Automatic Learning for Commercial Registration Renewal—The Case of Cámara de Comercio of Barranquilla-Colombia
This article shows the implementation of a prediction model of the payment behavior of the renewal concept of companies registered in the commercial registry of the Barranquilla Chamber of Commerce using machine learning techniques in a multilevel classification scenario, where it will offer the org...
- Autores:
-
Ríos-Henao, Cristian
Ariza Colpas, Paola Patricia
De-La-Hoz-Franco, Emiro
aziz, shariq
Piñeres Melo, Marlon Alberto
Perez Coronell, Leidy
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/10154
- Acceso en línea:
- https://hdl.handle.net/11323/10154
https://repositorio.cuc.edu.co/
- Palabra clave:
- Behavioral sciences
Finance
Companies
Registers
Law
Classification algorithms
Optimization
- Rights
- embargoedAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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Automatic Learning for Commercial Registration Renewal—The Case of Cámara de Comercio of Barranquilla-Colombia |
| title |
Automatic Learning for Commercial Registration Renewal—The Case of Cámara de Comercio of Barranquilla-Colombia |
| spellingShingle |
Automatic Learning for Commercial Registration Renewal—The Case of Cámara de Comercio of Barranquilla-Colombia Behavioral sciences Finance Companies Registers Law Classification algorithms Optimization |
| title_short |
Automatic Learning for Commercial Registration Renewal—The Case of Cámara de Comercio of Barranquilla-Colombia |
| title_full |
Automatic Learning for Commercial Registration Renewal—The Case of Cámara de Comercio of Barranquilla-Colombia |
| title_fullStr |
Automatic Learning for Commercial Registration Renewal—The Case of Cámara de Comercio of Barranquilla-Colombia |
| title_full_unstemmed |
Automatic Learning for Commercial Registration Renewal—The Case of Cámara de Comercio of Barranquilla-Colombia |
| title_sort |
Automatic Learning for Commercial Registration Renewal—The Case of Cámara de Comercio of Barranquilla-Colombia |
| dc.creator.fl_str_mv |
Ríos-Henao, Cristian Ariza Colpas, Paola Patricia De-La-Hoz-Franco, Emiro aziz, shariq Piñeres Melo, Marlon Alberto Perez Coronell, Leidy |
| dc.contributor.author.none.fl_str_mv |
Ríos-Henao, Cristian Ariza Colpas, Paola Patricia De-La-Hoz-Franco, Emiro aziz, shariq Piñeres Melo, Marlon Alberto Perez Coronell, Leidy |
| dc.subject.proposal.eng.fl_str_mv |
Behavioral sciences Finance Companies Registers Law Classification algorithms Optimization |
| topic |
Behavioral sciences Finance Companies Registers Law Classification algorithms Optimization |
| description |
This article shows the implementation of a prediction model of the payment behavior of the renewal concept of companies registered in the commercial registry of the Barranquilla Chamber of Commerce using machine learning techniques in a multilevel classification scenario, where it will offer the organization a tool that allows it to know in advance the behavior of the payment of the renewal of a company in such a way that it is able to design strategies to increase the success indicators in terms of the number of registration renewals, mercantile, and of the income collected for this concept. |
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2022 |
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2022-10-20 |
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2023-05-19T22:52:02Z |
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2023-05-19T22:52:02Z 2024-10-20 |
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C. Rios-Henao, P. P. Ariza-Colpas, E. De-la-Hoz-Franco, S. B. Aziz, M. A. P. Melo and L. Perez-Coronell, "Automatic Learning for Commercial Registration Renewal—The Case of Camara de Comercio of Barranquilla-Colombia," in IEEE Engineering Management Review, vol. 51, no. 1, pp. 26-40, 1 Firstquarter,march 2023, doi: 10.1109/EMR.2022.3216200. |
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0360-8581 |
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https://hdl.handle.net/11323/10154 |
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10.1109/EMR.2022.3216200 |
