Some Pragmatic Prevention’s Guidelines Regarding SARS-CoV-2 and COVID-19 in Latin-America Inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study: Colombia
We use an enhanced methodology combining specific forms of AI techniques, opinion mining and artificial mathematical intelligence (AMI), with public data on the spread of the coronavirus SARS-CoV-2 and the incidence of COVID-19 disease in Colombia during the first three months since the first report...
- Autores:
-
Gómez Ramírez, Danny Arlen de Jesús
Herrera Jaramillo, Yoe Alexander
Ortega Giraldo, Johana C.
Ardila García, Alex M.
- 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/3956
- Acceso en línea:
- https://dspace.tdea.edu.co/handle/tdea/3956
- Palabra clave:
- SARS-CoV-2
COVID-19
Inteligencia Artificial
Artificial intelligence
Inteligência Artificial
Intelligence artificielle
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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Some Pragmatic Prevention’s Guidelines Regarding SARS-CoV-2 and COVID-19 in Latin-America Inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study: Colombia |
| title |
Some Pragmatic Prevention’s Guidelines Regarding SARS-CoV-2 and COVID-19 in Latin-America Inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study: Colombia |
| spellingShingle |
Some Pragmatic Prevention’s Guidelines Regarding SARS-CoV-2 and COVID-19 in Latin-America Inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study: Colombia SARS-CoV-2 COVID-19 Inteligencia Artificial Artificial intelligence Inteligência Artificial Intelligence artificielle |
| title_short |
Some Pragmatic Prevention’s Guidelines Regarding SARS-CoV-2 and COVID-19 in Latin-America Inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study: Colombia |
| title_full |
Some Pragmatic Prevention’s Guidelines Regarding SARS-CoV-2 and COVID-19 in Latin-America Inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study: Colombia |
| title_fullStr |
Some Pragmatic Prevention’s Guidelines Regarding SARS-CoV-2 and COVID-19 in Latin-America Inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study: Colombia |
| title_full_unstemmed |
Some Pragmatic Prevention’s Guidelines Regarding SARS-CoV-2 and COVID-19 in Latin-America Inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study: Colombia |
| title_sort |
Some Pragmatic Prevention’s Guidelines Regarding SARS-CoV-2 and COVID-19 in Latin-America Inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study: Colombia |
| dc.creator.fl_str_mv |
Gómez Ramírez, Danny Arlen de Jesús Herrera Jaramillo, Yoe Alexander Ortega Giraldo, Johana C. Ardila García, Alex M. |
| dc.contributor.author.none.fl_str_mv |
Gómez Ramírez, Danny Arlen de Jesús Herrera Jaramillo, Yoe Alexander Ortega Giraldo, Johana C. Ardila García, Alex M. |
| dc.subject.decs.none.fl_str_mv |
SARS-CoV-2 COVID-19 Inteligencia Artificial Artificial intelligence Inteligência Artificial Intelligence artificielle |
| topic |
SARS-CoV-2 COVID-19 Inteligencia Artificial Artificial intelligence Inteligência Artificial Intelligence artificielle |
| description |
We use an enhanced methodology combining specific forms of AI techniques, opinion mining and artificial mathematical intelligence (AMI), with public data on the spread of the coronavirus SARS-CoV-2 and the incidence of COVID-19 disease in Colombia during the first three months since the first reported positive case. The results obtained, together with conceptual tools coming from the global taxonomy of fundamental cognitive mechanisms emerging in AMI and with suitable contextual information from Colombian public health and mainstream social media, allowed us to stating specific preventive guidelines for a better restructuring