An empirical study of machine learning techniques applied to image watermarking

A study of how the machine learning technique, known as gentleboost, could improve different digital watermarking methods such as LSB, DWT, DCT2 and Histogram shifting.

Multidimensional Adaptations for Open Learning Management Systems

Our work is focused on alleviating the workload for designers of adaptive courses on the complexity task of authoring adaptive learning designs adjusted to specific user characteristics and the user context. We propose an adaptation platform that consists in a set of intelligent agents where each agent carries out an independent adaptation task. The agents apply machine learning techniques to support the user modelling for the adaptation process

Processament de música basat en el seu contingut

Projecte de recerca elaborat a partir d’una estada a la National University of Singapore Singapur, entre juliol i octubre del 2007. Donada l'explosió de la música a l'internet i la ràpida expansió de les col•leccions de música digital, un repte clau en l'àrea de la informació musical és el desenvolupament de sistemes de processament musical eficients i confiables. L'objectiu de la investigació proposada ha estat treballar en diferents aspectes de l'extracció, modelatge i processat del contingut musical. En particular, s’ha treballat en l'extracció, l'anàlisi i la manipulació de descriptors d'àudio de baix nivell, el modelatge de processos musicals, l'estudi i desenvolupament de tècniques d'aprenentatge automàtic per a processar àudio, i la identificació i extracció d'atributs musicals d'alt nivell. S’han revisat i millorat alguns components d'anàlisis d'àudio i revisat components per a l'extracció de descriptors inter-nota i intra-nota en enregistraments monofónics d'àudio. S’ha aplicat treball previ en Tempo a la formalització de diferents tasques musicals. Finalment, s’ha investigat el processat d'alt nivell de música basandonos en el seu contingut. Com exemple d'això, s’ha investigat com músics professionals expressen i comuniquen la seva interpretació del contingut musical i emocional de peces musicals, i hem usat aquesta informació per a identificar automàticament intèrprets. S’han estudiat les desviacions en paràmetres com to, temps, amplitud i timbre a nivell inter-nota i intra-nota.

Guiding students through competence-based educational material

Student guidance is an always desired characteristic in any educational system, butit represents special difficulty if it has to be deployed in an automated way to fulfilsuch needs in a computer supported educational tool. In this paper we explorepossible avenues relying on machine learning techniques, to be included in a nearfuture -in the form of a tutoring navigational tool- in a teleeducation platform -InterMediActor- currently under development. Since no data from that platform isavailable yet, the preliminary experiments presented in this paper are builtinterpreting every subject in the Telecommunications Degree at Universidad CarlosIII de Madrid as an aggregated macro-competence (following the methodologicalconsiderations in InterMediActor), such that marks achieved by students can beused as data for the models, to be replaced in a near future by real data directlymeasured inside InterMediActor. We evaluate the predictability of students qualifications, and we deploy a preventive early detection system -failure alert-, toidentify those students more prone to fail a certain subject such that correctivemeans can be deployed with sufficient anticipation.

Alternative Techniques of Neural Signal Processing in Neuroengineering

Neural signal processing is a discipline within neuroengineering. This interdisciplinary approach combines principles from machine learning, signal processing theory, and computational neuroscience applied to problems in basic and clinical neuroscience. The ultimate goal of neuroengineering is a technological revolution, where machines would interact in real time with the brain. Machines and brains could interface, enabling normal function in cases of injury or disease, brain monitoring, and/or medical rehabilitation of brain disorders. Much current research in neuroengineering is focused on understanding the coding and processing of information in the sensory and motor systems, quantifying how this processing is altered in the pathological state, and how it can be manipulated through interactions with artificial devices including brain–computer interfaces and neuroprosthetics.