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1937-4178 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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C. Rios-Henao, P. P. Ariza-Colpas, E. De-la-Hoz-Franco, S. B. Aziz, M. A. P. Melo and L. Perez-Coronell, "Automatic Learning for Commercial Registration Renewal—The Case of Camara de Comercio of Barranquilla-Colombia," in IEEE Engineering Management Review, vol. 51, no. 1, pp. 26-40, 1 Firstquarter,march 2023, doi: 10.1109/EMR.2022.3216200. 0360-8581 10.1109/EMR.2022.3216200 1937-4178 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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1. "Funciones de las Cámaras de Comercio", Dec. 2021. 2. "Ley 28 de 1931". 3. "Decreto 410 de1971", 1971. 4. "Informe de Gestión 2015", 2015. 5. "Informe de Gestión 2016", 2016. 6. "Informe de Labores 2017", 2017. 7. "Informe de Gestión 2018", 2018. 8. M. Jupri and R. Sarno, "Taxpayer compliance classification using C4.5 SVM KNN Naive Bayes and MLP", Proceeding of the International Conference on Information and Communications Technology, pp. 297-303, 2018. 9. G. Warner et al., "Modeling tax evasion with genetic algorithms", Economics of Governance, vol. 16, no. 2, pp. 165-178, May 2015. 10. E. Rahimikia, S. Mohammadi, T. Rahmani and M. Ghazanfari, "Detecting corporate tax evasion using a hybrid intelligent system: A case study of Iran", International Journal of Accounting Information Systems, vol. 25, pp. 1-17, 2017. 11. W. Didimo, L. Giamminonni, G. Liotta, F. Montecchiani and D. Pagliuca, "A visual analytics system to support tax evasion discovery", Decision Support Systems, vol. 110, pp. 71-83, 2018. 12. N. D. Goumagias, D. Hristu-Varsakelis and Y. M. Assael, "Using deep Q-learning to understand the tax evasion behavior of risk-averse firms", Expert Systems with Applications, vol. 101, pp. 258-270, 2018. 13. M. G. Allingham and A. Sandmo, "Income tax evasion: A theoretical analysis", Journal of Public Economics, vol. 1, no. 3/4, pp. 323-338, 1972. 14. D. Micci-Barreca, "A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems", ACM SIGKDD Explorations Newsletter, vol. 3, no. 1, pp. 27-32, 2001. 15. P. Velmurugan, A. Kannagi and M. Varsha, "Superior fuzzy enumeration crop prediction algorithm for big data agriculture applications", Materials Today: Proceedings, 2021. 16. L. Trujillo, U. Lopez and P. Legrand, "SOAP: Semantic outliers automatic preprocessing", Information Sciences, vol. 526, pp. 86-101, 2020. 17. S. G. K. Patro and K. K. Sahu, "Normalization: A preprocessing stage", IARJSET, 2015. 18. S. García, J. Luengo and F. Herrera, Data Preprocessing in Data Mining, Cham, Switzerland:Springer, 2014. Show in Context Google Scholar 19. R. Vettukattil, "Preprocessing of raw metabonomic data", Methods in Molecular Biology, vol. 1277, pp. 123-136, 2015. 20. V. R. Algazi, P. L. Kelly and R. R. Estes, "Compression of binary facsimile images by preprocessing and color shrinking", IEEE Transactions on Communications, vol. 38, no. 9, pp. 1592-1598, Sep. 1990. 21. A. Singh, B. S. Prakash and K. Chandrasekaran, "A comparison of linear discriminant analysis and ridge classifier on Twitter data", Proceedings of the International Conference on Computing Communication and Automation, pp. 133-138, Apr. 2016. 22. K. Nagashri and J. Sangeetha, "Fake news detection using passive-aggressive classifier and other machine learning algorithms" in Advances in Computing and Network Communications, Singapore:Springer, pp. 221-233, 2021. 23. I. Levner, "Feature selection and nearest centroid classification for protein mass spectrometry", BMC Bioinformatics, vol. 6, no. 1, 2005. Show in Context CrossRef Google Scholar 24. R. Rosipal, L. J. Trejo and B. Matthews, "Kernel PLS-SVC for linear and nonlinear classification", Proceedings of the 20th International Conference on Machine Learning, pp. 640-647, 2003. 25. H. A. A. Alfeilat et al., "Effects of distance measure choice on k-nearest neighbor classifier performance: A review", Big Data, vol. 7, no. 4, pp. 221-248, 2019. 26. F. Kabir, S. Siddique, M. R. A. Kotwal and M. N. Huda, "Bangla text document categorization using stochastic gradient descent (SGD) classifier", Proceedings of the International Conference on Cognitive Computing and Information Processing, pp. 1-4, Mar. 2015. 27. P. H. Swain and H. Hauska, "The decision tree classifier: Design and potential", IEEE Transactions on Geoscience Electronics, vol. 15, no. 3, pp. 142-147, Jul. 1977. 