El núcleo de red en redes de nueva generación

El presente proyecto fin de carrera tiene como objetivo realizar un estudio del núcleo de red en las de redes de nueva generación (NGN) y de cómo la evolución de las redes actuales hacia estos conceptos producirá un cambio en la forma de pensar y desarrollar las redes de comunicaciones del futuro. El estudio esta desglosado en tres grandes partes y se inicia con el análisis de la evolución que ha sufrido el núcleo de red en las redes de comunicaciones móviles digitales desde la implantación de las primeras redes digitales hasta la actualidad abarcando tanto la evolución de las redes troncales como de las redes de acceso así como los cambios que han tenido lugar tanto dentro de las propias estructuras de red de los operadores como la forma de interconectarse entre sus redes. Una segunda parte que constituye el cuerpo teórico del trabajo donde se estudia a nivel funcional y de arquitectura de red el desarrollo de los nuevos modelos de red proporcionados por los organismos de estandarización que dan lugar a la aparición de las redes de nueva generación (NGN) y que constituirán el siguiente paso en la evolución de las redes de comunicaciones hacia una infraestructura de red común para todas las redes de acceso actuales. Y una tercera parte que tiene como objetivo el estudio del grado de transformación que tienen que sufrir el núcleo de red en actuales redes troncales de comunicaciones móviles y terrestres, así como una valoración del estado actual de dicha integración, de las dificultades que están encontrando fabricantes y proveedores de servicio para la implementación de dichas redes en el contexto tecnológico y económico actual y su respectivo análisis de como afectará este cambio a los modelos de negocio de los proveedores de servicios de telecomunicaciones. Finalmente se estudia como se esta llevando a cabo este proceso por medio de un caso práctico de implantación e interconexión de la solución propuesta por un fabricante de equipamiento basándose en los modelos anteriormente expuestos en una red comercial de un operador en España y todas las implicaciones asociadas a esta caso concreto. The object of this work is to provide a deep view about the core network inside next generation network (NGN) and how the evolution of the current comunications networks towards the concepts introduced by these new networks brings a change in the way of think and develop communications networks of the future. This work is composed of three blocks and one real case and it starts with the analysis of the evolution of the core network in digital mobile comunications networks since the beginning of the digital mobile comunications networks deployments until nowadays both in core network side and access network side and how the providers have made changes inside their comunications infrastructure and how they interconnect them with other networks. A second part which is the central theoretical part of this work where it is studied the next generation network models stablished by telecomunications associations and how they will be the next step in the evolution of comunications networks towards a common network infrastructure for all existing access networks. A third part where it is studied the level of transformation that core network in mobile and terrestrial comunications networks have to experienced since current situation up to next generation scenarios and what it is the impact of these changes, the issues that are arising for developers, manufactures and service providers in this process, the way that these changes will improve and shift telecomunications business models and how the current economic and technological context is influencing in the whole process. Finally it is studied a actual case about a proposed solution by a manufacturer that based on the models exposed in second part take place a integration and interconection process in a the comercial network of one telecomunication service providers in Spain. This final part regards to all implications associated with this specific case.

Dam seepage analysis based on artificial neural networks: the hysteresis phenomenon

Seepage flow measurement is an important behavior indicator when providing information about dam performance. The main objective of this study is to analyze seepage by means of an artificial neural network model. The model is trained and validated with data measured at a case study. The dam behavior towards different water level changes is reproduced by the model and a hysteresis phenomenon detected and studied. Artificial neural network models are shown to be a powerful tool for predicting and understanding seepage phenomenon.

On nonlinearity in neural encoding models applied to the primary visual cortex

Within the regression framework, we show how different levels of nonlinearity influence the instantaneous firing rate prediction of single neurons. Nonlinearity can be achieved in several ways. In particular, we can enrich the predictor set with basis expansions of the input variables (enlarging the number of inputs) or train a simple but different model for each area of the data domain. Spline-based models are popular within the first category. Kernel smoothing methods fall into the second category. Whereas the first choice is useful for globally characterizing complex functions, the second is very handy for temporal data and is able to include inner-state subject variations. Also, interactions among stimuli are considered. We compare state-of-the-art firing rate prediction methods with some more sophisticated spline-based nonlinear methods: multivariate adaptive regression splines and sparse additive models. We also study the impact of kernel smoothing. Finally, we explore the combination of various local models in an incremental learning procedure. Our goal is to demonstrate that appropriate nonlinearity treatment can greatly improve the results. We test our hypothesis on both synthetic data and real neuronal recordings in cat primary visual cortex, giving a plausible explanation of the results from a biological perspective.

