Examinando por Autor "Raya, Laura"

Mostrando 1 - 10 de 10

A virtual reality data visualization tool for dimensionality reduction methods
(Springer, 2024) Morales-Vega, Juan C.; Raya, Laura; Rubio-Sánchez, Manuel; Sanchez, Alberto
In this paper, we present a virtual reality interactive tool for generating and manipulating visualizations for high-dimensional data in a natural and intuitive stereoscopic way. Our tool offers support for a diverse range of dimensionality reduction (DR) algorithms, enabling the transformation of complex data into insightful 2D or 3D representations within an immersive VR environment. The tool also allows users to include annotations with a virtual pen using hand tracking, to assign class labels to the data observations, and to perform simultaneous visualization with other users within the 3D environment to facilitate collaboration.
Enabling personalized VR experiences: a framework for real‑time adaptation and recommendations in VR environments
(Springer, 2024-06-24) Valmorisco, Sergio; Raya, Laura; Sanchez, Alberto
The personalization of user experiences through recommendation systems has been extensively explored in Internet applications, but this has yet to be fully addressed in Virtual Reality (VR) environments. The complexity of managing geometric 3D data, computational load, and natural interactions poses significant challenges in real-time adaptation in these immersive experiences. However, tailoring VR environments to individual user needs and interests holds promise for enhancing user experiences. In this paper, we present Virtual Reality Environment Adaptation through Recommendations (VR-EAR), a framework designed to address this challenge. VR-EAR employs customizable object metadata and a hybrid recommendation system modeling implicit user feedback in VR environments. We utilize VR optimization techniques to ensure efficient performance. To evaluate our framework, we designed a virtual store where product locations dynamically adjust based on user interactions. Our results demonstrate the effectiveness of VR-EAR in adapting and personalizing VR environments in real time. domains.
Feature selection based on star coordinates plots associated with Eigenvalue problems
(2021-02-01) Sanchez, Alberto; Raya, Laura; Mohedano-Munoz, Miguel Ángel; Rubio-Sánchez, Manuel
Feature selection consists of choosing a smaller number of variables to work with when analyzing high-dimensional data sets. Recently, several visualization tools, techniques, and feature relevance measures have been developed in order to help users carry out the feature selection. Some of these approaches are based on radial axes methods, where analysts perform backward feature elimination by discarding features that have a low impact on the visualizations. Similarly, in this paper, we propose a new feature relevance measure for star coordinates plots associated with the class of linear dimensionality reduction mappings defined through the solutions of eigenvalue problems, such as linear discriminant analysis or principal component analysis. We show that the approach leads to enhanced feature subsets for class separation or variance maximization in the plots for numerous data sets of the UCI repository. Lastly, in practice, the tool allows analysts to decide which features to discard by examining their relevance and by taking into account previous domain knowledge.
Guided Decision Tree: A Tool to Interactively Create Decision Trees Through Visualization of Subsequent LDA Diagrams
(MDPI, 2024-11-14) Mohedano-Munoz, Miguel A.; Raya, Laura; Sanchez, Alberto
Decision trees are a widely used machine learning technique due to their ease of interpretation. and construction. This method allows domain experts to learn from raw data, but they cannot include their prior knowledge in the analysis due to its automatic nature, which implies minimal human intervention in its computation. Conversely, interactive visualization methods have proven to be effective in gaining insights from data, as they incorporate the researcher’s criteria into the analysis process. In an effort to combine both methodologies, we have developed a tool to manually build decision trees according to subsequent visualizations of data mapping after applying linear discriminant analysis in combination with Star Coordinates in order to analyze the importance of each feature in the separation. The nodes’ information contains data about the features that can be used to split and their cut-off values, in order to select them in a guided manner. In this way, it is possible to produce simpler and more expertly driven decision trees than those obtained by automatic methods. The resulting decision trees reduces the tree size compared to those generated by automatic machine learning algorithms, obtaining a similar accuracy and therefore improving their understanding. The tool developed and presented here to manually create decision trees in a guided manner based on the subsequent visualizations of the data mapping facilitates the use of this method in real-world applications. The usefulness of this tool is demonstrated through a case study with a complex dataset used for motion recognition, where domain experts built their own decision trees by applying their prior knowledge and the visualizations provided by the tool in node construction. The resulting trees are more comprehensible and explainable, offering valuable insights into the data and confirming the relevance of upper body features and hand movements for motion recognition.
