second quarter of 2018 there is a 15.2% unemployment rate, some 3.4 million unemployed. Construction is one of the activity sectors that have suffered the most from the economic crisis. In addition, the economic crisis affected in different ways to the labour market in terms of occupation level or location. The aim of this paper is to discover how the labour market is organised taking into account the jobs that workers get during two periods: 2011-2013, which corresponds to the economic crisis period, and 2014-2016, which was a period of economic recovery. The data used are official records of the Spanish administration corresponding to 1.9 and 2.4 million job placements, respectively. The labour market was analysed by applying unsupervised machine learning techniques to obtain a clear and structured information on the employment generation process and the underlying labour mobility. We have applied two clustering methods with two different technologies, and the results indicate that there were some movements in the Spanish labour market which have changed the physiognomy of some of the jobs. The analysis reveals the changes in the labour market: the crisis forces greater geographical mobility and favours the subsequent emergence of new job sources. Nevertheless, there still exist some clusters that remain stable despite the crisis. We may conclude that we have achieved a characterisation of some important groups of workers in Spain. The methodology used, being supported by Big Data techniques, would serve to analyse any alternative job market.Ministerio de Economía y Competitividad TIN2014-55894-C2-R y TIN2017-88209-C2-2-R, CO2017-8678

Analysis of Measures of Quantitative Association Rules

This paper presents the analysis of relationships among different interestingness measures of quality of association rules as first step to select the best objectives in order to develop a multi-objective algorithm. For this purpose, the discovering of association rules is based on evolutionary techniques. Specifically, a genetic algorithm has been used in order to mine quantitative association rules and determine the intervals on the attributes without discretizing the data before. The algorithm has been applied in real-word climatological datasets based on Ozone and Earthquake data.Ministerio de Ciencia y Tecnología TIN2007-68084-C-00Junta de Andalucía P07-TIC-0261

External clustering validity index based on chi-squared statistical test

Clustering is one of the most commonly used techniques in data mining. Its main goal is to group objects into clusters so that each group contains objects that are more similar to each other than to objects in other clusters. The evaluation of a clustering solution is a task carried out through the application of validity indices. These indices measure the quality of the solution and can be classified as either internal that calculate the quality of the solution through the data of the clusters, or as external indices that measure the quality by means of external information such as the class. Generally, indices from the literature determine their optimal result through graphical representation, whose results could be imprecisely interpreted. The aim of this paper is to present a new external validity index based on the chi-squared statistical test named Chi Index, which presents accurate results that require no further interpretation. Chi Index was analyzed using the clustering results of 3 clustering methods in 47 public datasets. Results indicate a better hit rate and a lower percentage of error against 15 external validity indices from the literature.Ministerio de Economía y Competitividad TIN2014-55894-C2-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-

An approach to validity indices for clustering techniques in Big Data

Clustering analysis is one of the most used Machine Learning techniques to discover groups among data objects. Some clustering methods require the number of clus ters into which the data is going to be partitioned. There exist several cluster validity indices that help us to approximate the optimal number of clusters of the dataset. However, such indices are not suitable to deal with Big Data due to its size limitation and runtime costs. This paper presents two cluster ing validity indices that handle large amount of data in low computational time. Our indices are based on redefinitions of traditional indices by simplifying the intra-cluster distance calculation. Two types of tests have been carried out over 28 synthetic datasets to analyze the performance of the proposed indices. First, we test the indices with small and medium size datasets to verify that our indices have a similar effectiveness to the traditional ones. Subsequently, tests on datasets of up to 11 million records and 20 features have been executed to check their efficiency. The results show that both indices can handle Big Data in a very low computational time with an effectiveness similar to the traditional indices using Apache Spark framework.Ministerio de Economía y Competitividad TIN2014-55894-C2-1-

Measuring satisfaction and power in influence based decision systems

We introduce collective decision-making models associated with influence spread under the linear threshold model in social networks. We define the oblivious and the non-oblivious influence models. We also introduce the generalized opinion leader–follower model (gOLF) as an extension of the opinion leader–follower model (OLF) proposed by van den Brink et al. (2011). In our model we allow rules for the final decision different from the simple majority used in OLF. We show that gOLF models are non-oblivious influence models on a two-layered bipartite influence digraph. Together with OLF models, the satisfaction and the power measures were introduced and studied. We analyze the computational complexity of those measures for the decision models introduced in the paper. We show that the problem of computing the satisfaction or the power measure is #P-hard in all the introduced models even when the subjacent social network is a bipartite graph. Complementing this result, we provide two subfamilies of decision models in which both measures can be computed in polynomial time. We show that the collective decision functions are monotone and therefore they define an associated simple game. We relate the satisfaction and the power measures with the Rae index and the Banzhaf value of an associated simple game. This will allow the use of known approximation methods for computing the Banzhaf value, or the Rae index to their practical computation.Peer ReviewedPostprint (author's final draft

Satisfaction and power in unanimous majority influence decision models

We consider decision models associated with cooperative influence games, the oblivious and the non-oblivious influence models. In those models the satisfaction and the power measures were introduced and studied. We analyze the computational complexity of those measures when the in uence level is set to unanimity and the rule of decision is simple majority. We show that computing the satisfaction and the power measure in those systems are #P-hard.Peer ReviewedPostprint (author's final draft

An Approach to Silhouette and Dunn Clustering Indices Applied to Big Data in Spark

K-Means and Bisecting K-Means clustering algorithms need the optimal number into which the dataset may be divided. Spark implementations of these algorithms include a method that is used to calculate this number. Unfortunately, this measurement presents a lack of precision because it only takes into account a sum of intra-cluster distances misleading the results. Moreover, this measurement has not been well-contrasted in previous researches about clustering indices. Therefore, we introduce a new Spark implementation of Silhouette and Dunn indices. These clustering indices have been tested in previous works. The results obtained show the potential of Silhouette and Dunn to deal with Big Data.Ministerio de Economía y Competitividad TIN2014-55894-C2-1-