of initial safe and stable life conditions in Colombia, and in an extended manner in similar Latin American Countries. More specifically, we describe three major guidelines: (1) regular creative visualization and effective planning, (2) the continuous use of constructive linguistic frameworks, and (3) frequent and moderate use of kinesthetic routines. They should be understood as effective tools from a cognitive and behavioural perspective, rather than from a biological one. Even more, the first two guidelines should be acknowledged in integral cooperation with the third one regarding the global effect of COVID-19 in human beings as a whole, this includes the mind and the body. |
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2021 |
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2021 |
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2023-10-12T20:57:59Z |
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2023-10-12T20:57:59Z |
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Capítulo - Parte de Libro |
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http://purl.org/coar/resource_type/c_3248 |
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978-3-030-68654-3 |
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eng |
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541 |
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519 |
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Technological and Industrial Applications Associated with Intelligent Logistics |
| dc.relation.references.spa.fl_str_mv |
Alimadadi A, Aryal S, Manandhar I, Munroe PB, Joe B, Cheng X (2020) Artif Intell Mach Learn GHT covid-19 Bullock J, Pham KH, Lam CSN, Luengo-Oroz M et al (2020) Mapping the landscape of artificial intelligence applications against covid-19. arXiv:2003.11336 Ciolac EG (2012) High-intensity interval training and hypertension: maximizing the bene ts of exercise? Am J Cardiovasc Dis 2(2):102 Fauconnier G, Turner M (2003) The way we think. Basic Books Gawain S (2016) Creative visualization-: use the power of your imagination to create what you want in your life. New World Library Gómez Ramírez DAJ (2020) Artificial mathematical intelligence: cognitive, (meta)mathematical, physical and philosophical foundations. Springer International Publishing Guo Y-R, Cao Q-D, Hong Z-S, Tan Y-Y, Chen S-D, Jin H-J, Tan K-S, Wang D-Y, Yan Y (2020) The origin, transmission and clinical therapies on coronavirus disease 2019 (covid-19) outbreak – an update on the status. Military Med Res 7(1):1–10 Hand DJ, Adams NM (2014) Data mining. Wiley StatsRef: Statistics Reference Online, pp 1–7 Hota S, Pathak S (2018) KNN classifier based approach for multi-class sentiment analysis of twitter data. Int J Eng Technol 7(3):1372–1375. https://doi.org/10.14419/ijet.v7i3.12656. ISSN 2227-524X Hu B, Ge X, Wang L-F, Shi Z (2015) Bat origin of human coronaviruses. Virol J 12(1):221 Johnson J (1985) Exercise, aging and health. Occup Health Nurs 33(3):137–151 Laiton-Donato K, Villabona Arenas CJ, Usme Ciro JA, Franco Munoz C, Alvarez-Diaz DA, Villabona-Arenas LS, Echeverria-Londono S, Franco-Sierra ND, Cucunuba ZM, Florez-Sanchez AC, Ferro C, Ajami NJ, Walteros DM, Prieto-Alvarado FE, Duran-Camacho CA, Ospina-Martinez ML, Mercado-Reyes MM (2020) Genomic epidemiology of sars-cov-2 in colombia. https://doi.org/10.1101/2020.06.26.20135715 Lako G, Johnson M (1980) The metaphorical structure of the human conceptual system. Cogn Sci 4(2):195–208, Le B, Nguyen H (2015) Twitter sentiment analysis using machine learning techniques. In Le Thi HA, Nguyen NT, Do TV (eds) advanced computational methods for knowledge engineering. Springer International Publishing, Cham, pp 279–289. ISBN 978-3-319-17996-4 