Procesamiento de imágenes médicas en odontología como ayuda al diagnóstico

En el campo de la odontología es necesario e importante tener una herramienta que permita realizar distintos tipos de cálculos sobre una imagen dada, en este caso, sobre una radiografía mandibular. Con este fin, hemos aplicado los conocimientos adquiridos en procesamiento de imágenes y programación para crear una aplicación que permita obtener resultados para los objetivos requeridos. Uno de tales objetivos que a su vez constituye el fundamental de la aplicación es obtener el Índice Cortical Mandibular (ICM), se trata de una medida importante ya que permite detectar signos de osteoporosis a nivel maxilar y mandibular. Otro objetivo es el estudio sobre el análisis de las texturas óseas sobre radiografías panorámicas de la mandíbula. Esto permite al especialista poder detectar diferentes irregularidades bucales como puede ser una infección en las encías, aislar y realzar la zona afectada o detectar enfermedades en la mandíbula como la osteoporosis. Todo ello como consecuencia del estudio de las diferentes texturas presentes en la imagen bajo análisis. En este proyecto se han utilizado las técnicas propias de lo que se conoce como percepción computacional desde la perspectiva del procesado de imágenes. Aunque el objetivo principal es una automatización del proceso del cálculo del ICM y la descripción de texturas, en determinados pasos de la ejecución de la aplicación se deja al usuario libertad para escoger los parámetros requeridos por determinadas rutinas implementadas en la aplicación, esto es debido a la dificultad intrínseca de automatizar la localización de determinadas regiones necesarias para calcular el ICM. Para unir e integrar todas las funcionalidades y conceptos se hace uso de la herramienta Matlab (2017), que a través de la ventana gráfica permite un uso más intuitivo y sencillo de la aplicación

Learning Pedagogical Policies from Few Training Data

[Poster of] 17th European Conference on Artificial Intelligence (ECAI'06). Workshop on Planning, Learning and Monitoring with Uncertainty and Dynamic Worlds, Riva del Garda, Italy, August 8, 2006Learning a pedagogical policy in an Adaptive Educational System (AIES) fits as a Reinforcement Learning (RL) problem. However, to learn pedagogical policies requires to acquire a huge amount of experience interacting with the students, so applying RL to the AIES from scratch is infeasible. In this paper we describe RLATES, an AIES that uses RL to learn an accurate pedagogical policy to teach a course of Data Base Design. To reduce the experience required to learn the pedagogical policy, we propose to use an initial value function learned with simulated students, whose model is provided by an expert as a Markov Decision Process. Empirical results demonstrate that the value function learned with the simulated students and transferred to the AIES is a very accurate initial pedagogical policy. The evaluation is based on the interaction of more than 70 Computer Science undergraduate students, and demonstrates that an efficient guide through the contents of the educational system is obtained.This work was supported by the project GPS (TIN2004/07083

Reinforcement learning of pedagogical policies in adaptive and intelligent educational systems

In an adaptive and intelligent educational system (AIES), the process of learning pedagogical policies according the students needs fits as a Reinforcement Learning (RL) problem. Previous works have demonstrated that a great amount of experience is needed in order for the system to learn to teach properly, so applying RL to the AIES from scratch is unfeasible. Other works have previously demonstrated in a theoretical way that seeding the AIES with an initial value function learned with simulated students reduce the experience required to learn an accurate pedagogical policy. In this paper we present empirical results demonstrating that a value function learned with simulated students can provide the AIES with a very accurate initial pedagogical policy. The evaluation is based on the interaction of more than 70 Computer Science undergraduate students, and demonstrates that an efficient and useful guide through the contents of the educational system is obtained.Publicad

