Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data

Fuzzy clustering algorithms are helpful when there exists a dataset with subgroupings of points having indistinct boundaries and overlap between the clusters. Traditional methods have been extensively studied and used on real-world data, but require users to have some knowledge of the outcome a priori in order to determine howmany clusters to look for. Additionally, iterative algorithms choose the optimal number of clusters based on one of several performance measures. In this study, the authors compare the performance of three algorithms (fuzzy c-means, Gustafson-Kessel, and an iterative version of Gustafson-Kessel) when clustering a traditional data set as well as real-world geophysics data that were collected from an archaeological site in Wyoming. Areas of interest in the were identified using a crisp cutoff value as well as a fuzzy α-cut to determine which provided better elimination of noise and non-relevant points. Results indicate that the α-cut method eliminates more noise than the crisp cutoff values and that the iterative version of the fuzzy clustering algorithm is able to select an optimum number of subclusters within a point set (in both the traditional and real-world data), leading to proper indication of regions of interest for further expert analysis

Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data

Fuzzy clustering algorithms are helpful when there exists a dataset with subgroupings of points having indistinct boundaries and overlap between the clusters. Traditional methods have been extensively studied and used on real-world data, but require users to have some knowledge of the outcome a priori in order to determine howmany clusters to look for. Additionally, iterative algorithms choose the optimal number of clusters based on one of several performance measures. In this study, the authors compare the performance of three algorithms (fuzzy c-means, Gustafson-Kessel, and an iterative version of Gustafson-Kessel) when clustering a traditional data set as well as real-world geophysics data that were collected from an archaeological site in Wyoming. Areas of interest in the were identified using a crisp cutoff value as well as a fuzzy α-cut to determine which provided better elimination of noise and non-relevant points. Results indicate that the α-cut method eliminates more noise than the crisp cutoff values and that the iterative version of the fuzzy clustering algorithm is able to select an optimum number of subclusters within a point set (in both the traditional and real-world data), leading to proper indication of regions of interest for further expert analysis

Transformations and analysis of parallel real time programs

The problem of schedulability analysis of a set of real time programs form a NP complete problem. The exponential complexity of analysis is a direct result of the complexity in the real time programs, as a combinatorial explosion takes place when trying to determine access patterns of shared resources. Thus, to transform the original programs to a less complex form, while preserving its timing characteristics, is the only viable solution. By using such transformations to reduce the complexity of real time programs, it is possible to schedulability analyze programs at compile time efficiently, without adding an unnecessary overhead to the compilation time. A set of suitable transformations and run time scheduling algorithms are introduced and implemented in C++. A library of transformations and analysis routines are provided. The library routines can be used to build prototype schedulability analyzers for testing various analysis techniques. These transformations and the scheduling algorithm will be an integral part of the real time compiler for the real time language RTL. The RTL compiler will not only produce fast and efficient code for an arbitrarily specified real time hardware architecture, but also will provide the worst case timing characteristics for the programs

A theory of jet definition

A systematic framework for jet definition is developed from first principles of physical measurement, quantum field theory, and QCD. A jet definition is found which: is theoretically optimal in regard of both minimization of detector errors and inversion of hadronization; is similar to a cone algorithm with dynamically negotiated jet shapes and positions found via shape observables that generalize the thrust to any number of axes; involves no ad hoc conventions; allows a fast computer implementation [hep-ph/9912415]. The framework offers an array of options for systematic construction of quasi-optimal observables for specific applications.Comment: PS 45 pages; 2nd ed. Jan 2000: major addition

