Impact of augmentation methods in online signature verification
The aim of this paper is to investigate the impact of selected data augmentation techniques on the learning performance of neural networks for dynamic signature verification. The paper investigates selected data augmentation techniques in deep learning for verification purpose of dynamic signature....
- Autores:
-
Najda, Dawid
Saeed, Khalid
- 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:11323/10733
- Acceso en línea:
- https://hdl.handle.net/11323/10733
https://repositorio.cuc.edu.co/
- Palabra clave:
- Signature
Online signature
Biometrics
Verification
Augmentation
- Rights
- embargoedAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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| dc.title.none.fl_str_mv |
Impact of augmentation methods in online signature verification |
| title |
Impact of augmentation methods in online signature verification |
| spellingShingle |
Impact of augmentation methods in online signature verification Signature Online signature Biometrics Verification Augmentation |
| title_short |
Impact of augmentation methods in online signature verification |
| title_full |
Impact of augmentation methods in online signature verification |
| title_fullStr |
Impact of augmentation methods in online signature verification |
| title_full_unstemmed |
Impact of augmentation methods in online signature verification |
| title_sort |
Impact of augmentation methods in online signature verification |
| dc.creator.fl_str_mv |
Najda, Dawid Saeed, Khalid |
| dc.contributor.author.none.fl_str_mv |
Najda, Dawid Saeed, Khalid |
| dc.subject.proposal.eng.fl_str_mv |
Signature Online signature Biometrics Verification Augmentation |
| topic |
Signature Online signature Biometrics Verification Augmentation |
| description |
The aim of this paper is to investigate the impact of selected data augmentation techniques on the learning performance of neural networks for dynamic signature verification. The paper investigates selected data augmentation techniques in deep learning for verification purpose of dynamic signature. Two neural networks were used as classifiers: MLP and LSTM-FCN. Investigation of five selected augmentation methods and experiments were performed on the open source signature database SVC2004. The authors tested both classifiers without augmentation and then with data augmentation for three extensions of the learning set and three sizes of the user database. They presented the results of the experiments in tabular form for each augmentation method. The results were compared with the existing dynamic signature verification methods and given in the paper. |
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2022 |
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2022 |
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2023 2024-02-19T21:03:10Z |
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2024-02-19T21:03:10Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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Najda, D., Saeed, K. Impact of augmentation methods in online signature verification. Innovations Syst Softw Eng (2022). https://doi.org/10.1007/s11334-022-00464-4 |
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1614-5046 |
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https://hdl.handle.net/11323/10733 |
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10.1007/s11334-022-00464-4 |
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1614-5054 |
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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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Najda, D., Saeed, K. Impact of augmentation methods in online signature verification. Innovations Syst Softw Eng (2022). https://doi.org/10.1007/s11334-022-00464-4 1614-5046 10.1007/s11334-022-00464-4 1614-5054 Corporación Universidad de la Costa REDICUC – Repositorio CUC |
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https://hdl.handle.net/11323/10733 https://repositorio.cuc.edu.co/ |
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eng |
| language |
eng |
| dc.relation.ispartofjournal.spa.fl_str_mv |
Innovations in Systems and Software Engineering |