Mejora diagnóstica de hepatopatías de afectación difusa mediante técnicas de inteligencia artificial

The automatic diagnostic discrimination is an application of artificial intelligence techniques that can solve clinical cases based on imaging. Diffuse liver diseases are diseases of wide prominence in the population and insidious course, yet early in its progression. Early and effective diagnosis is necessary because many of these diseases progress to cirrhosis and liver cancer. The usual technique of choice for accurate diagnosis is liver biopsy, an invasive and not without incompatibilities one. It is proposed in this project an alternative non-invasive and free of contraindications method based on liver ultrasonography. The images are digitized and then analyzed using statistical techniques and analysis of texture. The results are validated from the pathology report. Finally, we apply artificial intelligence techniques as Fuzzy k-Means or Support Vector Machines and compare its significance to the analysis Statistics and the report of the clinician. The results show that this technique is significantly valid and a promising alternative as a noninvasive diagnostic chronic liver disease from diffuse involvement. Artificial Intelligence classifying techniques significantly improve the diagnosing discrimination compared to other statistics.

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

Learning and adaptation in physical heterogeneous teams of robots

In this paper we present a novel approach to assigning roles to robots in a team of physical heterogeneous robots. Its members compete for these roles and get rewards for them. The rewards are used to determine each agent’s preferences and which agents are better adapted to the environment. These aspects are included in the decision making process. Agent interactions are modelled using the concept of an ecosystem in which each robot is a species, resulting in emergent behaviour of the whole set of agents. One of the most important features of this approach is its high adaptability. Unlike some other learning techniques, this approach does not need to start a whole exploitation process when the environment changes. All this is exemplified by means of experiments run on a simulator. In addition, the algorithm developed was applied as applied to several teams of robots in order to analyse the impact of heterogeneity in these systems

Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification

Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering.

A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning

Peer-reviewed

Design and evaluation of learning algorithms for dynamic resource management in virtual networks

Network virtualisation is considerably gaining attentionas a solution to ossification of the Internet. However, thesuccess of network virtualisation will depend in part on how efficientlythe virtual networks utilise substrate network resources.In this paper, we propose a machine learning-based approachto virtual network resource management. We propose to modelthe substrate network as a decentralised system and introducea learning algorithm in each substrate node and substrate link,providing self-organization capabilities. We propose a multiagentlearning algorithm that carries out the substrate network resourcemanagement in a coordinated and decentralised way. The taskof these agents is to use evaluative feedback to learn an optimalpolicy so as to dynamically allocate network resources to virtualnodes and links. The agents ensure that while the virtual networkshave the resources they need at any given time, only the requiredresources are reserved for this purpose. Simulations show thatour dynamic approach significantly improves the virtual networkacceptance ratio and the maximum number of accepted virtualnetwork requests at any time while ensuring that virtual networkquality of service requirements such as packet drop rate andvirtual link delay are not affected.

Sistemas de detección y extracción semiautomática de siglas: estado de la cuestión

Informe de investigación realizado a partir de una estancia en el Équipe de Recherche en Syntaxe et Sémantique de la Université de Toulouse-Le Mirail, Francia, entre julio y setiembre de 2006. En la actualidad existen diversos diccionarios de siglas en línea. Entre ellos sobresalen Acronym Finder, Abbreviations.com y Acronyma; todos ellos dedicados mayoritariamente a las siglas inglesas. Al igual que los diccionarios en papel, este tipo de diccionarios presenta problemas de desactualización por la gran cantidad de siglas que se crean a diario. Por ejemplo, en 2001, un estudio de Pustejovsky et al. mostraba que en los abstracts de Medline aparecían mensualmente cerca de 12.000 nuevas siglas. El mecanismo de actualización empleado por estos recursos es la remisión de nuevas siglas por parte de los usuarios. Sin embargo, esta técnica tiene la desventaja de que la edición de la información es muy lenta y costosa. Un ejemplo de ello es el caso de Abbreviations.com que en octubre de 2006 tenía alrededor de 100.000 siglas pendientes de edición e incorporación definitiva. Como solución a este tipo de problema, se plantea el diseño de sistemas de detección y extracción automática de siglas a partir de corpus. El proceso de detección comporta dos pasos; el primero, consiste en la identificación de las siglas dentro de un corpus y, el segundo, la desambiguación, es decir, la selección de la forma desarrollada apropiada de una sigla en un contexto dado. En la actualidad, los sistemas de detección de siglas emplean métodos basados en patrones, estadística, aprendizaje máquina, o combinaciones de ellos. En este estudio se analizan los principales sistemas de detección y desambiguación de siglas y los métodos que emplean. Cada uno se evalúa desde el punto de vista del rendimiento, medido en términos de precisión (porcentaje de siglas correctas con respecto al número total de siglas extraídas por el sistema) y exhaustividad (porcentaje de siglas correctas identificadas por el sistema con respecto al número total de siglas existente en el corpus). Como resultado, se presentan los criterios para el diseño de un futuro sistema de detección de siglas en español.

