The role of computational logic as a hinge paradigm among deduction, problem solving, programming, and parallelism

This paper presents some brief considerations on the role of Computational Logic in the construction of Artificial Intelligence systems and in programming in general. It does not address how the many problems in AI can be solved but, rather more modestly, tries to point out some advantages of Computational Logic as a tool for the AI scientist in his quest. It addresses the interaction between declarative and procedural views of programs (deduction and action), the impact of the intrinsic limitations of logic, the relationship with other apparently competing computational paradigms, and finally discusses implementation-related issues, such as the efficiency of current implementations and their capability for efficiently exploiting existing and future sequential and parallel hardware. The purpose of the discussion is in no way to present Computational Logic as the unique overall vehicle for the development of intelligent systems (in the firm belief that such a panacea is yet to be found) but rather to stress its strengths in providing reasonable solutions to several aspects of the task.

Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Artificial Neural Networks still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning ANN parameters. In recent years the use of hybrid technologies, combining Artificial Neural Networks and Genetic Algorithms, has been utilized to. In this work, several ANN topologies were trained and tested using Artificial Neural Networks and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out.

User-independent accelerometer-based gesture recognition for mobile devices

Many mobile devices embed nowadays inertial sensors. This enables new forms of human-computer interaction through the use of gestures (movements performed with the mobile device) as a way of communication. This paper presents an accelerometer-based gesture recognition system for mobile devices which is able to recognize a collection of 10 different hand gestures. The system was conceived to be light and to operate in a user -independent manner in real time. The recognition system was implemented in a smart phone and evaluated through a collection of user tests, which showed a recognition accuracy similar to other state-of-the art techniques and a lower computational complexity. The system was also used to build a human -robot interface that enables controlling a wheeled robot with the gestures made with the mobile phone.

Improving web learning through model optimization using bootstrap for a tour-guide robot

We perform a review of Web Mining techniques and we describe a Bootstrap Statistics methodology applied to pattern model classifier optimization and verification for Supervised Learning for Tour-Guide Robot knowledge repository management. It is virtually impossible to test thoroughly Web Page Classifiers and many other Internet Applications with pure empirical data, due to the need for human intervention to generate training sets and test sets. We propose using the computer-based Bootstrap paradigm to design a test environment where they are checked with better reliability.

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

Methodological guidelines for reusing general ontologies

Currently, there is a great deal of well-founded explicit knowledge formalizing general notions, such as time concepts and the part_of relation. Yet, it is often the case that instead of reusing ontologies that implement such notions (the so-called general ontologies), engineers create procedural programs that implicitly implement this knowledge. They do not save time and code by reusing explicit knowledge, and devote effort to solve problems that other people have already adequately solved. Consequently, we have developed a methodology that helps engineers to: (a) identify the type of general ontology to be reused; (b) find out which axioms and definitions should be reused; (c) make a decision, using formal concept analysis, on what general ontology is going to be reused; and (d) adapt and integrate the selected general ontology in the domain ontology to be developed. To illustrate our approach we have employed use-cases. For each use case, we provide a set of heuristics with examples. Each of these heuristics has been tested in either OWL or Prolog. Our methodology has been applied to develop a pharmaceutical product ontology. Additionally, we have carried out a controlled experiment with graduated students doing a MCs in Artificial Intelligence. This experiment has yielded some interesting findings concerning what kind of features the future extensions of the methodology should have.

Increasing the Intelligence of Virtual Sales Assistants through Knowledge Modeling Techniques

Shopping agents are web-based applications that help consumers to find appropriate products in the context of e-commerce. In this paper we argue about the utility of advanced model-based techniques that recently have been proposed in the fields of Artificial Intelligence and Knowledge Engineering, in order to increase the level of support provided by this type of applications. We illustrate this approach with a virtual sales assistant that dynamically configures a product according to the needs and preferences of customers.

Computer analysis and design of concrete shell roofs

This paper is a preliminary version of Chapter 3 of a State-of-the-Art Report by the IASS Working Group 5: Concrete Shell Roofs. The intention of this chapter is to set forth for those who intend to design concrete shell roofs information and advice about the selection, verification and utilization of commercial computer tools for analysis and design tasks.The computer analysis and design steps for a concrete shell roof are described. Advice follows on the aspects to be considered in the application of commercial finite element (FE)computer programs to concrete shell analysis, starting with recommendations on how novices can gain confidence and competence in the use of software. To establish vocabulary and provide background references, brief surveys are presented of, first,element types and formulations for shells and, second, challenges presented by advanced analyses of shells. The final section of the chapter indicates what capabilities to seek in selecting commercial FE software for the analysis and design of concrete shell roofs. Brief concluding remarks summarize advice regarding judicious use of computer analysis in design practice.

