Use of data mining to identify trends between variables to improve implementation of an immersive environment

Globally, the implementation of immersive environments for leaming activities have been in constant growth whch indcates that their development must improve daily. For this reason, this study identifies trends (co-occurrences) and relatiomhps between variables associated with an immersive environmen...

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Autores:
Zamora Musa, Ronald
Velez, Jeimy
Tipo de recurso:
Article of journal
Fecha de publicación:
2017
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/1810
Acceso en línea:
https://hdl.handle.net/11323/1810
https://repositorio.cuc.edu.co/
Palabra clave:
Association rules mining
Colombia
Data mining educational data mining
Immersive environment e-learning
Rights
openAccess
License
Atribución – No comercial – Compartir igual
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network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Use of data mining to identify trends between variables to improve implementation of an immersive environment
title Use of data mining to identify trends between variables to improve implementation of an immersive environment
spellingShingle Use of data mining to identify trends between variables to improve implementation of an immersive environment
Association rules mining
Colombia
Data mining educational data mining
Immersive environment e-learning
title_short Use of data mining to identify trends between variables to improve implementation of an immersive environment
title_full Use of data mining to identify trends between variables to improve implementation of an immersive environment
title_fullStr Use of data mining to identify trends between variables to improve implementation of an immersive environment
title_full_unstemmed Use of data mining to identify trends between variables to improve implementation of an immersive environment
title_sort Use of data mining to identify trends between variables to improve implementation of an immersive environment
dc.creator.fl_str_mv Zamora Musa, Ronald
Velez, Jeimy
dc.contributor.author.spa.fl_str_mv Zamora Musa, Ronald
Velez, Jeimy
dc.subject.eng.fl_str_mv Association rules mining
Colombia
Data mining educational data mining
Immersive environment e-learning
topic Association rules mining
Colombia
Data mining educational data mining
Immersive environment e-learning
description Globally, the implementation of immersive environments for leaming activities have been in constant growth whch indcates that their development must improve daily. For this reason, this study identifies trends (co-occurrences) and relatiomhps between variables associated with an immersive environment to improve its implementation. Results were found which show that a good design of information guides, organization of menus and useful instructiom generates that the users enjoy using the immersive environment for leaming and foments recommendations of use to other users.
publishDate 2017
dc.date.issued.none.fl_str_mv 2017
dc.date.accessioned.none.fl_str_mv 2018-11-24T20:32:50Z
dc.date.available.none.fl_str_mv 2018-11-24T20:32:50Z
dc.type.spa.fl_str_mv Artículo de revista
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Corporación Universidad de la Costa
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv Ahmed, A. B. E. D., & Elaraby, I. S. (2014). Data mining: A prediction for student's performance using classification method. World Journal of Computer Application and Technology, 2(2), 43-47 Angeli, C., Howard, S., Ma, J., Yang, J., & Kirschner, P. (2017). Data mining in educational technology classroom research: Can it make a contribution?. Computers & Education, 113, 226-242. Arantes, E., Stadler, A., Del Corso, J., & Catapan, A. (2016). Contribuições da educação profissional na modalidade a distância para a gestão e valorização da diversidade. Espacios, 37(22), E-1. Baker, R. S., & Siemens, G. (2014). Educational data mining and learning analytics. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (2nd ed., pp. 253e274). NY: Cambridge University Press Buja, A., & Lee, Y. S. (2001, August). Data mining criteria for tree-based regression and classification. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 27e36). ACM Chen, L. & Yang, Q. (2014). A group division method based on collaborative learning elements. In The 26th Chinese Control and Decision Conference (pp. 1701-1705). Changsha. Cho, Y. H., Yim, S. Y., & Paik, S. (2015). Physical and social presence in 3D virtual role-play for preservice teachers. The Internet and Higher Education, 25, 70–77 Comas-Gonzalez, Z., Echeverri-Ocampo, I., ZamoraMusa, R., Velez, J., Sarmiento, R. and Orellana, M. (2017). Tendencias recientes de la Educación Virtual y su fuerte conexión con los Entornos Inmersivos. Espacios, 38(15), p.4. Freire, P., Dandolini, G., De Souza, J., Trierweiller, A., Da Silva, S., & Sell, D. et al. (2016). Universidade Corporativa em Rede: Considerações Iniciais para um Novo