Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap data
This paper presents a feature selection comparison oriented to human action recognition only with the kinematic features of skeleton representation. For this purpose, three relevance methods are used to rank the contribution of kinematic features for classifying an action is employed. Particularly,...
- Autores:
-
Pulgarín Giraldo, Juan Diego
Alvarez Meza, Andres Marino
Ruales Tores, A. A.
Castellanos Dominguez, German
- Tipo de recurso:
- Part of book
- Fecha de publicación:
- 2017
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/11615
- Acceso en línea:
- http://hdl.handle.net/10614/11615
https://link.springer.com/chapter/10.1007/978-3-319-59740-9_23
https://link.springer.com/content/pdf/10.1007%2F978-3-319-59740-9.pdf
https://doi.org/10.1007/978-3-319-59773-7_51
- Palabra clave:
- Center kernel alignment
Feature selection
Human motion
Kinematics
Relevance
ReliefF
Motion capture data
Principal Component Analysis
Kinematics
Human beings--Attitude and movement
Cinemática
Actitud y movimiento del hombre
Motion
Mechanical movements
Movimiento
Movimientos mecánicos
- Rights
- openAccess
- License
- Derechos Reservados - Universidad Autónoma de Occidente
id |
REPOUAO2_fd2646e948c3814bdcd4cb6d78806b72 |
---|---|
oai_identifier_str |
oai:red.uao.edu.co:10614/11615 |
network_acronym_str |
REPOUAO2 |
network_name_str |
RED: Repositorio Educativo Digital UAO |
repository_id_str |
|
dc.title.eng.fl_str_mv |
Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap data |
title |
Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap data |
spellingShingle |
Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap data Center kernel alignment Feature selection Human motion Kinematics Relevance ReliefF Motion capture data Principal Component Analysis Kinematics Human beings--Attitude and movement Cinemática Actitud y movimiento del hombre Motion Mechanical movements Movimiento Movimientos mecánicos |
title_short |
Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap data |
title_full |
Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap data |
title_fullStr |
Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap data |
title_full_unstemmed |
Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap data |
title_sort |
Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap data |
dc.creator.fl_str_mv |
Pulgarín Giraldo, Juan Diego Alvarez Meza, Andres Marino Ruales Tores, A. A. Castellanos Dominguez, German |
dc.contributor.author.none.fl_str_mv |
Pulgarín Giraldo, Juan Diego |
dc.contributor.author.spa.fl_str_mv |
Alvarez Meza, Andres Marino Ruales Tores, A. A. Castellanos Dominguez, German |
dc.subject.eng.fl_str_mv |
Center kernel alignment Feature selection Human motion Kinematics Relevance ReliefF Motion capture data Principal Component Analysis |
topic |
Center kernel alignment Feature selection Human motion Kinematics Relevance ReliefF Motion capture data Principal Component Analysis Kinematics Human beings--Attitude and movement Cinemática Actitud y movimiento del hombre Motion Mechanical movements Movimiento Movimientos mecánicos |
dc.subject.lemb.eng.fl_str_mv |
Kinematics Human beings--Attitude and movement |
dc.subject.lemb.spa.fl_str_mv |
Cinemática Actitud y movimiento del hombre |
dc.subject.armarc.eng.fl_str_mv |
Motion Mechanical movements |
dc.subject.armarc.spa.fl_str_mv |
Movimiento Movimientos mecánicos |
description |