28. J. H. Hong, S. Campbell and P. Yeh, "Optical pattern classifier with perceptron learning" in Landmark Papers on Photorefractive Nonlinear Optics, Singapore:World Scientific, pp. 551-557, 1995. Show in Context CrossRef Google Scholar 29. J. A. K. Suykens and J. Vandewalle, "Training multilayer perceptron classifiers based on a modified support vector method", IEEE Transactions on Neural Networks, vol. 10, no. 4, pp. 907-911, Jul. 1999. 30. A. M. Kibriya, E. Frank, B. Pfahringer and G. Holmes, "Multinomial naive Bayes for text categorization revisited", Proceedings of the Australasian Joint Conference on Artificial Intelligence, pp. 488-499, Dec. 2004. 31. G. Singh, B. Kumar, L. Gaur and A. Tyagi, "Comparison between multinomial and Bernoulli naïve Bayes for text classification", Proceedings of the International Conference on Automation Computational and Technology Management, pp. 593-596, Apr. 2019. 32. A. H. Jahromi and M. Taheri, "A non-parametric mixture of Gaussian naive Bayes classifiers based on local independent features", Proceedings of the Artificial Intelligence and Signal Processing Conference, pp. 209-212, Oct. 2017. 33. B. Seref and E. Bostanci, "Sentiment analysis using naive Bayes and complement naive Bayes classifier algorithms on Hadoop framework", Proceedings of the 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies, pp. 1-7, Oct. 2018. 34. T.-K. An and M.-H. Kim, "A new diverse AdaBoost classifier", Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence, vol. 1, pp. 359-363, Oct. 2010. 35. A. Natekin and A. Knoll, "Gradient boosting machines a tutorial", Frontiers in Neurorobotics, vol. 7, 2013. 36. P. Ariza Colpas, E. Vicario, E. De-La-Hoz-Franco, M. Pineres-Melo, A. Oviedo-Carrascal and F. Patara, "Unsupervised human activity recognition using the clustering approach: A review", Sensors, vol. 20, no. 9, 2020. 37. M. Hasnain, M. F. Pasha, I. Ghani, M. Imran, M. Y. Alzahrani and R. Budiarto, "Evaluating trust prediction and confusion matrix measures for web services ranking", IEEE Access, vol. 8, pp. 90847-90861, 2020. 38. D. Chicco, N. Tötsch and G. Jurman, "The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy bookmaker informedness and markedness in two-class confusion matrix evaluation", BioData Mining, vol. 14, no. 1, 2021. 39. E. Milanov, "An alternative confusion matrix implementation for PreCall", 2020. 40. T.-S. Lim, W.-Y. Loh and Y.-S. Shih, "A comparison of prediction accuracy complexity and training time of thirty-three old and new classification algorithms", Machine Learning, vol. 40, no. 3, pp. 203-228, 2000. 41. B. Biggio et al., "Evasion attacks against machine learning at test time", Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 387-402, Sep. 2013. 42. A. Omar, T. M. Mahmoud and T. Abd-El-Hafeez, "Comparative performance of machine learning and deep learning algorithms for Arabic hate speech detection in OSNs", Proceedings of the International Conference on Artificial Intelligence and Computer Vision, pp. 247-257, Apr. 2020. 43. S. Xu, Y. Li and Z. Wang, "Bayesian multinomial Naïve Bayes classifier to text classification", Proceedings of the International Conference on Advanced Multimedia and Ubiquitous Engineering, pp. 347-352, 2017. |
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Atribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfRíos-Henao, CristianAriza Colpas, Paola PatriciaDe-La-Hoz-Franco, Emiroaziz, shariq Piñeres Melo, Marlon AlbertoPerez Coronell, LeidyCámara de ComercioBarranquillaColombia2023-05-19T22:52:02Z2024-10-202023-05-19T22:52:02Z2022-10-20C. Rios-Henao, P. P. Ariza-Colpas, E. De-la-Hoz-Franco, S. B. Aziz, M. A. P. Melo and L. Perez-Coronell, "Automatic Learning for Commercial Registration Renewal—The Case of Camara de Comercio of Barranquilla-Colombia," in IEEE Engineering Management Review, vol. 51, no. 1, pp. 26-40, 1 Firstquarter,march 2023, doi: 10.1109/EMR.2022.3216200.0360-8581https://hdl.handle.net/11323/1015410.1109/EMR.2022.32162001937-4178Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This article shows the implementation of a prediction model of the payment behavior of the renewal concept of companies registered in the commercial registry of the Barranquilla Chamber of Commerce using