{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T13:35:52Z","timestamp":1758893752035,"version":"3.37.3"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"DOI":"10.1007\/s10489-022-03752-5","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T03:26:31Z","timestamp":1655868391000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Active constrained deep embedded clustering with dual source"],"prefix":"10.1007","author":[{"given":"R.","family":"Hazratgholizadeh","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5898-0871","authenticated-orcid":false,"given":"M. A.","family":"Balafar","sequence":"additional","affiliation":[]},{"given":"M. R. F.","family":"Derakhshi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,22]]},"reference":[{"issue":"8","key":"3752_CR1","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"AK Jain","year":"2010","unstructured":"Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31(8):651\u2013666","journal-title":"Pattern Recogn Lett"},{"issue":"6","key":"3752_CR2","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1016\/j.ipm.2018.08.001","volume":"54","author":"B Alt\u0131nel","year":"2018","unstructured":"Alt\u0131nel B, Ganiz MC (2018) Semantic text classification: a survey of past and recent advances. Inf Process Manag 54(6):1129\u20131153","journal-title":"Inf Process Manag"},{"key":"3752_CR3","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.neucom.2017.05.046","volume":"266","author":"HK Kim","year":"2017","unstructured":"Kim HK, Kim H, Cho S (2017) Bag-of-concepts: comprehending document representation through clustering words in distributed representation. Neurocomputing 266:336\u2013352","journal-title":"Neurocomputing"},{"key":"3752_CR4","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.knosys.2018.02.020","volume":"148","author":"S Huang","year":"2018","unstructured":"Huang S, Xu Z, Lv J (2018) Adaptive local structure learning for document co-clustering. Knowl-Based Syst 148:74\u201384","journal-title":"Knowl-Based Syst"},{"key":"3752_CR5","doi-asserted-by":"publisher","first-page":"108161","DOI":"10.1016\/j.sigpro.2021.108161","volume":"188","author":"K Zhao","year":"2021","unstructured":"Zhao K, Dai Y, Jia Z, Ji Y (2021) General fuzzy C-means clustering algorithm using Minkowski metric. Signal Processing 188:108161","journal-title":"Signal Processing"},{"key":"3752_CR6","doi-asserted-by":"publisher","unstructured":"Dinler D, Tural MK (2016) A survey of constrained clustering. In: Celebi M, Aydin K (eds) Unsupervised Learning Algorithms. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-24211-8_9","DOI":"10.1007\/978-3-319-24211-8_9"},{"key":"3752_CR7","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.neucom.2018.10.016","volume":"325","author":"Y Ren","year":"2019","unstructured":"Ren Y, Hu K, Dai X, Pan L, Hoi SC, Xu Z (2019) Semi-supervised deep embedded clustering. Neurocomputing 325:121\u2013130","journal-title":"Neurocomputing"},{"key":"3752_CR8","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.patcog.2018.10.026","volume":"88","author":"A Adolfsson","year":"2019","unstructured":"Adolfsson A, Ackerman M, Brownstein NC (2019) To cluster, or not to cluster: an analysis of clusterability methods. Pattern Recogn 88:13\u201326","journal-title":"Pattern Recogn"},{"issue":"2","key":"3752_CR9","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","volume":"109","author":"JE Van Engelen","year":"2020","unstructured":"Van Engelen JE, Hoos HH (2020) A survey on semi-supervised learning. Mach Learn 109(2):373\u2013440","journal-title":"Mach Learn"},{"issue":"2","key":"3752_CR10","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s10115-012-0507-8","volume":"35","author":"Y Fu","year":"2013","unstructured":"Fu Y, Zhu X, Li B (2013) A survey on instance selection for active learning. Knowl Inf Syst 