Relevant bibliographies by topics / Attributes data

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Attributes data'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Attributes data"

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Xu, Xiaofeng, Ivor W. Tsang, and Chuancai Liu. "Improving Generalization via Attribute Selection on Out-of-the-Box Data." Neural Computation 32, no. 2 (February 2020): 485–514. http://dx.doi.org/10.1162/neco_a_01256.

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Zero-shot learning (ZSL) aims to recognize unseen objects (test classes) given some other seen objects (training classes) by sharing information of attributes between different objects. Attributes are artificially annotated for objects and treated equally in recent ZSL tasks. However, some inferior attributes with poor predictability or poor discriminability may have negative impacts on the ZSL system performance. This letter first derives a generalization error bound for ZSL tasks. Our theoretical analysis verifies that selecting the subset of key attributes can improve the generalization performance of the original ZSL model, which uses all the attributes. Unfortunately, previous attribute selection methods have been conducted based on the seen data, and their selected attributes have poor generalization capability to the unseen data, which is unavailable in the training stage of ZSL tasks. Inspired by learning from pseudo-relevance feedback, this letter introduces out-of-the-box data—pseudo-data generated by an attribute-guided generative model—to mimic the unseen data. We then present an iterative attribute selection (IAS) strategy that iteratively selects key attributes based on the out-of-the-box data. Since the distribution of the generated out-of-the-box data is similar to that of the test data, the key attributes selected by IAS can be effectively generalized to test data. Extensive experiments demonstrate that IAS can significantly improve existing attribute-based ZSL methods and achieve state-of-the-art performance.
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Ma, Xiaofei, Yi Feng, Yi Qu, and Yang Yu. "Attribute Selection Method based on Objective Data and Subjective Preferences in MCDM." International Journal of Computers Communications & Control 13, no. 3 (May 27, 2018): 391–407. http://dx.doi.org/10.15837/ijccc.2018.3.3188.

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Decision attributes are important parameters when choosing an alternative in a multiple criteria decision-making (MCDM) problem. In order to select the optimal set of decision attributes, an analysis framework is proposed to illustrate the attribute selection problem. Then a two-step attribute selection procedure is presented based on the framework: In the first step, attributes are filtered by using correlation algorithm. In the second step, a multi-objective optimization model is constructed to screen attributes from the results of the first step. Finally, a case study is given to illustrate and verify this method. The advantage of this method is that both external attribute data and subjective decision preferences are utilized in a sequential procedure. It enhances the reliability of decision attributes and matches the actual decision-making scenarios better.
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Zhao, Tao, Fangyu Li, and Kurt J. Marfurt. "Seismic attribute selection for unsupervised seismic facies analysis using user-guided data-adaptive weights." GEOPHYSICS 83, no. 2 (March 1, 2018): O31—O44. http://dx.doi.org/10.1190/geo2017-0192.1.

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With the rapid development in seismic attribute and interpretation techniques, interpreters can be overwhelmed by the number of attributes at their disposal. Pattern recognition-driven seismic facies analysis provides a means to identify subtle variations across multiple attributes that may only be partially defined on a single attribute. Typically, interpreters intuitively choose input attributes for multiattribute facies analysis based on their experience and the geologic target of interest. However, such an approach may overlook unsuspected or subtle features hidden in the data. We therefore augment this qualitative attribute selection process with quantitative measures of candidate attributes that best differentiate features of interest. Instead of selecting a group of attributes and assuming all the selected attributes contribute equally to the facies map, we weight the interpreter-selected input attributes based on their response from the unsupervised learning algorithm and the interpreter’s knowledge. In other words, we expect the weights to represent “which attribute is ‘favored’ by an interpreter as input for unsupervised learning” from an interpretation perspective and “which attribute is ‘favored’ by the learning algorithm” from a data-driven perspective. Therefore, we claim the weights are user guided and data adaptive, as the derivation of weight for each input attribute is embedded into the learning algorithm, providing a specific measurement tailored to the selected learning algorithm, while still taking the interpreter’s knowledge into account. We develop our workflow using Barnett Shale surveys and an unsupervised self-organizing map seismic facies analysis algorithm. We found that the proposed weighting-based attribute selection method better differentiates features of interest than using equally weighted input attributes. Furthermore, the weight values provide insights into dependency among input attributes.
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Zhang, Lin, and Jian Li Zhang. "Classification Algorithm Based on Category Attribute’s Mathematical Expectation." Advanced Materials Research 659 (January 2013): 103–7. http://dx.doi.org/10.4028/www.scientific.net/amr.659.103.