Reconocimiento facial

El presente proyecto trata sobre uno de los campos más problemáticos de la inteligencia artificial, el reconocimiento facial. Algo tan sencillo para las personas como es reconocer una cara conocida se traduce en complejos algoritmos y miles de datos procesados en cuestión de segundos. El proyecto comienza con un estudio del estado del arte de las diversas técnicas de reconocimiento facial, desde las más utilizadas y probadas como el PCA y el LDA, hasta técnicas experimentales que utilizan imágenes térmicas en lugar de las clásicas con luz visible. A continuación, se ha implementado una aplicación en lenguaje C++ que sea capaz de reconocer a personas almacenadas en su base de datos leyendo directamente imágenes desde una webcam. Para realizar la aplicación, se ha utilizado una de las librerías más extendidas en cuanto a procesado de imágenes y visión artificial, OpenCV. Como IDE se ha escogido Visual Studio 2010, que cuenta con una versión gratuita para estudiantes. La técnica escogida para implementar la aplicación es la del PCA ya que es una técnica básica en el reconocimiento facial, y además sirve de base para soluciones mucho más complejas. Se han estudiado los fundamentos matemáticos de la técnica para entender cómo procesa la información y en qué se datos se basa para realizar el reconocimiento. Por último, se ha implementado un algoritmo de testeo para poder conocer la fiabilidad de la aplicación con varias bases de datos de imágenes faciales. De esta forma, se puede comprobar los puntos fuertes y débiles del PCA. ABSTRACT. This project deals with one of the most problematic areas of artificial intelligence, facial recognition. Something so simple for human as to recognize a familiar face becomes into complex algorithms and thousands of data processed in seconds. The project begins with a study of the state of the art of various face recognition techniques, from the most used and tested as PCA and LDA, to experimental techniques that use thermal images instead of the classic visible light images. Next, an application has been implemented in C + + language that is able to recognize people stored in a database reading images directly from a webcam. To make the application, it has used one of the most outstretched libraries in terms of image processing and computer vision, OpenCV. Visual Studio 2010 has been chosen as the IDE, which has a free student version. The technique chosen to implement the software is the PCA because it is a basic technique in face recognition, and also provides a basis for more complex solutions. The mathematical foundations of the technique have been studied to understand how it processes the information and which data are used to do the recognition. Finally, an algorithm for testing has been implemented to know the reliability of the application with multiple databases of facial images. In this way, the strengths and weaknesses of the PCA can be checked.