This paper presents a feature selection comparison oriented to human action recognition only with the kinematic features of skeleton representation. For this purpose, three relevance methods are used to rank the contribution of kinematic features for classifying an action is employed. Particularly, the method with the best results includes the supervised information regarding the action to find out a relevant set of features, encoding the most discriminative information. Experimental results are obtained on a well-known public data (MSR Action3D). Results are encouraging to use kernel theory methods to get better kinematic feature selection for each action with a good generalization indistinct to the subject |
publishDate |
2017 |
dc.date.issued.spa.fl_str_mv |
2017-05-27 |
dc.date.accessioned.spa.fl_str_mv |
2019-11-28T20:35:57Z |
dc.date.available.spa.fl_str_mv |
2019-11-28T20:35:57Z |
dc.type.spa.fl_str_mv |
Capítulo - Parte de Libro |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_3248 |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/bookPart |
dc.type.redcol.spa.fl_str_mv |
https://purl.org/redcol/resource_type/CAP_LIB |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
http://purl.org/coar/resource_type/c_3248 |
status_str |
publishedVersion |
dc.identifier.citation.eng.fl_str_mv |
Pulgarin-Giraldo J.D., Ruales-Torres A.A., Alvarez-Meza A.M., Castellanos-Dominguez G. (2017) Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap Data. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science, vol 10338. Springer, Cham |
dc.identifier.issn.spa.fl_str_mv |
1611-3349 (en línea) 0302-9743 (impresa) 978-3-319-59773-7 (en línea) 9783319597720 (impreso) |
dc.identifier.uri.spa.fl_str_mv |
http://hdl.handle.net/10614/11615 https://link.springer.com/chapter/10.1007/978-3-319-59740-9_23 https://link.springer.com/content/pdf/10.1007%2F978-3-319-59740-9.pdf |
dc.identifier.doi.spa.fl_str_mv |
https://doi.org/10.1007/978-3-319-59773-7_51 |
identifier_str_mv |
Pulgarin-Giraldo J.D., Ruales-Torres A.A., Alvarez-Meza A.M., Castellanos-Dominguez G. (2017) Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap Data. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science, vol 10338. Springer, Cham 1611-3349 (en línea) 0302-9743 (impresa) 978-3-319-59773-7 (en línea) 9783319597720 (impreso) |
url |
http://hdl.handle.net/10614/11615 https://link.springer.com/chapter/10.1007/978-3-319-59740-9_23 https://link.springer.com/content/pdf/10.1007%2F978-3-319-59740-9.pdf https://doi.org/10.1007/978-3-319-59773-7_51 |
dc.language.iso.eng.fl_str_mv |
eng |
language |
eng |
dc.relation.eng.fl_str_mv |
Biomedical Applications Based on Natural and Artificial Computing : International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Corunna, Spain, June 19-23, 2017, Proceedings, Part II. Páginas 501-509, (2017) |
dc.relation.haspart.eng.fl_str_mv |
Lecture Notes in Computer Science. 10337. Theoretical Computer Science and General Issues. 10337 |
dc.rights.spa.fl_str_mv |
Derechos Reservados - Universidad Autónoma de Occidente |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.uri.spa.fl_str_mv |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.creativecommons.spa.fl_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) |
rights_invalid_str_mv |
Derechos Reservados - Universidad Autónoma de Occidente https://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.spa.fl_str_mv |
application/pdf |
dc.format.extent.spa.fl_str_mv |
Páginas 501-509 |
dc.coverage.spatial.spa.fl_str_mv |
Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí |
dc.publisher.eng.fl_str_mv |
Springer, Cham |
dc.source.spa.fl_str_mv |
instname:Universidad Autónoma de Occidente reponame:Repositorio Institucional UAO |
instname_str |
Universidad Autónoma de Occidente |
institution |
Universidad Autónoma de Occidente |
reponame_str |
Repositorio Institucional UAO |
collection |
Repositorio Institucional UAO |
dc.source.bibliographiccitation.spa.fl_str_mv |
Kale, G.V., Patil, V.H.: A study of vision based human motion recognition and analysis. IJACI 7(2), 75–92 (2016) Mandery, C., Plappert, M., Sol, J.B., Asfour, T.: Dimensionality reduction for whole-body human motion recognition. In: 19th International Conference on Information Fusion, FUSION, Heidelberg, Germany, 5–8 July 2016, pp. 355–362 (2016) Althloothi, S., Mahoor, M.H., Zhang, X., Voyles, R.M.: Human activity recognition using multi-features and multiple kernel learning. Pattern Recogn. 47(5), 1800–1812 (2014) Li, M., Leung, H., Liu, Z., Zhou, L.: 3D human motion retrieval using graph kernels based on adaptive graph construction. Comput. Graph. Pergamon 54, 104–112 (2016) Wang, L., Zhao, G., Cheng, L., Pietikainen, M.: Machine Learning for Vision-Based Motion Analysis, 1st edn. Springer, Heidelberg (2011) Wei, X.K., Chai, J.: Modeling 3D human poses from uncalibrated monocular images. In: IEEE 12th International Conference on Computer Vision, ICCV, Kyoto, Japan, 27 September–4 October 2009, pp. 1873–1880 (2009) Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR Workshopps 2010, San Francisco, CA, USA, 13–18 June 2010, pp. 9–14 (2010) Ravet, T., Tilmanne, J., d’Alessandro, N.: Hidden Markov model based real-time motion recognition and following. In: Proceedings of the 2014 International Workshop on Movement, Computing, MOCO 2014, pp. 82–87. ACM, New York (2014) Jiang, Y., Saxena, A.: Modeling high-dimensional humans for activity anticipation using Gaussian process latent CRFs. In: Robotics: Science and Systems X, University of California, Berkeley, USA, 12–16 July 2014 (2014) García-Vega, S., Álvarez-Meza, A.M., Castellanos-Domínguez, C.G.: MoCap data segmentation and classification using kernel based multi-channel analysis. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013. LNCS, vol. 8259, pp. 495–502. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41827-3_62 Pulgarin-Giraldo, J.D., Alvarez-Meza, A.M., Melo-Betancourt, L.G., Ramos-Bermudez, S., Castellanos-Dominguez, G.: A similarity indicator for differentiating kinematic performance between qualified tennis players. In: Beltrán-Castañón, C., Nyström, I., Famili, F. (eds.) CIARP 2016. LNCS, vol. 10125, pp. 309–317. Springer, Cham (2017). doi: 10.1007/978-3-319-52277-7_38 Diaz-Martinez, N.F., Pulgarin-Giraldo, J.D., Vinasco-Isaza, L.E., Agredo, W.: Analysis of the alignment angles and flexion angle in women with patellofemoral pain syndrome. In: Torres, I., Bustamante, J., Sierra, D. (eds.) CLAIB 2016. IFMBE Proceedings. Springer, Singapore (2016) Yang, X., Tian, Y.: Effective 3D action recognition using eigenjoints. J. Vis. Commun. Image Represent. 25(1), 2–11 (2014) Yang, X., Tian, Y.: Eigenjoints-based action recognition using Naïve-Bayes-Nearest-Neighbor. In: CVPR Workshops, pp. 14–19. IEEE Computer Society (2012) Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: Sequence of the most informative joints (SMIJ): a new representation for human skeletal action recognition. J. Vis. Commun. Image Represent. 