On wideband deconvolution using wavelet transform

A discussion on the expression proposed in [1]–[3] for deconvolving the wideband density function is presented. We prove here that such an expression reduces to be proportional to the wideband correlation receiver output, or continuous wavelet transform of the received signal with respect to the transmitted one. Moreover, we show that the same result has been implicitly assumed in [1], when the deconvolution equation is derived. We stress the fact that the analyzed approach is just the orthogonal projection of the density function onto the image of the wavelet transform with respect to the transmitted signal. Consequently, the approach can be considered a good representation of the density function only under the prior knowledge that the density function belongs to such a subspace. The choice of the transmitted signal is thus crucial to this approach.Peer Reviewe

Probabilistic policy reuse for safe reinforcement learning

This work introducesPolicy Reuse for Safe Reinforcement Learning, an algorithm that combines ProbabilisticPolicy Reuse and teacher advice for safe exploration in dangerous and continuous state and action reinforce-ment learning problems in which the dynamic behavior is reasonably smooth and the space is Euclidean. Thealgorithm uses a continuously increasing monotonic risk function that allows for the identification of theprobability to end up in failure from a given state. Such a risk function is defined in terms of how far such astate is from the state space known by the learning agent. Probabilistic Policy Reuse is used to safely balancethe exploitation of actual learned knowledge, the exploration of new actions, and the request of teacher advicein parts of the state space considered dangerous. Specifically, thepi-reuse exploration strategy is used. Usingexperiments in the helicopter hover task and a business management problem, we show that thepi-reuseexploration strategy can be used to completely avoid the visit to undesirable situations while maintainingthe performance (in terms of the classical long-term accumulated reward) of the final policy achieved.This paper has been partially supported by the Spanish Ministerio de Economía y Competitividad TIN2015-65686-C5-1-R and the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement No. 730086 (ERGO). Javier García is partially supported by the Comunidad de Madrid (Spain) funds under the project 2016-T2/TIC-1712

On the inverse windowed Fourier transform

The inversion problem concerning the windowed Fourier transform is considered. It is shown that, out of the infinite solutions that the problem admits, the windowed Fourier transform is the "optimal" solution according to a maximum-entropy selection criterion.Peer Reviewe

The continuous wavelet transform as a maximum entropy solution of the corresponding inverse problem

The continuous wavelet transform is obtained as a maximum entropy solution of the corresponding inverse problem. It is well known that although a signal can be reconstructed from its wavelet transform, the expansion is not unique due to the redundancy of continuous wavelets. Hence, the inverse problem has no unique solution. If we want to recognize one solution as "optimal", then an appropriate decision criterion has to be adopted. We show here that the continuous wavelet transform is an "optimal" solution in a maximum entropy sense.Peer Reviewe

Entrepreneurial skills of adult continuing education in Andalusia. Perception of Teacher

En el presente trabajo analizamos la percepción que tiene el profesorado de Educación Permanente de Personas Adultas en Andalucía (España) acerca del grado de presencia de competencias emprendedoras en su alumnado. Para dar respuesta al objetivo planteado adoptamos un diseño de investigación de tipo descriptivo, donde el enfoque de recolección y análisis de datos se ha definido como mixto, en el que se integran técnicas cualitativas (entrevistas en profundidad) y cuantitativas (cuestionario). Para la validación del cuestionario, se utilizó la técnica de juicio de expertos, seleccionados mediante el procedimiento de “Coeficiente de competencia experta” o “Coeficiente K”. El análisis de la fiabilidad del cuestionario a través de la prueba de Cronbach fue de 0,957. El cuestionario ha sido respondido por 549 profesores y profesoras de Centros de Educación Permanente de Andalucía y se han realizado 23 entrevistas en profundidad a informantes claves (directores de centros y coordinadores de sección). Los resultados muestran con claridad la percepción, tanto del profesorado como de aquellos que ejercen el ejercicio de la dirección, que el alumnado de estos centros educativos no están lo suficientemente preparados para iniciar por su cuenta un proyecto empresarial, a pesar de cursar contenidos curriculares de cultura emprendedora. Consideran que aún no poseen las competencias emprendedoras necesarias para crear y gestionar eficazmente un proyecto empresarial, constituyendo la falta de creatividad uno de los mayores obstáculos para dicho fin. Así mismo, se desprende del estudio la escasa incidencia que ejercen los programas de cultura emprendedora que se desarrollan en este tipo de centros.In the present study, we analyze the teaching staff perception of Adult Continuing Education in Andalusia (Spain) in relation to the degree of entrepreneurial skills present in their students. To give an answer the objective proposed, we assume a descriptive research design, where the collection approach and analysis of data have been defined as mixed, which include qualitative (interviews) and quantitative (questionnaire) techniques. For questionnaire validation the expert judgment technique was used, as they were selected by the "expert competence coefficient" or "K coefficient" procedure. The reliability analysis of the questionnaire through the Cronbach test was 0.957. The questionnaire was answered by 549 teachers from Continuing Education Centres in Andalusia and there were 23 in-depth interviews with key informants (centre directors and section coordinators). The results clearly show the perception, both of teachers and of those who exercise leadership, that the students of these schools are not sufficiently prepared to start their own business project, in spite of studying subjects of an enterprising culture. They consider that have not yet acquired the entrepreneurial skills necessary to create and manage an efficient business project, with lack of creativity constituting one of the biggest obstacles to that end. Also, the study shows the low incidence of programs of an entrepreneurial culture developed in these centres

