Browsing by Keyword "Visualization"

Now showing 1 - 5 of 5
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    Collision module integration in a specific graphic engine for terrain visualization
    (2004) López, Susana; Fernández, Borja; De Arrieta, Arkaitz Glz; De Segura, José Daniel Gómez; Garcia-Alonso, Alex; Infraestructuras y Servicios Corporativos; WEATHER AND CLIMATE INTELLIGENCE FOR BUSINESS
    This paper presents the design and implementation of a collision management module. This module is part of a specific graphic engine that manages large size terrain data. This engine has been used for example in a Basque Country terrain demo with a database up to 2.5Gb and texture resolution of 1024×1024 (3mts/pixel). As data textures are increasing resolution it seems convenient to allow near-terrain navigations. This behavior makes more complex the collision problem while navigating a large database. The main purpose of this project is the integration of a new collision module without any remarkable decrease in the performance of the engine. This work comprises the following research topics: preprocessed collision data structure definition, collision data management algorithm in execution time, collision detection algorithms and collision response algorithm. It has provided more flexibility in the interaction of the engine with the user, and more realism overflying terrains.
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    jMetalPy: A Python framework for multi-objective optimization with metaheuristics: A Python framework for multi-objective optimization with metaheuristics
    (2019-12) Benítez-Hidalgo, Antonio; Nebro, Antonio J.; García-Nieto, José; Oregi, Izaskun; Del Ser, Javier; Quantum; IA
    This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a different programming language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation, constrained and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy offers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it.
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    Multi-domain Adversarial Variational Bayesian Inference for Domain Generalization
    (2022) Gao, Zhifan; Guo, Saidi; Xu, Chenchu; Zhang, Jinglin; Gong, Mingming; Del Ser, Javier; Li, Shuo; IA
    Domain generalization aims to learn common knowledge from multiple observed source domains and transfer it to unseen target domains, e.g. the object recognition in varieties of visual environments. Traditional domain generalization methods aim to learn the feature representation of the raw data with its distribution invariant across domains. This relies on the assumption that the two posterior distributions (the distributions of the label given the feature distribution and given the raw data) are stable in different domains. However, this does not always hold in many practical situations. In this paper, we relax the above assumption by permitting the posterior distribution of the label given the raw data changes in difference domains, and thus focuses on a more realistic learning problem that infers the conditional domain-invariant feature representation. Specifically, a multi-domain adversarial variational Bayesian inference approach is proposed to minimize the inter-domain discrepancy of the conditional distributions of the feature given the label. Besides, it is imposed by the constraints from the adversarial learning and feedback mechanism to enhance the condition invariant feature representation. The extensive experiments on two datasets demonstrate the effectiveness of our approach, as well as the state-of-the-art performance comparing with thirteen methods.
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    Visualization of Numerical Association Rules by Hill Slopes
    (Springer, 2020-10-27) Fister, Iztok; Fister, Dušan; Iglesias, Andres; Galvez, Akemi; Osaba, Eneko; Del Ser, Javier; Analide, Cesar; Novais, Paulo; Camacho, David; Yin, Hujun; Quantum; IA
    Association Rule Mining belongs to one of the more prominent methods in Data Mining, where relations are looked for among features in a transaction database. Normally, algorithms for Association Rule Mining mine a lot of association rules, from which it is hard to extract knowledge. This paper proposes a new visualization method capable of extracting information hidden in a collection of association rules using numerical attributes, and presenting them in the form inspired by prominent cycling races (i.e., the Tour de France). Similar as in the Tour de France cycling race, where the hill climbers have more chances to win the race when the race contains more hills to overcome, the virtual hill slopes, reflecting a probability of one attribute to be more interesting than the other, help a user to understand the relationships among attributes in a selected association rule. The visualization method was tested on data obtained during the sports training sessions of a professional athlete that were processed by the algorithms for Association Rule Mining using numerical attributes.
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    Visualizations for the evolution of Variant-Rich Systems: A systematic mapping study: A systematic mapping study
    (2023-02) Medeiros, Raul; Martinez, Jabier; Díaz, Oscar; Falleri, Jean-Rémy; SWT
    Context: Variant-Rich Systems (VRSs), such as Software Product Lines or variants created through clone & own, aim at reusing existing assets. The long lifespan of families of variants, and the scale of both the code base and the workforce make VRS maintenance and evolution a challenge. Visualization tools are a needed companion. Objective: We aim at mapping the current state of visualization interventions in the area of VRS evolution. We tackle evolution in both functionality and architecture. Three research questions are posed: What sort of analysis is being conducted to assess VRS evolution? (Analysis perspective); What sort of visualizations are displayed? (Visualization perspective); What is the research maturity of the reported interventions? (Maturity perspective). Methods: We performed a systematic mapping study including automated search in digital libraries, expert knowledge, and snowballing. Results: The study reports on 41 visualization approaches to cope with VRS evolution. Analysis wise, feature identification and location is the most popular scenario, followed by variant integration towards a Software Product Line. As for visualization, nodelink diagram visualization is predominant while researchers have come up with a wealth of ingenious visualization approaches. Finally, maturity wise, almost half of the studies are solution proposals. Most of the studies provide proof-of-concept, some of them also include publicly available tools, yet very few face proof-of-value. Conclusions: This study introduces a comparison framework where to frame future studies. It also points out distinct research gaps worth investigating as well as shortcomings in the evidence about relevance and contextual considerations (e.g., scalability).