Semantic and Morpho-Syntactic Prevention Guidelines for COVID-19 Based on Cognitively Inspired Artificial Intelligence and Data Mining. Case Study: Europe, North America, and South America
Based on a combination of cognitively inspired methods in artificial intelligence such as artificial mathematical intelligence and data mining, we study the correlation between the COVID-19 pandemic and the sentiment analysis (qualitative ontological nature) of tweets and their linguistic patterns f...
- Autores:
-
Herrera Jaramillo, Yoe Alexander
Gómez Ramírez, Danny Arlen de Jesús
- Tipo de recurso:
- Part of book
- Fecha de publicación:
- 2021
- Institución:
- Tecnológico de Antioquia
- Repositorio:
- Repositorio Tdea
- Idioma:
- eng
- OAI Identifier:
- oai:dspace.tdea.edu.co:tdea/3957
- Acceso en línea:
- https://dspace.tdea.edu.co/handle/tdea/3957
- Palabra clave:
- Inteligencia Artificial
Artificial Intelligence
Inteligência Artificial
Intelligence artificielle
COVID-19
Minería de datos
Data mining
Mineração de dados
Fouille de données
Prevención
Prevention
Prevenção
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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Semantic and Morpho-Syntactic Prevention Guidelines for COVID-19 Based on Cognitively Inspired Artificial Intelligence and Data Mining. Case Study: Europe, North America, and South America |
| title |
Semantic and Morpho-Syntactic Prevention Guidelines for COVID-19 Based on Cognitively Inspired Artificial Intelligence and Data Mining. Case Study: Europe, North America, and South America |
| spellingShingle |
Semantic and Morpho-Syntactic Prevention Guidelines for COVID-19 Based on Cognitively Inspired Artificial Intelligence and Data Mining. Case Study: Europe, North America, and South America Inteligencia Artificial Artificial Intelligence Inteligência Artificial Intelligence artificielle COVID-19 Minería de datos Data mining Mineração de dados Fouille de données Prevención Prevention Prevenção |
| title_short |
Semantic and Morpho-Syntactic Prevention Guidelines for COVID-19 Based on Cognitively Inspired Artificial Intelligence and Data Mining. Case Study: Europe, North America, and South America |
| title_full |
Semantic and Morpho-Syntactic Prevention Guidelines for COVID-19 Based on Cognitively Inspired Artificial Intelligence and Data Mining. Case Study: Europe, North America, and South America |
| title_fullStr |
Semantic and Morpho-Syntactic Prevention Guidelines for COVID-19 Based on Cognitively Inspired Artificial Intelligence and Data Mining. Case Study: Europe, North America, and South America |
| title_full_unstemmed |
Semantic and Morpho-Syntactic Prevention Guidelines for COVID-19 Based on Cognitively Inspired Artificial Intelligence and Data Mining. Case Study: Europe, North America, and South America |
| title_sort |
Semantic and Morpho-Syntactic Prevention Guidelines for COVID-19 Based on Cognitively Inspired Artificial Intelligence and Data Mining. Case Study: Europe, North America, and South America |
| dc.creator.fl_str_mv |
Herrera Jaramillo, Yoe Alexander Gómez Ramírez, Danny Arlen de Jesús |
| dc.contributor.author.none.fl_str_mv |
Herrera Jaramillo, Yoe Alexander Gómez Ramírez, Danny Arlen de Jesús |
| dc.subject.decs.none.fl_str_mv |
Inteligencia Artificial Artificial Intelligence Inteligência Artificial Intelligence artificielle COVID-19 Minería de datos Data mining Mineração de dados Fouille de données |
| topic |
Inteligencia Artificial Artificial Intelligence Inteligência Artificial Intelligence artificielle COVID-19 Minería de datos Data mining Mineração de dados Fouille de données Prevención Prevention Prevenção |
| dc.subject.tee.none.fl_str_mv |
Prevención Prevention Prevenção |
| description |
Based on a combination of cognitively inspired methods in artificial intelligence such as artificial mathematical intelligence and data mining, we study the correlation between the COVID-19 pandemic and the sentiment analysis (qualitative ontological nature) of tweets and their linguistic patterns from the presidents and the populations of five countries from Europe (Spain and the United Kingdom), North America (The United States of America), and South America (Chile and Colombia). The results show that tweets classified as negative are the most common in all presidential tweeter accounts, except in one country, Colombia. However, tweets classified as neutral are dominant in the population tweets in each country examined. Based on the results obtained and on some of the foundational cognitive techniques of artificial mathematical intelligence, we conclude by providing COVID-19 prevention guidelines at the linguistic and cognitive levels. |
