{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:20:50Z","timestamp":1750220450509,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1145\/3454127.3456615","type":"proceedings-article","created":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T05:13:07Z","timestamp":1637989987000},"page":"1-5","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["State of art of PLS Regression for non quantitative data and in Big Data context"],"prefix":"10.1145","author":[{"given":"Yasmina","family":"Al Marouni","sequence":"first","affiliation":[{"name":"Ibn Tofail University, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youssef","family":"Bentaleb","sequence":"additional","affiliation":[{"name":"Ibn Tofail University, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,11,26]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"A comparison of methods for analysing logistic regression models with both clinical and genomic variables. (July","author":"Bazzoli Caroline","year":"2017","unstructured":"Caroline Bazzoli and Sophie Lambert-Lacroix . 2017. A comparison of methods for analysing logistic regression models with both clinical and genomic variables. (July 2017 ). Caroline Bazzoli and Sophie Lambert-Lacroix. 2017. A comparison of methods for analysing logistic regression models with both clinical and genomic variables. (July 2017)."},{"key":"e_1_3_2_1_2_1","volume-title":"Iteratively Reweighted Partial Least Squares estimation for Generalized Linear Regression.Technometrics","author":"Marx B.D.","year":"1996","unstructured":"B.D. Marx . 1996. Iteratively Reweighted Partial Least Squares estimation for Generalized Linear Regression.Technometrics ( 1996 ), 374\u2013381. B.D.Marx. 1996. Iteratively Reweighted Partial Least Squares estimation for Generalized Linear Regression.Technometrics (1996), 374\u2013381."},{"key":"e_1_3_2_1_3_1","volume-title":"Altheimer\u2019s Disease Neuroimaging Initiative","author":"Beaton Derek","year":"2016","unstructured":"Derek Beaton , J. Dunlop , and Herv\u00e9 Abdi . 2016. Altheimer\u2019s Disease Neuroimaging Initiative ( 2016 ), \u2018Partial Least Squares Correspondence Analysis: A Framework to Simultaneously Analyte Behavioral and Genetic Data\u2019, Psychological Methods . (2016). Derek Beaton, J. Dunlop, and Herv\u00e9 Abdi. 2016. Altheimer\u2019s Disease Neuroimaging Initiative (2016), \u2018Partial Least Squares Correspondence Analysis: A Framework to Simultaneously Analyte Behavioral and Genetic Data\u2019, Psychological Methods. (2016)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-40643-5_6"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Derek Beaton Gilbert Saporta and Herv\u00e9 Abdi. 2020. A generalization of partial least squares regression and correspondence analysis for categorical and mixed data: An application with the ADNI data. (2020).  Derek Beaton Gilbert Saporta and Herv\u00e9 Abdi. 2020. A generalization of partial least squares regression and correspondence analysis for categorical and mixed data: An application with the ADNI data. (2020).","DOI":"10.1101\/598888"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-40643-5_1"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Peter B\u00fchlmann and N. Meinshausen. 2015. Maximin effects in inhomogeneous large-scale data. Ann. Statist (2015).  Peter B\u00fchlmann and N. Meinshausen. 2015. Maximin effects in inhomogeneous large-scale data. Ann. Statist (2015).","DOI":"10.1214\/15-AOS1325"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Peter B\u00fchlmann and N. Meinshausen. 2016. Magging: maximin aggregation for inhomogeneous large-scale. (2016).  Peter B\u00fchlmann and N. Meinshausen. 2016. Magging: maximin aggregation for inhomogeneous large-scale. (2016).","DOI":"10.1109\/JPROC.2015.2494161"},{"key":"e_1_3_2_1_9_1","unstructured":"Gabriele Cantaluppi. 2012. A Partial Least Squares Algorithm Handling Ordinal Variables also in Presence of a Small Number of Categories. (2012).  Gabriele Cantaluppi. 2012. A Partial Least Squares Algorithm Handling Ordinal Variables also in Presence of a Small Number of Categories. (2012)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Gabriele Cantaluppi and Giuseppe Boari. 