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1. Zareen FJ, Jabin S (2013) A comparative study of the recent trends in biometric signature verification. In 2013 sixth international conference on contemporary computing (IC3), August 2013, pp 354–358 2. Lei H, Govindaraju V (2005) A comparative study on the consistency of features in on-line signature verification. Pattern Recogn Lett 26(15):2483–2489 3. Iwana BK, Uchida S (2020) An empirical survey of data augmentation for time series classification with neural networks. arXiv, 2020 4. Um TT, Pfister FMJ, Pichler D, Endo S, Lang M, Hirche S (2017) Data augmentation of wearable sensor data for Parkinson’s disease monitoring using convolutional neural networks. In: ACM ICMI, 2017, pp 216–220 5. Le Guennec A,Malinowski S, Tavenard R (201) Data augmentation for time series classification using convolutional neural networks. In: IWAATD, 2016 6. Sawicki A, Zieli ´nski SK (2020) Augmentation of segmented motion capture data for improving generalization of deep neural networks. In: CISIM. Springer, pp 278–290 7. , Jahan MV, Farimani SA (2018) An hmm for online signature verification based on velocity and hand movement directions. In: 2018 6th Iranian joint congress on fuzzy and intelligent systems (CFIS). IEEE, pp 205–209 8. Malik MI, Ahmed S, Marcelli A, Pal U, Blumenstein M, Alewijns L, Liwicki M (2015) Icdar2015 competition on signature verification and writer identification for on-and off-line neural computing and applications skilled forgeries. In: 2015 13th international conference on document analysis and recognition (ICDAR). IEEE, pp 1186–1190 9. Lai S, Jin L, Yang W (2017) Online signature verification using recurrent neural network and length-normalized path signature descriptor. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR) , vol 1. IEEE, pp 400–405 10. Bibi K, Naz S, Rehman A (2020) Biometric signature authentication using machine learning techniques: current trends, challenges and opportunities. Multimed Tools Appl 79(1):289–340 11. Liwicki M, Malik MI, Van Den Heuvel CE, Chen X, Berger C, Stoel R, Blumenstein M, Found B (2011) Signature verification competition for online and offline skilled forgeries. In: 2011 international conference on document analysis and recognition. IEEE, pp 1480–1484 12. Ureche O, Plamondon R (2000) Digital payment systems for internet commerce: the state of 1201” the art. World Wide Web 3(1):1–11 13. Gruber C, Gruber T, Krinninger S, Sick B (2010) Online signature verification with support vector machines based on LCSS kernel functions. IEEE Trans Syst Man Cybern Part B (Cybern) 40(4):1088–1100 14. Gruber C, Gruber T, Sick B (2005) Online signature verification with new time series kernels for support vector machines. Springer, Berlin, pp 500–508 15. Van BL, Garcia-Salicetti S, Dorizzi B (2007) On using the viterbi path along with HMM likelihood information for online signature verification. IEEE Trans Syst Man Cybern Part B 37(5):1237–1247 16. Fierrez J, Ortega-Garcia J, Ramos D, Gonzalez-Rodriguez J (2007) Hmm-based on-line signature verification: feature extraction and signature modeling. Pattern Recogn Lett 28(16):2325–2334 17. Sundaram S, Sharma A (2017) A novel online signature verification system based on gmm features in a dtw framework. IEEE Trans Inf Forensics Secur 12(3):705–718 18. Faundez-Zanuy M (2007) On-line signature recognition based on vq-dtw. Pattern Recogn 40(3):981–992 19. Kar B, Dutta PK, Basu TK, VielHauer C, Dittmann J (2006) DTW based verification scheme of biometric signatures. In 2006 IEEE international conference on industrial technology, December 2006, pp 381–386 20. Songxuan L, Lianwen J, Weixin Y (2017) Online signature verification using recurrent neural network and length-normalized path signature descriptor. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR) 21. Zapata G, Arias-Londoño JD, Vargas-Bonilla J, Orozco JR (2016) Online signature verification using gaussian mixture models and small-sample learning strategies. Rev Fac Ing 2016:06 22. Yeung D-Y, Chang H, Xiong Y, George S, Kashi R, Matsumoto T, Rigoll G (2004) SVC2004: first international signature verification competition, 2004 23. Lin A, Wang L (2007) Style-preserving english handwriting synthesis. Pattern Recogn 40(7):2097–2109 24. Galbally J, Fierrez J, Martinez-Diaz M, Ortega-Garcia J (2009) Improving the enrollment in dynamic signature verification with synthetic samples. In: 10th international conference on document analysis and recognition, 2009 25. Dahea W, Fadewar HS (2018) Multimodal biometric system: a review. Int J Eng Technol 4:25–31 26. Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks: a strong baseline. In: International joint conference on neural networks, pp 1578–1585 27. Gruber C, Gruber T, Krinninger S, Sick B (2010) Online signature verification with support vector machines based on LCSS kernel functions. IEEE Trans Syst Man Cybern Part B Cybern 40:1088–1100 28. Barkoula K, Economou G, Fotopoulos S (2013) Online signature verification based on signatures turning angle representation using longest common subsequence matching. Int J Doc Anal Recogn (IJDAR) 16:261–272 29. Yahyatabar ME, Baleghi Y, Karami MR (2013) Online signature verification: a Persian-language specific approach. In: 21st Iranian Conference on electrical engineering (ICEE), 2013, pp 1–6 30. Liu Y, Yang Z, Yang L (2017) Online signature verification based on DCT and