Plataforma de evaluación de algoritmos para la identificación de usuarios

La minería de datos es un campo de las ciencias de la computación referido al proceso que intenta descubrir patrones en grandes volúmenes de datos. La minería de datos busca generar información similar a la que podría producir un experto humano. Además es el proceso de descubrir conocimientos interesantes, como patrones, asociaciones, cambios, anomalías y estructuras significativas a partir de grandes cantidades de datos almacenadas en bases de datos, data warehouses o cualquier otro medio de almacenamiento de información. El aprendizaje automático o aprendizaje de máquinas es una rama de la Inteligencia artificial cuyo objetivo es desarrollar técnicas que permitan a las computadoras aprender. De forma más concreta, se trata de crear programas capaces de generalizar comportamientos a partir de una información no estructurada suministrada en forma de ejemplos. La minería de datos utiliza métodos de aprendizaje automático para descubrir y enumerar patrones presentes en los datos. En los últimos años se han aplicado las técnicas de clasificación y aprendizaje automático en un número elevado de ámbitos como el sanitario, comercial o de seguridad. Un ejemplo muy actual es la detección de comportamientos y transacciones fraudulentas en bancos. Una aplicación de interés es el uso de las técnicas desarrolladas para la detección de comportamientos fraudulentos en la identificación de usuarios existentes en el interior de entornos inteligentes sin necesidad de realizar un proceso de autenticación. Para comprobar que estas técnicas son efectivas durante la fase de análisis de una determinada solución, es necesario crear una plataforma que de soporte al desarrollo, validación y evaluación de algoritmos de aprendizaje y clasificación en los entornos de aplicación bajo estudio. El proyecto planteado está definido para la creación de una plataforma que permita evaluar algoritmos de aprendizaje automático como mecanismos de identificación en espacios inteligentes. Se estudiarán tanto los algoritmos propios de este tipo de técnicas como las plataformas actuales existentes para definir un conjunto de requisitos específicos de la plataforma a desarrollar. Tras el análisis se desarrollará parcialmente la plataforma. Tras el desarrollo se validará con pruebas de concepto y finalmente se verificará en un entorno de investigación a definir. ABSTRACT. The data mining is a field of the sciences of the computation referred to the process that it tries to discover patterns in big volumes of information. The data mining seeks to generate information similar to the one that a human expert might produce. In addition it is the process of discovering interesting knowledge, as patterns, associations, changes, abnormalities and significant structures from big quantities of information stored in databases, data warehouses or any other way of storage of information. The machine learning is a branch of the artificial Intelligence which aim is to develop technologies that they allow the computers to learn. More specifically, it is a question of creating programs capable of generalizing behaviors from not structured information supplied in the form of examples. The data mining uses methods of machine learning to discover and to enumerate present patterns in the information. In the last years there have been applied classification and machine learning techniques in a high number of areas such as healthcare, commercial or security. A very current example is the detection of behaviors and fraudulent transactions in banks. An application of interest is the use of the techniques developed for the detection of fraudulent behaviors in the identification of existing Users inside intelligent environments without need to realize a process of authentication. To verify these techniques are effective during the phase of analysis of a certain solution, it is necessary to create a platform that support the development, validation and evaluation of algorithms of learning and classification in the environments of application under study. The project proposed is defined for the creation of a platform that allows evaluating algorithms of machine learning as mechanisms of identification in intelligent spaces. There will be studied both the own algorithms of this type of technologies and the current existing platforms to define a set of specific requirements of the platform to develop. After the analysis the platform will develop partially. After the development it will be validated by prove of concept and finally verified in an environment of investigation that would be define.

Aplicación de técnicas de inteligencia artificial para contribuir en la detección de microcalcificaciones en mamografía digitalizada