2016. A Partial Least Squares Algorithm Handling Ordinal Variables. (2016).  Gabriele Cantaluppi and Giuseppe Boari. 2016. A Partial Least Squares Algorithm Handling Ordinal Variables. (2016).","DOI":"10.1007\/978-3-319-40643-5_22"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"PL. de Micheaux B. Liquet and M. Sutton. 2019. PLS for Big Data: A Unified Parallel Algorithm for Regularized Group PLS.Statistics Surveys (2019).  PL. de Micheaux B. Liquet and M. Sutton. 2019. PLS for Big Data: A Unified Parallel Algorithm for Regularized Group PLS.Statistics Surveys (2019).","DOI":"10.1214\/19-SS125"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"E. Demidenko. 2016. The P-value you can\u2019t buy. The American Statistician(2016) 33\u201337.  E. Demidenko. 2016. The P-value you can\u2019t buy. The American Statistician(2016) 33\u201337.","DOI":"10.1080\/00031305.2015.1069760"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"R.\u00a0Cook Dennis and Liliana Forzani. 2017. Big data and partial least-squares prediction The Canadian Journal of Statistics. La revue canadienne de statistique(2017) 1\u201317.  R.\u00a0Cook Dennis and Liliana Forzani. 2017. Big data and partial least-squares prediction The Canadian Journal of Statistics. La revue canadienne de statistique(2017) 1\u201317.","DOI":"10.1002\/cjs.11316"},{"key":"e_1_3_2_1_14_1","unstructured":"Domo. 2020. Data neverc sleep. https:\/\/www.domo.com\/learn\/data-never-sleeps-8 https:\/\/www.domo.com\/learn\/data-never-sleeps-8.  Domo. 2020. Data neverc sleep. https:\/\/www.domo.com\/learn\/data-never-sleeps-8 https:\/\/www.domo.com\/learn\/data-never-sleeps-8."},{"key":"e_1_3_2_1_15_1","unstructured":"L. Eriksson E. Johansson N. Kettaneh-Wold and S. Wold. 2001. Multi-and Megavariate Data Analysis. (2001).  L. Eriksson E. Johansson N. Kettaneh-Wold and S. Wold. 2001. Multi-and Megavariate Data Analysis. (2001)."},{"key":"e_1_3_2_1_16_1","volume-title":"Classification using partial least squares with penalized logistic regression.Bioinformatics","author":"Fort Gersende","year":"2005","unstructured":"Gersende Fort and Sophie Lambert-Lacroix . 2005. Classification using partial least squares with penalized logistic regression.Bioinformatics ( 2005 ), 1104\u20131111. Gersende Fort and Sophie Lambert-Lacroix. 2005. Classification using partial least squares with penalized logistic regression.Bioinformatics (2005), 1104\u20131111."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.10.525"},{"key":"e_1_3_2_1_18_1","volume-title":"Theory and Applications of Correspondence Analysis","author":"Greenacre Michael","year":"1984","unstructured":"Michael Greenacre . 1984. Theory and Applications of Correspondence Analysis . Academic Press ( 1984 ). Michael Greenacre. 1984. Theory and Applications of Correspondence Analysis. Academic Press (1984)."},{"key":"e_1_3_2_1_19_1","volume-title":"Correspondence analysis","author":"Greenacre Michael","year":"2010","unstructured":"Michael Greenacre . 2010. Correspondence analysis . Wiley Interdisciplinary Reviews : Computational Statistics ( 2010 ), 613\u2013619. https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1002\/wics Michael Greenacre. 2010. Correspondence analysis. Wiley Interdisciplinary Reviews: Computational Statistics (2010), 613\u2013619. https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1002\/wics"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"T. Hastie R. Tibshirani and J. Friedman. 2009. The Elements of Statistical Learning: Data Mining Inference and Prediction.New York Springer (2009).  T. Hastie R. Tibshirani and J. Friedman. 2009. The Elements of Statistical Learning: Data Mining Inference and Prediction.New York Springer (2009).","DOI":"10.1007\/978-0-387-84858-7"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"C. Hayashi. 1952. On the prediction of phenomena from qualitative data and the quantification of qualitative data from the mathematicostatistical point of view. Ann. Inst. Statist. Math(1952) 69\u201398.  C. Hayashi. 