25(1), 24–38 (2014) Alvarez-Meza, A.M., Velasquez-Martinez, L.F., Castellanos-Dominguez, G.: Time-series discrimination using feature relevance analysis in motor imagery classification. Neurocomputing 151(Part 1), 122–129 (2015) Brockmeier, A.J., Choi, J.S., Kriminger, E.G., Francis, J.T., Principe, J.C.: Neural decoding with kernel-based metric learning. Neural Comput. 26(6), 1080–1107 (2014) Zeng, X., Wang, Q., Zhang, C., Cai, H.: Feature selection based on reliefF and PCA for underwater sound classification. In: Proceedings of 3rd International Conference on Computer Science and Network Technology, pp. 442–445 (2013) |
bitstream.url.fl_str_mv |
https://red.uao.edu.co/bitstreams/72fbd1a1-5342-4d6a-b9ae-2198e6fbffce/download https://red.uao.edu.co/bitstreams/24db5418-7436-4323-ad07-e0dccc87ad41/download |
bitstream.checksum.fl_str_mv |
4460e5956bc1d1639be9ae6146a50347 20b5ba22b1117f71589c7318baa2c560 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 |
repository.name.fl_str_mv |
Repositorio Digital Universidad Autonoma de Occidente |
repository.mail.fl_str_mv |
repositorio@uao.edu.co |
_version_ |
1814259853151961088 |
spelling |
Pulgarín Giraldo, Juan Diegovirtual::4145-1jdpulgaring@unal.edu.coAlvarez Meza, Andres Marino5154abb266e67961a71f8dc28e883cbf-1Ruales Tores, A. A.3e3a8475001cc9f7d3845a5b97b052ad-1Castellanos Dominguez, German3de8cb9245317beddda7d5f7dade0b1b-1Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí2019-11-28T20:35:57Z2019-11-28T20:35:57Z2017-05-27Pulgarin-Giraldo J.D., Ruales-Torres A.A., Alvarez-Meza A.M., Castellanos-Dominguez G. (2017) Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap Data. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science, vol 10338. Springer, Cham1611-3349 (en línea)0302-9743 (impresa)978-3-319-59773-7 (en línea)9783319597720 (impreso)http://hdl.handle.net/10614/11615https://link.springer.com/chapter/10.1007/978-3-319-59740-9_23https://link.springer.com/content/pdf/10.1007%2F978-3-319-59740-9.pdfhttps://doi.org/10.1007/978-3-319-59773-7_51This paper presents a feature selection comparison oriented to human action recognition only with the kinematic features of skeleton representation. For this purpose, three relevance methods are used to rank the contribution of kinematic features for classifying an action is employed. Particularly, the method with the best results includes the supervised information regarding the action to find out a relevant set of features, encoding the most discriminative information. Experimental results are obtained on a well-known public data (MSR Action3D). Results are encouraging to use kernel theory methods to get better kinematic feature selection for each action with a good generalization indistinct to the subjectapplication/pdfPáginas 501-509engSpringer, ChamBiomedical Applications Based on Natural and Artificial Computing : International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Corunna, Spain, June 19-23, 2017, Proceedings, Part II. Páginas 501-509, (2017)Lecture Notes in Computer Science. 10337. Theoretical Computer Science and General Issues. 10337Derechos Reservados - Universidad Autónoma de Occidentehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2instname:Universidad Autónoma de Occidentereponame:Repositorio Institucional UAOKale, G.V., Patil, V.H.: A study of vision based human motion recognition and analysis. IJACI 7(2), 75–92 (2016)Mandery, C., Plappert, M., Sol, J.B., Asfour, T.: Dimensionality reduction for whole-body human motion recognition. In: 19th International Conference on Information Fusion, FUSION, Heidelberg, Germany, 5–8 July 2016, pp. 355–362 (2016)Althloothi, S., Mahoor, M.H., Zhang, X., Voyles, R.M.: Human activity recognition using multi-features and multiple kernel learning. Pattern Recogn. 