35(2):249\u2013283","journal-title":"Knowl Inf Syst"},{"key":"3752_CR11","doi-asserted-by":"publisher","first-page":"107628","DOI":"10.1016\/j.sigpro.2020.107628","volume":"174","author":"J Maggu","year":"2020","unstructured":"Maggu J, Majumdar A, Chouzenoux E, Chierchia G (2020) Deeply transformed subspace clustering. Signal Process 174:107628","journal-title":"Signal Process"},{"issue":"4","key":"3752_CR12","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1007\/s11390-020-9487-4","volume":"35","author":"P Kumar","year":"2020","unstructured":"Kumar P, Gupta A (2020) Active learning query strategies for classification, regression, and clustering: a survey. J Comput Sci Technol 35(4):913\u2013945","journal-title":"J Comput Sci Technol"},{"key":"3752_CR13","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.patcog.2016.11.003","volume":"64","author":"R Sheikhpour","year":"2017","unstructured":"Sheikhpour R, Sarram MA, Gharaghani S, Chahooki MAZ (2017) A survey on semi-supervised feature selection methods. Pattern Recogn 64:141\u2013158","journal-title":"Pattern Recogn"},{"issue":"2","key":"3752_CR14","doi-asserted-by":"publisher","first-page":"3513","DOI":"10.1007\/s10586-018-2199-7","volume":"22","author":"X Mai","year":"2019","unstructured":"Mai X, Cheng J, Wang S (2019) Research on semi supervised K-means clustering algorithm in data mining. Clust Comput 22(2):3513\u20133520","journal-title":"Clust Comput"},{"key":"3752_CR15","unstructured":"Olsson F (2009) A literature survey of active machine learning in the context of natural language processing. Swedish Institute of Computer Science. https:\/\/www.ccs.neu.edu\/home\/vip\/teach\/MLcourse\/4_boosting\/materials\/SICS-T--2009-06--SE.pdf"},{"issue":"1","key":"3752_CR16","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/TKDE.2013.22","volume":"26","author":"S Xiong","year":"2013","unstructured":"Xiong S, Azimi J, Fern XZ (2013) Active learning of constraints for semi-supervised clustering. IEEE Trans Knowl Data Eng 26(1):43\u201354","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"3752_CR17","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/TPAMI.2016.2539965","volume":"39","author":"C Xiong","year":"2016","unstructured":"Xiong C, Johnson DM, Corso JJ (2016) Active clustering with model-based uncertainty reduction. IEEE Trans Pattern Anal Mach Intell 39(1):5\u201317","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3752_CR18","doi-asserted-by":"crossref","unstructured":"Basu S, Banerjee A, Mooney RJ (2004) Active semi-supervision for pairwise constrained clustering. In: Proceedings of the 2004 SIAM international conference on data mining: 2004: SIAM, 333\u2013344","DOI":"10.1137\/1.9781611972740.31"},{"key":"3752_CR19","doi-asserted-by":"crossref","unstructured":"Bilenko M, Basu S, Mooney RJ (2004) Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of the twenty-first international conference on Machine learning: 2004, 11","DOI":"10.1145\/1015330.1015360"},{"issue":"2","key":"3752_CR20","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/s10618-020-00734-4","volume":"35","author":"H Zhang","year":"2021","unstructured":"Zhang H, Zhan T, Basu S, Davidson I (2021) A framework for deep constrained clustering. Data Min Knowl Disc 35(2):593\u2013620","journal-title":"Data Min Knowl Disc"},{"issue":"2","key":"3752_CR21","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1007\/s11280-019-00723-8","volume":"23","author":"X Li","year":"2020","unstructured":"Li X, Yin H, Zhou K, Zhou X (2020) Semi-supervised clustering with deep metric learning and graph embedding. World Wide Web 23(2):781\u2013798","journal-title":"World Wide Web"},{"key":"3752_CR22","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.neunet.2020.04.017","volume":"127","author":"M \u015amieja","year":"2020","unstructured":"\u015amieja M, Struski \u0141, Figueiredo