Diez años innovando en la enseñanza de los fundamentos de la programación : resultados y conclusiones

Este artículo presenta los cambios experimentados en las asignaturas relacionadas con la introducción a la programación, impartidas en las titulaciones de Ingeniería Informática de la Universidad de Sevilla durante el período 2001-2011. El artículo hace especial hincapié en las principales innovaciones realizadas a lo largo de dicho período, entre las que destacan: la apuesta por la orientación a objetos en detrimento de la programación estructurada como paradigma de iniciación, el uso de un lenguaje real (Java) y la consiguiente eliminación del pseudocódigo como lenguaje de iniciación, y la introducción de esquemas y librerías (Guava) orientados a facilitar la tarea de programación de los futuros profesionales. Finalmente, mostramos los resultados obtenidos por nuestros alumnos a lo largo de este período y las principales conclusiones que se pueden extraer de su análisis.SUMMARY This article presents the changes in the subjects related to the introduction to programming taught in the Computer Engineering degree at University of Seville in the period 2001-2011. The article puts special emphasis on major innovations during this period: object-orientation in lieu of structured programming as a paradigm of initiation, use of real languages (Java) and subsequent elimination of the pseudocode as a language of initiation, and the introduction of patterns and libraries (guava) designed to facilitate the programming tasks of future professionals. Finally, we show the results obtained by our students during this period and show the main conclusions can be drawn from the analysis of the results.Peer Reviewe

An Experimental Review on Deep Learning Architectures for Time Series Forecasting

In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the temporal dependencies present in time series. However, selecting the most convenient type of deep neural network and its parametrization is a complex task that requires considerable expertise. Therefore, there is a need for deeper studies on the suitability of all existing architectures for different forecasting tasks. In this work, we face two main challenges: a comprehensive review of the latest works using deep learning for time series forecasting; and an experimental study comparing the performance of the most popular architectures. The comparison involves a thorough analysis of seven types of deep learning models in terms of accuracy and efficiency. We evaluate the rankings and distribution of results obtained with the proposed models under many different architecture configurations and training hyperparameters. The datasets used comprise more than 50000 time series divided into 12 different forecasting problems. By training more than 38000 models on these data, we provide the most extensive deep learning study for time series forecasting. Among all studied models, the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve comparable performance with less variability of results under different parameter configurations, while also being more efficient

Local models-based regression trees for very short-term wind speed prediction

This paper evaluates the performance of different types of Regression Trees (RTs) in a real problem of very short-term wind speed prediction from measuring data in wind farms. RT is a solidly established methodology that, contrary to other soft-computing approaches, has been under-explored in problems of wind speed prediction in wind farms. In this paper we comparatively evaluate eight different types of RTs algorithms, and we show that they are able obtain excellent results in real problems of very short-term wind speed prediction, improving existing classical and soft-computing approaches such as multi-linear regression approaches, different types of neural networks and support vector regression algorithms in this problem.We also show that RTs have a very small computation time, that allows the retraining of the algorithms whenever new wind speed data are collected from the measuring towers.Ministerio de Ciencia y Tecnología ECO2010-22065-C03-02Ministerio de Ciencia y Tecnología TIN2011-28956-C02Junta de Andalucía P12-TIC-1728Universidad Pablo de Olavide APPB81309

Una propuesta para asignaturas de Introducción a la Programación

En este artículo se presentan las asignaturas de Introducción a la Programación que se imparten en las titulaciones de Ingeniero en Informática de la Universidad de Sevilla. Los rasgos más destacados son: introducción a la POO y la abstracción de datos, el uso de C y C++ en el laboratorio y una metodología basada en diapositivas, código para experimentos y el acceso a toda la documentación a través de la web

Diez años innovando en la enseñanza de los fundamentos de la programación: resultados y conclusiones