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The thesis introduced a classification algorithm- CAME which based on the training set’s mathematical expectation of each class attribute for unknown data. This algorithm converted the non-numerical or discrete attributes to the corresponding numerical data first, then calculate the mathematical expectation of data which belonging to different categories of numerical attributes. When a new data is needed to predict its classification, let each attribute’s mathematical expectation with existing categories as coordinate, then calculate the distance from new data attribute to various categories. The new data will belong to the category that has the shortest distance to the new data. This algorithm is not affected by attribute’s property or the number of category, and has high accuracy and good scalability.
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Duffy, Leanne M., and Shane P. Griffiths. "Assessing attribute redundancy in the application of productivity-susceptibility analysis to data-limited fisheries." Aquatic Living Resources 32 (2019): 20. http://dx.doi.org/10.1051/alr/2019018.

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Productivity-susceptibility analysis (PSA) is a widely used data-limited method to assess the relative vulnerability of species impacted by fisheries. Despite its widespread use, few authors have evaluated the impacts of attribute weightings and correlation of productivity attributes that may bias species' vulnerability scores. We evaluated the PSA methodology and performed sensitivity analyses to determine the impacts of correlation among productivity attributes used in the PSA, given that several of these attributes are strongly correlated. A PSA for species caught in the eastern Pacific Ocean tuna purse-seine fishery was used as an example to assess potential bias introduced by attribute weightings and correlation of productivity attributes on species' vulnerability scores. Redundancy was observed among three pairs of attributes. We demonstrated that manipulation of attribute weightings and removal of correlated attributes did not appreciably change any species' overall vulnerability status. Our results suggest that after removal of redundant attributes, PSAs can be conducted more rapidly with fewer data inputs than previous implementations, while retaining comparable vulnerability scores.
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Albattah, Waleed, Rehan Ullah Khan, and Khalil Khan. "Attributes Reduction in Big Data." Applied Sciences 10, no. 14 (July 17, 2020): 4901. http://dx.doi.org/10.3390/app10144901.

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Processing big data requires serious computing resources. Because of this challenge, big data processing is an issue not only for algorithms but also for computing resources. This article analyzes a large amount of data from different points of view. One perspective is the processing of reduced collections of big data with less computing resources. Therefore, the study analyzed 40 GB data to test various strategies to reduce data processing. Thus, the goal is to reduce this data, but not to compromise on the detection and model learning in machine learning. Several alternatives were analyzed, and it is found that in many cases and types of settings, data can be reduced to some extent without compromising detection efficiency. Tests of 200 attributes showed that with a performance loss of only 4%, more than 80% of the data could be ignored. The results found in the study, thus provide useful insights into large data analytics.
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Alfonseca, Enrique, Guillermo Garrido, Jean-Yves Delort, and Anselmo Peñas. "WHAD: Wikipedia historical attributes data." Language Resources and Evaluation 47, no. 4 (May 28, 2013): 1163–90. http://dx.doi.org/10.1007/s10579-013-9232-5.

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Zhao, Huimin. "Matching Attributes across Overlapping Heterogeneous Data Sources Using Mutual Information." Journal of Database Management 21, no. 4 (October 2010): 91–110. http://dx.doi.org/10.4018/jdm.2010100105.

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Identifying matching attributes across heterogeneous data sources is a critical and time-consuming step in integrating the data sources. In this paper, the author proposes a method for matching the most frequently encountered types of attributes across overlapping heterogeneous data sources. The author uses mutual information as a unified measure of dependence on various types of attributes. An example is used to demonstrate the utility of the proposed method, which is useful in developing practical attribute matching tools.
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Agrawal, Ankit, Sanchit Misra, Ramanathan Narayanan, Lalith Polepeddi, and Alok Choudhary. "Lung Cancer Survival Prediction using Ensemble Data Mining on Seer Data." Scientific Programming 20, no. 1 (2012): 29–42. http://dx.doi.org/10.1155/2012/920245.

