{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:30:51Z","timestamp":1773804651774,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,11,14]],"date-time":"2020-11-14T00:00:00Z","timestamp":1605312000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,14]],"date-time":"2020-11-14T00:00:00Z","timestamp":1605312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61941113"],"award-info":[{"award-number":["61941113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["30918015103"],"award-info":[{"award-number":["30918015103"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["30918012204"],"award-info":[{"award-number":["30918012204"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Nanjing Science and Technology Development Plan Project","award":["201805036"],"award-info":[{"award-number":["201805036"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s10489-020-01953-4","type":"journal-article","created":{"date-parts":[[2020,11,14]],"date-time":"2020-11-14T04:53:00Z","timestamp":1605329580000},"page":"3475-3489","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Batch mode active learning via adaptive criteria weights"],"prefix":"10.1007","volume":"51","author":[{"given":"Hao","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2219-067X","authenticated-orcid":false,"given":"Yongli","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanchao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruxin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,14]]},"reference":[{"key":"1953_CR1","unstructured":"Ash JT, Zhang C, Krishnamurthy A, Langford J, Agarwal A (2019) Deep batch active learning by diverse, uncertain gradient lower bounds arXiv: Learning"},{"key":"1953_CR2","doi-asserted-by":"crossref","unstructured":"Balcan MF, Broder A, Zhang T (2007) Margin based active learning. In: International conference on computational learning theory. Springer, pp 35\u201350","DOI":"10.1007\/978-3-540-72927-3_5"},{"key":"1953_CR3","doi-asserted-by":"crossref","unstructured":"Beluch WH, Genewein T, N\u00fcrnberger A, K\u00f6hler JM (2018) The power of ensembles for active learning in image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9368\u20139377","DOI":"10.1109\/CVPR.2018.00976"},{"issue":"14","key":"1953_CR4","doi-asserted-by":"publisher","first-page":"e49","DOI":"10.1093\/bioinformatics\/btl242","volume":"22","author":"KM Borgwardt","year":"2006","unstructured":"Borgwardt KM, Gretton A, Rasch MJ, Kriegel HP, Sch\u00f6lkopf B, Smola A (2006) Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22(14):e49\u2013e57","journal-title":"Bioinformatics"},{"key":"1953_CR5","unstructured":"Brinker K (2003) Incorporating diversity in active learning with support vector machines. In: Proceedings of the 20th international conference on machine learning (ICML-03), pp 59\u201366"},{"issue":"2","key":"1953_CR6","first-page":"15","volume":"36","author":"W Cai","year":"2017","unstructured":"Cai W, Zhang Y, Zhang Y, Zhou S, Wang W, Chen Z, Ding C (2017) Active learning for classification with maximum model change. ACM Trans Inform Sys (TOIS) 36(2):15","journal-title":"ACM Trans Inform Sys (TOIS)"},{"issue":"8","key":"1953_CR7","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1109\/TNNLS.2014.2356470","volume":"26","author":"S Chakraborty","year":"2015","unstructured":"Chakraborty S, Balasubramanian V, Panchanathan S (2015) Adaptive batch mode active learning. IEEE Trans Neural Netw Learning Sys 26(8):1747\u20131760","journal-title":"IEEE Trans Neural Netw Learning Sys"},{"issue":"10","key":"1953_CR8","doi-asserted-by":"publisher","first-page":"1945","DOI":"10.1109\/TPAMI.2015.2389848","volume":"37","author":"S Chakraborty","year":"2015","unstructured":"Chakraborty S, Balasubramanian V, Sun Q, Panchanathan S, Ye J (2015) Active batch selection via convex relaxations with guaranteed solution bounds. IEEE Trans Pattern Anal Mach Intell 37 (10):1945\u20131958","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1953_CR9","unstructured":"Chattopadhyay R, Fan W, Davidson I, Panchanathan S, Ye J (2013) Joint transfer and batch-mode