Ponencias

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https://hdl.handle.net/20.500.14454/1637

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Examinando Ponencias por Autor "Almeida, Aitor"

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    Ítem
    Analysing the impact of images and text for predicting human creativity through encoders
    (Science and Technology Publications, Lda, 2025) Pikatza Huerga, Amaia; Matanzas de Luis, Pablo; Fernandez De Retana Uribe, Miguel; Peña Lasa, Javier; Zulaika Zurimendi, Unai; Almeida, Aitor
    This study explores the application of multimodal machine learning techniques to evaluate the originality and complexity of drawings. Traditional approaches in creativity assessment have primarily focused on visual analysis, often neglecting the potential insights derived from accompanying textual descriptions. The research assesses four target features: drawings’ originality, flexibility and elaboration level, and titles’ creativity, all labelled by expert psychologists. The research compares different image encoding and text embeddings to examine the effectiveness and impact of individual and combined modalities. The results indicate that incorporating textual information enhances the predictive accuracy for all features, suggesting that text provides valuable contextual insights that images alone may overlook. This work demonstrates the importance of a multimodal approach in creativity assessment, paving the way for more comprehensive and nuanced evaluations of artistic expression.
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    Ítem
    IEM: a unified lifecycle orchestrator for multilingual IaC deployments
    (Association for Computing Machinery, Inc, 2023-04-15) Díaz de Arcaya Serrano, Josu; Osaba, Eneko ; Benguria, Gorka; Etxaniz Errazkin, Iñaki; López Lobo, Jesús ; Alonso, Juncal; Torre Bastida, Ana Isabel ; Almeida, Aitor
    Over the last few years, DevOps methodologies have promoted a more streamlined operationalization of software components in production environments. Infrastructure as Code (IaC) technologies play a key role in the lifecycle management of applications, as they promote the delivery of the infrastructural elements alongside the application components. This way, IaC technologies aspire to minimize the problems associated with the environment by providing a repeatable and traceable process. However, there are a large variety of IaC frameworks, each of them focusing on a different phase of the operationalization lifecycle, hence the necessity to master numerous technologies. In this research, we present the IaC Execution Manager (IEM), a tool devoted to providing a unified framework for the operationalization of software components that encompasses the various stages and technologies involved in the application lifecycle. We analyze an industrial use case to improve the current approach and conclude the IEM is a suitable tool for solving the problem as it promotes automation, while reducing the learning curve associated with the required IaC technologies.
  • No hay miniatura disponible
    Ítem
    Influence Functions for interpretable link prediction in Knowledge Graphs for Intelligent Environments
    (Institute of Electrical and Electronics Engineers Inc., 2022) Zulaika Zurimendi, Unai ; Almeida, Aitor; López de Ipiña González de Artaza, Diego
    Knowledge graphs are large, graph-structured databases used in many use-case scenarios such as Intelligent Environments. Many Artificial Intelligent latent feature models are used to infer new facts in Knowledge Graphs. Despite their success, the lack of interpretability remains a challenge to overcome. This paper applies influence functions to obtain the most significant facts when predicting new knowledge and allows users to understand these models. However, Influence Functions do not scale well. We present an efficient method to scale up influence functions to large Knowledge Graphs to overcome such an issue. It drastically reduces the number of training samples when computing influences and uses fast curvature matrix-vector products to linearize the computation steps required for the inverse Hessian. We conduct experiments on different sized Knowledge Graphs demonstrating the scalability of our approach and its effectiveness in measuring the most influential facts. Our method provides an intuitive understanding of link prediction behaviour in Knowledge Graphs and Intelligent Environments.