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We analyze the lung cancer data available from the SEER program with the aim of developing accurate survival prediction models for lung cancer. Carefully designed preprocessing steps resulted in removal/modification/splitting of several attributes, and 2 of the 11 derived attributes were found to have significant predictive power. Several supervised classification methods were used on the preprocessed data along with various data mining optimizations and validations. In our experiments, ensemble voting of five decision tree based classifiers and meta-classifiers was found to result in the best prediction performance in terms of accuracy and area under the ROC curve. We have developed an on-line lung cancer outcome calculator for estimating the risk of mortality after 6 months, 9 months, 1 year, 2 year and 5 years of diagnosis, for which a smaller non-redundant subset of 13 attributes was carefully selected using attribute selection techniques, while trying to retain the predictive power of the original set of attributes. Further, ensemble voting models were also created for predicting conditional survival outcome for lung cancer (estimating risk of mortality after 5 years of diagnosis, given that the patient has already survived for a period of time), and included in the calculator. The on-line lung cancer outcome calculator developed as a result of this study is available at http://info.eecs.northwestern.edu:8080/LungCancerOutcomeCalculator/.
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Michelson, M., and C. A. Knoblock. "Creating Relational Data from Unstructured and Ungrammatical Data Sources." Journal of Artificial Intelligence Research 31 (March 28, 2008): 543–90. http://dx.doi.org/10.1613/jair.2409.

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In order for agents to act on behalf of users, they will have to retrieve and integrate vast amounts of textual data on the World Wide Web. However, much of the useful data on the Web is neither grammatical nor formally structured, making querying difficult. Examples of these types of data sources are online classifieds like Craigslist and auction item listings like eBay. We call this unstructured, ungrammatical data "posts." The unstructured nature of posts makes query and integration difficult because the attributes are embedded within the text. Also, these attributes do not conform to standardized values, which prevents queries based on a common attribute value. The schema is unknown and the values may vary dramatically making accurate search difficult. Creating relational data for easy querying requires that we define a schema for the embedded attributes and extract values from the posts while standardizing these values. Traditional information extraction (IE) is inadequate to perform this task because it relies on clues from the data, such as structure or natural language, neither of which are found in posts. Furthermore, traditional information extraction does not incorporate data cleaning, which is necessary to accurately query and integrate the source. The two-step approach described in this paper creates relational data sets from unstructured and ungrammatical text by addressing both issues. To do this, we require a set of known entities called a "reference set." The first step aligns each post to each member of each reference set. This allows our algorithm to define a schema over the post and include standard values for the attributes defined by this schema. The second step performs information extraction for the attributes, including attributes not easily represented by reference sets, such as a price. In this manner we create a relational structure over previously unstructured data, supporting deep and accurate queries over the data as well as standard values for integration. Our experimental results show that our technique matches the posts to the reference set accurately and efficiently and outperforms state-of-the-art extraction systems on the extraction task from posts.
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Dissertations / Theses on the topic "Attributes data"

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Li, Wei. "Multi-attribute decision making a test on the impact of data attributes dependency /." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/5989.

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Thesis (M.S.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on November 9, 2007) Includes bibliographical references.
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Dawara, Santosh. "Grouping related attributes /." Link to online version, 2004. https://ritdml.rit.edu/dspace/handle/1850/438.

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Shongwe, Sandile Charles. "Contributions to control charts for attributes data." Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/79185.

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Kalibjian, J. R., T. J. Voss, J. J. Yio, and B. Hedeline. "Automated Binding of Attributes to Telemetry Data." International Foundation for Telemetering, 1993. http://hdl.handle.net/10150/608880.