47(5), 1800–1812 (2014)Li, M., Leung, H., Liu, Z., Zhou, L.: 3D human motion retrieval using graph kernels based on adaptive graph construction. Comput. Graph. Pergamon 54, 104–112 (2016)Wang, L., Zhao, G., Cheng, L., Pietikainen, M.: Machine Learning for Vision-Based Motion Analysis, 1st edn. Springer, Heidelberg (2011)Wei, X.K., Chai, J.: Modeling 3D human poses from uncalibrated monocular images. In: IEEE 12th International Conference on Computer Vision, ICCV, Kyoto, Japan, 27 September–4 October 2009, pp. 1873–1880 (2009)Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR Workshopps 2010, San Francisco, CA, USA, 13–18 June 2010, pp. 9–14 (2010)Ravet, T., Tilmanne, J., d’Alessandro, N.: Hidden Markov model based real-time motion recognition and following. In: Proceedings of the 2014 International Workshop on Movement, Computing, MOCO 2014, pp. 82–87. ACM, New York (2014)Jiang, Y., Saxena, A.: Modeling high-dimensional humans for activity anticipation using Gaussian process latent CRFs. In: Robotics: Science and Systems X, University of California, Berkeley, USA, 12–16 July 2014 (2014)García-Vega, S., Álvarez-Meza, A.M., Castellanos-Domínguez, C.G.: MoCap data segmentation and classification using kernel based multi-channel analysis. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013. LNCS, vol. 8259, pp. 495–502. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41827-3_62Pulgarin-Giraldo, J.D., Alvarez-Meza, A.M., Melo-Betancourt, L.G., Ramos-Bermudez, S., Castellanos-Dominguez, G.: A similarity indicator for differentiating kinematic performance between qualified tennis players. In: Beltrán-Castañón, C., Nyström, I., Famili, F. (eds.) CIARP 2016. LNCS, vol. 10125, pp. 309–317. Springer, Cham (2017). doi: 10.1007/978-3-319-52277-7_38Diaz-Martinez, N.F., Pulgarin-Giraldo, J.D., Vinasco-Isaza, L.E., Agredo, W.: Analysis of the alignment angles and flexion angle in women with patellofemoral pain syndrome. In: Torres, I., Bustamante, J., Sierra, D. (eds.) CLAIB 2016. IFMBE Proceedings. Springer, Singapore (2016)Yang, X., Tian, Y.: Effective 3D action recognition using eigenjoints. J. Vis. Commun. Image Represent. 25(1), 2–11 (2014)Yang, X., Tian, Y.: Eigenjoints-based action recognition using Naïve-Bayes-Nearest-Neighbor. In: CVPR Workshops, pp. 14–19. IEEE Computer Society (2012)Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: Sequence of the most informative joints (SMIJ): a new representation for human skeletal action recognition. J. Vis. Commun. Image Represent. 25(1), 24–38 (2014)Alvarez-Meza, A.M., Velasquez-Martinez, L.F., Castellanos-Dominguez, G.: Time-series discrimination using feature relevance analysis in motor imagery classification. Neurocomputing 151(Part 1), 122–129 (2015)Brockmeier, A.J., Choi, J.S., Kriminger, E.G., Francis, J.T., Principe, J.C.: Neural decoding with kernel-based metric learning. Neural Comput. 