Máster INFTEL: Transición de la universidad a la empresa en el campo de las comunicaciones móviles

Las actividades de los estudiantes en la universidad tienen características claramente diferentes a las actividades en una empresa. Al pasar el estudiante de un lugar al otro necesita un periodo de adaptación. Reducir ese periodo de adaptación permite que la persona se integre en la actividad profesional en menos tiempo y de forma más eficaz. Esto es más cierto aún en el ámbito de los teléfonos móviles y los desarrollos informáticos vinculados a ellos, dada su reciente aparición que requiere de una formación especializada. Con este doble enfoque nació el Máster Universitario en Informática aplicada a las Telecomunicaciones Móviles (Máster INFTEL), que alcanza ya la XI edición, habiéndose graduado diez promociones. Con esta experiencia, planteamos en este trabajo hacer un breve recorrido sobre cuál fue su origen, enfoque, resultados alcanzados y plantear las perspectivas futurasUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

GNUsmail: Open framework for on-line email classification

Real-time classification of massive email data is a challenging task that presents its own particular difficulties. Since email data presents an important temporal component, several problems arise: emails arrive continuously, and the criteria used to classify those emails can change, so the learning algorithms have to be able to deal with concept drift. Our problem is more general than spam detection, which has received much more attention in the literature. In this paper we present GNUsmail, an open-source extensible framework for email classification, which structure supports incremental and on-line learning. This framework enables the incorporation of algorithms developed by other researchers, such as those included in WEKA and MOA. We evaluate this framework, characterized by two overlapping phases (pre-processing and learning), using the ENRON dataset, and we compare the results achieved by WEKA and MOA algorithms

Mining Web-based Educational Systems to Predict Student Learning Achievements

Educational Data Mining (EDM) is getting great importance as a new interdisciplinary research field related to some other areas. It is directly connected with Web-based Educational Systems (WBES) and Data Mining (DM, a fundamental part of Knowledge Discovery in Databases). The former defines the context: WBES store and manage huge amounts of data. Such data are increasingly growing and they contain hidden knowledge that could be very useful to the users (both teachers and students). It is desirable to identify such knowledge in the form of models, patterns or any other representation schema that allows a better exploitation of the system. The latter reveals itself as the tool to achieve such discovering. Data mining must afford very complex and different situations to reach quality solutions. Therefore, data mining is a research field where many advances are being done to accommodate and solve emerging problems. For this purpose, many techniques are usually considered. In this paper we study how data mining can be used to induce student models from the data acquired by a specific Web-based tool for adaptive testing, called SIETTE. Concretely we have used top down induction decision trees algorithms to extract the patterns because these models, decision trees, are easily understandable. In addition, the conducted validation processes have assured high quality models

StreetQR Project. Device for Information Assistance in Streets and Places of Interest

En este trabajo se expone un ejemplo de transferencia de conocimiento desde la universidad hacia la sociedad, dentro del campo de la Inteligencia Artificial, con vista a obtener un encadenamiento productivo universidad-empresa. Así, se describe el proyecto StreetQR, cuyo objetivo es implementar el dispositivo de dicho nombre en el campus de la Universidad de Málaga, y que está actualmente en desarrollo. El StreetQR es un dispositivo de asistencia informativa para placas de calle y lugares de interés, que permite tres funciones: informar de manera situacional a los ciudadanos que están en una ciudad, captar información del flujo vehicular y peatonal de dicha ciudad, y alertar a la población en caso de situaciones especiales. En el trabajo se explica el dispositivo y su funcionamiento, así como el marco institucional que ha ofrecido la Universidad de Málaga para poder pasar de una patente a un proyecto que tiene por objetivo obtener un prototipo funcional del dispositivo en el campus universitario. También se expondrá el estado actual de desarrollo del proyecto.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Educational Data Mining for Personalized Prediction of Academic Performance