MAT (2020) A classification-based approach to semi-supervised clustering with pairwise constraints. Neural Netw 127:193\u2013203","journal-title":"Neural Netw"},{"issue":"9","key":"3752_CR23","doi-asserted-by":"publisher","first-page":"1497","DOI":"10.1007\/s10994-017-5643-7","volume":"106","author":"T Van Craenendonck","year":"2017","unstructured":"Van Craenendonck T, Blockeel H (2017) Constraint-based clustering selection. Mach Learn 106(9):1497\u20131521","journal-title":"Mach Learn"},{"key":"3752_CR24","unstructured":"Settles B (2009) Active learning literature survey. Computer sciences technical report 1648. University of Wisconsin-Madison. https:\/\/minds.wisconsin.edu\/handle\/1793\/60660"},{"key":"3752_CR25","doi-asserted-by":"crossref","unstructured":"Li Y, Wang Y, Yu D, Ye N, Hu P, Zhao R (2020) ASCENT: active supervision for semi-supervised learning. IEEE Trans Knowl Data Eng 32(5):868\u2013882","DOI":"10.1109\/TKDE.2019.2897307"},{"issue":"3","key":"3752_CR26","doi-asserted-by":"publisher","first-page":"2381","DOI":"10.1007\/s00500-019-04069-1","volume":"24","author":"X Wang","year":"2020","unstructured":"Wang X, Ding S, Jia W (2020) Active constraint spectral clustering based on hessian matrix. Soft Comput 24(3):2381\u20132390","journal-title":"Soft Comput"},{"issue":"9","key":"3752_CR27","doi-asserted-by":"publisher","first-page":"3247","DOI":"10.1109\/TPAMI.2020.2979699","volume":"43","author":"L Bai","year":"2021","unstructured":"Bai L, Liang J, Cao F (2021) Semi-supervised clustering with constraints of different types from multiple information sources. IEEE Trans Pattern Anal Mach Intell 43(9):3247\u20133258","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3752_CR28","doi-asserted-by":"publisher","first-page":"22794","DOI":"10.1109\/ACCESS.2018.2817845","volume":"6","author":"Z Wang","year":"2018","unstructured":"Wang Z, Fang X, Tang X, Wu C (2018) Multi-class active learning by integrating uncertainty and diversity. IEEE Access 6:22794\u201322803","journal-title":"IEEE Access"},{"key":"3752_CR29","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.ins.2017.10.027","volume":"507","author":"H Yu","year":"2018","unstructured":"Yu H, Wang X, Wang G, Zeng X (2018) An active three-way clustering method via low-rank matrices for multi-view data. Inf Sci 507:50\u201360","journal-title":"Inf Sci"},{"issue":"12","key":"3752_CR30","doi-asserted-by":"publisher","first-page":"2591","DOI":"10.1109\/TCSVT.2016.2589879","volume":"27","author":"K Wang","year":"2016","unstructured":"Wang K, Zhang D, Li Y, Zhang R, Lin L (2016) Cost-effective active learning for deep image classification. IEEE Trans Circuits Syst Video Technol 27(12):2591\u20132600","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"4","key":"3752_CR31","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.jfds.2017.05.001","volume":"2","author":"G Zhong","year":"2016","unstructured":"Zhong G, Wang L-N, Ling X, Dong J (2016) An overview on data representation learning: from traditional feature learning to recent deep learning. J Finan Data Sci 2(4):265\u2013278","journal-title":"J Finan Data Sci"},{"key":"3752_CR32","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.neucom.2012.02.021","volume":"89","author":"Y Ren","year":"2012","unstructured":"Ren Y, Zhang G, Yu G, Li X (2012) Local and global structure preserving based feature selection. Neurocomputing 89:147\u2013157","journal-title":"Neurocomputing"},{"key":"3752_CR33","doi-asserted-by":"crossref","unstructured":"Guo X, Gao L, Liu X, Yin J (2017) Improved deep embedded clustering with local structure preservation. In: Ijcai: 2017, 1753\u20131759","DOI":"10.24963\/ijcai.2017\/243"},{"key":"3752_CR34","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1007\/s10489-021-02515-y","volume":"52","author":"V Ili\u0107","year":"2021","unstructured":"Ili\u0107 