Framework for the development of articulatory characterization studies over mri images

En este artículo se presenta un marco de trabajo tecnológico innovador diseñado y desarrollado por nuestro grupo de investigación para posibilitar la realización de estudios de caracterización articulatoria de los sonidos de una lengua a partir de medidas tomadas sobre secuencias de imágenes de cine-MRI. Como elemento fundamental se tiene la herramienta software de producción propia DicomPas, que permite realizar la toma de medidas de parámetros articulatorios sobre las secuencias de imágenes MRI y la ejecución de algoritmos ad hoc sobre dichas medidas, de cara al procesamiento de los datos, con vistas a la posterior extracción del conocimiento, en forma de generación de inferencias estadísticas o de inteligencia artificial. En estos momentos este marco de trabajo está siendo aplicado a la realización de diversos estudios en euskara y español de Euskadi, disponiéndose para ello de una base de datos con dos repositorios de imágenes tomadas en el plano medio sagital, correspondientes a 18 informantes diferentes.In this paper an innovative framework is presented, designed and developed by our research team to enable the accomplishment of research works concerning the articulatory characterization of the sounds of a language from measures taken over MRI image sequences. As fundamental element there is the DicomPas software tool, developed by our team, which allows to carry out the measures of articulatory parameters over the MRI image sequences and the execution of ad hoc algorithms over such measures, facing the data processing, with the view to the subsequent extraction of knowledge, in the form of the generation of statistical or artificial intelligence inferences. This framework is currently being applied to the achievement of diverse studies in Basque and Spanish of the Basque Country. To do so, a database with two repositories of images taken in the midsagittal plane, corresponding to 18 different informants, is available

Nueva metodología de enseñanza de procesado digital de la señal utilizando la API “joPAS”

Este artículo presenta la API de programación “JoPAS” desarrollada por el grupo de investigación PAS de la universidad de Deusto. joPAS permite el uso de variables y funciones de Octave desde un programa realizado en Java. Esta API posibilita a los estudiantes el rápido desarrollo de aplicaciones de procesado digital de señal, haciendo uso de la sencillez de diseño y potencia de interfaces gráficas en leguaje Java y el cálculo científico en Octave. Esta nueva herramienta docente está siendo utilizada por alumnos de ingeniería informática e ingeniería técnica de telecomunicación

Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning

Lifelong learning enables professionals to update their skills to face challenges in their changing work environments. In view of the wide range of courses on offer, it is important for professionals to have recommendation systems that can link them to suitable courses. Based on this premise and on our previous research, this paper proposes the use of ontology to model job sectors and areas of knowledge, and to represent professional skills that can be automatically updated using the profiled data and machine learning for clustering entities. A three-stage hybrid system is proposed for the recommendation process: semantic filtering, content filtering and heuristics. The proposed system was evaluated with a set of more than 100 user profiles that were used in a previous version of the proposed recommendation system, which allowed the two systems to be compared. The proposed recommender showed 15% improvement when using ontology and clustering with DBSCAN in recall and serendipity metrics, and a six-point increase in harmonic mean over the stored data-based recommender system

Recommendation Systems for Education: Systematic Review

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented

Primary Prevention of Asymptomatic Cardiovascular Disease Using Physiological Sensors Connected to an iOS App

International audienc