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International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada
An automated method is described for binding attributes to extracted data from a telemetry stream. These attributes can be used by post processing utilities to facilitate efficient analysis. A practical implementation of such a scheme is described.
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MARTINS, LEONARDO DE OLIVEIRA. "ESTIMATES OF VOLUMETRIC CURVATURE ATTRIBUTES IN SEISMIC DATA." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2012. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35204@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Atributos de curvatura são uma importante ferramenta para visualização e interpretação de feições estruturais em dados sísmicos. Tais medidas podem realçar falhas e fraturas sutis que não estavam evidentes no dado de amplitude, fornecendo ao intérprete informações importantes para a construção do modelo geológico da área de interesse. Neste trabalho é apresentado um método para estimar atributos de curvatura volumérica em dados sísmicos empilhados. A partir do dado de amplitude, é computado um atributo identificador de horizonte, o qual permite que horizontes sísmicos sejam representados como superfícies de nível. Dessa maneira, o gradiente desse atributo fornece uma representação coerente do campo de normais do volume. Fórmulas para o cálculo de curvatura em superfícies implícitas são usadas para obter vários atributos de curvatura úteis na delineação e predição de importantes feições estratigráficas. Testes realizados com dados sintéticos e reais mostram que o método proposto é capaz de fornecer estimativas coerentes de atributos de curvatura a um baixo custo de processamento. São avaliados três atributos identificadores de horizontes: fase instantânea, derivada vertical e atributo de ridges.
Curvature attributes are powerful tools for visualization and interpretation of structural features in seismic data. Such measures may highlight faults and subtle fractures that were not evident in amplitude data, providing important information to the interpreter to build the geological model of the area of interest. This paper presents a method for estimating volumetric curvature attributes in post-stack seismic data. Using amplitude volume, an horizon identifier attribute is computed, in order to represent seismic horizons as level surfaces. Thus, the gradient of this attribute provides a coherent estimate of volumetric normal field. Formulas for the calculation of curvature in implicit surfaces are used to compute several curvature attributes useful in the delineation and prediction of important stratigraphic features. Tests with synthetic and real data show that the proposed method is able to provide consistent estimates of attributes of curvature at low cost processing. Three horizon identifer attributes are evaluated: instantaneous phase, vertical derivative and ridge attribute.
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Qaiser, Elizae. "Quantization of Real-Valued Attributes for Data Mining." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin983500840.

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Najmuddin, Ilyas Juzer. "Austin Fracture mapping using frequency data derived from seismic data." Texas A&M University, 2003. http://hdl.handle.net/1969/34.

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Pham, Thanh H. "Dynamic Update Techniques for Online Maps and Attributes Data." NSUWorks, 2001. http://nsuworks.nova.edu/gscis_etd/771.

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Online databases containing geographic and related tabular data for maps and attributes often require continuous updates from widely distributed sources afield. For some applications, these data are dynamic, and thus are of little value if they do not reflect the latest information or changes. A status map that depicts graphically temporal data affecting accountability is an example of this type of data. How can accommodations be made collectively for the perpetual data updates in the database and the need to deliver online information in real time without making concessions? The goal of the dissertation was to analyze and evaluate techniques and technology for data collection and storage, online data delivery, and real-time upload. The result of this analysis culminated in the design and prototype of a system that allowed real-time delivery of up-to-date maps and attributes information. A literature review revealed that an ample amount of research material existed on the theory and practice of developing dynamic update techniques. Despite that fact, no research literature was available that specifically dealt with dynamic update techniques that provide for real-time delivery of up-to-date maps while allowing online update of attributes information. This dissertation was the first attempt at providing research material in this important area. The procedure consisted of five major steps encompassing a number of small steps, and culminated in the development of a prototype. The steps included gathering data collection and storage information, investigating technological advances in data delivery and access, studying dynamic update techniques, assessing the feasibility of an implementation solution, and developing a prototype. The results revealed that the dynamic update technique as implemented in the prototype met the need for timely delivery of accountability, geospatial, and metadata information within an infrastructure.
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NOGUEIRA, DANIEL MOURA. "DATA VISUALIZATION: THE PERSUASIVE SPEECH OF VISUAL ATTRIBUTES IN INFOGRAPHICS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=24672@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
Esta dissertação aborda o tema do discurso persuasivo nos infográficos, um dos produtos do Design da Informação. Os infográficos são amplamente usados como ferramenta de comunicação pela mídia, com o intuito de transmitir informações de modo sintético, rápido e atraente por meio de representações visuais diagramáticas. Examina e analisa os atributos visuais dos gráficos e infográficos sob o ponto de vista da retórica visual. Investiga o uso do ferramental disponível para a elaboração de visualizações de dados persuasivas, que comuniquem de forma eloquente e eficiente o discurso desejado, obtendo a adesão do leitor. Os aportes teóricos da pesquisa se encontram na proposta de uma Retórica do Design, de Almeida Junior, fundamentada na Nova Retórica, de Chaïm Perelman e Lucie Olbrechts-Tyteca, nas investigações sobre a Percepção Visual voltada à visualização de dados, nas pesquisas de Colin Ware e Stephen Few, e na Semiótica de Charles Sanders Peirce, como elemento transdisciplinar, perpassando pelos diferentes tópicos como forma de integrá-los. Foram tomados, como casos exemplares, infográficos da seção Jornais da Sexta Mostra Nacional de Infografia de 2012, o Infolide. Os infográficos analisados mostram a intensa presença de recursos e ferramentas de persuasão na infografia impressa. Como resultado, chegou-se à conclusão de que é possível o designer intensificar o poder persuasivo dos seus infográficos, aprofundando-se acerca dos sistemas cognitivos da linguagem que regem a compreensão do leitor, ou seja, do seu auditório.
This dissertation addresses the topic of persuasive speech in infographics, one of the products of the Information Design. The infographics are widely used as a communication tool by the media, in order to transmit information in a synthetic, fast and attractive way using visual diagrammatic representations. Examines and analyzes the visual attributes of the data graphs and infographics from the point of view of visual rhetoric. Investigates the use of the tools available for developing compelling data visualizations that communicate eloquently and efficiently the desired speech, with the adherence of the reader. The theoretical references of the research are the proposal of a Rhetoric of Design, by Almeida Junior, based on the New Rhetoric of Chaïm Perelman and Lucie Olbrechts- Tyteca, the investigations on Visual Perception oriented to data visualization, in surveys of Colin Ware and Stephen Few, and the Semiotics of Charles Sanders Peirce, as a transdisciplinary element, passing through the different topics, integrating them. Were taken, as special cases, infographics from the Newspapers section of the 6th National Exhibition Infographics 2012, Infolide. The analyzed infographics show the intense presence of persuasive tools in printed infographics. As a result, the conclusion that the designer can enhance the persuasive power of their infographics deepening his knowledge about cognitive and language systems that govern the reader s understanding, ie, of his audience.
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Sajja, Sunitha. "Data Mining of Medical Datasets with Missing Attributes from Different Sources." Youngstown State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1300298263.