26(6), 1080–1107 (2014)Zeng, X., Wang, Q., Zhang, C., Cai, H.: Feature selection based on reliefF and PCA for underwater sound classification. In: Proceedings of 3rd International Conference on Computer Science and Network Technology, pp. 442–445 (2013)Center kernel alignmentFeature selectionHuman motionKinematicsRelevanceReliefFMotion capture dataPrincipal Component AnalysisKinematicsHuman beings--Attitude and movementCinemáticaActitud y movimiento del hombreMotionMechanical movementsMovimientoMovimientos mecánicosRelevant Kinematic Feature Selection to Support Human Action Recognition in MoCap dataCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttps://purl.org/redcol/resource_type/CAP_LIBinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Publication33e9b6b4-bd6d-4b86-b500-ae237e1e9a98virtual::4145-133e9b6b4-bd6d-4b86-b500-ae237e1e9a98virtual::4145-1https://scholar.google.com.co/citations?user=Bwuc2BkAAAAJ&hl=envirtual::4145-10000-0002-6409-5104virtual::4145-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000207497virtual::4145-1CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://red.uao.edu.co/bitstreams/72fbd1a1-5342-4d6a-b9ae-2198e6fbffce/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/24db5418-7436-4323-ad07-e0dccc87ad41/download20b5ba22b1117f71589c7318baa2c560MD5310614/11615oai:red.uao.edu.co:10614/116152024-03-13 14:21:16.824https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos Reservados - Universidad Autónoma de Occidentemetadata.onlyhttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.coRUwgQVVUT1IgYXV0b3JpemEgYSBsYSBVbml2ZXJzaWRhZCBBdXTDs25vbWEgZGUgT2NjaWRlbnRlLCBkZSBmb3JtYSBpbmRlZmluaWRhLCBwYXJhIHF1ZSBlbiBsb3MgdMOpcm1pbm9zIGVzdGFibGVjaWRvcyBlbiBsYSBMZXkgMjMgZGUgMTk4MiwgbGEgTGV5IDQ0IGRlIDE5OTMsIGxhIERlY2lzacOzbiBhbmRpbmEgMzUxIGRlIDE5OTMsIGVsIERlY3JldG8gNDYwIGRlIDE5OTUgeSBkZW3DoXMgbGV5ZXMgeSBqdXJpc3BydWRlbmNpYSB2aWdlbnRlIGFsIHJlc3BlY3RvLCBoYWdhIHB1YmxpY2FjacOzbiBkZSBlc3RlIGNvbiBmaW5lcyBlZHVjYXRpdm9zLiBQQVJBR1JBRk86IEVzdGEgYXV0b3JpemFjacOzbiBhZGVtw6FzIGRlIHNlciB2w6FsaWRhIHBhcmEgbGFzIGZhY3VsdGFkZXMgeSBkZXJlY2hvcyBkZSB1c28gc29icmUgbGEgb2JyYSBlbiBmb3JtYXRvIG8gc29wb3J0ZSBtYXRlcmlhbCwgdGFtYmnDqW4gcGFyYSBmb3JtYXRvIGRpZ2l0YWwsIGVsZWN0csOzbmljbywgdmlydHVhbCwgcGFyYSB1c29zIGVuIHJlZCwgSW50ZXJuZXQsIGV4dHJhbmV0LCBpbnRyYW5ldCwgYmlibGlvdGVjYSBkaWdpdGFsIHkgZGVtw6FzIHBhcmEgY3VhbHF1aWVyIGZvcm1hdG8gY29ub2NpZG8gbyBwb3IgY29ub2Nlci4gRUwgQVVUT1IsIGV4cHJlc2EgcXVlIGVsIGRvY3VtZW50byAodHJhYmFqbyBkZSBncmFkbywgcGFzYW50w61hLCBjYXNvcyBvIHRlc2lzKSBvYmpldG8gZGUgbGEgcHJlc2VudGUgYXV0b3JpemFjacOzbiBlcyBvcmlnaW5hbCB5IGxhIGVsYWJvcsOzIHNpbiBxdWVicmFudGFyIG5pIHN1cGxhbnRhciBsb3MgZGVyZWNob3MgZGUgYXV0b3IgZGUgdGVyY2Vyb3MsIHkgZGUgdGFsIGZvcm1hLCBlbCBkb2N1bWVudG8gKHRyYWJham8gZGUgZ3JhZG8sIHBhc2FudMOtYSwgY2Fzb3MgbyB0ZXNpcykgZXMgZGUgc3UgZXhjbHVzaXZhIGF1dG9yw61hIHkgdGllbmUgbGEgdGl0dWxhcmlkYWQgc29icmUgw6lzdGUuIFBBUkFHUkFGTzogZW4gY2FzbyBkZSBwcmVzZW50YXJzZSBhbGd1bmEgcmVjbGFtYWNpw7NuIG8gYWNjacOzbiBwb3IgcGFydGUgZGUgdW4gdGVyY2VybywgcmVmZXJlbnRlIGEgbG9zIGRlcmVjaG9zIGRlIGF1dG9yIHNvYnJlIGVsIGRvY3VtZW50byAoVHJhYmFqbyBkZSBncmFkbywgUGFzYW50w61hLCBjYXNvcyBvIHRlc2lzKSBlbiBjdWVzdGnDs24sIEVMIEFVVE9SLCBhc3VtaXLDoSBsYSByZXNwb25zYWJpbGlkYWQgdG90YWwsIHkgc2FsZHLDoSBlbiBkZWZlbnNhIGRlIGxvcyBkZXJlY2hvcyBhcXXDrSBhdXRvcml6YWRvczsgcGFyYSB0b2RvcyBsb3MgZWZlY3RvcywgbGEgVW5pdmVyc2lkYWQgIEF1dMOzbm9tYSBkZSBPY2NpZGVudGUgYWN0w7phIGNvbW8gdW4gdGVyY2VybyBkZSBidWVuYSBmZS4gVG9kYSBwZXJzb25hIHF1ZSBjb25zdWx0ZSB5YSBzZWEgZW4gbGEgYmlibGlvdGVjYSBvIGVuIG1lZGlvIGVsZWN0csOzbmljbyBwb2Ryw6EgY29waWFyIGFwYXJ0ZXMgZGVsIHRleHRvIGNpdGFuZG8gc2llbXByZSBsYSBmdWVudGUsIGVzIGRlY2lyIGVsIHTDrXR1bG8gZGVsIHRyYWJham8geSBlbCBhdXRvci4gRXN0YSBhdXRvcml6YWNpw7NuIG5vIGltcGxpY2EgcmVudW5jaWEgYSBsYSBmYWN1bHRhZCBxdWUgdGllbmUgRUwgQVVUT1IgZGUgcHVibGljYXIgdG90YWwgbyBwYXJjaWFsbWVudGUgbGEgb2JyYS4K |