V, Tadi\u0107 J (2021) Active learning using a self-correcting neural network (ALSCN). Appl Intell 52:1956\u20131968","journal-title":"Appl Intell"},{"issue":"4","key":"3752_CR35","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1007\/s10489-019-01581-7","volume":"50","author":"W Guo","year":"2020","unstructured":"Guo W, Cai J, Wang S (2020) Unsupervised discriminative feature representation via adversarial auto-encoder. Appl Intell 50(4):1155\u20131171","journal-title":"Appl Intell"},{"key":"3752_CR36","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neucom.2020.12.094","volume":"433","author":"B Diallo","year":"2021","unstructured":"Diallo B, Hu J, Li T, Khan GA, Liang X, Zhao Y (2021) Deep embedding clustering based on contractive autoencoder. Neurocomputing 433:96\u2013107","journal-title":"Neurocomputing"},{"key":"3752_CR37","doi-asserted-by":"publisher","first-page":"11093","DOI":"10.1109\/ACCESS.2019.2891970","volume":"7","author":"J Enguehard","year":"2019","unstructured":"Enguehard J, O\u2019Halloran P, Gholipour A (2019) Semi-supervised learning with deep embedded clustering for image classification and segmentation. IEEE Access 7:11093\u201311104","journal-title":"IEEE Access"},{"issue":"7","key":"3752_CR38","doi-asserted-by":"publisher","first-page":"2496","DOI":"10.1109\/TPAMI.2020.2973634","volume":"43","author":"X Jia","year":"2021","unstructured":"Jia X, Jing XY, Zhu X, Chen S, Du B, Cai Z, He Z, Yue D (2021) Semi-supervised multi-view deep discriminant representation learning. IEEE Trans Pattern Anal Mach Intell 43(7):2496\u20132509","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"8","key":"3752_CR39","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798\u20131828","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"3752_CR40","doi-asserted-by":"publisher","first-page":"1721","DOI":"10.1007\/s11063-018-9794-8","volume":"48","author":"MT Ngoc","year":"2018","unstructured":"Ngoc MT, Park D-C (2018) Centroid neural network with pairwise constraints for semi-supervised learning. Neural Process Lett 48(3):1721\u20131747","journal-title":"Neural Process Lett"},{"key":"3752_CR41","unstructured":"Peng X, Xiao S, Feng J, Yau W-Y, Yi Z (2016) Deep subspace clustering with sparsity prior. In: IJCAI: 2016, 1925\u20131931"},{"key":"3752_CR42","unstructured":"Xie J, Girshick R, Farhadi A (2016) Unsupervised deep embedding for clustering analysis. In: International conference on machine learning: 2016: PMLR, 478\u2013487"},{"key":"3752_CR43","doi-asserted-by":"publisher","first-page":"106190","DOI":"10.1016\/j.knosys.2020.106190","volume":"204","author":"AQ Ohi","year":"2020","unstructured":"Ohi AQ, Mridha MF, Safir FB, Hamid MA, Monowar MM (2020) Autoembedder: a semi-supervised DNN embedding system for clustering. Knowl-Based Syst 204:106190","journal-title":"Knowl-Based Syst"},{"key":"3752_CR44","unstructured":"Wagstaff K, Cardie C, Rogers S, Schr\u00f6dl S (2001) Constrained k-means clustering with background knowledge. In: Icml: 2001, 577\u2013584"},{"key":"3752_CR45","unstructured":"Basu S (2003) Semi-supervised clustering: learning with limited user feedback: computer science department, University of Texas at Austin"},{"key":"3752_CR46","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition: 2015, 815\u2013823","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"3752_CR47","unstructured":"Hsu Y-C, Kira Z (2015) Neural network-based clustering using pairwise constraints. arXiv preprint arXiv:151106321"},{"issue":"9","key":"3752_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3472291","volume":"54","author":"P Ren","year":"2021","unstructured":"Ren P, Xiao Y, Chang X, Huang P-Y, Li Z, Gupta BB, Chen X, Wang X (2021) A survey of deep active learning. ACM Comput Surv (CSUR) 54(9):1\u201340","journal-title":"ACM Comput Surv (CSUR)"},{"key":"3752_CR49","doi-asserted-by":"crossref","unstructured":"Greene D, Cunningham P (2007) Constraint selection by committee: An ensemble approach to identifying informative constraints for semi-supervised clustering. In: European Conference on Machine Learning: 2007: Springer, 140\u2013151","DOI":"10.1007\/978-3-540-74958-5_16"},{"issue":"12","key":"3752_CR50","doi-asserted-by":"publisher","first-page":"2394","DOI":"10.1109\/TKDE.2018.2818729","volume":"30","author":"Z Yu","year":"2018","unstructured":"Yu Z, Luo P, Liu J, Wong H, You J, Han G, Zhang J (2018) Semi-supervised ensemble clustering based on selected constraint projection. IEEE Trans Knowl Data Eng 30(12):2394\u20132407","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3752_CR51","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.neucom.2017.01.001","volume":"235","author":"F Yang","year":"2017","unstructured":"Yang F, Li T, Zhou Q, Xiao H (2017) Cluster ensemble selection with constraints. Neurocomputing 235:59\u201370","journal-title":"Neurocomputing"},{"issue":"3","key":"3752_CR52","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1109\/TKDE.2015.2499200","volume":"28","author":"Z Yu","year":"2015","unstructured":"Yu Z, Luo P, You J, Wong H-S, Leung H, Wu S, Zhang J, Han G (2015) Incremental semi-supervised clustering ensemble for high dimensional data clustering. IEEE Trans Knowl Data Eng 28(3):701\u2013714","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3752_CR53","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.asoc.2017.01.023","volume":"54","author":"RM de Oliveira","year":"2017","unstructured":"de Oliveira RM, Chaves AA, Lorena LAN (2017) A comparison of two hybrid methods for constrained clustering problems. Appl Soft Comput 54:256\u2013266","journal-title":"Appl Soft Comput"},{"key":"3752_CR54","doi-asserted-by":"publisher","first-page":"46255","DOI":"10.1109\/ACCESS.2020.2978404","volume":"8","author":"Q Lei","year":"2020","unstructured":"Lei Q, Li T (2020) Semi-supervised selective affinity propagation ensemble clustering with active constraints. IEEE Access 8:46255\u201346266","journal-title":"IEEE Access"},{"key":"3752_CR55","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.neucom.2014.09.106","volume":"188","author":"X Xu","year":"2016","unstructured":"Xu X, He P (2016) Improving clustering with constrained communities. Neurocomputing 188:239\u2013252","journal-title":"Neurocomputing"},{"key":"3752_CR56","doi-asserted-by":"crossref","unstructured":"Mallapragada PK, Jin R, Jain AK (2008) Active query selection for semi-supervised clustering. In: 2008 19Th international conference on pattern recognition: 2008: IEEE, 1\u20134","DOI":"10.1109\/ICPR.2008.4761792"},{"key":"3752_CR57","unstructured":"Liu X (2017) Joint constrained clustering and feature learning based on deep neural networks. Applied Sciences: School of Computing Science"},{"key":"3752_CR58","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.patrec.2020.07.028","volume":"138","author":"MM Fard","year":"2020","unstructured":"Fard MM, Thonet T, Gaussier E (2020) Deep k-means: jointly clustering with k-means and learning representations. Pattern Recogn Lett 138:185\u2013192","journal-title":"Pattern Recogn Lett"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03752-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03752-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03752-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T04:45:38Z","timestamp":1655873138000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03752-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,22]]},"references-count":58,"alternative-id":["3752"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03752-5","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2022,6,22]]},"assertion":[{"value":"9 May 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}