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Artículos de revistas

Literatura académica sobre el tema "Attributs temporels"

Autor: Grafiati

Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Attributs temporels"

1

RANDALL, D. J., H. J. HAMILTON, and R. J. HILDERMAN. "TEMPORAL GENERALIZATION WITH DOMAIN GENERALIZATION GRAPHS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 02 (1999): 195–217. http://dx.doi.org/10.1142/s0218001499000124.

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This paper addresses the problem of using domain generalization graphs to generalize temporal data extracted from relational databases. A domain generalization graph associated with an attribute defines a partial order which represents a set of generalization relations for the attribute. We propose formal specifications for domain generalization graphs associated with calendar (date and time) attributes. These graphs are reusable (i.e. can be used to generalize any calendar attributes), adaptable (i.e. can be extended or restricted as appropriate for particular applications), and transportable

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2

Nguyen, Ha, and Wendy V. Wismer. "Temporal Sensory Profiles of Regular and Sodium-Reduced Foods Elicited by Temporal Dominance of Sensations (TDS) and Temporal Check-All-That-Apply (TCATA)." Foods 11, no. 3 (2022): 457. http://dx.doi.org/10.3390/foods11030457.

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Temporal sensory methods can be used to highlight the impact of sodium reduction on the dynamic sensory profile of foods targeted for sodium reduction. Study aims were to compare the temporal sensory attribute profiles of regular and sodium-reduced food products elicited by TDS and TCATA, over single and multiple oral intakes. A total of 20 semi-trained participants evaluated commercially available regular and sodium-reduced canned corn, cooked ham (single intakes), potato chips and cream of mushroom soup (5 intakes) using both TDS and TCATA. Regular and sodium-reduced products differed in not

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3

Chalyi, Serhii, and Ievgen Bogatov. "METHOD OF CONSTRUCTING AN ATTRIBUTE DESCRIPTION OF THE BUSINESS PROCESS "AS IS" IN THE PROCESS APPROACH TO ENTERPRISE MANAGEMENT." EUREKA: Physics and Engineering 6 (November 30, 2018): 35–40. http://dx.doi.org/10.21303/2461-4262.2018.00786.

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The problem of constructing an attribute description of a business process with the automated formation of process models “as is” using logs of information systems in which the tracks of individual processes are not identified is considered. It is shown that to solve this problem, it is advisable to distinguish the distinctive properties of individual business processes represented by the attributes of log events. A method for constructing an attribute description of a business process is proposed. The method is based on the comparison of combinations of attributes for intervals of events of a

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4

Fujisaki, Waka, and Shin'ya Nishida. "Sensory Attribute Identification Time Cannot Explain the Common Temporal Limit of Binding Different Attributes and Modalities." i-Perception 2, no. 8 (2011): 758. http://dx.doi.org/10.1068/ic758.

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5

Wu, Bo, Xun Liang, Xiangping Zheng, and Jun Wang. "Enhancing Dynamic GCN for Node Attribute Forecasting with Meta Spatial-Temporal Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16360–61. http://dx.doi.org/10.1609/aaai.v37i13.27040.

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Node attribute forecasting has recently attracted considerable attention. Recent attempts have thus far utilize dynamic graph convolutional network (GCN) to predict future node attributes. However, few prior works have notice that the complex spatial and temporal interaction between nodes, which will hamper the performance of dynamic GCN. In this paper, we propose a new dynamic GCN model named meta-DGCN, leveraging meta spatial-temporal tasks to enhance the ability of dynamic GCN for better capturing node attributes in the future. Experiments show that meta-DGCN effectively modeling comprehens

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6

Fan, Wenfei, Resul Tugay, Yaoshu Wang, Min Xie, and Muhammad Asif Ali. "Learning and Deducing Temporal Orders." Proceedings of the VLDB Endowment 16, no. 8 (2023): 1944–57. http://dx.doi.org/10.14778/3594512.3594524.

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This paper studies how to determine temporal orders on attribute values in a set of tuples that pertain to the same entity, in the absence of complete timestamps. We propose a creator-critic framework to learn and deduce temporal orders by combining deep learning and rule-based deduction, referred to as GATE (Get the lATEst). The creator of GATE trains a ranking model via deep learning, to learn temporal orders and rank attribute values based on correlations among the attributes. The critic then validates the temporal orders learned and deduces more ranked pairs by chasing the data with curren

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7

Colloc, Joël, Relwendé Aristide Yameogo, Peter Summons, Lilian Loubet, Jean-Bernard Cavelier, and Paul Bridier. "A Temporal Case-Based Reasoning Platform Relying on a Fuzzy Vector Spaces Object-Oriented Model and a Method to Design Knowledge Bases and Decision Support Systems in Multiple Domains." Algorithms 15, no. 2 (2022): 66. http://dx.doi.org/10.3390/a15020066.

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Knowledge bases in complex domains must take into account many attributes describing numerous objects that are themselves components of complex objects. Temporal case-based reasoning (TCBR) requires comparing the structural evolution of component objects and their states (attribute values) at different levels of granularity. This paper provides some significant contributions to computer science. It extends a fuzzy vector space object-oriented model and method (FVSOOMM) to present a new platform and a method guideline capable of designing objects and attributes that represent timepoint knowledg

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8

Newton, Peter F. "Developmental Trends of Black Spruce Fibre Attributes in Maturing Plantations." International Journal of Forestry Research 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/7895289.

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This study assessed the temporal developmental patterns of commercially relevant fibre attributes (tracheid length and diameters, wall thickness, specific surface area, wood density, microfibril angle, fibre coarseness, and modulus of elasticity) and their interrelationships within maturing black spruce (Picea mariana (Mill.) B.S.P.) plantations. A size-based stratified random sample procedure within 5 semimature plantations located in the Canadian Boreal Forest Region was used to select 50 trees from which radial cross-sectional xylem sequences at breast-height (1.3 m) were cut and analyzed.

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9

Altuna, Begoña, María Jesús Aranzabe, and Arantza Díaz de Ilarraza. "EusTimeML: A mark-up language for temporal information in Basque." Research in Corpus Linguistics 8 (2020): 86–104. http://dx.doi.org/10.32714/ricl.08.01.06.

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We present EusTimeML, a mark-up language for temporal information in texts written in Basque. It is compliant with the TimeML specifications, while offering some adapted attributes and attribute values in order to represent the language-specific features of Basque. In particular, alterations have been carried out for verb tense, aspect and modality coding, as well as for time expression and signal annotation. EusTimeML also provides a major extension to the existing TimeML schemes, since the attributes and values for factuality annotation have been added to the existing temporal information an

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10

Liu, Yaqin, Yunsi Chen, Qing He, and Qian Yu. "Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective." Systems 11, no. 2 (2023): 82. http://dx.doi.org/10.3390/systems11020082.

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With the cyclical development of emerging technologies, in reality, the evolution dynamics of their innovation networks will inevitably show obvious time attributes. Numerous network analyses of real complex systems usually focus on static networks; however, it is difficult to describe that most real networks undergo topological evolutions over time. Temporal networks, which incorporate time attributes into traditional static network models, can more accurately depict the temporal features of network evolution. Here, we introduced the time attribute of the life cycle of emerging technology int

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