La Minería de Datos Educativos (Educational Data Mining - EDM) está adquiriendo gran importancia como un nuevo campo de investigación interdisciplinario relacionado con algunas otras áreas. Está directamente relacionado con los Sistemas Educativos basados en la Web (Web-based Educational Systems - WBES) y la Minería de Datos (Data Mining - DM), siendo esta última una parte fundamental del Descubrimiento de Conocimiento en Bases de Datos (Knowledge Discovery in Databases - KDD). Los WBES almacenan y administran grandes cantidades de datos. Estos datos están creciendo cada vez más y contienen conocimientos ocultos que podrían ser muy útiles para los usuarios (tanto profesores como estudiantes). Es conveniente identificar tales conocimientos en forma de modelos, patrones o cualquier otro esquema de repre- sentación que permita una mejor explotación del sistema. La minería de datos se revela como la herramienta para lograr tal descubrimiento, dando lugar a la EDM. En este contexto complejo se suelen utilizar distintas técnicas y algoritmos de aprendizaje para obtener los mejores resultados. En este trabajo se estudia, para una asignatura de Informática Teórica, concretamente la asignatura “Teoría de Autómatas y Lenguajes Formales”, cómo predecir el rendimiento académico alcanzado por los estudiantes, a partir de la realización de controles intermedios. Para ello se han aplicado y comparado distintos tipos de algoritmos de aprendizaje (vecinos más cercanos, árboles de decisión, multiclasificadores). Todo el proceso de control y evaluación de los estudiantes durante el curso se ha llevado a cabo a través de la herramienta web denominada SIETTE, desarrollada en nuestro departamento, y que además se utiliza en ámbitos fuera de nuestra propia universidad.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Este trabajo ha sido parcialmente financiado por el I Plan Propio de Investigacion y Transferencia de la Universidad de Malaga

Sobre el número máximo de factores frecuentes distintos en una cadena de símbolos

Las cadenas de sımbolos, como fuente de informacion, siempre han sido un recurso del que poder extraer conocimiento y, actualmente, el numero de aplicaciones y casos reales que las usan sigue creciendo, de forma que avances en este ambito repercutiran en multiples disciplinas. En esta comunicacion se estudia la complejidad del problema de descubrir factores (subcadenas) frecuentes en cadenas de sımbolos de longitud n, añadiendo la caracterıstica de que dicha busqueda pueda estar dirigida por un soporte (frecuencia) k mınimo que deben alcanzar dichos factores. Se analiza como afecta este resultado a algoritmos conocidos para este problema y se calcula de manera efectiva el numero maximo de factores k-frecuentes en una cadena. Se llega a demostrar que, aunque la complejidad en general es cuadratica en la longitud n de la cadena, si el soporte k es al menosraiz(n), la complejidad es lineal en n. Ese soporte es suficientemente interesante.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Este trabajo ha sido parcialmente financiado por el I Plan Propio de Investigacion y Transferencia de la Universidad de Malaga

Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds

I. Frías-Blanco, J. d. Campo-Ávila, G. Ramos-Jiménez, R. Morales-Bueno, A. Ortiz-Díaz and Y. Caballero-Mota, "Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds," in IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 3, pp. 810-823, 1 March 2015 doi: 10.1109/TKDE.2014.2345382. © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Incremental and online learning algorithms are more relevant in the data mining context because of the increasing necessity to process data streams. In this context, the target function may change over time, an inherent problem of online learning (known as concept drift). In order to handle concept drift regardless of the learning model, we propose new methods to monitor the performance metrics measured during the learning process, to trigger drift signals when a significant variation has been detected. To monitor this performance, we apply some probability inequalities that assume only independent, univariate and bounded random variables to obtain theoretical guarantees for the detection of such distributional changes. Some common restrictions for the online change detection as well as relevant types of change (abrupt and gradual) are considered. Two main approaches are proposed, the first one involves moving averages and is more suitable to detect abrupt changes. The second one follows a widespread intuitive idea to deal with gradual changes using weighted moving averages. The simplicity of the proposed methods, together with the computational efficiency make them very advantageous. We use a Naïve Bayes classifier and a Perceptron to evaluate the performance of the methods over synthetic and real data.Supported in part by the SESAAME project number TIN2008-06582-C03-03 of the MICINN, Spain. Supported in part by the AUIP (Asociación Universitaria Iberoamericana de Postgrado)