Lee SY (2019) Document vectorization method using network information of words. PLOS ONE 14(7):1–13. https://doi.org/10.1371/jour-nal.pone.0219389 Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135. ISSN 1554-0669. https://doi.org/10.1561/1500000011 Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, Barnaby DP, Becker LB, Chelico JD, Cohen SL et al (2020) Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with covid-19 in the New York City area. Jama Rodriguez-Morales AJ, Gallego V, Escalera-Antezana JP, Mendez CA, Zambrano LI, Franco-Paredes C, Suarez JA, Rodriguez-Enciso HD, Balbin-Ramon GJ, Savio-Larriera E et al (2020) Covid-19 in latin america: the implications of the first confirmed case in brazil. Travel Med Infect Dis Ross A, Thomas S (2010) The health benefits of yoga and exercise: a review of comparison studies. J Altern Complement Med 16(1):3–12 Sailunaz K, Alhajj R (2019) Emotion and sentiment analysis from twitter text. J Comput Sci 36:101003. ISSN 1877-7503. https://doi.org/10.1016/j.jocs.2019.05.009 Shereen MA, Khan S, Kazmi A, Bashir N, Siddique R (2020) Covid-19 infection: origin, transmission, and characteristics of human coron-aviruses. J Adv Res Simbana-Rivera K, Gomez-Barreno L, Guerrero J, Simbana-Guaycha F, Fernandez R, Lopez-Cortes A, Lister A, Ortiz-Prado E (2020) Interim analysis of pandemic coronavirus disease 2019 (covid-19) and the sars-cov-2 virus in latin america and the caribbean: morbidity, mortality and molecular testing trends in the region. medRxiv Sparkes AC, Smith B (2013) Qualitative research methods in sport, exercise and health: from process to product. Routledge Vaishya R, Javaid M, Khan IH, Haleem A (2020) Artificial intelligence (ai) applications for covid-19 pandemic. Diabetes Metab Syndr: Clin Res & Rev Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, Ji R, Wang H, Wang Y, Zhou Y (2020) Prevalence of comorbidities and its effects in patients infected with sars-cov-2: a systematic review and meta-analysis. Int J Infect Dis 94:91–95 Yaqub U, Sharma N, Pabreja R, Chun SA, Atluri V, Vaidya J (2018) Analysis and visualization of subjectivity and polarity of twitter location data. In: Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, dg.o ’18, New York, NY, USA. Association for Computing Ma-chinery. ISBN 9781450365260. https://doi.org/10.1145/3209281.3209313 Ye Z-W, Yuan S, Yuen K-S, Fung S-Y, Chan C-P, Jin D-Y (2020) Zoonotic origins of human coronaviruses. Int J Biol Sci 16(10):1686 |
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Gómez Ramírez, Danny Arlen de Jesús8972dd0d-c727-41e1-8a42-c071934e1eccHerrera Jaramillo, Yoe Alexanderae82ab16-3913-4702-babe-4cc669e57d46Ortega Giraldo, Johana C.e9791eaf-f691-416d-bd0a-4fbf8f837b49Ardila García, Alex M.fd22a85a-6ccd-4614-bbc1-9228b9b6a1982023-10-12T20:57:59Z2023-10-12T20:57:59Z2021978-3-030-68654-3https://dspace.tdea.edu.co/handle/tdea/3956978-3-030-68655-0We use an enhanced methodology combining specific forms of AI techniques, opinion mining and artificial mathematical intelligence (AMI), with public data on the spread of the coronavirus SARS-CoV-2 and the incidence of COVID-19 disease in Colombia during the first three months since the first reported positive case. The results obtained, together with conceptual tools coming from the global taxonomy of fundamental cognitive mechanisms emerging in AMI and with suitable contextual information from Colombian public health and mainstream social media, allowed us to stating specific preventive guidelines for a better restructuring of initial safe and stable life conditions in Colombia, and in an extended manner in similar Latin American Countries. More specifically, we describe three major guidelines: (1) regular