1952. On the prediction of phenomena from qualitative data and the quantification of qualitative data from the mathematicostatistical point of view. Ann. Inst. Statist. Math(1952) 69\u201398.","DOI":"10.1007\/BF02949778"},{"key":"e_1_3_2_1_22_1","volume-title":"On the convergence of the partial least squares path modeling algorithm.Comput Stat","author":"Henseler J\u00f6rg","year":"2010","unstructured":"J\u00f6rg Henseler . 2010. On the convergence of the partial least squares path modeling algorithm.Comput Stat ( 2010 ), 107\u2013120. J\u00f6rg Henseler. 2010. On the convergence of the partial least squares path modeling algorithm.Comput Stat (2010), 107\u2013120."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Kjetil J\u00f8rgensen Vegard Segtnan Kari Thyholt and Tormod N\u00e6s. 2004. A comparison of methods for analysing regression models with both spectral and designed variables.Journal of Chemometrics(2004) 451\u2013464.  Kjetil J\u00f8rgensen Vegard Segtnan Kari Thyholt and Tormod N\u00e6s. 2004. A comparison of methods for analysing regression models with both spectral and designed variables.Journal of Chemometrics(2004) 451\u2013464.","DOI":"10.1002\/cem.890"},{"key":"e_1_3_2_1_24_1","volume-title":"CA and PLS with very large data sets. (November","author":"Kettaneha Nouna","year":"2003","unstructured":"Nouna Kettaneha , Anders Berglundb , and Svante Wold . 2003. CA and PLS with very large data sets. (November 2003 ). Nouna Kettaneha, Anders Berglundb, and Svante Wold. 2003. CA and PLS with very large data sets. (November 2003)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.06.061"},{"key":"e_1_3_2_1_26_1","volume-title":"1984. Multivariate descriptive statistical analysis: correspondence analysis and related techniques for large matrices","author":"Lebart L.","year":"1984","unstructured":"L. Lebart , A. Morineau , and M. Warwick , K. 1984. Multivariate descriptive statistical analysis: correspondence analysis and related techniques for large matrices . Wiley . ( 1984 ). L. Lebart, A. Morineau, and M. Warwick, K.1984. Multivariate descriptive statistical analysis: correspondence analysis and related techniques for large matrices. Wiley. (1984)."},{"key":"e_1_3_2_1_27_1","volume-title":"Latent variable path modeling with partial least squares","author":"Lohm\u00f6ller B.","year":"2013","unstructured":"J.\u00a0 B. Lohm\u00f6ller . 2013. Latent variable path modeling with partial least squares . Springer Science and Business Media( 2013 ). J.\u00a0B. Lohm\u00f6ller. 2013. Latent variable path modeling with partial least squares. Springer Science and Business Media(2013)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"T. Mehmood H. Martens S. S\u00e6b\u00f8 J. Warringer and L. Snipen. 2011. A partial least squares based algorithm for parsimonious variable selection. Algorithms for Molecular Biology. 27 6 (2011).  T. Mehmood H. Martens S. S\u00e6b\u00f8 J. Warringer and L. Snipen. 2011. A partial least squares based algorithm for parsimonious variable selection. Algorithms for Molecular Biology. 27 6 (2011).","DOI":"10.1186\/1748-7188-6-27"},{"key":"e_1_3_2_1_29_1","volume-title":"Multivariate Procedure for Variable Selection and Classification of High Dimensional Heterogeneous Data Communications for Statistical Applications and Methods.22 (November","author":"Mehmood Tahir","year":"2015","unstructured":"Tahir Mehmood and Zahid Rasheeda . 2015. Multivariate Procedure for Variable Selection and Classification of High Dimensional Heterogeneous Data Communications for Statistical Applications and Methods.22 (November 2015 ), 575\u2013587. https:\/\/doi.org\/10.5351\/CSAM.2015.22.6.575 10.5351\/CSAM.2015.22.6.575 Tahir Mehmood and Zahid Rasheeda. 2015. Multivariate Procedure for Variable Selection and Classification of High Dimensional Heterogeneous Data Communications for Statistical Applications and Methods.22 (November 2015), 575\u2013587. https:\/\/doi.org\/10.5351\/CSAM.2015.22.6.575"},{"key":"e_1_3_2_1_30_1","article-title":"Comparaison de variantes de r\u00e9gressions logistiques PLS et de r\u00e9gression PLS sur variables qualitatives : application aux donn\u00e9es d\u2019all\u00e9lotypage","volume":"151","author":"Meyer Nicolas","year":"2010","unstructured":"Nicolas Meyer , Myriam Maumy-Bertrand , and Fr\u00e9d\u00e9ric Bertrand . 