Este artículo presenta los cambios experimentados en las asignaturas relacionadas con la introducción a la programación, impartidas en las titulaciones de Ingeniería Informática de la Universidad de Sevilla durante el período 2001-2011. El artículo hace especial hincapié en las principales innovaciones realizadas a lo largo de dicho período, entre las que destacan: la apuesta por la orientación a objetos en detrimento de la programación estructurada como paradigma de iniciación, el uso de un lenguaje real (Java) y la consiguiente eliminación del pseudocódigo como lenguaje de iniciación, y la introducción de esquemas y librerías (Guava) orientados a facilitar la tarea de programación de los futuros profesionales. Finalmente, mostramos los resultados obtenidos por nuestros alumnos a lo largo de este período y las principales conclusiones que se pueden extraer de su análisis.This article presents the changes in the subjects related to the introduction to programming taught in the Computer Engineering degree at University of Seville in the period 2001-2011. The article puts special emphasis on major innovations during this period: object-orientation in lieu of structured programming as a paradigm of initiation, use of real languages (Java) and subsequent elimination of the pseudocode as a language of initiation, and the introduction of patterns and libraries (guava) designed to facilitate the programming tasks of future professionals. Finally, we show the results obtained by our students during this period and show the main conclusions can be drawn from the analysis of the results

Evolutionary association rules for total ozone content modeling from satellite observations

In this paper we propose an evolutionary method of association rules discovery (EQAR, Evolutionary Quan titative Association Rules) that extends a recently published algorithm by the authors and we describe its ap plication to a problem of Total Ozone Content (TOC) modeling in the Iberian Peninsula. We use TOC data from the Total Ozone Mapping Spectrometer (TOMS) on board the NASA Nimbus-7 satellite measured at three lo cations (Lisbon, Madrid and Murcia) of the Iberian Peninsula. As prediction variables for the association rules we consider several meteorological variables, such as Outgoing Long-wave Radiation (OLR), Temperature at 50 hPa level, Tropopause height, and wind vertical velocity component at 200 hPa. We show that the best as sociation rules obtained by EQAR are able to accurate modeling the TOC data in the three locations consid ered, providing results which agree to previous works in the literatur

Social Emotional Health Survey-Secondary (SEHS-S): A Universal Screening Measure of Social-Emotional Strengths for Spanish-Speaking Adolescents

The Social Emotional Health Survey-Secondary (SEHS-S), which is a measure of core psychological assets based on a higher-order model of Covitality, is comprised of 36 items and four latent traits (with three measured subscales): belief in self (self-efficacy, self-awareness, and persistence), belief in others (school support, family coherence, and peer support), emotional competence (emotional regulation, behavioral self-control, and empathy), and engaged living (gratitude, zest, and optimism). Previous international studies have supported the psychometric properties of the SEHS-S. The present study extended this research by examining the psychometric properties of a Spanish-language adaptation with a sample of 1042 Spanish adolescents (Mage = 14.49, SD = 1.65.). Confirmatory factor analyses replicated the original factorial structure, with hierarchical omega between 0.66–0.93, with 0.94 for the total score. Factorial invariance across genders revealed small latent mean differences. A path model evaluated concurrent validity, which revealed a significant association between Covitality and bidimensional mental health (psychological distress and well-being). Specifically, correlational analyses showed a negative association with internalizing/externalizing symptoms, and positive associations with subjective well-being, health-related quality of life, and prosocial behaviors. This study provides an example of a culturally relevant adaptation of an international tool to measure student strengths, which is critical to planning school programming and policy.The Ministry of Economy, Industry, and Competitiveness of the Government of Spain (I+D+i Projects, 2017, reference number: PSI2017-88280-R, and Research Networks PSI2015-70943-REDT and PSI2017-90650-REDT) and the Department of Education, Research, Culture, and Sport from Valencian Community of Spain through two grants for the hiring of PhD research assistant awarded to M.R-R. [ACIF/2015/155; VALi+d Program) and R.F. (ACIF/2019/052; VALi+d Program) funded this research