sparse representation. IEEE Trans Cybern 45:2498–2511 31. Song X, Xia X, Luan F (2017) Online signature verification based on stable features extracted dynamically. IEEE Trans Syst Man Cybern Syst 47:2663–2676 32. Sharma A, Sundaram S (2018) On the exploration of information from the DTW cost matrix for online signature verification. IEEE Trans Cybern 48:611–624 33. Jia Y, Huang L, Chen H (2018) A two-stage method for online signature verification using shape contexts and function features. In: 2019, extended version of paper published in PRCV 2018: Chinese conference on pattern recognition and computer vision, Guangzhou, China, 23–26 November 2018 34. Karim F, Majumdar S, Darabi H, Chen S (2018) LSTM fully convolutional networks for time series classification. IEEE Access 1662–1669 35. Tolosana R, Vera-Rodriguez R, González C, Fierrez J, Morales A, Ortega-Garcia J, Ruiz-Garcia J, Romero-Tapiador S, Rengifo S, Caruana M, Jiang J, Lai S, Jin L, Zhu Y, Galbally J, Diaz M, Ferrer M, Gomez-Barrero M, Hodashinsky I, Jabin S (2022) SVC-onGoing: signature verification competition. Pattern Recogn 127:108609 |
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© The Author(s) 2022 |
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Atribución 4.0 Internacional (CC BY 4.0) |
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Atribución 4.0 Internacional (CC BY 4.0)© The Author(s) 2022https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfNajda, Dawid56a1ebe75cc0b8b8893b1df9a40f30deSaeed, Khalid2dae71af04f365846e3b11bd740ab1ab2024-02-19T21:03:10Z20232024-02-19T21:03:10Z2022Najda, D., Saeed, K. Impact of augmentation methods in online signature verification. Innovations Syst Softw Eng (2022). https://doi.org/10.1007/s11334-022-00464-41614-5046https://hdl.handle.net/11323/1073310.1007/s11334-022-00464-41614-5054Corporación Universidad de la CostaREDICUC – Repositorio CUChttps://repositorio.cuc.edu.co/The aim of this paper is to investigate the impact of selected data augmentation techniques on the learning performance of neural networks for dynamic signature verification. The paper investigates selected data augmentation techniques in deep learning for verification purpose of dynamic signature. Two neural networks were used as classifiers: MLP and LSTM-FCN. Investigation of five selected augmentation methods and experiments were performed on the open source signature database SVC2004. The authors tested both classifiers without augmentation and then with data augmentation for three extensions of the learning set and three sizes of the user database. They presented the results of the experiments in tabular form for each augmentation method. The results were compared with the existing dynamic signature verification methods and given in the paper.7 páginasapplication/pdfengSpringer LondonUnited Kingdomhttps://link.springer.com/article/10.1007/s11334-022-00464-4Impact of augmentation methods in online signature verificationArtículo 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_970fb48d4fbd8a85Innovations in Systems and Software Engineering1. Zareen FJ, Jabin S (2013) A comparative study of the recent trends in biometric signature verification. In 2013 sixth international conference on contemporary computing (IC3), August 2013, pp 354–3582. Lei H, Govindaraju V (2005) A comparative study on the consistency of features in on-line signature verification. Pattern Recogn Lett 26(15):2483–24893. Iwana BK, Uchida S (2020) An empirical survey of data augmentation for time series classification with neural networks. arXiv, 20204. Um TT, Pfister FMJ, Pichler D, Endo S, Lang M, Hirche S (2017) Data augmentation of wearable sensor data for Parkinson’s disease monitoring using convolutional neural networks. In: ACM ICMI, 2017, pp 216–2205. Le Guennec A,Malinowski S, Tavenard R (201) Data augmentation for time series classification using convolutional neural networks. In: IWAATD, 20166. Sawicki A, Zieli ´nski SK (2020) Augmentation of segmented motion capture data for improving generalization of deep neural networks. In: CISIM. Springer, pp 278–2907. , Jahan MV, Farimani SA (2018) An hmm for online signature verification based on velocity and hand movement directions. In: 2018 6th Iranian joint congress on fuzzy and intelligent systems (CFIS). IEEE, pp 205–2098. Malik MI, Ahmed S, Marcelli A, Pal U, Blumenstein M, Alewijns L, Liwicki M (2015) Icdar2015 competition on signature verification and writer identification for on-and off-line neural computing and applications skilled forgeries. In: 2015 13th international conference on document analysis and recognition (ICDAR). IEEE, pp 1186–11909. Lai S, Jin L, Yang W (2017) Online signature verification using recurrent neural network and length-normalized path signature descriptor. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR) , vol 1. IEEE, pp 400–40510. Bibi K, Naz S, Rehman A (2020) Biometric signature authentication using machine learning techniques: current trends, challenges and opportunities. Multimed Tools Appl 79(1):289–34011. Liwicki M, Malik MI, Van Den Heuvel CE, Chen X, Berger C, Stoel R, Blumenstein M, Found B (2011) Signature verification competition for online and offline skilled forgeries. In: 2011 international conference on document analysis and recognition. IEEE, pp 1480–148412. Ureche O, Plamondon R (2000) Digital payment systems for internet commerce: the state of 1201” the art. World Wide Web 3(1):1–1113. Gruber C, Gruber T, Krinninger S, Sick B (2010) Online signature verification with support vector machines based on LCSS kernel functions. IEEE Trans Syst Man Cybern Part B (Cybern) 40(4):1088–110014. Gruber C, Gruber T, Sick B (2005) Online signature verification with new time series kernels for support vector machines. Springer, Berlin, pp 500–50815. Van BL, Garcia-Salicetti S, Dorizzi B (2007) On using the viterbi path along with HMM likelihood information for online signature verification. IEEE Trans Syst Man Cybern Part B 37(5):1237–124716. Fierrez J, Ortega-Garcia J, Ramos D, Gonzalez-Rodriguez J (2007) Hmm-based on-line signature verification: feature extraction and signature modeling. Pattern Recogn Lett 28(16):2325–233417. Sundaram S, Sharma A (2017) A novel online signature verification system based on gmm features in a dtw framework. IEEE Trans Inf Forensics Secur 12(3):705–71818. Faundez-Zanuy M (2007) On-line signature recognition based on vq-dtw. Pattern Recogn 40(3):981–99219. Kar B, Dutta PK, Basu TK, VielHauer C, Dittmann J (2006) DTW based verification scheme of biometric signatures. In 2006 IEEE international conference on industrial technology, December 2006, pp 381–38620. Songxuan L, Lianwen J, Weixin Y (2017) Online signature verification using recurrent neural network and length-normalized path signature descriptor. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR)21. Zapata G, Arias-Londoño JD, Vargas-Bonilla J, Orozco JR (2016) Online signature verification using gaussian mixture models and small-sample learning strategies. Rev Fac Ing 2016:0622. Yeung D-Y, Chang H, Xiong Y, George S, Kashi R, Matsumoto T, Rigoll G (2004) SVC2004: first international signature verification competition, 200423. Lin A, Wang L (2007) Style-preserving english handwriting synthesis. Pattern Recogn 40(7):2097–210924. Galbally J, Fierrez J, Martinez-Diaz M, Ortega-Garcia J (2009) Improving the enrollment in dynamic signature verification with synthetic samples. In: 10th international conference on document analysis and recognition, 200925. Dahea W, Fadewar HS (2018) Multimodal biometric system: a review. Int J Eng Technol 4:25–3126. Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks: a strong baseline. In: International joint conference on neural networks, pp 1578–158527. Gruber C, Gruber T, Krinninger S, Sick B (2010) Online signature verification with support vector machines based on LCSS kernel functions. IEEE Trans Syst Man Cybern Part B Cybern 40:1088–110028. Barkoula K, Economou G, Fotopoulos S (2013) Online signature verification based on signatures turning angle representation using longest common subsequence matching. Int J Doc Anal Recogn (IJDAR) 16:261–27229. Yahyatabar ME, Baleghi Y, Karami MR (2013) Online signature verification: a Persian-language specific approach. In: 21st Iranian Conference on electrical engineering (ICEE), 2013, pp 1–630. Liu Y, Yang Z, Yang L (2017) Online signature verification based on DCT and sparse representation. IEEE Trans Cybern 45:2498–251131. Song X, Xia X, Luan F (2017) Online signature verification based on stable features extracted dynamically. IEEE Trans Syst Man Cybern Syst 47:2663–267632. Sharma A, Sundaram S (2018) On the exploration of information from the DTW cost matrix for online signature verification. IEEE Trans Cybern 48:611–62433. Jia Y, Huang L, Chen H (2018) A two-stage method for online signature verification using shape contexts and function features. In: 2019, extended version of paper published in PRCV 2018: Chinese conference on pattern recognition and computer vision, Guangzhou, China, 23–26 November 201834. Karim F, Majumdar S, Darabi H, Chen S (2018) LSTM fully convolutional networks for time series classification. IEEE Access 1662–166935. Tolosana R, Vera-Rodriguez R, González C, Fierrez J, Morales A, Ortega-Garcia J, Ruiz-Garcia J, Romero-Tapiador S, Rengifo S, Caruana M, Jiang J, Lai S, Jin L, Zhu Y, Galbally J, Diaz M, Ferrer M, Gomez-Barrero M, Hodashinsky I, Jabin S (2022) SVC-onGoing: signature verification competition. 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corporada 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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