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Books on the topic "Attributes data"

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Scarpa, Riccardo. Reliability of benefit value transfers from contingent valuation data with forest-specific attributes. Milan: Fondazione Eni Enrico Mattei, 2000.

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Byrne, John C. A data structure for describing sampling designs to aid in compilation of stand attributes. Ogden, UT: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1988.

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Byrne, John C. A data structure for describing sampling designs to aid in compilation of stand attributes. Ogden, UT: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1988.

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Muus, Kyle J. Health-related attributes of North Dakota adults with disabilities: Analysis of 2001-2007 BRFSS data. Minot, ND: North Dakota Disability Health Project, North Dakota Center for Persons with Disabilities, Minot State University, 2009.

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Lambaounas, Andreas. Data mining by attribute oriented induction. Manchester: UMIST, 1994.

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Waite, William M. Generator for attributed grammars: Abstract data type. Sankt Augustin, W.-Germany: Gesellschaft f"ur Mathematik und Datenverarbeitung, 1986.

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Wang, Y. Richard. Toward quality data: An attribute-based approach. Cambridge, MA, USA: Productivity From Information Technology, "PROFIT" Research Initiative, Sloan School of Management, Massachusetts Institute of Technology, 1992.

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Reeve, Derek E. Attribute data and database theory: Course notes. 4th ed. Manchester: Manchester Metropolitan University Department of Environmental and Geographical Sciences, 1994.

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Prediger, D. J. Linking occupational attribute preferences to occupations. Iowa City, Iowa: American College Testing Program, 1996.

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Evangeliou, N. Simulating statistical quality control procedures for handling attribute data. Manchester: UMIST, 1996.

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Book chapters on the topic "Attributes data"

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von Frese, Ralph R. B. "Data Attributes." In Basic Environmental Data Analysis for Scientists and Engineers, 15–34. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429291210-2.

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Bramer, Max. "Continuous Attributes." In Principles of Data Mining, 93–119. London: Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-7307-6_8.

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Bramer, Max. "Continuous Attributes." In Principles of Data Mining, 93–119. London: Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-7493-6_8.

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Bramer, Max. "Continuous Attributes." In Principles of Data Mining, 93–119. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4884-5_8.