Tutorització Intel·ligent de Comunitats Virtuals d'Aprenentatge

L’evolució de la tecnologia ha produït canvis profunds en els paradigmes de l'ensenyament i, particularment, en l'aplicació d'aquests a l’aprenentatge en línia (e-learning). De fet va ser la pròpia revolució tecnològica la que va fer néixer aquest nou model d'aprenentatge virtual i, actualment, poques són les institucions que no compten amb alguna aplicació de l'e-learning, ja sigui com a alternativa al model educatiu tradicional o com a complement (blended learning). La introducció de l'e-learning, i en general de les Tecnologies de la Informació i Comunicació (TIC), al món educatiu ha fet que la teoria instructivista de l'educació tradicional s'hagi desplaçat cap a un paradigma constructivista, generant un model molt més centrat en l'alumne. Les eines educatives han anat evolucionant cap aquest nou paradigma, on la personalització i l’adaptació són fils conductors, i els Sistemes Tutors Intel·ligents (STI) en són un bon exemple. Tanmateix, l'arribada de la Web 2.0 ha desencadenat un moment social que ha acabat marcant de nou el món educatiu. El desplegament de la teoria connectivista, sorgida de l'aplicació de la Web Social en l’àmbit educatiu, i la implantació de múltiples iniciatives d'e-learning han afavorit la proliferació d'Entorns Virtuals d'Aprenentatge (EVA) i de diferents tecnologies educatives basades en Web. Atès que la tecnologia associada a Internet està en constant evolució, però, tot fa pensar que els entorns d’aprenentatge hauran d’evolucionar en els propers anys de manera paral·lela a com ho està fent la pròpia Web. Així, és probable que les següents generacions d'e-learning implementin característiques pròpies de la Web 3.0 (semàntica) i de la Web 4.0 (simbiòtica) i esdevinguin entorns on els agents intel·ligents hi tinguin un paper significatiu. En aquesta tesi s’analitza en primer lloc quina ha estat la trajectòria que ha seguit l’educació al llarg de la història i quina influència ha tingut en la implantació dels sistemes d’aprenentatge en línia, des dels més senzills i poc adaptatius, fins als més moderns i pensats per millorar l’experiència en l’aprenentatge. A més, en vistes de la trajectòria tecnològica que es divisa, es proposa una nova arquitectura que permeti incloure, d’una banda, les capacitats dels entorns ja existents d’aprenentatge en línia, i, de l’altra, els agents intel·ligents que convertiran l’experiència de l’ensenyament a distància en una experiència adaptativa i social, on el concepte de grup tindrà cabdal importància. Els sistemes educatius intel·ligents futurs, per tant, hauran de disposar d'una part complexa de computació avançada, aspecte abordat des del camp de la Intel·ligència Artificial, que permeti reconèixer quina és l’evolució de l’alumne en el seu aprenentatge i com aquest està interactuant i rendint amb els companys de la seva classe virtual. A més, la quantitat d'interaccions produïdes en aquests entorns generarà un gran volum de dades educatives, la Big Learning Data, amb informació vital que caldrà processar per millorar i adaptar el sistema a l’alumne a mesura que el curs avança, i per recollir informació valuosa per a la seva tutorització. Així, la darrera part d’aquesta tesi mostra les contribucions realitzades en Intel·ligència Artificial i els resultats de la seva implementació per crear la part intel·ligent d’aquesta arquitectura, podent extreure d'aquesta manera el màxim rendiment d’aquests nous entorns d’aprenentatge col·laboratiu que seran realitat d’aquí a pocs anys.La evolución de la tecnología ha producido cambios profundos en los paradigmas de la enseñanza y, particularmente, en la aplicación de éstos en el aprendizaje en línea (e-learning). De hecho fue la propia revolución tecnológica la que hizo nacer este nuevo modelo de aprendizaje virtual y, actualmente, pocas son las instituciones que no cuentan con alguna aplicación del e-learning, ya sea como alternativa al modelo educativo tradicional o como complemento (blended learning). La introducción del e-learning, y en general de las Tecnologías de la Información y Comunicación (TIC), en el mundo educativo ha hecho que la teoría instructivista de la educación tradicional se haya desplazado hacia un paradigma constructivista, generando un modelo mucho más centrado en el alumno. Las herramientas educativas han ido evolucionando hacia este nuevo paradigma, donde la personalización y la adaptación son hilos conductores, y los Sistemas Tutores Inteligentes (STI) son un buen ejemplo. Sin embargo, la llegada de la Web 2.0 ha desencadenado un momento social que ha marcado de nuevo el mundo educativo. El despliegue de la teoría conectivista, surgida de la aplicación de la Web Social en el ámbito educativo, y la implantación de varias iniciativas de e-learning han favorecido la proliferación de entornos virtuales de aprendizaje y de diferentes tecnologías educativas basadas en Web. Dado que la tecnología asociada a Internet está en constante evolución, todo hace pensar que los entornos de aprendizaje deberán evolucionar en los próximos años de manera paralela a como lo está haciendo la propia Web. Así, es probable que las siguientes generaciones de e-learning implementen características propias de la Web 3.0 (semántica) y de la Web 4.0 (simbiótica) y se conviertan en entornos donde los agentes inteligentes tengan un papel significativo. En esta tesis se analiza en primer lugar cuál ha sido la trayectoria que ha seguido la educación a lo largo de la historia y qué influencia ha tenido en la implantación del e-learning, desde los más sencillos y poco adaptativos, hasta los más modernos y pensados para mejorar la experiencia en el aprendizaje. Además, en vistas de la trayectoria tecnológica que se divisa, se propone una nueva arquitectura que permita incluir, por un lado, las capacidades de los entornos ya existentes de aprendizaje en línea, y, por otro, los agentes inteligentes que convertirán la experiencia de la enseñanza a distancia en una experiencia adaptativa y social, donde el concepto de grupo tendrá capital importancia. Los sistemas educativos inteligentes futuros, por tanto, deberán disponer de una parte compleja de computación avanzada, aspecto abordado desde el campo de la Inteligencia Artificial, que permita reconocer cuál es la evolución del alumno en su aprendizaje y como éste está interactuando y rindiendo con los compañeros de su clase virtual. Además, la cantidad de interacciones producidas en estos entornos generará un gran volumen de datos educativos, la Big Learning Data, con información vital que habrá que procesar para mejorar y adaptar el sistema al alumno a medida que el curso avanza, y para recoger información valiosa para su tutorización. Así, la última parte de esta tesis muestra las contribuciones realizadas en Inteligencia Artificial y los resultados de su implementación para crear la parte inteligente de esta arquitectura, pudiendo extraer de este modo el máximo rendimiento de estos nuevos entornos de aprendizaje colaborativo que serán realidad dentro de pocos años.The evolution of technology has produced profound changes in the paradigms of teaching and, particularly, in their application in online learning (e-learning). In fact it was the technological revolution itself that gave birth to this new model of virtual learning and there are currently few institutions that do not have an e-learning application, either as an alternative to traditional methods or to complement them (blended learning). The introduction of e-learning, and in general of the Information Technology and Communication (ICT) in the educational world has made the instructivist traditional education theory move to a constructivist paradigm, creating a more focused learning model. Educational tools have evolved towards this new paradigm, where customization and adaptation are the backbone of the model. Intelligent Tutoring Systems (ITS) provide a good example of this new methodology. However, the advent of Web 2.0 has created a social era which has rebranded the educational world. The deployment of the connectionist theory, arising from the implementation of the Social Web in education, and the implementation of various e-learning initiatives have led to the proliferation of virtual learning environments and different educational Web-based technologies. Since the technology associated with the Internet is constantly evolving, everything suggests that learning environments should evolve in the coming years in parallel with the Web itself. Thus it is likely that the next generation of e-learning implements own Web 3.0 (semantic) and Web 4.0 (symbiotic) characteristics and create environments where intelligent agents have a significant role. In this thesis we first analyze the path of education throughout history and discuss the influence it has had on the implementation of e-learning, from the simplest and less adaptive measures to the most modern, designed methods to enhance the learning experience. Furthermore, in view of the visible technological background, we propose a new architecture to include, on the one hand, the capabilities of existing online learning environments, and secondly, intelligent agents which can convert the experiences acquired in distance learning into an adaptive and social experience, where the group concept is of paramount importance. Future intelligent educational systems must therefore have an intricate part of advanced computing, an aspect from the field of Artificial Intelligence, which recognizes the evolution of students in their learning and how they interact and perform with their virtual class mates. In addition, the number of interactions produced in these environments will generate a large volume of educational data, the Big Data Learning with vital information that must be processed to improve and adapt the system to the student as the course progresses, and to collect valuable information for tutorship. So, the last part of this thesis shows the contributions made in Artificial Intelligence and the results of their implementation to create the intelligent part of this architecture. The benefits of these new collaborative learning environments will enable us to optimize performance in coming years