Modelo de Educação Corporativa. Espacios, 37(5), E-5. Gunasekara, R., Wijegunasekara, M., & Dias, N. (2014). A Study on How to Improve the Perfomance of k-mean Data Mining Algorithm in a Parallel Environment. Journal Of Engineering And Applied Sciences, 9(10), 441 - 446. Kovács, P., Murray, N., Rozinaj, G., Sulema, Y., & Rybárová, R. (2015). Application of immersive technologies for education: State of the art. In 2015 International Conference on Interactive Mobile Communication Technologies and Learning (IMCL) (pp. 283 - 288). Thessaloniki. Kumar Ameta, G., & Pathak, V. (2016). A Survey on Improved Association Rule Mining for market based analysis. International Journal Of Advances In Computer Science And Technology, 5(12), 173-175. Lin, W., Alvarez, S. A., & Ruiz, C. (2002). Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery, 6(1), 83-105. Lovkesh. (2016). Enhancing E-Learning Through Data Mining in the Context of Education Data. International Conference On Computing For Sustainable Global Development (Indiacom) - IEEE, 109 - 113. Marengo, A., Pagano, A., & Barbone, A. (2013). Data mining methods to assess student behavior in adaptive e-learning processes. Fourth International Conference On E-Learning "Best Practices In Management, Design And Development Of ECourses: Standards Of Excellence And Creativity" - IEEE, 303 - 309. Maqsood, A. (2017). Study of Big Data: An Industrial Revolution Review of applications and challenges. International Journal Of Advanced Trends In Computer Science And Engineering, 6(3), 31-34. Medvedev, V., Kurasova, O., Bernatavičienė, J., Treigys, P., Marcinkevičius, V., & Dzemyda, G. (2017). A new web-based solution for modelling data mining processes. Simulation Modelling Practice And Theory, 76, 34-46. Mendoza, F., De la Hoz, A., De la Hoz, E., & Ariza, P. (2015). Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems. Journal Of Theoretical And Applied Information Technology, 82(2), 291 - 298. Merceron, A., & Yacef, K. (2010). Measuring correlation of strong symmetric assocation rules in educational data. In C. Romero, S. Ventura, M. Pechenizkiy, & R. S. J. D. Baker (Eds.), Handbook of educational data mining (pp. 245e255). Boca Raton: Taylor & Francis Group Mohajer, A., Somarin, A., Yaghoobzadeh, M., & Gudakahriz, S. (2016). A Method Based on Data Mining for Detection of Intrusion in Distributed Databases. Journal Of Engineering And Applied Sciences, 11(7), 1493 - 1501. Mustami, M., Suryadin and Suardi Wekke, I. (2017). Learning Model Combined with Mind Maps and Cooperative Strategies for Junior High School Student. Journal of Engineering and Applied Sciences, 12(7), pp.1681 - 1686. Paez, H., Zabala, V. and Zamora, R. (2017). Análisis y actualización del programa de la asignatura Automatización Industrial en la formación profesional de ingenieros electrónicos. Educación en Ingeniería, 11(21), pp.39 - 44. Peng, J., Tan, W., & Liu, G. (2015). Virtual Experiment in Distance Education: Based on 3D Virtual Learning Environment. In 2015 International Conference of Educational Innovation through Technology (EITT) (pp. 81-84). Wuhan. Pollock, C. & Biles, J. (2016). Discovering the Lived Experience of Students Learning in Immersive Simulation. Clinical Simulation in Nursing, 12(8), 313-319. Poorani, M., Nithya, P., & Umamaheshwari, B. (2014). A Method for Mining Infrequent Causal Associations with Swarm Intelligence Optimization for Finding Adverse Drug Reaction. International Journal Of Computing, Communications And Networking, 3(1), 25-32. Romero, C., & Ventura, S. (2012). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining And Knowledge Discovery, 3(1), 12-27. Tawil, N., Zaharim, A., Shaari, I., Ismail, N. and Embi, M. (2012). The Acceptance of E-Learning in Engineering Mathematics in Enhancing Engineering Education. Journal of Engineering and Applied Sciences, 7(3), pp.279-284. Udupi, P., Sharma, N., & Jha, S. (2016). Educational Data Mining and Big Data Framework for eLearning Environment. 5Th International Conference On Reliability, Infocom Technologies And Optimization (ICRITO) (Trends And Future Directions) - IEEE, 258 - 261. Zamora-Musa, R. and Villa, J. (2013). Estudio de la alternativa de ambientes virtuales colaborativos como herramienta de apoyo a laboratorios teleoperados en ingeniería. WEEF – World Engineering Education Forum. Zamora, R., Velez, J. and Villa, J. (2016). Contributions of Collaborative and Immersive Environments in Development a Remote Access Laboratory: From Point of View of Effectiveness in Learning. In: F. Mendes Neto, R. de Souza and A. Sandro Gomes, ed., Handbook of Research on 3-D Virtual Environments and Hypermedia for Ubiquitous Learning, 1st ed. Pennsylvania: IGIGlobal, pp.1-28. Zamora-Musa, R., Velez, J., Paez-Logreira, H., Coba, J., Cano-Cano, C. and Martinez, O. (2017). Implementación de un recurso educativo abierto a través del modelo del diseño universal para el aprendizaje teniendo en cuenta evaluación de competencias y las necesidades individuales de los estudiantes. Espacios, 38(5), p.3.