Distributed models in P-Systems architectures to reduce computation time

Membrane systems are computational equivalent to Turing machines. However, their distributed and massively parallel nature obtains polynomial solutions opposite to traditional non-polynomial ones. At this point, it is very important to develop dedicated hardware and software implementations exploiting those two membrane systems features. Dealing with distributed implementations of P systems, the bottleneck communication problem has arisen. When the number of membranes grows up, the network gets congested. The purpose of distributed architectures is to reach a compromise between the massively parallel character of the system and the needed evolution step time to transit from one configuration of the system to the next one, solving the bottleneck communication problem. The goal of this paper is twofold. Firstly, to survey in a systematic and uniform way the main results regarding the way membranes can be placed on processors in order to get a software/hardware simulation of P-Systems in a distributed environment. Secondly, we improve some results about the membrane dissolution problem, prove that it is connected, and discuss the possibility of simulating this property in the distributed model. All this yields an improvement in the system parallelism implementation since it gets an increment of the parallelism of the external communication among processors. Proposed ideas improve previous architectures to tackle the communication bottleneck problem, such as reduction of the total time of an evolution step, increase of the number of membranes that could run on a processor and reduction of the number of processors.

Aerodynamic Optimization of High-Speed Trains Nose using a Genetic Algorithm and Artificial Neural Network

An aerodynamic optimization of the train aerodynamic characteristics in term of front wind action sensitivity is carried out in this paper. In particular, a genetic algorithm (GA) is used to perform a shape optimization study of a high-speed train nose. The nose is parametrically defined via Bézier Curves, including a wider range of geometries in the design space as possible optimal solutions. Using a GA, the main disadvantage to deal with is the large number of evaluations need before finding such optimal. Here it is proposed the use of metamodels to replace Navier-Stokes solver. Among all the posibilities, Rsponse Surface Models and Artificial Neural Networks (ANN) are considered. Best results of prediction and generalization are obtained with ANN and those are applied in GA code. The paper shows the feasibility of using GA in combination with ANN for this problem, and solutions achieved are included.

Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

The main purpose of a gene interaction network is to map the relationships of the genes that are out of sight when a genomic study is tackled. DNA microarrays allow the measure of gene expression of thousands of genes at the same time. These data constitute the numeric seed for the induction of the gene networks. In this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. The interactions induced by the Bayesian classifiers are based both on the expression levels and on the phenotype information of the supervised variable. Feature selection and bootstrap resampling add reliability and robustness to the overall process removing the false positive findings. The consensus among all the induced models produces a hierarchy of dependences and, thus, of variables. Biologists can define the depth level of the model hierarchy so the set of interactions and genes involved can vary from a sparse to a dense set. Experimental results show how these networks perform well on classification tasks. The biological validation matches previous biological findings and opens new hypothesis for future studies

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39)

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson’s patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson’s disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.

Runtime variability for dynamic reconfiguration in wireless sensor network product lines

Runtime variability is a key technique for the success of Dynamic Software Product Lines (DSPLs), as certain application demand reconfiguration of system features and execution plans at runtime. In this emerging research work we address the problem of dynamic changes in feature models in sensor networks product families, where nodes of the network demand dynamic reconfiguration at post-deployment time.

Electrical Power Fluctuations in a Network of DC/AC inverters in a Large PV Plant: relationship between correlation, distance and time scale

This paper analyzes the correlation between the fluctuations of the electrical power generated by the ensemble of 70 DC/AC inverters from a 45.6 MW PV plant. The use of real electrical power time series from a large collection of photovoltaic inverters of a same plant is an impor- tant contribution in the context of models built upon simplified assumptions to overcome the absence of such data. This data set is divided into three different fluctuation categories with a clustering proce- dure which performs correctly with the clearness index and the wavelet variances. Afterwards, the time dependent correlation between the electrical power time series of the inverters is esti- mated with the wavelet transform. The wavelet correlation depends on the distance between the inverters, the wavelet time scales and the daily fluctuation level. Correlation values for time scales below one minute are low without dependence on the daily fluctuation level. For time scales above 20 minutes, positive high correlation values are obtained, and the decay rate with the distance depends on the daily fluctuation level. At intermediate time scales the correlation depends strongly on the daily fluctuation level. The proposed methods have been implemented using free software. Source code is available as supplementary material.

