Computational intelligent methods for trusting in social networks

104 p.This Thesis covers three research lines of Social Networks. The first proposed reseach line is related with Trust. Different ways of feature extraction are proposed for Trust Prediction comparing results with classic methods. The problem of bad balanced datasets is covered in this work. The second proposed reseach line is related with Recommendation Systems. Two experiments are proposed in this work. The first experiment is about recipe generation with a bread machine. The second experiment is about product generation based on rating given by users. The third research line is related with Influence Maximization. In this work a new heuristic method is proposed to give the minimal set of nodes that maximizes the influence of the network

Enhancing Confusion Entropy (CEN) for Binary and Multiclass Classification

Different performance measures are used to assess the behaviour, and to carry out the comparison, of classifiers in Machine Learning. Many measures have been defined on the literature, and among them, a measure inspired by Shannon's entropy named the Confusion Entropy (CEN). In this work we introduce a new measure, MCEN, by modifying CEN to avoid its unwanted behaviour in the binary case, that disables it as a suitable performance measure in classification. We compare MCEN with CEN and other performance measures, presenting analytical results in some particularly interesting cases, as well as some heuristic computational experimentation.This work was supported by Ministerio de Economía y Competitividad, Gobierno de España, MTM2015 67802-P to R.D. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Bayesian network-based over-sampling method (BOSME) with application to indirect cost-sensitive learning

Traditional supervised learning algorithms do not satisfactorily solve the classification problem on imbalanced data sets, since they tend to assign the majority class, to the detriment of the minority class classification. In this paper, we introduce the Bayesian network-based over-sampling method (BOSME), which is a new over-sampling methodology based on Bayesian networks. Over-sampling methods handle imbalanced data by generating synthetic minority instances, with the benefit that classifiers learned from a more balanced data set have a better ability to predict the minority class. What makes BOSME different is that it relies on a new approach, generating artificial instances of the minority class following the probability distribution of a Bayesian network that is learned from the original minority classes by likelihood maximization. We compare BOSME with the benchmark synthetic minority over-sampling technique (SMOTE) through a series of experiments in the context of indirect cost-sensitive learning, with some state-of-the-art classifiers and various data sets, showing statistical evidence in favor of BOSME, with respect to the expected (misclassification) cost.The authors are supported by Ministerio de Ciencia, Innovación y Universidades, Gobierno de España, project ref. PGC2018-097848-B-I0

Reviewing and Discussing Graph Reduction in Edge Computing Context

Much effort has been devoted to transferring efficiently different machine-learning algorithms, and especially deep neural networks, to edge devices in order to fulfill, among others, real-time, storage and energy-consumption issues. The limited resources of edge devices and the necessity for energy saving to lengthen the durability of their batteries, has encouraged an interesting trend in reducing neural networks and graphs, while keeping their predictability almost untouched. In this work, an alternative to the latest techniques for finding these reductions in networks size is proposed, seeking to figure out a simplistic way to shrink networks while maintaining, as far as possible, their predictability testing on well-known datasets

Cuestionarios para evaluar la violencia escolar en Educación Primaria y en Educación Secundaria: CUVE3-EP y CUVE3-ESO

El CUVE3-EP y el CUVE3-ESO son dos cuestionarios diseñados para analizar la frecuencia con la que el alumnado considera que aparecen diferentes tipos de violencia escolar, protagonizada por los estudiantes o el profesorado de su clase. El CUVE3-EP fue contrastado y baremado con 1041 estudiantes de tercer ciclo de Primaria, pertenecientes a 20 centros educativos de Asturias (España). El CUVE3-ESO fue contrastado y baremado con 2597 estudiantes de Educación Secundaria Obligatoria, pertenecientes a 18 centros educativos de Asturias. Como resultado, se han obtenido dos instrumentos de evaluación con unas apropiadas garantías psicométricas. Pero, sobre todo, se han creado dos herramientas que han demostrado su utilidad tanto para investigadores, en análisis descriptivos o de impacto de intervenciones, como para orientadores psicopedagógicos o equipos directivos, en la evaluación de sus centros. La aplicación informática que acompaña al cuestionario permite obtener, de forma sencilla, informes por aula, nivel o centro educativo.CUVE3-EP and CUVE3-ESO are two questionnaires that have been designed to identify how often students perceive different kinds of school violence committed by their teachers or classmates. CUVE3-EP was applied to 1041 third cycle of primary education students, from 20 schools in Asturias (Spain). CUVE3-ESO was applied to 2597 compulsory secondary education students, from 18 high schools in Asturias. Both questionnaires have shown adequate psychometric properties. But the most important, they were useful to investigators to perform descriptive analysis and to analyze intervention effects and also to school psychologists and directive teams interested in assessing their school´s climate. The software offered with the questionnaire allows to easily create classroom or school reports

A Review of Possible EEG Markers of Abstraction, Attentiveness and Memorisation in Cyber-Physical Systems for Special Education

[EN]Cyber-physical systems (CPSs) for special education rely on effective mental and brain processing during the lesson, performed with the assistance of humanoid robots. The improved diagnostic ability of the CPS is a prerogative of the system for efficient technological support of the pedagogical process. The article focuses on the available knowledge of possible EEG markers of abstraction, attentiveness, and memorisation (in some cases combined with eye tracking) related to predicting effective mental and brain processing during the lesson. The role of processing abstraction is emphasised as the learning mechanism, which is given priority over the other mechanisms by the cognitive system. The main markers in focus are P1, N170, Novelty P3, RewP, N400, and P600. The description of the effects is accompanied by the analysis of some implications for the design of novel educational scenarios in inclusive classes.The presented research received funding from the EC for project CybSPEED,. 777720, H2020-MSCA-RISE-2017; the Bulgarian Research Fund project,. KP-06-H42/4 (2020-2023); and the project Competence Center "Intelligent mechatronic, eco-and energy saving systems and technologies". BG05M2OP0011.002-0023, OP Science and Education for Smart Growth (2014-2020)