active learning. In: International conference on machine learning, pp 253\u2013261"},{"issue":"3","key":"1953_CR10","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1145\/2513092.2513094","volume":"7","author":"R Chattopadhyay","year":"2013","unstructured":"Chattopadhyay R, Wang Z, Fan W, Davidson I, Panchanathan S, Ye J (2013) Batch mode active sampling based on marginal probability distribution matching. ACM Transactions on Knowledge Discovery from Data (TKDD) 7(3):13","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"key":"1953_CR11","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L, Li K, Feifei L (2009) Imagenet: a large-scale hierarchical image database. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"2-3","key":"1953_CR12","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1007\/s10994-015-5500-5","volume":"100","author":"A Dhurandhar","year":"2015","unstructured":"Dhurandhar A, Sankaranarayanan K (2015) Improving classification performance through selective instance completion. Mach Learn 100(2-3):425\u2013447","journal-title":"Mach Learn"},{"key":"1953_CR13","doi-asserted-by":"crossref","unstructured":"Donmez P, Carbonell J, Bennett PN (2007) Dual strategy active learning. In: European conference on machine learning. Springer, pp 116\u2013127","DOI":"10.1007\/978-3-540-74958-5_14"},{"issue":"1","key":"1953_CR14","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/TCYB.2015.2496974","volume":"47","author":"B Du","year":"2017","unstructured":"Du B, Wang Z, Zhang L, Zhang L, Liu W, Shen J, Tao D (2017) Exploring representativeness and informativeness for active learning. IEEE Trans Cybern 47(1):14\u201326","journal-title":"IEEE Trans Cybern"},{"key":"1953_CR15","doi-asserted-by":"crossref","unstructured":"Esna Ashari Z, Ghasemzadeh H (2019) Mindful active learning. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence, pp 2265\u20132271","DOI":"10.24963\/ijcai.2019\/314"},{"issue":"2","key":"1953_CR16","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88(2):303\u2013338","journal-title":"Int J Comput Vis"},{"issue":"2-3","key":"1953_CR17","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1023\/A:1007330508534","volume":"28","author":"Y Freund","year":"1997","unstructured":"Freund Y, Seung HS, Shamir E, Tishby N (1997) Selective sampling using the query by committee algorithm. Machine Learning 28(2-3):133\u2013168","journal-title":"Machine Learning"},{"key":"1953_CR18","doi-asserted-by":"crossref","unstructured":"Fu W, Wang M, Hao S, Wu X (2018) Scalable active learning by approximated error reduction. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. ACM, pp 1396\u20131405","DOI":"10.1145\/3219819.3219954"},{"key":"1953_CR19","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1007\/s10489-018-1315-y","volume":"49","author":"H Fujita","year":"2019","unstructured":"Fujita H, Gaeta A, Loia V, Orciuoli F (2019) Improving awareness in early stages of security analysis: a zone partition method based on grc. Appl Intell 49:1063\u20131077","journal-title":"Appl Intell"},{"issue":"5","key":"1953_CR20","first-page":"1835","volume":"49","author":"H Fujita","year":"2019","unstructured":"Fujita H, Gaeta A, Loia V, Orciuoli F (2019) Resilience analysis of critical infrastructures: a cognitive approach based on granular computing. IEEE Trans Sys Man Cybern 49(5):1835\u20131848","journal-title":"IEEE Trans Sys Man Cybern"},{"key":"1953_CR21","unstructured":"Gilad-Bachrach R, Navot A, Tishby N (2006) Query by committee made real. In: Advances in neural information processing systems, pp 443\u2013450"},{"issue":"Mar","key":"1953_CR22","first-page":"723","volume":"13","author":"A Gretton","year":"2012","unstructured":"Gretton A, Borgwardt KM, Rasch MJ, Sch\u00f6lkopf B, Smola A (2012) A kernel two-sample test. J Mach Learn Res 13(Mar):723\u2013773","journal-title":"J Mach Learn Res"},{"key":"1953_CR23","unstructured":"Guo Y, Greiner R (2007) Optimistic active-learning using mutual information. In: IJCAI, vol 7, pp 823\u2013829"},{"key":"1953_CR24","unstructured":"Guo Y, Schuurmans D (2008) Discriminative