Monitoreo de Cadena de Bloques con Aprendizaje Automático

Etherum se ejecuta en redes de cadena de Bloques (Blockchain) a través de código personalizado, facilitando su implementación en diferentes áreas. Aprovecha la inmutabilidad y trazabilidad para aprobar transacciones independientes de una entidad central. Sin embargo, se han identificado problemas tr...

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Autores:
Giraldo Mejía, Juan Camilo
Grisales Zuluaga, Julián
Vargas Agudelo, Fabio Alberto
Reyes Gamboa, Adriana Xiomara
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Tecnológico de Antioquia
Repositorio:
Repositorio Tdea
Idioma:
spa
OAI Identifier:
oai:dspace.tdea.edu.co:tdea/3803
Acceso en línea:
https://dspace.tdea.edu.co/handle/tdea/3803
Palabra clave:
Rendimiento
Rendement
Yields
Rendimento
Aprendizaje automático
Apprentissage machine
Machine learning
Aprendizagem electrónica
Cadena de Bloques
Blockchain
Chaine de blocs
Ethereum
Métricas
Metrics
Cuello de Botella
Bottleneck
Rights
openAccess
License
https://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.none.fl_str_mv Monitoreo de Cadena de Bloques con Aprendizaje Automático
dc.title.translated.none.fl_str_mv Blockchain Monitoring with Machine Learning
title Monitoreo de Cadena de Bloques con Aprendizaje Automático
spellingShingle Monitoreo de Cadena de Bloques con Aprendizaje Automático
Rendimiento
Rendement
Yields
Rendimento
Aprendizaje automático
Apprentissage machine
Machine learning
Aprendizagem electrónica
Cadena de Bloques
Blockchain
Chaine de blocs
Ethereum
Métricas
Metrics
Cuello de Botella
Bottleneck
title_short Monitoreo de Cadena de Bloques con Aprendizaje Automático
title_full Monitoreo de Cadena de Bloques con Aprendizaje Automático
title_fullStr Monitoreo de Cadena de Bloques con Aprendizaje Automático
title_full_unstemmed Monitoreo de Cadena de Bloques con Aprendizaje Automático
title_sort Monitoreo de Cadena de Bloques con Aprendizaje Automático
dc.creator.fl_str_mv Giraldo Mejía, Juan Camilo
Grisales Zuluaga, Julián
Vargas Agudelo, Fabio Alberto
Reyes Gamboa, Adriana Xiomara
dc.contributor.author.none.fl_str_mv Giraldo Mejía, Juan Camilo
Grisales Zuluaga, Julián
Vargas Agudelo, Fabio Alberto
Reyes Gamboa, Adriana Xiomara
dc.subject.agrovoc.none.fl_str_mv Rendimiento
Rendement
Yields
Rendimento
Aprendizaje automático
Apprentissage machine
Machine learning
Aprendizagem electrónica
topic Rendimiento
Rendement
Yields
Rendimento
Aprendizaje automático
Apprentissage machine
Machine learning
Aprendizagem electrónica
Cadena de Bloques
Blockchain
Chaine de blocs
Ethereum
Métricas
Metrics
Cuello de Botella
Bottleneck
dc.subject.decs.none.fl_str_mv Cadena de Bloques
Blockchain
Chaine de blocs
dc.subject.proposal.none.fl_str_mv Ethereum
Métricas
Metrics
Cuello de Botella
Bottleneck
description Etherum se ejecuta en redes de cadena de Bloques (Blockchain) a través de código personalizado, facilitando su implementación en diferentes áreas. Aprovecha la inmutabilidad y trazabilidad para aprobar transacciones independientes de una entidad central. Sin embargo, se han identificado problemas transaccionales como cuellos de botella. Este artículo presenta un modelo de desempeño de transacciones con Aprendizaje Automático, que analiza métricas en tiempo real, detectando anomalías. El modelo se probó con una secuencia de transacciones ideales y adversas determinando su efectividad. Palabras-clave: Cadena de Bloques, Ethereum, Rendimiento, Aprendizaje Automático, Métricas, Cuello de Botella.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-08-22T00:53:05Z
dc.date.available.none.fl_str_mv 2023-08-22T00:53:05Z
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dc.type.spa.fl_str_mv Artículo de revista
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dc.relation.references.spa.fl_str_mv Aoki, Y., Otsuki, K., Kaneko, T., Banno, R., & Shudo, K. (2019, April). Simblock: A blockchain network simulator. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 325329). IEEE
Bandara, E., Ng, W. K., De Zoysa, K., Fernando, N., Tharaka, S., Maurakirinathan, P., & Jayasuriya, N. (2018, December). Mystiko—blockchain meets big data. In 2018 IEEE international conference on big data (big data) (pp. 30243032). IEEE.