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Sharmanska, Viktoriia, and Novi Quadrianto. "In the Era of Deep Convolutional Features: Are Attributes Still Useful Privileged Data?" In Visual Attributes, 31–48. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50077-5_3.

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Chandra, Yanto, and Liang Shang. "Data Attributes and Memos." In Qualitative Research Using R: A Systematic Approach, 107–23. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3170-1_9.

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Kauderer, Harald, and Hans-Joachim Mucha. "Supervised Learning with Qualitative and Mixed Attributes." In Classification, Data Analysis, and Data Highways, 374–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72087-1_40.

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Nanda, Niranjan C. "Analysing Seismic Attributes." In Seismic Data Interpretation and Evaluation for Hydrocarbon Exploration and Production, 171–85. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-26491-2_10.

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Nanda, Niranjan C. "Analysing Seismic Attributes." In Seismic Data Interpretation and Evaluation for Hydrocarbon Exploration and Production, 205–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75301-6_10.

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Hamilton, Howard J., and Dee Jay Randall. "Data Mining with Calendar Attributes." In Temporal, Spatial, and Spatio-Temporal Data Mining, 117–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45244-3_10.

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Conference papers on the topic "Attributes data"

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Singh, Prem Kumar, and Aswani Kumar. "Attribute implications in data with fuzzy attributes using armstrong axioms." In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, 2017. http://dx.doi.org/10.1109/icecds.2017.8389514.

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Belohlavek, Radim, and Vilem Vychodil. "Similarity issues in attribute implications from data with fuzzy attributes." In Proceedings of the 2006 IEEE International Conference on Information Reuse and Integration. IEEE, 2006. http://dx.doi.org/10.1109/iri.2006.252401.

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Protasov, Maxim I., Vladimir A. Tcheverda, and Valery V. Shilikov. "Diffraction images and their attributes based on asymmetric summation: real data application." In Недропользование. Горное дело. Направления и технологии поиска, разведки и разработки месторождений полезных ископаемых. Экономика. Геоэкология. Федеральное государственное бюджетное учреждение науки Институт нефтегазовой геологии и геофизики им. А.А. Трофимука Сибирского отделения Российской академии наук, 2020. http://dx.doi.org/10.18303/b978-5-4262-0102-6-2020-062.

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The paper deals with a 3D diffraction imaging with the subsequent diffraction attribute calculation. The imaging is based on an asymmetric summation of seismic data and provides three diffraction attributes: structural diffraction attribute, point diffraction attribute, an azimuth of structural diffraction. These attributes provide differentiating fractured and cavernous objects and to determine the fractures orientations. Approbation of the approach was provided on several real data sets.
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Dong, Tingting, Shoji Nishimura, and Jianquan Liu. "Refining Image Search Results using Multiple Attributes." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005685.

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Li, Nan, and Longin Jan Latecki. "Affinity Learning for Mixed Data Clustering." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/302.

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In this paper, we propose a novel affinity learning based framework for mixed data clustering, which includes: how to process data with mixed-type attributes, how to learn affinities between data points, and how to exploit the learned affinities for clustering. In the proposed framework, each original data attribute is represented with several abstract objects defined according to the specific data type and values. Each attribute value is transformed into the initial affinities between the data point and the abstract objects of attribute. We refine these affinities and infer the unknown affinities between data points by taking into account the interconnections among the attribute values of all data points. The inferred affinities between data points can be exploited for clustering. Alternatively, the refined affinities between data points and the abstract objects of attributes can be transformed into new data features for clustering. Experimental results on many real world data sets demonstrate that the proposed framework is effective for mixed data clustering.
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Bienati, N. "Impact of Geometry of Minimum Data Sets on 3D Velocity Analysis." In EAGE/SEG Workshop - Depth Imaging of Reservoir Attributes. European Association of Geoscientists & Engineers, 1998. http://dx.doi.org/10.3997/2214-4609.201406708.

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Trabelsi, Mohamed, Brian D. Davison, and Jeff Heflin. "Improved Table Retrieval Using Multiple Context Embeddings for Attributes." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005681.

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Yo, Take, and Kazutoshi Sasahara. "Inference of personal attributes from tweets using machine learning." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258295.

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Wei, Xiaokai, Bokai Cao, Weixiang Shao, Chun-Ta Lu, and Philip S. Yu. "Community detection with partially observable links and node attributes." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840670.