A nivel mundial, el cáncer de mama es el tipo de cáncer más frecuente además de una de las principales causas de muerte entre la población femenina. Actualmente, el método más eficaz para detectar lesiones mamarias en una etapa temprana es la mamografía. Ésta contribuye decisivamente al diagnóstico precoz de esta enfermedad que, si se detecta a tiempo, tiene una probabilidad de curación muy alta. Uno de los principales y más frecuentes hallazgos en una mamografía, son las microcalcificaciones, las cuales son consideradas como un indicador importante de cáncer de mama. En el momento de analizar las mamografías, factores como la capacidad de visualización, la fatiga o la experiencia profesional del especialista radiólogo hacen que el riesgo de omitir ciertas lesiones presentes se vea incrementado. Para disminuir dicho riesgo es importante contar con diferentes alternativas como por ejemplo, una segunda opinión por otro especialista o un doble análisis por el mismo. En la primera opción se eleva el coste y en ambas se prolonga el tiempo del diagnóstico. Esto supone una gran motivación para el desarrollo de sistemas de apoyo o asistencia en la toma de decisiones. En este trabajo de tesis se propone, se desarrolla y se justifica un sistema capaz de detectar microcalcificaciones en regiones de interés extraídas de mamografías digitalizadas, para contribuir a la detección temprana del cáncer demama. Dicho sistema estará basado en técnicas de procesamiento de imagen digital, de reconocimiento de patrones y de inteligencia artificial. Para su desarrollo, se tienen en cuenta las siguientes consideraciones: 1. Con el objetivo de entrenar y probar el sistema propuesto, se creará una base de datos de imágenes, las cuales pertenecen a regiones de interés extraídas de mamografías digitalizadas. 2. Se propone la aplicación de la transformada Top-Hat, una técnica de procesamiento digital de imagen basada en operaciones de morfología matemática. La finalidad de aplicar esta técnica es la de mejorar el contraste entre las microcalcificaciones y el tejido presente en la imagen. 3. Se propone un algoritmo novel llamado sub-segmentación, el cual está basado en técnicas de reconocimiento de patrones aplicando un algoritmo de agrupamiento no supervisado, el PFCM (Possibilistic Fuzzy c-Means). El objetivo es encontrar las regiones correspondientes a las microcalcificaciones y diferenciarlas del tejido sano. Además, con la finalidad de mostrar las ventajas y desventajas del algoritmo propuesto, éste es comparado con dos algoritmos del mismo tipo: el k-means y el FCM (Fuzzy c-Means). Por otro lado, es importante destacar que en este trabajo por primera vez la sub-segmentación es utilizada para detectar regiones pertenecientes a microcalcificaciones en imágenes de mamografía. 4. Finalmente, se propone el uso de un clasificador basado en una red neuronal artificial, específicamente un MLP (Multi-layer Perceptron). El propósito del clasificador es discriminar de manera binaria los patrones creados a partir de la intensidad de niveles de gris de la imagen original. Dicha clasificación distingue entre microcalcificación y tejido sano. ABSTRACT Breast cancer is one of the leading causes of women mortality in the world and its early detection continues being a key piece to improve the prognosis and survival. Currently, the most reliable and practical method for early detection of breast cancer is mammography.The presence of microcalcifications has been considered as a very important indicator ofmalignant types of breast cancer and its detection and classification are important to prevent and treat the disease. However, the detection and classification of microcalcifications continue being a hard work due to that, in mammograms there is a poor contrast between microcalcifications and the tissue around them. Factors such as visualization, tiredness or insufficient experience of the specialist increase the risk of omit some present lesions. To reduce this risk, is important to have alternatives such as a second opinion or a double analysis for the same specialist. In the first option, the cost increases and diagnosis time also increases for both of them. This is the reason why there is a great motivation for development of help systems or assistance in the decision making process. This work presents, develops and justifies a system for the detection of microcalcifications in regions of interest extracted fromdigitizedmammographies to contribute to the early detection of breast cancer. This systemis based on image processing techniques, pattern recognition and artificial intelligence. For system development the following features are considered: With the aim of training and testing the system, an images database is created, belonging to a region of interest extracted from digitized mammograms. The application of the top-hat transformis proposed. This image processing technique is based on mathematical morphology operations. The aim of this technique is to improve the contrast betweenmicrocalcifications and tissue present in the image. A novel algorithm called sub-segmentation is proposed. The sub-segmentation is based on pattern recognition techniques applying a non-supervised clustering algorithm known as Possibilistic Fuzzy c-Means (PFCM). The aim is to find regions corresponding to the microcalcifications and distinguish them from the healthy tissue. Furthermore,with the aim of showing themain advantages and disadvantages this is compared with two algorithms of same type: the k-means and the fuzzy c-means (FCM). On the other hand, it is important to highlight in this work for the first time the sub-segmentation is used for microcalcifications detection. Finally, a classifier based on an artificial neural network such as Multi-layer Perceptron is used. The purpose of this classifier is to discriminate froma binary perspective the patterns built from gray level intensity of the original image. This classification distinguishes between microcalcifications and healthy tissue.