Intestinal content detection in capsule endoscopy using robust features

This work covers two aspects. First, it generally compares and summarizes the similarities and differences of state of the art feature detector and descriptor and second it presents a novel approach of detecting intestinal content (in particular bubbles) in capsule endoscopy images. Feature detectors and descriptors providing invariance to change of perspective, scale, signal-noise-ratio and lighting conditions are important and interesting topics in current research and the number of possible applications seems to be numberless. After analysing a selection of in the literature presented approaches, this work investigates in their suitability for applications information extraction in capsule endoscopy images. Eventually, a very good performing detector of intestinal content in capsule endoscopy images is presented. A accurate detection of intestinal content is crucial for all kinds of machine learning approaches and other analysis on capsule endoscopy studies because they occlude the field of view of the capsule camera and therefore those frames need to be excluded from analysis. As a so called “byproduct” of this investigation a graphical user interface supported Feature Analysis Tool is presented to execute and compare the discussed feature detectors and descriptor on arbitrary images, with configurable parameters and visualized their output. As well the presented bubble classifier is part of this tool and if a ground truth is available (or can also be generated using this tool) a detailed visualization of the validation result will be performed.

Projecte pilot d'innovació docent de l'assignatura Estructura de Computadors I

L'objectiu del projecte ha estat la millora de la qualitat docent de l'assignatura Estructura de Computadors I, impartida a la Facultat d'Informàtica de Barcelona (UPC) dins els estudis d'Enginyeria Informàtica, Enginyeria Tècnica en Informàtica de Sistemes i Enginyeria Tècnica en Informàtica de Gestió. S'ha treballat en quatre línies d'actuació: (i) aplicació de tècniques d'aprenentatge actiu a les classes; (ii) aplicació de tècniques d'aprenentage cooperatiu no presencials; (iii) implantació de noves TIC i adaptació de les ja emprades per tal d'habilitar mecanismes d'autoavaluació i de realimentació de la informació referent a l'avaluació; i (iv) difusió de les experiències derivades de les diferents actuacions. Referent a les dues primeres mesures s'avalua l'impacte de metodologies docents que afavoreixen l'aprenentatge actiu tant de forma presencial com no presencial, obtenint-se clares millores en el rendiment respecte a altres metodologies utilitzades anteriorment enfocades a la realització de classes del tipus magistral, en què únicament es posa a l'abast dels alumnes la documentació de l'assignatura per a què puguin treballar de forma responsable. Les noves metodologies fan especial èmfasi en el treball en grup a classe i la compartició de les experiències fora de classe a través de fòrums de participació. La mesura que ha requerit més esforç en aquest projecte és la tercera, amb el desenvolupament d'un entorn d'interfície web orientat a la correcció automàtica de programes escrits en llenguatge assemblador. Aquest entorn permet l'autoavaluació per part dels alumnes dels exercicis realitzats a l'assignatura, amb obtenció d'informació detallada sobre les errades comeses. El treball realitzat dins d'aquest projecte s'ha publicat en congressos rellevants en l'àrea docent tant a nivell estatal com internacional. El codi font de l'entorn esmentat anteriorment es posa a disposició pública a través d'un enllaç a la web.