A framework for building distributed social network websites

Las redes son la esencia de comunidades y sociedades humanas; constituyen el entramado en el que nos relacionamos y determinan cómo lo hacemos, cómo se disemina la información o incluso cómo las cosas se llevan a cabo. Pero el protagonismo de las redes va más allá del que adquiere en las redes sociales. Se encuentran en el seno de múltiples estructuras que conocemos, desde las interaciones entre las proteínas dentro de una célula hasta la interconexión de los routers de internet. Las redes sociales están presentes en internet desde sus principios, en el correo electrónico por tomar un ejemplo. Dentro de cada cliente de correo se manejan listas contactos que agregadas constituyen una red social. Sin embargo, ha sido con la aparición de los sitios web de redes sociales cuando este tipo de aplicaciones web han llegado a la conciencia general. Las redes sociales se han situado entre los sitios más populares y con más tráfico de la web. Páginas como Facebook o Twitter manejan cifras asombrosas en cuanto a número de usuarios activos, de tráfico o de tiempo invertido en el sitio. Pero las funcionalidades de red social no están restringidas a las redes sociales orientadas a contactos, aquellas enfocadas a construir tu lista de contactos e interactuar con ellos. Existen otros ejemplos de sitios que aprovechan las redes sociales para aumentar la actividad de los usuarios y su involucración alrededor de algún tipo de contenido. Estos ejemplos van desde una de las redes sociales más antiguas, Flickr, orientada al intercambio de fotografías, hasta Github, la red social de código libre más popular hoy en día. No es una casualidad que la popularidad de estos sitios web venga de la mano de sus funcionalidades de red social. El escenario es más rico aún, ya que los sitios de redes sociales interaccionan entre ellos, compartiendo y exportando listas de contactos, servicios de autenticación y proporcionando un valioso canal para publicitar la actividad de los usuarios en otros sitios web. Esta funcionalidad es reciente y aún les queda un paso hasta que las redes sociales superen su condición de bunkers y lleguen a un estado de verdadera interoperabilidad entre ellas, tal como funcionan hoy en día el correo electrónico o la mensajería instantánea. Este trabajo muestra una tecnología que permite construir sitios web con características de red social distribuída. En primer lugar, se presenta una tecnología para la construcción de un componente intermedio que permite proporcionar cualquier característica de gestión de contenidos al popular marco de desarrollo web modelo-vista-controlador (MVC) Ruby on Rails. Esta técnica constituye una herramienta para desarrolladores que les permita abstraerse de las complejidades de la gestión de contenidos y enfocarse en las particularidades de los propios contenidos. Esta técnica se usará también para proporcionar las características de red social. Se describe una nueva métrica de reusabilidad de código para demostrar la validez del componente intermedio en marcos MVC. En segundo lugar, se analizan las características de los sitios web de redes sociales más populares, con el objetivo de encontrar los patrones comunes que aparecen en ellos. Este análisis servirá como base para definir los requisitos que debe cumplir un marco para construir redes sociales. A continuación se propone una arquitectura de referencia que proporcione este tipo de características. Dicha arquitectura ha sido implementada en un componente, Social Stream, y probada en varias redes sociales, tanto orientadas a contactos como a contenido, en el contexto de una asociación vecinal tanto como en proyectos de investigación financiados por la UE. Ha sido la base de varios proyectos fin de carrera. Además, ha sido publicado como código libre, obteniendo una comunidad creciente y está siendo usado más allá del ámbito de este trabajo. Dicha arquitectura ha permitido la definición de un nuevo modelo de control de acceso social que supera varias limitaciones presentes en los modelos de control de acceso para redes sociales. Más aún, se han analizado casos de estudio de sitios de red social distribuídos, reuniendo un conjunto de caraterísticas que debe cumplir un marco para construir redes sociales distribuídas. Por último, se ha extendido la arquitectura del marco para dar cabida a las características de redes sociales distribuídas. Su implementación ha sido validada en proyectos de investigación financiados por la UE. Abstract Networks are the substance of human communities and societies; they constitute the structural framework on which we relate to each other and determine the way we do it, the way information is diseminated or even the way people get things done. But network prominence goes beyond the importance it acquires in social networks. Networks are found within numerous known structures, from protein interactions inside a cell to router connections on the internet. Social networks are present on the internet since its beginnings, in emails for example. Inside every email client, there are contact lists that added together constitute a social network. However, it has been with the emergence of social network sites (SNS) when these kinds of web applications have reached general awareness. SNS are now among the most popular sites in the web and with the higher traffic. Sites such as Facebook and Twitter hold astonishing figures of active users, traffic and time invested into the sites. Nevertheless, SNS functionalities are not restricted to contact-oriented social networks, those that are focused on building your own list of contacts and interacting with them. There are other examples of sites that leverage social networking to foster user activity and engagement around other types of content. Examples go from early SNS such as Flickr, the photography related networking site, to Github, the most popular social network repository nowadays. It is not an accident that the popularity of these websites comes hand-in-hand with their social network capabilities The scenario is even richer, due to the fact that SNS interact with each other, sharing and exporting contact lists and authentication as well as providing a valuable channel to publize user activity in other sites. These interactions are very recent and they are still finding their way to the point where SNS overcome their condition of data silos to a stage of full interoperability between sites, in the same way email and instant messaging networks work today. This work introduces a technology that allows to rapidly build any kind of distributed social network website. It first introduces a new technique to create middleware that can provide any kind of content management feature to a popular model-view-controller (MVC) web development framework, Ruby on Rails. It provides developers with tools that allow them to abstract from the complexities related with content management and focus on the development of specific content. This same technique is also used to provide the framework with social network features. Additionally, it describes a new metric of code reuse to assert the validity of the kind of middleware that is emerging in MVC frameworks. Secondly, the characteristics of top popular SNS are analysed in order to find the common patterns shown in them. This analysis is the ground for defining the requirements of a framework for building social network websites. Next, a reference architecture for supporting the features found in the analysis is proposed. This architecture has been implemented in a software component, called Social Stream, and tested in several social networks, both contact- and content-oriented, in local neighbourhood associations and EU-founded research projects. It has also been the ground for several Master’s theses. It has been released as a free and open source software that has obtained a growing community and that is now being used beyond the scope of this work. The social architecture has enabled the definition of a new social-based access control model that overcomes some of the limitations currenly present in access control models for social networks. Furthermore, paradigms and case studies in distributed SNS have been analysed, gathering a set of features for distributed social networking. Finally the architecture of the framework has been extended to support distributed SNS capabilities. Its implementation has also been validated in EU-founded research projects.

