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Bibliografías temáticas / Multimodal Knowledge Representation

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Artículos de revistas

Literatura académica sobre el tema "Multimodal Knowledge Representation"

Autor: Grafiati

Publicado: 10 de marzo de 2023

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Artículos de revistas sobre el tema "Multimodal Knowledge Representation"

1

Azañón, Elena, Luigi Tamè, Angelo Maravita, et al. "Multimodal Contributions to Body Representation." Multisensory Research 29, no. 6-7 (2016): 635–61. http://dx.doi.org/10.1163/22134808-00002531.

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Our body is a unique entity by which we interact with the external world. Consequently, the way we represent our body has profound implications in the way we process and locate sensations and in turn perform appropriate actions. The body can be the subject, but also the object of our experience, providing information from sensations on the body surface and viscera, but also knowledge of the body as a physical object. However, the extent to which different senses contribute to constructing the rich and unified body representations we all experience remains unclear. In this review, we aim to bri

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2

Coelho, Ana, Paulo Marques, Ricardo Magalhães, Nuno Sousa, José Neves, and Victor Alves. "A Knowledge Representation and Reasoning System for Multimodal Neuroimaging Studies." Inteligencia Artificial 20, no. 59 (2017): 42. http://dx.doi.org/10.4114/intartif.vol20iss59pp42-52.

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Multimodal neuroimaging analyses are of major interest for both research and clinical practice, enabling the combined evaluation of the structure and function of the human brain. These analyses generate large volumes of data and consequently increase the amount of possibly useful information. Indeed, BrainArchive was developed in order to organize, maintain and share this complex array of neuroimaging data. It stores all the information available for each participant/patient, being dynamic by nature. Notably, the application of reasoning systems to this multimodal data has the potential to pro

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3

Bruni, E., N. K. Tran, and M. Baroni. "Multimodal Distributional Semantics." Journal of Artificial Intelligence Research 49 (January 23, 2014): 1–47. http://dx.doi.org/10.1613/jair.4135.

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Distributional semantic models derive computational representations of word meaning from the patterns of co-occurrence of words in text. Such models have been a success story of computational linguistics, being able to provide reliable estimates of semantic relatedness for the many semantic tasks requiring them. However, distributional models extract meaning information exclusively from text, which is an extremely impoverished basis compared to the rich perceptual sources that ground human semantic knowledge. We address the lack of perceptual grounding of distributional models by exploiting co

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4

Toraldo, Maria Laura, Gazi Islam, and Gianluigi Mangia. "Modes of Knowing." Organizational Research Methods 21, no. 2 (2016): 438–65. http://dx.doi.org/10.1177/1094428116657394.

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The current article argues that video-based methodologies offer unique potential for multimodal research applications. Multimodal research, further, can respond to the problem of “elusive knowledges,” that is, tacit, aesthetic, and embodied aspects of organizational life that are difficult to articulate in traditional methodological paradigms. We argue that the multimodal qualities of video, including but not limited to its visual properties, provide a scaffold for translating embodied, tacit, and aesthetic knowledge into discursive and textual forms, enabling the representation of organizatio

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5

Gül, Davut, and Bayram Costu. "To What Extent Do Teachers of Gifted Students Identify Inner and Intermodal Relations in Knowledge Representation?" Mimbar Sekolah Dasar 8, no. 1 (2021): 55–80. http://dx.doi.org/10.53400/mimbar-sd.v8i1.31333.

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Gifted students get bored of reading authoritative and descriptive multimodal texts. They need coherent, explanatory, and interactive texts. Moreover, because of the pandemic, gifted students took courses online, and teachers had to conduct their lessons on digital online tools with multimodal representations. They posted supplementary teaching materials as multimodal texts to the students. Hence, teachers of gifted students should pay attention to inner and intermodal relations to meet the needs of gifted students and support their learning experience. The research aims at examining to what e

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6

Tomskaya, Maria, and Irina Zaytseva. "MULTIMEDIA REPRESENTATION OF KNOWLEDGE IN ACADEMIC DISCOURSE." Verbum 8, no. 8 (2018): 129. http://dx.doi.org/10.15388/verb.2017.8.11357.

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The article focuses on academic presentations created with the help of multimedia programmes. The presentation is regarded as a special form of new academic knowledge representation. An academic presentation is explored as a multimodal phenomenon due to the fact that different channels or modes are activated during its perception. Data perception constitutes a part of the context which in itself is a semiotic event involving various components (an addresser, an addressee, the message itself, the channel of communication and the code). The choice of the code and the channel depends on different

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7

Cholewa, Wojciech, Marcin Amarowicz, Paweł Chrzanowski, and Tomasz Rogala. "Development Environment for Diagnostic Multimodal Statement Networks." Key Engineering Materials 588 (October 2013): 74–83. http://dx.doi.org/10.4028/www.scientific.net/kem.588.74.

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Development of effective diagnostic systems for the recognition of technical conditions of complex objects or processes requires the use of knowledge from multiple sources. Gathering of diagnostic knowledge acquired from diagnostic experiments as well as independent experts in the form of an information system database is one of the most important stages in the process of designing diagnostic systems. The task can be supported through suitable modeling activities and diagnostic knowledge management. Briefly, this paper presents an example of an application of multimodal diagnostic statement ne

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8

Prieto-Velasco, Juan Antonio, and Clara I. López Rodríguez. "Managing graphic information in terminological knowledge bases." Terminology 15, no. 2 (2009): 179–213. http://dx.doi.org/10.1075/term.15.2.02pri.

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The cognitive shift in Linguistics has affected the way linguists, lexicographers and terminologists understand and describe specialized language, and the way they represent scientific and technical concepts. The representation of terminological knowledge, as part of our encyclopaedic knowledge about the world, is crucial in multimedia terminological knowledge bases, where different media coexist to enhance the multidimensional character of knowledge representations. However, so far little attention has been paid in Terminology and Linguistics to graphic information, including visual resources

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9

Laenen, Katrien, and Marie-Francine Moens. "Learning Explainable Disentangled Representations of E-Commerce Data by Aligning Their Visual and Textual Attributes." Computers 11, no. 12 (2022): 182. http://dx.doi.org/10.3390/computers11120182.

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Understanding multimedia content remains a challenging problem in e-commerce search and recommendation applications. It is difficult to obtain item representations that capture the relevant product attributes since these product attributes are fine-grained and scattered across product images with huge visual variations and product descriptions that are noisy and incomplete. In addition, the interpretability and explainability of item representations have become more important in order to make e-commerce applications more intelligible to humans. Multimodal disentangled representation learning,

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10

Li, Jinghua, Runze Liu, Dehui Kong, et al. "Attentive 3D-Ghost Module for Dynamic Hand Gesture Recognition with Positive Knowledge Transfer." Computational Intelligence and Neuroscience 2021 (November 18, 2021): 1–12. http://dx.doi.org/10.1155/2021/5044916.

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Hand gesture recognition is a challenging topic in the field of computer vision. Multimodal hand gesture recognition based on RGB-D is with higher accuracy than that of only RGB or depth. It is not difficult to conclude that the gain originates from the complementary information existing in the two modalities. However, in reality, multimodal data are not always easy to acquire simultaneously, while unimodal RGB or depth hand gesture data are more general. Therefore, one hand gesture system is expected, in which only unimordal RGB or Depth data is supported for testing, while multimodal RGB-D d

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