machine learning techniques in a multilevel classification scenario, where it will offer the organization a tool that allows it to know in advance the behavior of the payment of the renewal of a company in such a way that it is able to design strategies to increase the success indicators in terms of the number of registration renewals, mercantile, and of the income collected for this concept.1 páginaapplication/pdfengInstitute of Electrical and Electronics Engineers Inc.United Stateshttps://ieeexplore.ieee.org/abstract/document/9925575/keywords#keywordsAutomatic Learning for Commercial Registration Renewal—The Case of Cámara de Comercio of Barranquilla-ColombiaArtículo 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_970fb48d4fbd8a85IEEE Engineering Management Review1. "Funciones de las Cámaras de Comercio", Dec. 2021.2. "Ley 28 de 1931".3. "Decreto 410 de1971", 1971.4. "Informe de Gestión 2015", 2015.5. "Informe de Gestión 2016", 2016.6. "Informe de Labores 2017", 2017.7. "Informe de Gestión 2018", 2018.8. M. Jupri and R. Sarno, "Taxpayer compliance classification using C4.5 SVM KNN Naive Bayes and MLP", Proceeding of the International Conference on Information and Communications Technology, pp. 297-303, 2018.9. G. Warner et al., "Modeling tax evasion with genetic algorithms", Economics of Governance, vol. 16, no. 2, pp. 165-178, May 2015.10. E. Rahimikia, S. Mohammadi, T. Rahmani and M. Ghazanfari, "Detecting corporate tax evasion using a hybrid intelligent system: A case study of Iran", International Journal of Accounting Information Systems, vol. 25, pp. 1-17, 2017.11. W. Didimo, L. Giamminonni, G. Liotta, F. Montecchiani and D. Pagliuca, "A visual analytics system to support tax evasion discovery", Decision Support Systems, vol. 110, pp. 71-83, 2018.12. N. D. Goumagias, D. Hristu-Varsakelis and Y. M. Assael, "Using deep Q-learning to understand the tax evasion behavior of risk-averse firms", Expert Systems with Applications, vol. 101, pp. 258-270, 2018.13. M. G. Allingham and A. Sandmo, "Income tax evasion: A theoretical analysis", Journal of Public Economics, vol. 1, no. 3/4, pp. 323-338, 1972.14. D. Micci-Barreca, "A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems", ACM SIGKDD Explorations Newsletter, vol. 3, no. 1, pp. 27-32, 2001.15. P. Velmurugan, A. Kannagi and M. Varsha, "Superior fuzzy enumeration crop prediction algorithm for big data agriculture applications", Materials Today: Proceedings, 2021.16. L. Trujillo, U. Lopez and P. Legrand, "SOAP: Semantic outliers automatic preprocessing", Information Sciences, vol. 526, pp. 86-101, 2020.17. S. G. K. Patro and K. K. Sahu, "Normalization: A preprocessing stage", IARJSET, 2015.18. S. García, J. Luengo and F. Herrera, Data Preprocessing in Data Mining, Cham, Switzerland:Springer, 2014. Show in Context Google Scholar19. R. Vettukattil, "Preprocessing of raw metabonomic data", Methods in Molecular Biology, vol. 1277, pp. 123-136, 2015.20. V. R. Algazi, P. L. Kelly and R. R. Estes, "Compression of binary facsimile images by preprocessing and color shrinking", IEEE Transactions on Communications, vol. 38, no. 9, pp. 1592-1598, Sep. 1990.21. A. Singh, B. S. Prakash and K. Chandrasekaran, "A comparison of linear discriminant analysis and ridge classifier on Twitter data", Proceedings of the International Conference on Computing Communication and Automation, pp. 133-138, Apr. 2016.22. K. Nagashri and J. Sangeetha, "Fake news detection using passive-aggressive classifier and other machine learning algorithms" in Advances in Computing and Network Communications, Singapore:Springer, pp. 221-233, 2021.23. I. Levner, "Feature selection and nearest centroid classification for protein mass spectrometry", BMC Bioinformatics, vol. 6, no. 1, 2005. Show in Context CrossRef Google Scholar24. R. Rosipal, L. J. Trejo and B. Matthews, "Kernel PLS-SVC for linear and nonlinear classification", Proceedings of the 20th International Conference on Machine Learning, pp. 640-647, 2003.25. H. A. A. Alfeilat et al., "Effects of distance measure choice on k-nearest neighbor classifier performance: A review", Big Data, vol. 7, no. 4, pp. 221-248, 2019.26. F. Kabir, S. Siddique, M. R. A. Kotwal and M. N. Huda, "Bangla text document categorization using stochastic gradient descent (SGD) classifier", Proceedings