2010 . Comparaison de variantes de r\u00e9gressions logistiques PLS et de r\u00e9gression PLS sur variables qualitatives : application aux donn\u00e9es d\u2019all\u00e9lotypage . Journal de la Soci\u00e9t\u00e9 Fran\u00e7aise de Statistique 151 , 2(2010). Nicolas Meyer, Myriam Maumy-Bertrand, and Fr\u00e9d\u00e9ric Bertrand. 2010. Comparaison de variantes de r\u00e9gressions logistiques PLS et de r\u00e9gression PLS sur variables qualitatives : application aux donn\u00e9es d\u2019all\u00e9lotypage. Journal de la Soci\u00e9t\u00e9 Fran\u00e7aise de Statistique 151, 2(2010).","journal-title":"Journal de la Soci\u00e9t\u00e9 Fran\u00e7aise de Statistique"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"D. Nguyen and D. Rocke. 2002. Tumor classification by Partial Least Squares using microarray gene expression data.Bioinformatics (2002) 39\u201350.  D. Nguyen and D. Rocke. 2002. Tumor classification by Partial Least Squares using microarray gene expression data.Bioinformatics (2002) 39\u201350.","DOI":"10.1093\/bioinformatics\/18.1.39"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1038\/35021093"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"S.\u00a0L. Pomeroy P. Tamayo and M. Gaasenbeek. 2002. reduction of central nervous system embryonal tumour outcome based on gene expression.Nature 415(2002) 436\u2013442.  S.\u00a0L. Pomeroy P. Tamayo and M. Gaasenbeek. 2002. reduction of central nervous system embryonal tumour outcome based on gene expression.Nature 415(2002) 436\u2013442.","DOI":"10.1038\/415436a"},{"key":"e_1_3_2_1_34_1","volume-title":"Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach","author":"P\u00e9rez-Enciso Miguel","year":"2003","unstructured":"Miguel P\u00e9rez-Enciso and Michel Tenenhaus . 2003. Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach . Springer-Verlag ( 2003 ). Miguel P\u00e9rez-Enciso and Michel Tenenhaus. 2003. Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach. Springer-Verlag (2003)."},{"key":"e_1_3_2_1_35_1","volume-title":"A robust and scalable approach to face identification.Lecture Notes in Computer Science 6316 (September","author":"Robson William","year":"2010","unstructured":"William Robson , Schwartz\u00a0Huimin Guo , and Larry\u00a0 S. Davis . 2010. A robust and scalable approach to face identification.Lecture Notes in Computer Science 6316 (September 2010 ), 476\u2013489. https:\/\/doi.org\/10.1007\/978-3-642-15567-3_35 10.1007\/978-3-642-15567-3_35 William Robson, Schwartz\u00a0Huimin Guo, and Larry\u00a0S. Davis. 2010. A robust and scalable approach to face identification.Lecture Notes in Computer Science 6316 (September 2010), 476\u2013489. https:\/\/doi.org\/10.1007\/978-3-642-15567-3_35"},{"key":"e_1_3_2_1_36_1","unstructured":"Roman Rosipal. [n.d.]. Nonlinear Partial Least Squares: An Overview.([n.\u00a0d.]).  Roman Rosipal. [n.d.]. Nonlinear Partial Least Squares: An Overview.([n.\u00a0d.])."},{"key":"e_1_3_2_1_37_1","volume-title":"A Proposal for Handling Categorical Predictors in PLS Regression Framework","author":"Russolillo Giorgio","year":"2008","unstructured":"Giorgio Russolillo and Carlo\u00a0Natale Lauro . 2008. A Proposal for Handling Categorical Predictors in PLS Regression Framework . Springer , Berlin, Heidelberg publisher( 2008 ). Giorgio Russolillo and Carlo\u00a0Natale Lauro. 2008. A Proposal for Handling Categorical Predictors in PLS Regression Framework. Springer, Berlin, Heidelberg publisher(2008)."},{"key":"e_1_3_2_1_38_1","volume-title":"Dijkstra","author":"Schuberth Florian","year":"2016","unstructured":"Florian Schuberth , J\u00f6rg Henseler , and Theo K . Dijkstra . 2016 . Partial least squares path modeling using ordinal categorical indicators. 52 (2016). https:\/\/doi.org\/10.1007\/s11135-016-0401-7 10.1007\/s11135-016-0401-7 Florian Schuberth, J\u00f6rg Henseler, and Theo K.Dijkstra. 2016. Partial least squares path modeling using ordinal categorical indicators. 