Aprendizaje por refuerzo en espacios de estados continuos

El aprendizaje por refuerzo es un modelo de aprendizaje que permite implementar comportamientos inteligentes de forma automática. La mayor parte de la teoría del aprendizaje por refuerzo tiene su fundamento en la programación dinámica, y por tanto, en lo que se denominan funciones de valor. Sin embargo, la implementación tradicional de estas funciones en forma tabular no es práctica cuando el espacio de estados es muy grande, o incluso infinito. Cuando se produce esta situación, se deben aplicar métodos de generalización que permitan extrapolar la experiencia adquirida para un conjunto limitado de estados, a la totalidad del espacio. Existen dos aproximaciones básicas para resolver este problema. Por un lado, están aquellas técnicas que se basan en obtener una discretización adecuada del espacio de estados. Por otro lado, están los métodos basados en implementar las funciones de valor con algún método supervisado de aproximación de funciones, como, por ejemplo, una red de neuronas. En esta tesis doctoral se pretende desarrollar métodos de aprendizaje por refuerzo que sean aplicables en dominios con espacios de estados continuos, partiendo de las dos aproximaciones planteadas anteriormente, fundiendo las ventajas de una y otra en un método eficaz y eficiente que permita que el aprendizaje sea un proceso totalmente automático.Reinforcement Learning is a technique that aliows to implement intelli gent behaviours automatically without the need of introducing knowledge or modeis about the domain. Most of the reinforcement learning theory is based on dynamic programming, and hence, on value functions. These func tions provide information about how good it is, in order to solve a defined task, to be in a given situation in the dornain, typically narned state, or even how good it is to execute a defined action if the system is in a given state. These functions, typically implernented using look-up tables, are used to represent the action policy that must guide the behaviour of the system. However, the traditional implementation of these functions as look-up tables is not practical when the state space is very large, or even infinite. When one of these situations appears, generalization methods must be applied in order to extrapolate the acquired experience for a limited set of states, to the whole space, so optirnal behaviours can be achieved, even when the whole domain has not been explored. Two main approaches can be found in the literature. Qn the one hand, there are methods based on learning an adequate state space discretization, so the continuous state space is mapped to a finite and reduced one. Qn the other hand, methods based oil irnplementing the value functions with sorne supervised learning technique for function approximation, for instance, a neural network, can be found. This dissertation tries to develop reinfor cernent learning methods that can be applied in domains with a continuous state space. The start point is given by the two approaches aboye, and it tries to j oin the advantages of one and another in an efficient and effective method that aliows the learning process be a fully automatic process where the designer has to introduce the less possible arnount of information about the task to solve