| publishDate |
2021 |
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2021 |
| dc.date.accessioned.none.fl_str_mv |
2023-10-12T22:45:15Z |
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2023-10-12T22:45:15Z |
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Capítulo - Parte de Libro |
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http://purl.org/coar/resource_type/c_3248 |
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Text |
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info:eu-repo/semantics/bookPart |
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978-3-030-69743-3 |
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978-3-030-69744-0 |
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eng |
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eng |
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Studies in Systems, Decision and Control;Volumen 358 |
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519 |
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501 |
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Investigación e Innovación en Ingeniería de Software |
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Abebe, E.C., Dejenie, T.A., Shiferaw, M.Y., Malik, T.: The newly emerged covid-19 disease: a systemic review. Virol. J. 17(1), 1–8 (2020) Dong, E., Du, H., Gardner, L.: An interactive web-based dashboard to track covid-19 in real time. Lancet Infect. Dis. (2020). https://doi.org/10.1016/S1473-3099(20)30120-1 General, W.D.: Who director-general’s opening remarks at the media briefing on covid-19-11 March 2020. World Health Organization Website (2020). https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19. (11-Mar-2020) Gomez-Ramirez, D.A.J.: Artificial Mathematical Intelligence: Cognitive, (Meta)mathemticial, Physical and Philosophical Foundations. Springer International Publishing (2020). ISBN 978-3-030-50272-0 Gómez-Ramírez, D.A.J.: Conceptual blending in mathematical creation/invention. In: Artificial Mathematical Intelligence, pp. 109–131. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_7 Gómez-Ramírez, D.A.J.: Conceptual substratum. In: Artificial Mathematical Intelligence, pp. 147–163. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_9 Gómez-Ramírez, D.A.J.: Dathematics: a meta-isomorphic version of “standard” mathematics based on proper classes. In: Artificial Mathematical Intelligence, pp. 91–105. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_6 Gómez-Ramírez, D.A.J.: Formal analogical reasoning in concrete mathematical research. In: Artificial Mathematical Intelligence, pp. 133–146. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_8 Gómez-Ramírez, D.A.J.: General considerations for the new cognitive foundations’ program. In: Artificial Mathematical Intelligence, pp. 41–52. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_3 Gómez-Ramírez, D.A.J.: Global introduction to the artificial mathematical intelligence general program. In: Artificial Mathematical Intelligence, pp. 1–17. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_1 Gómez-Ramírez, D.A.J.: (Initial) global taxonomy of the most fundamental cognitive (metamathematical) mechanisms used in mathematical creation/invention. In: Artificial Mathematical Intelligence, pp. 165–198. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_10 Gómez-Ramírez, D.A.J.: Meta-modeling of classic and modern proofs and concepts. In: Artificial Mathematical Intelligence, pp. 201–249. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_11 Gómez-Ramírez, D.A.J.: The most outstanding (future) challenges towards global ami and its plausible extensions. In: Artificial Mathematical Intelligence, pp. 251–259. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_12 Gómez-Ramírez, D.A.J.: The physical numbers. In: Artificial Mathematical Intelligence, pp. 67–89. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_5 Gómez-Ramírez, D.A.J.: Some basic technical (meta)mathematical preliminaries for cognitive metamathematics. In: Artificial Mathematical Intelligence, pp. 19–38. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_2 Gómez-Ramírez, D.A.J.: Towards the (cognitive) reality of mathematics and the mathematics of the (cognitive) reality. In: Artificial Mathematical Intelligence, pp. 53–66. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_4 Gómez-Ramírez, D.A.J., Cardona, J.P.: Una aproximación multidisciplinaria a la formación del substrato ontológico-natural local de las estructuras matemáticas. In: Logos y Filosofía: Temas y Debates Contemporáneos, pp. 112–136. Editorial Bonaventuriana (2020) Hota, S., Pathak, S.: Knn classifier based approach for multi-class sentiment analysis of twitter data. Int. J. Eng. Technol. 7(3), 1372–1375 (2018). ISSN 2227-524X. 10.14419/ijet.v7i3.12656. https://www.sciencepubco.com/index.php/ijet/article/view/12656 Le, B., Nguyen, H.: Twitter sentiment analysis using machine learning techniques. In: Le Thi, H.A., Nguyen, N.T., Do, T.V. (eds.) Advanced Computational Methods for Knowledge Engineering, pp. 279–289. Springer International Publishing, Cham (2015). ISBN 978-3-319-17996-4 Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press (2015). https://doi.org/10.1017/CBO9781139084789 Lopez-Lopez, A.F. (ed.): Logos y Filosofía: Temas y Debates Contemporáneos. Editorial Bonaventuriana (2020). ISBN 978-958-8474-95-3 Maalouf, M.: Logistic regression in data analysis: an overview. Int. J. Data Anal. Tech. Strateg. 3(3), 281 (2011). ISSN 1755-8050, 1755-8069. https://doi.org/10.1504/IJDATS.2011.041335. http://www.inderscience.com/link.php?id=41335 Roser, M., Ortiz-Ospina, E., Ritchie, H., Hasell, J.: Coronavirus pandemic (covid-19). In: Our World in Data (2020). https://ourworldindata.org/coronavirus Moore, J.P., Klasse, P.: Covid-19 vaccines: “warp speed” needs mind melds, not warped minds. J. Virol. 94(17) (2020) Richardson, S., Hirsch, J.S., Narasimhan, M., Crawford, J.M., McGinn, T., Davidson, K.W., Barnaby, D.P., Becker, L.B., Chelico, J.D., Cohen, S.L., et al.: Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with covid-19 in the New York City area. Jama (2020) Rudkowsky, E., Haselmayer, M., Wastian, M., Jenny, M., Emrich, Š., Sedlmair, M.: More than bags of words: Sentiment analysis with word embeddings. Commun. Methods Meas. 12(2–3), 140–157 (2018). https://doi.org/10.1080/19312458.2018.1455817 Ruz, G.A., Henríquez, P.A., Mascareño, A.: Sentiment analysis of twitter data during critical events through bayesian networks classifiers. Future Generat. Comput. Syst. 106, 92–104 (2020). ISSN 0167-739X. https://doi.org/10.1016/j.future.2020.01.005. http://www.sciencedirect.com/science/article/pii/S0167739X19303322 Saad, S., Saberi, B.: Sentiment analysis or opinion mining: a review. Int. J. Adv. Sci. Eng. Inf. Technol. 7(5), 1660–1666 (2017.) ISSN 2088-5334. https://doi.org/10.18517/ijaseit.7.5.2137. http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2137 Surveillances, V.: The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (covid-19)–China, 2020. China CDC Weekly 2(8), 113–122 (2020) Wadawadagi, R., Pagi, V.: Sentiment analysis with deep neural networks: comparative study and performance assessment. Artif. Intell. Rev. 53(8), 6155–6195 (2020). ISSN 1573-7462. https://doi.org/10.1007/s10462-020-09845-2 |
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Herrera Jaramillo, Yoe Alexanderae82ab16-3913-4702-babe-4cc669e57d46Gómez Ramírez, Danny Arlen de Jesús8972dd0d-c727-41e1-8a42-c071934e1eccEuropaNorte AméricaSur América2023-10-12T22:45:15Z2023-10-12T22:45:15Z2021978-3-030-69743-3https://dspace.tdea.edu.co/handle/tdea/3957978-3-030-69744-0Based on a combination of cognitively inspired methods in artificial intelligence such as artificial mathematical intelligence and data mining, we study the correlation between the COVID-19 pandemic and the sentiment analysis (qualitative ontological nature) of tweets and their linguistic patterns from the presidents and the populations of five countries from Europe (Spain and the United