52 (2016). https:\/\/doi.org\/10.1007\/s11135-016-0401-7"},{"key":"e_1_3_2_1_39_1","volume-title":"IEEE 11th International Symposium on Biomedical Imaging (ISBI)","author":"Sheng J.","year":"2014","unstructured":"J. Sheng , S. Kim , J. Yan , J. Moore , A. Saykin , and L. Shen . 2014. Data synthesis and method evaluation for brain imaging genetics . IEEE 11th International Symposium on Biomedical Imaging (ISBI) ( 2014 ), 1202\u20131205. J. Sheng, S. Kim, J. Yan, J. Moore, A. Saykin, and L. Shen. 2014. Data synthesis and method evaluation for brain imaging genetics. IEEE 11th International Symposium on Biomedical Imaging (ISBI) (2014), 1202\u20131205."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Y. Tabei H. Saigo Y. Yamanishi and Simon\u00a0J. Puglisi. 2016. Scalable partial least squares regression on grammar-compressed data matrices. (2016).  Y. Tabei H. Saigo Y. Yamanishi and Simon\u00a0J. Puglisi. 2016. Scalable partial least squares regression on grammar-compressed data matrices. (2016).","DOI":"10.1145\/2939672.2939864"},{"key":"e_1_3_2_1_41_1","volume-title":"Editions TECHNIP.","author":"Tenenhaus M.","year":"1998","unstructured":"M. Tenenhaus . 1998. La r\u00e9gression PLS: th\u00e9orie et pratique , Editions TECHNIP. ( 1998 ). M. Tenenhaus. 1998. La r\u00e9gression PLS: th\u00e9orie et pratique, Editions TECHNIP. (1998)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"H. Wold. 1975. Soft modelling by latent variables: the non-linear iterative partial least squares (NIPALS) approach. Journal of Applied Probability(1975) 117\u2013142.  H. Wold. 1975. Soft modelling by latent variables: the non-linear iterative partial least squares (NIPALS) approach. Journal of Applied Probability(1975) 117\u2013142.","DOI":"10.1017\/S0021900200047604"},{"volume-title":"Partial least squares.Encyclopedia of statistical sciences(1985)","author":"Wold H.","key":"e_1_3_2_1_43_1","unstructured":"H. Wold . 1985. Partial least squares.Encyclopedia of statistical sciences(1985) . H. Wold. 1985. Partial least squares.Encyclopedia of statistical sciences(1985)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"S. Wold K. Esbensen and P. Geladi. 1987. Principal components analysis.(1987) 37\u201352.  S. Wold K. Esbensen and P. Geladi. 1987. Principal components analysis.(1987) 37\u201352.","DOI":"10.1016\/0169-7439(87)80084-9"},{"key":"e_1_3_2_1_45_1","series-title":"SIAM J. Sci. Statist. Comput.(1984), 735\u2013743","volume-title":"The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses","author":"Wold S.","unstructured":"S. Wold , A. Ruhe , H. Wold , and Dunn. 1984. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses . SIAM J. Sci. Statist. Comput.(1984), 735\u2013743 . S. Wold, A. Ruhe, H. Wold, and Dunn. 1984. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM J. Sci. Statist. Comput.(1984), 735\u2013743."},{"key":"e_1_3_2_1_46_1","volume-title":"Incremental partial least squares analysis of big streaming data. 47","author":"Zeng Xue-Qiang","year":"2014","unstructured":"Xue-Qiang Zeng and Guo-Zheng Li. 2014. Incremental partial least squares analysis of big streaming data. 47 ( 2014 ), 3726\u20133735. Xue-Qiang Zeng and Guo-Zheng Li. 2014. Incremental partial least squares analysis of big streaming data. 47 (2014), 3726\u20133735."}],"event":{"name":"NISS2021: The 4th International Conference on Networking, Information Systems & Security.","acronym":"NISS2021","location":"KENITRA AA Morocco"},"container-title":["Proceedings of the 4th International Conference on Networking, Information Systems &amp; Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3454127.3456615","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3454127.3456615","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:52Z","timestamp":1750193272000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3454127.3456615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4]]},"references-count":46,"alternative-id":["10.1145\/3454127.3456615","10.1145\/3454127"],"URL":"https:\/\/doi.org\/10.1145\/3454127.3456615","relation":{},"subject":[],"published":{"date-parts":[[2021,4]]},"assertion":[{"value":"2021-11-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}