Controlled grain-size thermochromic VO2 coatings by the fast oxidation of sputtered vanadium or vanadium oxide films deposited at glancing angles

An original, simple and cost-effective oxidation strategy to attain thermochromic vanadium dioxide thin films is reported. This two-step procedure comprises the initial deposition of DC magnetron-sputtered vanadium or vanadium oxide films by the combination of glancing angle deposition and, if needed, reactive gas pulsing process, followed by fast oxidation of such layers in air atmosphere at high temperatures. Thanks to the careful control of the thermal treatment parameters, and taking advantage of the superior reactivity of the high surface-to-volume porous deposited structures, the formation of pure VO2 (M1) layers was achieved. The comprehensive characterization of such oxidized systems by means of scanning electron microscopy, Raman spectroscopy and scanning-transmission electron microscopy techniques such as electron energy-loss spectroscopy, not only confirmed the presence of the VO2 (M1) phase, but also allowed to shed light on the key role that reaction time plays in the selective formation of vanadium dioxide films of adjustable grain size and crystallinity. The optimal conditions to stabilize thermochromic VO2 consists in using large deposition angles (85 degrees) and short oxygen pulses (<= 2 s) during the growth, followed by fast and short thermal treatments (<= 45 s with a heating rate of 42 degrees C s(-1)) in air atmosphere at 550 degrees C. The metal-to-insulator response of the accomplished VO2 layers was finally evaluated by means of temperature dependent Kelvin probe force microscopy measurements, evidencing surface potential drops at heating, greater than those reported in the literature to date for VO2 thin films.A. J. Santos would like to thank the IMEYMAT Institute and the Spanish Ministerio de Educacion y Cultura for the concessions of grants (ICARO-173873 and FPU16-04386) . The "Talent Attraction Program" of the University of Cadiz is acknowledged by supporting B. Lacroix contract code E-11-2017-0117214. University of Cadiz and IMEYMAT are also agreed by financing the mutual facilities available at the UCA R&D Central Services (SC-ICYT) , the UCA projects reference "PUENTE PR2018-040 '' and "PUENTE PR2020-003 '', and the IMEYMAT project references "LINEAS PRIORITARIAS PLP2019120-3 and PLP2021120-1 ''. This work was supported by the Spanish State R&D project (Retos y Generacion de Conocimiento) ref. PID2020-114418RB-I00/AEI/10.13039/501100011033. The regional government of Andalusia with FEDER cofunding also participates through the projects AT-5983 Trewa 1157178 and FEDER-UCA18-10788. M. Dominguez acknowledges financial support from the Spanish Ministerio de Ciencia e Innovacion under project reference EQC 2018-004704-P FEDER 2014-2020, "Modernizacion del Servicio de Microscopia de Fuerza Atomica (AFM) del IMEYMAT"

Texture in ITO films deposited at oblique incidence by ion beam sputtering

Texture of crystalline In2O3:Sn (ITO) thin films prepared by combining ion beam sputtering (IBS) at room temperature and oblique angle deposition (OAD) has been studied depending on the vapor incidence on Si substrates (alpha, ranging from 50 degrees to 85 degrees) and the ions used to sputter the target (argon or xenon accelerated at 1.2 keV). Films obtained using Xe ions show an unusual evolution depending on the deposition angle alpha, with the development of a dual biaxial (1 1 1) off-axis texture for alpha 70 degrees that leads to a biaxial (0 0 1) texture at highest deposition angles. These behaviors are well described by mechanisms involving a maximization of the direct capture of the adatoms on {1 1 1} planes, which can however be hindered when mobilities are exalted such as in the case of Ar deposition. The tuning of adatoms mobilities through the IBS process mixed with the control of the deposition angle offered by the OAD geometry appears as an efficient route to achieve an upgraded texture engineering in nanostructured ITO thin films