Kingdom), North America (The United States of America), and South America (Chile and Colombia). The results show that tweets classified as negative are the most common in all presidential tweeter accounts, except in one country, Colombia. However, tweets classified as neutral are dominant in the population tweets in each country examined. Based on the results obtained and on some of the foundational cognitive techniques of artificial mathematical intelligence, we conclude by providing COVID-19 prevention guidelines at the linguistic and cognitive levels.19 páginasimage/jpegengSpringerSuizaStudies in Systems, Decision and Control;Volumen 358519501Investigación e Innovación en Ingeniería de SoftwareAbebe, E.C., Dejenie, T.A., Shiferaw, M.Y., Malik, T.: The newly emerged covid-19 disease: a systemic review. Virol. J. 17(1), 1–8 (2020)Dong, E., Du, H., Gardner, L.: An interactive web-based dashboard to track covid-19 in real time. Lancet Infect. Dis. (2020). https://doi.org/10.1016/S1473-3099(20)30120-1General, W.D.: Who director-general’s opening remarks at the media briefing on covid-19-11 March 2020. World Health Organization Website (2020). https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19. (11-Mar-2020)Gomez-Ramirez, D.A.J.: Artificial Mathematical Intelligence: Cognitive, (Meta)mathemticial, Physical and Philosophical Foundations. Springer International Publishing (2020). ISBN 978-3-030-50272-0Gómez-Ramírez, D.A.J.: Conceptual blending in mathematical creation/invention. In: Artificial Mathematical Intelligence, pp. 109–131. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_7Gómez-Ramírez, D.A.J.: Conceptual substratum. In: Artificial Mathematical Intelligence, pp. 147–163. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_9Gómez-Ramírez, D.A.J.: Dathematics: a meta-isomorphic version of “standard” mathematics based on proper classes. In: Artificial Mathematical Intelligence, pp. 91–105. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_6Gómez-Ramírez, D.A.J.: Formal analogical reasoning in concrete mathematical research. In: Artificial Mathematical Intelligence, pp. 133–146. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_8Gómez-Ramírez, D.A.J.: General considerations for the new cognitive foundations’ program. In: Artificial Mathematical Intelligence, pp. 41–52. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_3Gómez-Ramírez, D.A.J.: Global introduction to the artificial mathematical intelligence general program. In: Artificial Mathematical Intelligence, pp. 1–17. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_1Gómez-Ramírez, D.A.J.: (Initial) global taxonomy of the most fundamental cognitive (metamathematical) mechanisms used in mathematical creation/invention. In: Artificial Mathematical Intelligence, pp. 165–198. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_10Gómez-Ramírez, D.A.J.: Meta-modeling of classic and modern proofs and concepts. In: Artificial Mathematical Intelligence, pp. 201–249. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_11Gómez-Ramírez, D.A.J.: The most outstanding (future) challenges towards global ami and its plausible extensions. In: Artificial Mathematical Intelligence, pp. 251–259. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_12Gómez-Ramírez, D.A.J.: The physical numbers. In: Artificial Mathematical Intelligence, pp. 67–89. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_5Gómez-Ramírez, D.A.J.: Some basic technical (meta)mathematical preliminaries for cognitive metamathematics. In: Artificial Mathematical Intelligence, pp. 19–38. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_2Gómez-Ramírez, D.A.J.: Towards the (cognitive) reality of mathematics and the mathematics of the (cognitive) reality. In: Artificial Mathematical Intelligence, pp. 53–66. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50273-7_4Gómez-Ramírez, D.A.J., Cardona, J.P.: Una aproximación multidisciplinaria a la formación del substrato ontológico-natural local de las estructuras matemáticas. In: Logos y Filosofía: Temas y Debates Contemporáneos, pp. 112–136. Editorial Bonaventuriana (2020)Hota, S., Pathak, S.: Knn classifier based approach for multi-class sentiment analysis of twitter data. Int. J. Eng. Technol. 