Chan, W., & Olmsted, A. (2017, December). Ethereum transaction graph analysis. In 2017 12th international conference for internet technology and secured transactions (ICITST) (pp. 498-500). IEEE
Deshpande, A. (2018). “Design and Implementation of an Ethereum-like Blockchain Simulation Framework,”.
Faria, C., & Correia, M. (2019, July). BlockSim: blockchain simulator. In 2019 IEEE International Conference on Blockchain (Blockchain) (pp. 439-446). IEEE.
Jepkemei, B., & Kipkebut, A. (2019). Blockchain-a disruptive technology in financial assets. IRE Journals, 2(9), 38-47
Leppelsack, H. F., Kinkelin, H., Liebald, S., & Geyer, F. (2018). Experimental performance evaluation of private distributed ledger implementations. Experimentelle Performanz Evaluierung von privaten Distributed Ledger Implementierungen), Master’s thesis in Informatics, Technical University of Munich
McConaghy, T., Marques, R., Müller, A., De Jonghe, D., McConaghy, T., McMullen, G., ... & Granzotto, A. (2016). Bigchaindb: a scalable blockchain database. white paper, BigChainDB
Mechkaroska, D., Dimitrova, V., & Popovska-Mitrovikj, A. (2018, November). Analysis of the possibilities for improvement of blockchain technology. In 2018 26th Telecommunications Forum (TELFOR) (pp. 1-4). IEEE
Memon, R. A., Li, J. P., & Ahmed, J. (2019). Simulation model for blockchain systems using queuing theory. Electronics, 8(2), 234.
Mingxiao, D., Xiaofeng, M., Zhe, Z., Xiangwei, W., & Qijun, C. (2017, October). A review on consensus algorithm of blockchain. In 2017 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 2567-2572). IEEE.
Schüssler, F., Nasirifard, P., & Jacobsen, H. A. (2018, December). Attack and vulnerability simulation framework for bitcoin-like blockchain technologies. In Proceedings of the 19th International Middleware Conference (Posters) (pp. 5-6).
Xiwei, X., Qinghua, L., Yue, L., Liming, Z., & Haonan, Y. (2019). Vasilakos Athanasios V. 2019. Designing blockchain-based applications a case study for imported product traceability. Fut. Gener. Comput. Syst, 92, 399-406.
Yasaweerasinghelage, R., Staples, M., & Weber, I. (2017, April). Predicting latency of blockchain-based systems using architectural modelling and simulation. In 2017 IEEE International Conference on Software Architecture (ICSA) (pp. 253-256). IEEE.
Zheng, P., Zheng, Z., Luo, X., Chen, X., & Liu, X. (2018, May). A detailed and realtime performance monitoring framework for blockchain systems. In 2018 IEEE/ACM 40th International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP) (pp. 134-143). IEEE.
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spelling Giraldo Mejía, Juan Camilo02954ed8-c1c7-4449-a226-011845bc4f81Grisales Zuluaga, Julián7a177ac8-193f-4335-a1a0-5d7e42e4f998Vargas Agudelo, Fabio Alberto430096c5-fb9c-4699-be17-5ff6154470edReyes Gamboa, Adriana Xiomarae315677b-7b18-456b-83c7-e84c1c98ec0f2023-08-22T00:53:05Z2023-08-22T00:53:05Z20231646-9895https://dspace.tdea.edu.co/handle/tdea/38032183-0126Etherum se ejecuta en redes de cadena de Bloques (Blockchain) a través de código personalizado, facilitando su implementación en diferentes áreas. Aprovecha la inmutabilidad y trazabilidad para aprobar transacciones independientes de una entidad central. Sin embargo, se han identificado problemas transaccionales como cuellos de botella. Este artículo presenta un modelo de desempeño de transacciones con Aprendizaje Automático, que analiza métricas en tiempo real, detectando anomalías. El modelo se probó con una secuencia de transacciones ideales y adversas determinando su efectividad. Palabras-clave: Cadena de Bloques, Ethereum, Rendimiento, Aprendizaje Automático, Métricas, Cuello de Botella.Etherum runs on Blockchain networks through custom code, facilitating its implementation in different areas. It leverages immutability and traceability to approve transactions independent of a central entity. However, transactional problems have been identified as bottlenecks. This paper presents a transaction performance model with Machine Learning, which analyzes metrics in real time, detecting anomalies. The model was tested with a sequence of ideal and adverse transactions to determine its effectiveness. Keywords: Blockchain, Ethereum, Performance, Machine Learning, Metrics, Bottleneck.17 páginasapplication/pdfspaUniversidade de CoimbraPortugalhttps://creativecommons.org/licenses/by-nc-nd/4.0/Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2https://www.proquest.com/openview/79f1543b41abfbd2df1b304e675bdc17/1?pq-origsite=gscholar&cbl=1006393Monitoreo de Cadena de Bloques con Aprendizaje AutomáticoBlockchain Monitoring with Machine LearningArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a8563756621RISTIAoki, Y., Otsuki, K., Kaneko, T., Banno, R., & Shudo, K. (2019, April). Simblock: A blockchain network simulator. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 325329). IEEEBandara, E., Ng, W. K., De Zoysa, K., Fernando, N., Tharaka, S., Maurakirinathan, P., & Jayasuriya, N. (2018, December). Mystiko—blockchain meets big data. In 2018 IEEE international conference on big data (big data) (pp. 30243032). IEEE.Chan, W., & Olmsted, A. (2017, December). Ethereum transaction graph analysis. In 2017 12th international conference for internet technology and secured transactions (ICITST) (pp. 498-500). IEEEDeshpande, A. (2018). “Design and Implementation of an Ethereum-like Blockchain Simulation Framework,”.Faria, C., & Correia, M. (2019, July). BlockSim: blockchain simulator. In 2019 IEEE International Conference on Blockchain (Blockchain) (pp. 439-446). IEEE.Jepkemei, B., & Kipkebut, A. (2019). Blockchain-a disruptive technology in financial assets. IRE Journals, 2(9), 38-47Leppelsack, H. F., Kinkelin, H., Liebald, S., & Geyer, F. (2018). Experimental performance evaluation of private distributed ledger implementations. Experimentelle Performanz Evaluierung von privaten Distributed Ledger Implementierungen), Master’s thesis in Informatics, Technical University of MunichMcConaghy, T., Marques, R., Müller, A., De Jonghe, D., McConaghy, T., McMullen, G., ... & Granzotto, A. (2016). Bigchaindb: a scalable blockchain database. white paper, BigChainDBMechkaroska, D., Dimitrova, V., & Popovska-Mitrovikj, A. (2018, November). Analysis of the possibilities for improvement of blockchain technology. In 2018 26th Telecommunications Forum (TELFOR) (pp. 1-4). IEEEMemon, R. A., Li, J. P., & Ahmed, J. (2019). Simulation model for blockchain systems using queuing theory. Electronics, 8(2), 234.Mingxiao, D., Xiaofeng, M., Zhe, Z., Xiangwei, W., & Qijun, C. (2017, October). A review on consensus algorithm of blockchain. In 2017 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 2567-2572). IEEE.Schüssler, F., Nasirifard, P., & Jacobsen, H. A. (2018, December). Attack and vulnerability simulation framework for bitcoin-like blockchain technologies. In Proceedings of the 19th International Middleware Conference (Posters) (pp. 5-6).Xiwei, X., Qinghua, L., Yue, L., Liming, Z., & Haonan, Y. (2019). Vasilakos Athanasios V. 2019. Designing blockchain-based applications a case study for imported product traceability. Fut. Gener. Comput. Syst, 92, 399-406.Yasaweerasinghelage, R., Staples, M., & Weber, I. (2017, April). Predicting latency of blockchain-based systems using architectural modelling and simulation. In 2017 IEEE International Conference on Software Architecture (ICSA) (pp. 253-256). IEEE.Zheng, P., Zheng, Z., Luo, X., Chen, X., & Liu, X. (2018, May). A detailed and realtime performance monitoring framework for blockchain systems. In 2018 IEEE/ACM 40th International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP) (pp. 134-143). IEEE.RendimientoRendementYieldsRendimentoAprendizaje automáticoApprentissage machineMachine learningAprendizagem electrónicaCadena de BloquesBlockchainChaine de blocsEthereumMétricasMetricsCuello de BotellaBottleneckTHUMBNAILMonitoreo de Cadena de Bloques con Aprendizaje Automático.pdf.jpgMonitoreo de Cadena de Bloques con Aprendizaje Automático.pdf.jpgGenerated Thumbnailimage/jpeg11033https://dspace.tdea.edu.co/bitstream/tdea/3803/4/Monitoreo%20de%20Cadena%20de%20Bloques%20con%20Aprendizaje%20Autom%c3%a1tico.pdf.jpgef98ad7c7af793926f12bf887d9e835dMD54open accessTEXTMonitoreo de Cadena de Bloques con Aprendizaje Automático.pdf.txtMonitoreo de Cadena de Bloques con Aprendizaje Automático.pdf.txtExtracted texttext/plain28116https://dspace.tdea.edu.co/bitstream/tdea/3803/3/Monitoreo%20de%20Cadena%20de%20Bloques%20con%20Aprendizaje%20Autom%c3%a1tico.pdf.txta0b95ce183e888cf172935b1b43cc85aMD53open accessLICENSElicense.txtlicense.txttext/plain; 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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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