Derivación deductiva de programas funcionales con patrones

Una de las dificultades principales en el desarrollo de software es la ausencia de un marco conceptual adecuado para su estudio. Una propuesta la constituye el modelo transformativo, que entiende el desarrollo de software como un proceso iterativo de transformación de especificaciones: se parte de una especificación inicial que va transformándose sucesivamente hasta obtener una especificación final que se toma como programa. Este modelo básico puede llevarse a la práctica de varias maneras. En concreto, la aproximación deductiva toma una sentencia lógica como especificación inicial y su proceso transformador consiste en la demostración de la sentencia; como producto secundario de la demostración se deriva un programa que satisface la especificación inicial. La tesis desarrolla un método deductivo para la derivación de programas funcionales con patrones, escritos en un lenguaje similar a Hope. El método utiliza una lógica multigénero, cuya relación con el lenguaje de programación es estudiada. También se identifican los esquemas de demostración necesarios para la derivación de funciones con patrones, basados en la demostración independiente de varias subsentencias. Cada subsentencia proporciona una subespecificación de una ecuación del futuro programa a derivar. Nuestro método deductivo está inspirado en uno previo de Zohar Manna y Richard Waldinger, conocido como el cuadro deductivo, que deriva programas en un lenguaje similar a Lisp. El nuevo método es una modificación del cuadro de estos autores, que incorpora géneros y permite demostrar una especificación mediante varios cuadros. Cada cuadro demuestra una subespecificación y por tanto deriva una ecuación del programa. Se prevén mecanismos para que los programas derivados puedan contener definiciones locales con patrones y variables anónimas y sinónimas y para que las funciones auxiliares derivadas no usen variables de las funciones principales. La tesis se completa con varios ejemplos de aplicación, un mecanismo que independentiza el método del lenguaje de programación y un prototipo de entorno interactivo de derivación deductiva. Categorías y descriptores de materia CR D.l.l [Técnicas de programación]: Programación funcional; D.2.10 [Ingeniería de software]: Diseño - métodos; F.3.1 [Lógica y significado de los programas]: Especificación, verificación y razonamiento sobre programas - lógica de programas; F.3.3 [Lógica y significado de los programas]: Estudios de construcciones de programas - construcciones funcionales; esquemas de programa y de recursion; 1.2.2 [Inteligencia artificial]: Programación automática - síntesis de programas; 1.2.3 [Inteligencia artificial]: Deducción y demostración de teoremas]: extracción de respuesta/razón; inducción matemática. Términos generales Programación funcional, síntesis de programas, demostración de teoremas. Otras palabras claves y expresiones Funciones con patrones, cuadro deductivo, especificación parcial, inducción estructural, teorema de descomposición.---ABSTRACT---One of the main difficulties in software development is the lack of an adequate conceptual framework of study. The transformational model is one such proposal that conceives software development as an iterative process of specifications transformation: an initial specification is developed and successively transformed until a final specification is obtained and taken as a program. This basic model can be implemented in several ways. The deductive approach takes a logical sentence as the initial specification and its proof constitutes the transformational process; as a byproduct of the proof, a program which satisfies the initial specification is derived. In the thesis, a deductive method for the derivation of Hope-like functional programs with patterns is developed. The method uses a many-sorted logic, whose relation to the programming language is studied. Also the proof schemes necessary for the derivation of functional programs with patterns, based on the independent proof of several subsentences, are identified. Each subsentence provides a subspecification of one equation of the future program to be derived. Our deductive method is inspired on a previous one by Zohar Manna and Richard Waldinger, known as the deductive tableau, which derives Lisp-like programs. The new method incorporates sorts in the tableau and allows to prove a sentence with several tableaux. Each tableau proves a subspecification and therefore derives an equation of the program. Mechanisms are included to allow the derived programs to contain local definitions with patterns and anonymous and synonymous variables; also, the derived auxiliary functions cannot reference parameters of their main functions. The thesis is completed with several application examples, i mechanism to make the method independent from the programming language and an interactive environment prototype for deductive derivation. CR categories and subject descriptors D.l.l [Programming techniques]: Functional programming; D.2.10 [Software engineering]: Design - methodologies; F.3.1 [Logics and meanings of programa]: Specifying and verifying and reasoning about programs - logics of programs; F.3.3 [Logics and meanings of programs]: Studies of program constructs - functional constructs; program and recursion schemes; 1.2.2 [Artificial intelligence]: Automatic programming - program synthesis; 1.2.3 [Artificial intelligence]: Deduction and theorem proving - answer/reason extraction; mathematical induction. General tenas Functional programming, program synthesis, theorem proving. Additional key words and phrases Functions with patterns, deductive tableau, structural induction, partial specification, descomposition theorem.

Aprendizaje colaborativo en enseñanza a distancia: entorno genérico para configurar, realizar y analizar actividades en grupo