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Walter, Robert A., and Leonora Katz-Rhoads. "NHTSA Vehicle Safety Attributes Data Book." In SAE International Congress and Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1987. http://dx.doi.org/10.4271/870347.

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Reports on the topic "Attributes data"

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Byrne, John C., and Albert R. Stage. A data structure for describing sampling designs to aid in compilation of stand attributes. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station, 1988. http://dx.doi.org/10.2737/int-gtr-247.

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Olson, Jon E., Larry W. Lake, and Steve E. Laubach. Advanced technology for predicting the fluid flow attributes of naturally fractured reservoirs from quantitative geological data and modeling. Office of Scientific and Technical Information (OSTI), November 2004. http://dx.doi.org/10.2172/838717.

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Jon E. Olson, Larry W. Lake, and Steve E. Laubach. ADVANCED TECHNOLOGY FOR PREDICTING THE FLUID FLOW ATTRIBUTES OF NATURALLY FRACTURED RESERVOIRS FROM QUANTITATIVE GEOLOGIC DATA AND MODELING. Office of Scientific and Technical Information (OSTI), April 2003. http://dx.doi.org/10.2172/820623.

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Olson, Jon E., Larry W. Lake, and Steve E. Laubach. Advanced technology for predicting the fluid flow attributes of naturally fractured reservoirs from quantitative geological data and modeling. Office of Scientific and Technical Information (OSTI), May 2003. http://dx.doi.org/10.2172/820624.

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Loftis, Charles, Tennyson Chen, and Jonathan Cirella. Attribute-level encryption of data in public android databases. Research Triangle Park, NC: RTI Press, September 2013. http://dx.doi.org/10.3768/rtipress.2013.op.0016.1309.

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Sither, Mark A. Spatial Data Transformation: Feature Attribute Conversion Issues and Practical Experience. Fort Belvoir, VA: Defense Technical Information Center, March 1991. http://dx.doi.org/10.21236/ada254829.

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Sither, Mark A. Spatial Data Transformation: Feature Attribute Conversion Issues and Practical Experience. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada254238.

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Arbeit, Caren A., Alexander Bentz, Emily Forrest Cataldi, and Herschel Sanders. Alternative and Independent: The universe of technology-related “bootcamps". RTI Press, February 2019. http://dx.doi.org/10.3768/rtipress.2019.rr.0033.1902.

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In recent years, nontraditional workforce training programs have proliferated inside and outside of traditional postsecondary institutions. A subset of these programs, bootcamps, advertise high job placement rates and have been hailed by policymakers as key to training skilled workers. However, few formal data exist on the number, types, prices, location, or other descriptive details of program offerings. We fill this void by studying the universe of bootcamp programs offered as of June 30, 2017. In this report, we discuss the attributes of the 1,010 technology-related programs offered in the United States, Canada, and online. We find more diversity among bootcamp providers and programs than would be expected from public discourse. This primarily relates to the mode of delivery (online vs. in person), intensity (part time/full time), cost, and program types. Based on the data we collected, we present a classification structure for bootcamps focused on five distinct program types.
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Ferraiolo, David F., Ramaswamy Chandramouli, Vincent C. Hu, and D. Richard R. Kuhn. A Comparison of Attribute Based Access Control (ABAC) Standards for Data Service Applications. National Institute of Standards and Technology, October 2016. http://dx.doi.org/10.6028/nist.sp.800-178.

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Cafferata, Fernando G., Bridget Lynn Hoffmann, and Carlos Scartascini. How Can We Improve Air Pollution?: Try Increasing Trust First. Inter-American Development Bank, August 2021. http://dx.doi.org/10.18235/0003453.

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Environmental policies are characterized by salient short-term costs and long-term benefits that are difficult to observe and to attribute to the government's efforts. These characteristics imply that citizens' support for environmental policies is highly dependent on their trust in the government's capability to implement solutions and commitment to investments in those policies. Using novel survey data from Mexico City, we show that trust in the government is positively correlated with citizens' willingness to support an additional tax approximately equal to a days minimum wage to improve air quality and greater preference for government retention of revenues from fees collected from polluting firms. We find similar correlations using the perceived quality of public goods as a measure of government competence. These results provide evidence that mistrust can be an obstacle to better environmental outcomes.
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