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spelling Zamora Musa, RonaldVelez, Jeimy2018-11-24T20:32:50Z2018-11-24T20:32:50Z20171816949Xhttps://hdl.handle.net/11323/1810Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Globally, the implementation of immersive environments for leaming activities have been in constant growth whch indcates that their development must improve daily. For this reason, this study identifies trends (co-occurrences) and relatiomhps between variables associated with an immersive environment to improve its implementation. Results were found which show that a good design of information guides, organization of menus and useful instructiom generates that the users enjoy using the immersive environment for leaming and foments recommendations of use to other users.Zamora Musa, Ronald-0000-0003-4949-4438-600Velez, Jeimy-0a3e1d27-25f6-41d7-a09d-f36f69e8d219-0engJournal Of Engineering And Applied SciencesAtribución – No comercial – Compartir igualinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Association rules miningColombiaData mining educational data miningImmersive environment e-learningUse of data mining to identify trends between variables to improve implementation of an immersive environmentArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionAhmed, A. B. E. D., & Elaraby, I. S. (2014). Data mining: A prediction for student's performance using classification method. World Journal of Computer Application and Technology, 2(2), 43-47 Angeli, C., Howard, S., Ma, J., Yang, J., & Kirschner, P. (2017). Data mining in educational technology classroom research: Can it make a contribution?. Computers & Education, 113, 226-242. Arantes, E., Stadler, A., Del Corso, J., & Catapan, A. (2016). Contribuições da educação profissional na modalidade a distância para a gestão e valorização da diversidade. Espacios, 37(22), E-1. Baker, R. S., & Siemens, G. (2014). Educational data mining and learning analytics. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (2nd ed., pp. 253e274). NY: Cambridge University Press Buja, A., & Lee, Y. S. (2001, August). Data mining criteria for tree-based regression and classification. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 27e36). ACM Chen, L. & Yang, Q. (2014). A group division method based on collaborative learning elements. In The 26th Chinese Control and Decision Conference (pp. 1701-1705). Changsha. Cho, Y. H., Yim, S. Y., & Paik, S. (2015). Physical and social presence in 3D virtual role-play for preservice teachers. The Internet and Higher Education, 25, 70–77 Comas-Gonzalez, Z., Echeverri-Ocampo, I., ZamoraMusa, R., Velez, J., Sarmiento, R. and Orellana, M. (2017). Tendencias recientes de la Educación Virtual y su fuerte conexión con los Entornos Inmersivos. Espacios, 38(15), p.4. Freire, P., Dandolini, G., De Souza, J., Trierweiller, A., Da Silva, S., & Sell, D. et al. (2016). Universidade Corporativa em Rede: Considerações Iniciais para um Novo Modelo de Educação Corporativa. Espacios, 37(5), E-5. Gunasekara, R., Wijegunasekara, M., & Dias, N. (2014). A Study on How to Improve the Perfomance of k-mean Data Mining Algorithm in a Parallel Environment. Journal Of Engineering And Applied Sciences, 9(10), 441 - 446. Kovács, P., Murray, N., Rozinaj, G., Sulema, Y., & Rybárová, R. (2015). Application of immersive technologies for education: State of the art. In 2015 International Conference on Interactive Mobile Communication Technologies and Learning (IMCL) (pp. 283 - 288). Thessaloniki. Kumar Ameta, G., & Pathak, V. (2016). A Survey on Improved Association Rule Mining for market based analysis. International Journal Of Advances In Computer Science And Technology, 5(12), 173-175. Lin, W., Alvarez, S. A., & Ruiz, C. (2002). Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery, 6(1), 83-105. Lovkesh. (2016). Enhancing E-Learning Through Data Mining in the Context of Education Data. International Conference On Computing For Sustainable Global Development (Indiacom) - IEEE, 109 - 113. Marengo, A., Pagano, A., & Barbone, A. (2013). Data mining methods to assess student behavior in adaptive e-learning processes. Fourth International Conference On E-Learning "Best Practices In Management, Design And Development Of ECourses: Standards Of Excellence And Creativity" - IEEE, 303 - 309. Maqsood, A. (2017). Study of Big Data: An Industrial Revolution Review of applications and challenges. International Journal Of Advanced Trends In Computer Science And Engineering, 6(3), 31-34. Medvedev, V., Kurasova, O., Bernatavičienė, J., Treigys, P., Marcinkevičius, V., & Dzemyda, G. (2017). A new web-based solution for modelling data mining processes. Simulation Modelling Practice And Theory, 76, 34-46. Mendoza, F., De la Hoz, A., De la Hoz, E., & Ariza, P. (2015). Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems. Journal Of Theoretical And Applied Information Technology, 82(2), 291 - 298. Merceron, A., & Yacef, K. (2010). Measuring correlation of strong symmetric assocation rules in educational data. In C. Romero, S. Ventura, M. Pechenizkiy, & R. S. J. D. Baker (Eds.), Handbook of educational data mining (pp. 245e255). Boca Raton: Taylor & Francis Group Mohajer, A., Somarin, A., Yaghoobzadeh, M., & Gudakahriz, S. (2016). A Method Based on Data Mining for Detection of Intrusion in Distributed Databases. Journal Of Engineering And Applied Sciences, 11(7), 1493 - 1501. Mustami, M., Suryadin and Suardi Wekke, I. (2017). Learning Model Combined with Mind Maps and Cooperative Strategies for Junior High School Student. Journal of Engineering and Applied Sciences, 12(7), pp.1681 - 1686. Paez, H., Zabala, V. and Zamora, R. (2017). Análisis y actualización del programa de la asignatura Automatización Industrial en la formación profesional de ingenieros electrónicos. Educación en Ingeniería, 11(21), pp.39 - 44. Peng, J., Tan, W., & Liu, G. (2015). Virtual Experiment in Distance Education: Based on 3D Virtual Learning Environment. In 2015 International Conference of Educational Innovation through Technology (EITT) (pp. 81-84). Wuhan. Pollock, C. & Biles, J. (2016). Discovering the Lived Experience of Students Learning in Immersive Simulation. Clinical Simulation in Nursing, 12(8), 313-319. Poorani, M., Nithya, P., & Umamaheshwari, B. (2014). A Method for Mining Infrequent Causal Associations with Swarm Intelligence Optimization for Finding Adverse Drug Reaction. International Journal Of Computing, Communications And Networking, 3(1), 25-32. Romero, C., & Ventura, S. (2012). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining And Knowledge Discovery, 3(1), 12-27. Tawil, N., Zaharim, A., Shaari, I., Ismail, N. and Embi, M. (2012). The Acceptance of E-Learning in Engineering Mathematics in Enhancing Engineering Education. Journal of Engineering and Applied Sciences, 7(3), pp.279-284. Udupi, P., Sharma, N., & Jha, S. (2016). Educational Data Mining and Big Data Framework for eLearning Environment. 5Th International Conference On Reliability, Infocom Technologies And Optimization (ICRITO) (Trends And Future Directions) - IEEE, 258 - 261. Zamora-Musa, R. and Villa, J. (2013). Estudio de la alternativa de ambientes virtuales colaborativos como herramienta de apoyo a laboratorios teleoperados en ingeniería. WEEF – World Engineering Education Forum. Zamora, R., Velez, J. and Villa, J. (2016). Contributions of Collaborative and Immersive Environments in Development a Remote Access Laboratory: From Point of View of Effectiveness in Learning. In: F. Mendes Neto, R. de Souza and A. Sandro Gomes, ed., Handbook of Research on 3-D Virtual Environments and Hypermedia for Ubiquitous Learning, 1st ed. Pennsylvania: IGIGlobal, pp.1-28. Zamora-Musa, R., Velez, J., Paez-Logreira, H., Coba, J., Cano-Cano, C. and Martinez, O. (2017). Implementación de un recurso educativo abierto a través del modelo del diseño universal para el aprendizaje teniendo en cuenta evaluación de competencias y las necesidades individuales de los estudiantes. Espacios, 38(5), p.3.PublicationORIGINALUse Of Data Mining To Identify.pdfUse Of Data Mining To Identify.pdfapplication/pdf182213https://repositorio.cuc.edu.co/bitstreams/f7b5f88e-5d26-49a0-b614-386447ba96e1/download408b2a2b355908c3a87e2039a9e50177MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/45b144af-74c3-447f-9d8f-d45da1985d71/download8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILUse Of Data Mining To Identify.pdf.jpgUse Of Data Mining To Identify.pdf.jpgimage/jpeg73479https://repositorio.cuc.edu.co/bitstreams/a2f72d39-3034-49cd-bf66-1dd04464a1f6/downloadc96bfb76ca0f365f302bcc6e05e68886MD54TEXTUse Of Data Mining To Identify.pdf.txtUse Of Data Mining To Identify.pdf.txttext/plain20302https://repositorio.cuc.edu.co/bitstreams/5523eb66-2791-4129-bc6c-dad6b0cd5c5e/download4f7bfc201caa279c90c9a257000ad4b4MD5511323/1810oai:repositorio.cuc.edu.co:11323/18102024-09-17 12:50:37.736open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.coTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo=

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