of the International Conference on Cognitive Computing and Information Processing, pp. 1-4, Mar. 2015.27. P. H. Swain and H. Hauska, "The decision tree classifier: Design and potential", IEEE Transactions on Geoscience Electronics, vol. 15, no. 3, pp. 142-147, Jul. 1977.28. J. H. Hong, S. Campbell and P. Yeh, "Optical pattern classifier with perceptron learning" in Landmark Papers on Photorefractive Nonlinear Optics, Singapore:World Scientific, pp. 551-557, 1995. Show in Context CrossRef Google Scholar29. J. A. K. Suykens and J. Vandewalle, "Training multilayer perceptron classifiers based on a modified support vector method", IEEE Transactions on Neural Networks, vol. 10, no. 4, pp. 907-911, Jul. 1999.30. A. M. Kibriya, E. Frank, B. Pfahringer and G. Holmes, "Multinomial naive Bayes for text categorization revisited", Proceedings of the Australasian Joint Conference on Artificial Intelligence, pp. 488-499, Dec. 2004.31. G. Singh, B. Kumar, L. Gaur and A. Tyagi, "Comparison between multinomial and Bernoulli naïve Bayes for text classification", Proceedings of the International Conference on Automation Computational and Technology Management, pp. 593-596, Apr. 2019.32. A. H. Jahromi and M. Taheri, "A non-parametric mixture of Gaussian naive Bayes classifiers based on local independent features", Proceedings of the Artificial Intelligence and Signal Processing Conference, pp. 209-212, Oct. 2017.33. B. Seref and E. Bostanci, "Sentiment analysis using naive Bayes and complement naive Bayes classifier algorithms on Hadoop framework", Proceedings of the 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies, pp. 1-7, Oct. 2018.34. T.-K. An and M.-H. Kim, "A new diverse AdaBoost classifier", Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence, vol. 1, pp. 359-363, Oct. 2010.35. A. Natekin and A. Knoll, "Gradient boosting machines a tutorial", Frontiers in Neurorobotics, vol. 7, 2013.36. P. Ariza Colpas, E. Vicario, E. De-La-Hoz-Franco, M. Pineres-Melo, A. Oviedo-Carrascal and F. Patara, "Unsupervised human activity recognition using the clustering approach: A review", Sensors, vol. 20, no. 9, 2020.37. M. Hasnain, M. F. Pasha, I. Ghani, M. Imran, M. Y. Alzahrani and R. Budiarto, "Evaluating trust prediction and confusion matrix measures for web services ranking", IEEE Access, vol. 8, pp. 90847-90861, 2020.38. D. Chicco, N. Tötsch and G. Jurman, "The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy bookmaker informedness and markedness in two-class confusion matrix evaluation", BioData Mining, vol. 14, no. 1, 2021.39. E. Milanov, "An alternative confusion matrix implementation for PreCall", 2020.40. T.-S. Lim, W.-Y. Loh and Y.-S. Shih, "A comparison of prediction accuracy complexity and training time of thirty-three old and new classification algorithms", Machine Learning, vol. 40, no. 3, pp. 203-228, 2000.41. B. Biggio et al., "Evasion attacks against machine learning at test time", Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 387-402, Sep. 2013.42. A. Omar, T. M. Mahmoud and T. Abd-El-Hafeez, "Comparative performance of machine learning and deep learning algorithms for Arabic hate speech detection in OSNs", Proceedings of the International Conference on Artificial Intelligence and Computer Vision, pp. 247-257, Apr. 2020.43. S. Xu, Y. Li and Z. Wang, "Bayesian multinomial Naïve Bayes classifier to text classification", Proceedings of the International Conference on Advanced Multimedia and Ubiquitous Engineering, pp. 347-352, 2017.151Behavioral sciencesFinanceCompaniesRegistersLawClassification algorithmsOptimizationPublicationORIGINALAutomatic Learning for Commercial Registration Renewal.pdfAutomatic Learning for Commercial Registration Renewal.pdfArtículoapplication/pdf70260https://repositorio.cuc.edu.co/bitstreams/7e54794f-4bd4-4cd1-864e-1512b872f04f/download0dc7022c4196be6398574f68cc48e272MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/8f4b9778-8cc7-4413-98fe-c15e3661f718/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTAutomatic Learning for Commercial Registration Renewal.pdf.txtAutomatic Learning for Commercial Registration Renewal.pdf.txtExtracted 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ada 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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