7(3), 1372–1375 (2018). ISSN 2227-524X. 10.14419/ijet.v7i3.12656. https://www.sciencepubco.com/index.php/ijet/article/view/12656Le, B., Nguyen, H.: Twitter sentiment analysis using machine learning techniques. In: Le Thi, H.A., Nguyen, N.T., Do, T.V. (eds.) Advanced Computational Methods for Knowledge Engineering, pp. 279–289. Springer International Publishing, Cham (2015). ISBN 978-3-319-17996-4Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press (2015). https://doi.org/10.1017/CBO9781139084789Lopez-Lopez, A.F. (ed.): Logos y Filosofía: Temas y Debates Contemporáneos. Editorial Bonaventuriana (2020). ISBN 978-958-8474-95-3Maalouf, M.: Logistic regression in data analysis: an overview. Int. J. Data Anal. Tech. Strateg. 3(3), 281 (2011). ISSN 1755-8050, 1755-8069. https://doi.org/10.1504/IJDATS.2011.041335. http://www.inderscience.com/link.php?id=41335Roser, M., Ortiz-Ospina, E., Ritchie, H., Hasell, J.: Coronavirus pandemic (covid-19). In: Our World in Data (2020). https://ourworldindata.org/coronavirusMoore, J.P., Klasse, P.: Covid-19 vaccines: “warp speed” needs mind melds, not warped minds. J. Virol. 94(17) (2020)Richardson, S., Hirsch, J.S., Narasimhan, M., Crawford, J.M., McGinn, T., Davidson, K.W., Barnaby, D.P., Becker, L.B., Chelico, J.D., Cohen, S.L., et al.: Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with covid-19 in the New York City area. Jama (2020)Rudkowsky, E., Haselmayer, M., Wastian, M., Jenny, M., Emrich, Š., Sedlmair, M.: More than bags of words: Sentiment analysis with word embeddings. Commun. Methods Meas. 12(2–3), 140–157 (2018). https://doi.org/10.1080/19312458.2018.1455817Ruz, G.A., Henríquez, P.A., Mascareño, A.: Sentiment analysis of twitter data during critical events through bayesian networks classifiers. Future Generat. Comput. Syst. 106, 92–104 (2020). ISSN 0167-739X. https://doi.org/10.1016/j.future.2020.01.005. http://www.sciencedirect.com/science/article/pii/S0167739X19303322Saad, S., Saberi, B.: Sentiment analysis or opinion mining: a review. Int. J. Adv. Sci. Eng. Inf. Technol. 7(5), 1660–1666 (2017.) ISSN 2088-5334. https://doi.org/10.18517/ijaseit.7.5.2137. http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2137Surveillances, V.: The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (covid-19)–China, 2020. China CDC Weekly 2(8), 113–122 (2020)Wadawadagi, R., Pagi, V.: Sentiment analysis with deep neural networks: comparative study and performance assessment. Artif. Intell. Rev. 53(8), 6155–6195 (2020). ISSN 1573-7462. https://doi.org/10.1007/s10462-020-09845-2https://link.springer.com/chapter/10.1007/978-3-030-69744-0_28Semantic and Morpho-Syntactic Prevention Guidelines for COVID-19 Based on Cognitively Inspired Artificial Intelligence and Data Mining. Case Study: Europe, North America, and South AmericaCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttp://purl.org/redcol/resource_type/CAP_LIBinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbInteligencia ArtificialArtificial IntelligenceInteligência ArtificialIntelligence artificielleCOVID-19Minería de datosData miningMineração de dadosFouille de donnéesPrevenciónPreventionPrevençãoTHUMBNAILSemantic and Morpho-Syntactic Prevention Guidelines for COVID-19 Based on Cognitively Inspired Artificial Intelligence and Data Mining. Case Study_ Europe, North America, and South America.jpg.jpgSemantic and Morpho-Syntactic Prevention Guidelines for COVID-19 Based on Cognitively Inspired Artificial Intelligence and Data Mining. 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Case Study_ Europe, North America, and South America.jpgDatos del documentoimage/jpeg185024https://dspace.tdea.edu.co/bitstream/tdea/3957/1/Semantic%20and%20Morpho-Syntactic%20Prevention%20Guidelines%20for%20COVID-19%20Based%20on%20Cognitively%20Inspired%20Artificial%20Intelligence%20and%20Data%20Mining.%20Case%20Study_%20Europe%2c%20North%20America%2c%20and%20South%20America.jpg92956f473fc97955d0d8f9aa92b5b0deMD51open accesstdea/3957oai:dspace.tdea.edu.co:tdea/39572023-10-13 03:02:35.973open accessRepositorio Institucional Tecnologico de 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 incorporada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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