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Decision boundary for discrete Bayesian network classifiers

Bayesian network classifiers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classifiers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V-structures in the predictor sub-graph, we are also able to prove that this family of polynomials does in- deed characterize the specific classifier considered. We then use this representation to bound the number of decision functions representable by Bayesian network classifiers with a given structure and we compare these bounds to the ones obtained using Vapnik-Chervonenkis dimension.

Network assisted content protection architectures for a connected world

Content protection is a key component for the success of a multimedia services platform, as proven by the plethora of solutions currently on the market. In this paper we analyze a new network scenario where permanent bidirectional connectivity and video-aware encryption technologies allow a trustful operation of ubiquitous end devices. We propose new scalable models for a content protection architecture that may achieve dramatic improvement in robustness, reliability, and scalability. Selective ciphering and countermeasures are included in those models, together with several examples of their application.

Knowledge Modelling in Multiagent Systems: The Case of the Management of a National Network

This paper presents the knowledge model of a distributed decision support system, that has been designed for the management of a national network in Ukraine. It shows how advanced Artificial Intelligence techniques (multiagent systems and knowledge modelling) have been applied to solve this real-world decision support problem: on the one hand its distributed nature, implied by different loci of decision-making at the network nodes, suggested to apply a multiagent solution; on the other, due to the complexity of problem-solving for local network administration, it was useful to apply knowledge modelling techniques, in order to structure the different knowledge types and reasoning processes involved. The paper sets out from a description of our particular management problem. Subsequently, our agent model is described, pointing out the local problem-solving and coordination knowledge models. Finally, the dynamics of the approach is illustrated by an example.

Decision boundary for discrete Bayesian network classifiers

Bayesian network classifiers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classifiers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V-structures in the predictor sub-graph, we are also able to prove that this family of polynomials does in- deed characterize the specific classifier considered. We then use this representation to bound the number of decision functions representable by Bayesian network classifiers with a given structure and we compare these bounds to the ones obtained using Vapnik-Chervonenkis dimension.