Similarity, Retrieval, and Classification of Motion Capture Data

Three-dimensional motion capture data is a digital representation of the complex spatio-temporal structure of human motion. Mocap data is widely used for the synthesis of realistic computer-generated characters in data-driven computer animation and also plays an important role in motion analysis tasks such as activity recognition. Both for efficiency and cost reasons, methods for the reuse of large collections of motion clips are gaining in importance in the field of computer animation. Here, an active field of research is the application of morphing and blending techniques for the creation of new, realistic motions from prerecorded motion clips. This requires the identification and extraction of logically related motions scattered within some data set. Such content-based retrieval of motion capture data, which is a central topic of this thesis, constitutes a difficult problem due to possible spatio-temporal deformations between logically related motions. Recent approaches to motion retrieval apply techniques such as dynamic time warping, which, however, are not applicable to large data sets due to their quadratic space and time complexity. In our approach, we introduce various kinds of relational features describing boolean geometric relations between specified body points and show how these features induce a temporal segmentation of motion capture data streams. By incorporating spatio-temporal invariance into the relational features and induced segments, we are able to adopt indexing methods allowing for flexible and efficient content-based retrieval in large motion capture databases. As a further application of relational motion features, a new method for fully automatic motion classification and retrieval is presented. We introduce the concept of motion templates (MTs), by which the spatio-temporal characteristics of an entire motion class can be learned from training data, yielding an explicit, compact matrix representation. The resulting class MT has a direct, semantic interpretation, and it can be manually edited, mixed, combined with other MTs, extended, and restricted. Furthermore, a class MT exhibits the characteristic as well as the variational aspects of the underlying motion class at a semantically high level. Classification is then performed by comparing a set of precomputed class MTs with unknown motion data and labeling matching portions with the respective motion class label. Here, the crucial point is that the variational (hence uncharacteristic) motion aspects encoded in the class MT are automatically masked out in the comparison, which can be thought of as locally adaptive feature selection