Risk factors associated with cybervictimization in adolescence

AbstractThe aim of this work is to analyze the predictive value of several variables that may affect the likelihood of occasional or severe cibervictimization in adolescence, including sociodemographic (gender and age), psychological (self-esteem and shyness-social anxiety), educational (off-line school victimization, training and socio-emotional support, and academic achievement), family (parental control), and technological (frequency of use and risky Internet behaviors) factors. To achieve this, three self-reports were applied to 3,180 Compulsory Secondary Education students from Asturias (Spain), aged between 11 and 19 years old. The multinomial logistic regression results show that age, off-line school victimization, parental control, risky Internet behaviors, using online social networks or instant messaging applications and frequency of Internet use during weekends are statistically significant risk factors for both occasional and severe cybervictimization. Self-esteem is a protective factor for occasional cybervictimization. Having their own mobile phone, playing on-line with others and frequency of Internet use during weekdays are risk factors for severe cybervictimization. The implications of these results are discussed with regard to prevention, detection and treatment of cybervictimization

SALVADOR JIMÉNEZ CORONADO. ASTRÓNOMO, CIENTÍFICO, MATEMÁTICO, POLÍTICO Y ESCOLAPIO.

El presente artículo consta de dos partes bien diferenciadas. En la primera de ellas se realizará un breve recorrido por el estado de la Ciencia española durante los siglos XVIII y XIX, destacando los aspectos más importantes de dicha época. En la segunda, mostraremos los aspectos biográficos más importantes de la vida de Salvador Jiménez Coronado, primer director del Real Observatorio Astronómico de Madrid, así como sus aportaciones al campo de las ciencias, de la matemática y de la educación. No se puede acabar este resumen sin mencionar que, en este texto, se adjuntan dos documentos inéditos hasta la fecha como son su partida de nacimiento y su partida de defunción

Development of AI-Based Tools for Power Generation Prediction

This study presents a model for predicting photovoltaic power generation based on meteorological, temporal and geographical variables, without using irradiance values, which have traditionally posed challenges and difficulties for accurate predictions. Validation methods and evaluation metrics are used to analyse four different approaches that vary in the distribution of the training and test database, and whether or not location-independent modelling is performed. The coefficient of determination,R2, is used to measure the proportion of variation in photovoltaic power generation that can be explained by the model’s variables, while gCO2eq represents the amount of CO2 emissions equivalent to each unit of power generation. Both are used to compare model performance and environmental impact. The results show significant differences between the locations, with substantial improvements in some cases, while in others improvements are limited. The importance of customising the predictive model for each specific location is emphasised. Furthermore, it is concluded that environmental impact studies in model production are an additional step towards the creation of more sustainable and efficient models. Likewise, this research considers both the accuracy of solar energy predictions and the environmental impact of the computational resources used in the process, thereby promoting the responsible and sustainable progress of data science.This research is supported by the Bulgarian National Science Fund in the scope of the project ”Exploration the application of statistics and machine learning in electronics” under contract number κπ-06-H42/1

Violencia a través de las tecnologías de la información y la comunicación en estudiantes de secundaria

The aim of this paper was to analyze violence through Informa- tion and Communication Technologies (ICT) in Compulsory Secondary Education students. To achieve this objective, the School Violence Question- naire-Revised was applied to 638 Compulsory Secondary Education stu- dents, from six high schools in Asturias (Spain). Results show that vio- lence through ICT occurs with a remarkable frequency, although less than some kinds of traditional school violence. Statistically significative differ- ences were found among grades of Compulsory Secondary Education and between rural and urban centers. Violence through ICT is more frequent in second grade and in urban high schools. However, statistically significa- tive differences were barely found between genders. Also, a strong correla- tion between violence through ICT and some kinds of traditional school violence was found. These outcomes are compared with the results of the main previous works about this subject. Some educative implications of these results are discussed.El objetivo de este estudio fue analizar la violencia a través de las Tecnologías de la Información y la Comunicación (TIC) en estudiantes de Educación Secundaria Obligatoria. Para ello, se aplicó el Cuestionario de Violencia Escolar-Revisado (CUVE-R) a 638 estudiantes de 1º a 4º de ESO, pertenecientes a seis centros educativos de Asturias (España). Los resultados obtenidos muestran que, a pesar de ser menos habitual que otros tipos más tradicionales de violencia escolar, la violencia a través de las TIC aparece con una frecuencia que la hace digna de atención. Se han encontrado diferencias estadísticamente significativas en función del curso y del entorno -rural o urbano- del centro: la violencia a través de las TIC aparece con mayor frecuencia en 2º de ESO que en los demás niveles educativos, y en los centros urbanos que en los rurales. En cambio, apenas se han encontrado diferencias en función del género. Se ha hallado, asimismo, una fuerte correlación entre la presencia de violencia a través de las TIC y de otros tipos de violencia escolar más tradicionales. Estos resultados se contrastan con los obtenidos por los estudios previos más relevantes sobre esta temática. Se discuten algunas implicaciones educativas de estos resultados