Interactive Visual Clustering and Classification based on Dimensionality Reduction Mappings: A Case Study for Analyzing Patients with Dermatologic Conditions
(2021-06-01) Mohedano-Munoz, Miguel Ángel; Alique-García, Sergio; Rubio-Sánchez, Manuel; Raya, Laura; Sanchez, Alberto
Multidimensional data sets are becoming more frequent in practically all research fields, and require complex data analysis techniques in order to extract knowledge from them. In this paper, we propose an interactive visualization tool for performing exploratory data analysis. The tool combines unsupervised and supervised dimensionality reduction methods, such as linear discriminant analysis, or t-SNE, with clustering and classification techniques. Analysts can use several machine learning methods for extracting data structure, and can group data into clusters interactively or through clustering algorithms. In addition they can visualize projections of the data to evaluate the quality of obtained clusters, and to analyze the performance of classification methods. We have applied this tool to analyze a clinical data set related to patients with dermatologic conditions that are under photodynamic therapy. The analysis allowed medical doctors to identify several clinically interesting patient groups. In addition, clinicians discovered a greater efficacy in the treatment of patients with the photosensitizer 5-aminolaevulinic acid nanoemulsion gel compared to those treated with methyl-5-aminolaevulinate cream.
La máquina de Sumar: Aprendizaje por descubrimiento en la toma de contacto con Arquitectura de Computadores
(Jornadas de Innovación y TIC Educativas - JITICE'12. Boletin ETSII. Universidad Rey Juan Carlos, 2012-03) Garre, Carlos; Miraut, David; Raya, Laura; Sánchez, Alberto
La arquitectura de computadores es uno de los pilares sobre los que se sustenta la informática. En los planes universitarios, su estudio suele comenzar con los temas de Representación de la Información y Estructura y Diseño del Computador. En cambio, los alumnos que se enfrentan por primera vez a estos temas frecuentemente no comprenden la necesidad de estudiarlos, ya que aún no tienen el concepto de que, al abordar el diseño de una máquina capaz de procesar información, es necesario en primer lugar diseñar una forma de representar la información. En este artículo se presenta una sencilla actividad de aprendizaje por descubrimiento en la que el alumno debe abordar el diseño de una máquina que procesa información, una máquina de sumar. Enfrentarse a los retos que supone su diseño facilita al alumno la comprensión de la necesidad de abordar el estudio de los primeros conceptos de arquitectura de computadores.
Multi-Technique Redirected Walking Method
(IEEE Computer Society, 2022-06-07) Mayor, Jesus; Raya, Laura; Bayona, Sofia; Sanchez, Alberto
The Room-Scale locomotion method is one of the most realistic locomotion methods used in virtual reality technologies. This is due to the natural interaction obtained through the tracking of its controllers and the head-mounted display with six degrees of freedom. However, its mapping by position between the physical and the virtual world limits the user’s movement to the physical workspace provided by the corresponding device. Redirected Walking methods use gain algorithms that add modifications to the virtual movement of the user, optimizing the virtual workspace in the same physical workspace. In this paper, we develop a new Redirected Walking method, which combines the modification of three gain algorithms (curvature gain, rotation gain, and translation gain), a new algorithm (deviation gain), a path predictive method of the user’s locomotion, a proportional distance to the center function, and a user direction smoothing function that softens the effect of the algorithms. Complementing the new method, we offer an automatic calibration system that allows the user to use our method in a personalized way.
El razonamiento analógico activo en el estudio de Arquitectura e Ingeniería de los Computadores
(Boletín de la ETSII, 2012-03) Raya, Laura; Garre, Carlos; Miraut, David; Perez, Alvaro G.
La complejidad de ciertos conceptos de Arquitectura e Ingeniería de los Computadores requiere que el alumnado posea una sólida y amplia base de conocimientos previa para su completa comprensión. El carácter multidisciplinar de los grados o másteres universitarios provocan que un porcentaje elevado de los estudiantes no cuenten con la base apropiada. En el presente artículo se expone que el uso del razonamiento analógico activo por parte del estudiante podrá facilitar la asimilación de los nuevos conceptos a partir del razonamiento cognitivo del usuario, familiarizando los nuevos conocimientos a adquirir con conocimientos previos ya entendidos.
Test multi-respuesta: b) b) y b)
(Boletín de la ETSII, 2012-03) Miraut, David; Tenajas, Rebeca; Perez, Alvaro G.; Novalbos, Marcos; Garre, Carlos; Raya, Laura; Mendoza, Angela; S. Zurdo, Javier
En este artículo se presenta un paquete de LaTeX que asiste en la creación de test multirrespuesta, que pretende ser una alternativa más que ayude a hacer sostenible la evaluación continua en el marco de Espacio Europeo de Educación Superior. La solución propuesta está especialmente diseñada para el caso en el que el número de alumnos matriculados en una asignatura excede a los recursos humanos en profesorado para poder realizar esta tarea de forma tradicional, y el espacio del aula dificulta que los alumnos puedan estar lo suficientemente separados para evitar la tentación de copiar sus respuestas en las pruebas de evaluación.
Visualización de datos multidimensionales y multivariables en entornos tridimensionales, aplicados a la ingeniería civil
(2014-11-14) Sújar, Aaron; Raya, Laura; Mata, Susana
Este proyecto se centra en la facilitación de la visualización de datos multidimensionales y multivariables en entornos tridimensionales. Dentro de la visualización de datos, existen múltiples técnicas que permiten un mejor entendimiento de la información mostrada y la creación de interfaces de usuario más intuitivas y manejables