En esta tesis se ha profundizado en el estudio y desarrollo de modelos de soporte para el aprendizaje colaborativo a distancia, que ha permitido proponer una arquitectura fundamentada en los principios del paradigma CSCL (Computer Supported Collaborative Learning). La arquitectura propuesta aborda un tipo de problema concreto que requiere el uso de técnicas derivadas del Trabajo Colaborativo, la Inteligencia Artificial, Interfaces de Usuario así como ideas tomadas de la Pedagogía y la Psicología. Se ha diseñado una solución completa, abierta y genérica. La arquitectura aprovecha las nuevas tecnologías para lograr un sistema efectivo de apoyo a la educación a distancia. Está organizada en cuatro niveles: el de Configuración, el de Experiencia, el de Organización y el de Análisis. A partir de ella se ha implementado un sistema llamado DEGREE. En DEGREE, cada uno de los niveles de la arquitectura da lugar a un subsistema independiente pero relacionado con los otros. La aplicación saca partido del uso de espacios de trabajo estructurados. El subsistema Configurador de Experiencias permite definir los elementos de un espacio de trabajo y una experiencia y adaptarlos a cada tipo de usuario. El subsistema Manejador de Experiencias recoge las contribuciones de los usuarios para construir una solución conjunta de un problema. Las intervenciones de los alumnos se estructuran basándose en un grafo conversacional genérico. Además, se registran todas las acciones de los usuarios para representar explícitamente el proceso completo que lleva a la solución. Estos datos también se almacenan en una memoria común que constituye el subsistema llamado Memoria Organizativa de Experiencias. El subsistema Analizador estudia las intervenciones de los usuarios. Este análisis permite inferir conclusiones sobre la forma en que trabajan los grupos y sus actitudes frente a la colaboración, teniendo en cuenta además el conocimiento subjetivo del observador. El proceso de desarrollo en paralelo de la arquitectura y el sistema ha seguido un ciclo de refinamiento en cinco fases con sucesivas etapas de prototipado y evaluación formativa. Cada fase de este proceso se ha realizado con usuarios reales y se han considerado las opiniones de los usuarios para mejorar las funcionalidades de la arquitectura así como la interfaz del sistema. Esta aproximación ha permitido, además, comprobar la utilidad práctica y la validez de las propuestas que sustentan este trabajo.---ABSTRACT---In this thesis, we have studied in depth the development of support models for distance collaborative learning and subsequently devised an architecture based on the Computer Supported Collaborative Learning paradigm principles. The proposed architecture addresses a specific problem: coordinating groups of students to perform collaborative distance learning activities. Our approach uses Cooperative Work, Artificial Intelligence and Human-Computer Interaction techniques as well as some ideas from the fields of Pedagogy and Psychology. We have designed a complete, open and generic solution. Our architecture exploits the new information technologies to achieve an effective system for education purposes. It is organised into four levels: Configuration, Experience, Organisation and Reflection. This model has been implemented into a system called DEGREE. In DEGREE, each level of the architecture gives rise to an independent subsystem related to the other ones. The application benefits from the use of shared structured workspaces. The configuration subsystem allows customising the elements that define an experience and a workspace. The experience subsystem gathers the users' contributions to build joint solutions to a given problem. The students' interventions build up a structure based on a generic conversation graph. Moreover, all user actions are registered in order to represent explicitly the complete process for reaching the group solution. Those data are also stored into a common memory, which constitutes the organisation subsystem. The user interventions are studied by the reflection subsystem. This analysis allows us inferring conclusions about the way in which the group works and its attitudes towards collaboration. The inference process takes into account the observer's subjective knowledge. The process of developing both the architecture and the system in parallel has run through a five-pass cycle involving successive stages of prototyping and formative evaluation. At each stage of that process, we have considered the users' feedback for improving the architecture's functionalities as well as the system interface. This approach has allowed us to prove the usability and validity of our proposal.

Rational conchoid and offset constructions: algorithms and implementation

This paper is framed within the problem of analyzing the rationality of the components of two classical geometric constructions, namely the offset and the conchoid to an algebraic plane curve and, in the affirmative case, the actual computation of parametrizations. We recall some of the basic definitions and main properties on offsets (see [13]), and conchoids (see [15]) as well as the algorithms for parametrizing their rational components (see [1] and [16], respectively). Moreover, we implement the basic ideas creating two packages in the computer algebra system Maple to analyze the rationality of conchoids and offset curves, as well as the corresponding help pages. In addition, we present a brief atlas where the offset and conchoids of several algebraic plane curves are obtained, their rationality analyzed, and parametrizations are provided using the created packages.

Real-time incidents detection in the highways of the future

Due to ever increasing transportation of people and goods, automatic traffic surveillance is becoming a key issue for both providing safety to road users and improving traffic control in an efficient way. In this paper, we propose a new system that, exploiting the capabilities that both computer vision and machine learning offer, is able to detect and track different types of real incidents on a highway. Specifically, it is able to accurately detect not only stopped vehicles, but also drivers and passengers leaving the stopped vehicle, and other pedestrians present in the roadway. Additionally, a theoretical approach for detecting vehicles which may leave the road in an unexpected way is also presented. The system works in real-time and it has been optimized for working outdoor, being thus appropriate for its deployment in a real-world environment like a highway. First experimental results on a dataset created with videos provided by two Spanish highway operators demonstrate the effectiveness of the proposed system and its robustness against noise and low-quality videos.