creative visualization and effective planning, (2) the continuous use of constructive linguistic frameworks, and (3) frequent and moderate use of kinesthetic routines. They should be understood as effective tools from a cognitive and behavioural perspective, rather than from a biological one. Even more, the first two guidelines should be acknowledged in integral cooperation with the third one regarding the global effect of COVID-19 in human beings as a whole, this includes the mind and the body.23 páginasimage/jpegengSpringerSuizahttps://link.springer.com/chapter/10.1007/978-3-030-68655-0_26Some Pragmatic Prevention’s Guidelines Regarding SARS-CoV-2 and COVID-19 in Latin-America Inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study: ColombiaCapítulo - 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_970fb48d4fbd8a85Colombia541519Technological and Industrial Applications Associated with Intelligent LogisticsAlimadadi A, Aryal S, Manandhar I, Munroe PB, Joe B, Cheng X (2020) Artif Intell Mach Learn GHT covid-19Bullock J, Pham KH, Lam CSN, Luengo-Oroz M et al (2020) Mapping the landscape of artificial intelligence applications against covid-19. arXiv:2003.11336Ciolac EG (2012) High-intensity interval training and hypertension: maximizing the bene ts of exercise? Am J Cardiovasc Dis 2(2):102Fauconnier G, Turner M (2003) The way we think. Basic BooksGawain S (2016) Creative visualization-: use the power of your imagination to create what you want in your life. New World LibraryGómez Ramírez DAJ (2020) Artificial mathematical intelligence: cognitive, (meta)mathematical, physical and philosophical foundations. Springer International PublishingGuo Y-R, Cao Q-D, Hong Z-S, Tan Y-Y, Chen S-D, Jin H-J, Tan K-S, Wang D-Y, Yan Y (2020) The origin, transmission and clinical therapies on coronavirus disease 2019 (covid-19) outbreak – an update on the status. Military Med Res 7(1):1–10Hand DJ, Adams NM (2014) Data mining. Wiley StatsRef: Statistics Reference Online, pp 1–7Hota S, Pathak S (2018) KNN classifier based approach for multi-class sentiment analysis of twitter data. Int J Eng Technol 7(3):1372–1375. https://doi.org/10.14419/ijet.v7i3.12656. ISSN 2227-524XHu B, Ge X, Wang L-F, Shi Z (2015) Bat origin of human coronaviruses. Virol J 12(1):221Johnson J (1985) Exercise, aging and health. Occup Health Nurs 33(3):137–151Laiton-Donato K, Villabona Arenas CJ, Usme Ciro JA, Franco Munoz C, Alvarez-Diaz DA, Villabona-Arenas LS, Echeverria-Londono S, Franco-Sierra ND, Cucunuba ZM, Florez-Sanchez AC, Ferro C, Ajami NJ, Walteros DM, Prieto-Alvarado FE, Duran-Camacho CA, Ospina-Martinez ML, Mercado-Reyes MM (2020) Genomic epidemiology of sars-cov-2 in colombia. https://doi.org/10.1101/2020.06.26.20135715Lako G, Johnson M (1980) The metaphorical structure of the human conceptual system. Cogn Sci 4(2):195–208,Le B, Nguyen H (2015) Twitter sentiment analysis using machine learning techniques. In Le Thi HA, Nguyen NT, Do TV (eds) advanced computational methods for knowledge engineering. Springer International Publishing, Cham, pp 279–289. ISBN 978-3-319-17996-4Lee SY (2019) Document vectorization method using network information of words. PLOS ONE 14(7):1–13. https://doi.org/10.1371/jour-nal.pone.0219389Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135. ISSN 1554-0669. https://doi.org/10.1561/1500000011Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, Barnaby DP, Becker LB, Chelico JD, Cohen SL et al (2020) Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with covid-19 in the New York City area. JamaRodriguez-Morales AJ, Gallego V, Escalera-Antezana JP, Mendez CA, Zambrano LI, Franco-Paredes C, Suarez JA, Rodriguez-Enciso HD, Balbin-Ramon GJ, Savio-Larriera E et al (2020) Covid-19 in latin america: the implications of the first confirmed case in brazil. Travel Med Infect DisRoss A, Thomas S (2010) The health benefits of yoga and exercise: a review of comparison studies. J Altern Complement Med 16(1):3–12Sailunaz K, Alhajj R (2019) Emotion and sentiment analysis from twitter text. J Comput Sci 36:101003. ISSN 1877-7503. https://doi.org/10.1016/j.jocs.2019.05.009Shereen MA, Khan S, Kazmi A, Bashir N, Siddique R (2020) Covid-19 infection: origin, transmission, and characteristics of human coron-aviruses. J Adv ResSimbana-Rivera K, Gomez-Barreno L, Guerrero J, Simbana-Guaycha F, Fernandez R, Lopez-Cortes A, Lister A, Ortiz-Prado E (2020) Interim analysis of pandemic coronavirus disease 2019 (covid-19) and the sars-cov-2 virus in latin america and the caribbean: morbidity, mortality and molecular testing trends in the region. medRxivSparkes AC, Smith B (2013) Qualitative research methods in sport, exercise and health: from process to product. RoutledgeVaishya R, Javaid M, Khan IH, Haleem A (2020) Artificial intelligence (ai) applications for covid-19 pandemic. Diabetes Metab Syndr: Clin Res & RevYang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, Ji R, Wang H, Wang Y, Zhou Y (2020) Prevalence of comorbidities and its effects in patients infected with sars-cov-2: a systematic review and meta-analysis. Int J Infect Dis 94:91–95Yaqub U, Sharma N, Pabreja R, Chun SA, Atluri V, Vaidya J (2018) Analysis and visualization of subjectivity and polarity of twitter location data. In: Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, dg.o ’18, New York, NY, USA. Association for Computing Ma-chinery. ISBN 9781450365260. https://doi.org/10.1145/3209281.3209313Ye Z-W, Yuan S, Yuen K-S, Fung S-Y, Chan C-P, Jin D-Y (2020) Zoonotic origins of human coronaviruses. Int J Biol Sci 16(10):1686info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbSARS-CoV-2COVID-19Inteligencia ArtificialArtificial intelligenceInteligência ArtificialIntelligence artificielleTHUMBNAILSome Pragmatic Preventions Guidelines Regarding SARS-CoV-2 and COVID-19 in Latin-America Inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study_ Colombia.jpg.jpgSome Pragmatic Preventions Guidelines Regarding SARS-CoV-2 and COVID-19 in Latin-America Inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study_ Colombia.jpg.jpgGenerated Thumbnailimage/jpeg14383https://dspace.tdea.edu.co/bitstream/tdea/3956/3/Some%20Pragmatic%20Preventions%20Guidelines%20Regarding%20SARS-CoV-2%20and%20COVID-19%20in%20Latin-America%20Inspired%20by%20Mixed%20Machine%20Learning%20Techniques%20and%20Artificial%20Mathematical%20Intelligence.%20Case%20Study_%20Colombia.jpg.jpgc5fa12ad271e6aed2978ee5d0ec68a84MD53open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://dspace.tdea.edu.co/bitstream/tdea/3956/2/license.txt2f9959eaf5b71fae44bbf9ec84150c7aMD52open accessORIGINALSome Pragmatic Preventions Guidelines Regarding SARS-CoV-2 and COVID-19 in Latin-America Inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study_ Colombia.jpgSome Pragmatic Preventions Guidelines Regarding SARS-CoV-2 and COVID-19 in Latin-America Inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study_ Colombia.jpgimage/jpeg182926https://dspace.tdea.edu.co/bitstream/tdea/3956/1/Some%20Pragmatic%20Preventions%20Guidelines%20Regarding%20SARS-CoV-2%20and%20COVID-19%20in%20Latin-America%20Inspired%20by%20Mixed%20Machine%20Learning%20Techniques%20and%20Artificial%20Mathematical%20Intelligence.%20Case%20Study_%20Colombia.jpg41d8d3d0e17258385048e7df67e4e4bfMD51open accesstdea/3956oai:dspace.tdea.edu.co:tdea/39562023-10-13 03:02:05.728open 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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