batch mode active learning. In: Advances in neural information processing systems, pp 593\u2013600"},{"issue":"1","key":"1953_CR25","first-page":"254","volume":"19","author":"X He","year":"2009","unstructured":"He X (2009) Laplacian regularized d-optimal design for active learning and its application to image retrieval. IEEE Trans Image Process 19(1):254\u2013263","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"1953_CR26","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1109\/TKDE.2009.60","volume":"21","author":"SC Hoi","year":"2009","unstructured":"Hoi SC, Jin R, Lyu MR (2009) Batch mode active learning with applications to text categorization and image retrieval. IEEE Trans Knowl Data Eng 21(9):1233\u20131248","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"3","key":"1953_CR27","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1145\/1508850.1508854","volume":"27","author":"SC Hoi","year":"2009","unstructured":"Hoi SC, Jin R, Zhu J, Lyu MR (2009) Semisupervised svm batch mode active learning with applications to image retrieval. ACM Transactions on Information Systems (TOIS) 27(3):16","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"1953_CR28","unstructured":"Hu Y, Zhang D, Jin Z, Cai D, He X (2013) Active learning via neighborhood reconstruction. In: Proceedings of the twenty-third international joint conference on artificial intelligence, pp 1415\u20131421"},{"issue":"10","key":"1953_CR29","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1109\/TPAMI.2014.2307881","volume":"36","author":"SJ Huang","year":"2014","unstructured":"Huang SJ, Jin R, Zhou ZH (2014) Active learning by querying informative and representative examples. IEEE Trans Pattern Anal Mach Intell 36(10):1936\u20131949","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1953_CR30","doi-asserted-by":"crossref","unstructured":"Joshi AJ, Porikli F, Papanikolopoulos N (2009) Multi-class active learning for image classification. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 2372\u20132379","DOI":"10.1109\/CVPR.2009.5206627"},{"key":"1953_CR31","unstructured":"Kapoor A, Horvitz E, Basu S (2007) Selective supervision: guiding supervised learning with decision-theoretic active learning. In: IJCAI, vol 7, pp 877\u2013882"},{"key":"1953_CR32","unstructured":"Kirsch A, van Amersfoort J, Gal Y (2019) Batchbald: efficient and diverse batch acquisition for deep bayesian active learning. In: Advances in neural information processing systems, pp 7026\u20137037"},{"key":"1953_CR33","unstructured":"Konyushkova K, Sznitman R, Fua P (2017) Learning active learning from data. In: Advances in neural information processing systems 30, pp 4225\u20134235"},{"key":"1953_CR34","unstructured":"Li CL, Ferng CS, Lin HT (2012) Active learning with hinted support vector machine. In: Proceedings of asian conference on machine learning. PMLR, pp 221\u2013235"},{"issue":"5","key":"1953_CR35","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1109\/TKDE.2019.2897307","volume":"32","author":"Y Li","year":"2019","unstructured":"Li Y, Wang Y, Yu DJ, Ye N, Hu P, Zhao R (2019) Ascent: active supervision for semi-supervised learning. IEEE Trans Knowl Data Eng 32(5):868\u2013882","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1953_CR36","doi-asserted-by":"crossref","unstructured":"Long C, Hua G (2015) Multi-class multi-annotator active learning with robust gaussian process for visual recognition. In: Proceedings of the IEEE international conference on computer vision, pp 2839\u20132847","DOI":"10.1109\/ICCV.2015.325"},{"issue":"2","key":"1953_CR37","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/s11263-015-0834-9","volume":"116","author":"C Long","year":"2016","unstructured":"Long C, Hua G, Kapoor A (2016) A joint gaussian process model for active visual recognition with expertise estimation in crowdsourcing. Int J Comput Vis 116(2):136\u2013160","journal-title":"Int J Comput Vis"},{"key":"1953_CR38","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, pp 1\u20134. IEEE","DOI":"10.1109\/ICPR.2008.4761792"},{"key":"1953_CR39","unstructured":"Murugesan K, Carbonell J (2017) Active learning from peers. In: Advances in neural information processing systems 30, pp 7008\u20137017"},{"key":"1953_CR40","unstructured":"Nie F, Wang H, Huang H, Ding C (2013) Early active learning via robust representation and structured sparsity. In: Twenty-third international joint conference on artificial intelligence, pp 1572\u20131578"},{"key":"1953_CR41","doi-asserted-by":"crossref","unstructured":"Nie F, Wang X, Huang H (2014) Clustering and projected clustering with adaptive neighbors. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 977\u2013986","DOI":"10.1145\/2623330.2623726"},{"key":"1953_CR42","doi-asserted-by":"crossref","unstructured":"Paul S, Bappy JH, Roy-Chowdhury AK (2017) Non-uniform subset selection for active learning in structured data. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6846\u20136855","DOI":"10.1109\/CVPR.2017.95"},{"key":"1953_CR43","unstructured":"Pinsler R, Gordon J, Nalisnick E, Hernandezlobato JM (2019) Bayesian batch active learning as sparse subset approximation. In: Advances in neural information processing systems, pp 6356\u20136367"},{"issue":"4","key":"1953_CR44","first-page":"51","volume":"5","author":"AM Sandoval","year":"2019","unstructured":"Sandoval AM, Diaz J, Llanos LC, Redondo T (2019) Biomedical term extraction: Nlp techniques in computational medicine. Int J Interact Multimed Artificial Intell 5(4):51\u201359","journal-title":"Int J Interact Multimed Artificial Intell"},{"key":"1953_CR45","unstructured":"Settles B (2009) Active learning literature survey. Tech. rep., University of Wisconsin-Madison Department of Computer Sciences"},{"key":"1953_CR46","doi-asserted-by":"crossref","unstructured":"Seung HS, Opper M, Sompolinsky H (1992) Query by committee. In: Proceedings of the fifth annual workshop on computational learning theory, pp 287\u2013294. ACM","DOI":"10.1145\/130385.130417"},{"key":"1953_CR47","doi-asserted-by":"crossref","unstructured":"Tang YP, Huang SJ (2019) Self-paced active learning: query the right thing at the right time. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 5117\u2013 5124","DOI":"10.1609\/aaai.v33i01.33015117"},{"key":"1953_CR48","unstructured":"Tang YP, Li GX, Huang SJ (2019) ALiPy: active learning in python. Tech. rep., Nanjing University of Aeronautics and Astronautics. https:\/\/github.com\/NUAA-AL\/ALiPy. Available as arXiv:1901.03802"},{"issue":"Nov","key":"1953_CR49","first-page":"45","volume":"2","author":"S Tong","year":"2001","unstructured":"Tong S, Koller D (2001) Support vector machine active learning with applications to text classification. J Mach Learn Res 2(Nov):45\u201366","journal-title":"J Mach Learn Res"},{"key":"1953_CR50","doi-asserted-by":"crossref","unstructured":"Vasisht D, Damianou A, Varma M, Kapoor A (2014) Active learning for sparse bayesian multilabel classification. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 472\u2013481","DOI":"10.1145\/2623330.2623759"},{"key":"1953_CR51","doi-asserted-by":"crossref","unstructured":"Wang H, Chang X, Shi L, Yang Y, Shen YD (2018) Uncertainty sampling for action recognition via maximizing expected average precision. In: IJCAI, pp 964\u2013970","DOI":"10.24963\/ijcai.2018\/134"},{"key":"1953_CR52","doi-asserted-by":"crossref","unstructured":"Wang H, Du L, Zhou P, Shi L, Shen YD (2015) Convex batch mode active sampling via \u03b1-relative pearson divergence. In: Twenty-ninth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v29i1.9618"},{"key":"1953_CR53","doi-asserted-by":"crossref","unstructured":"Wang H, Zhou R, Shen YD (2019) Bounding uncertainty for active batch selection. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 5240\u20135247","DOI":"10.1609\/aaai.v33i01.33015240"},{"issue":"12","key":"1953_CR54","doi-asserted-by":"publisher","first-page":"2591","DOI":"10.1109\/TCSVT.2016.2589879","volume":"27","author":"K Wang","year":"2017","unstructured":"Wang K, Zhang D, Li Y, Zhang R, Lin L (2017) Cost-effective active learning for deep image classification. IEEE Trans Circ Sys Video Technol 27(12):2591\u20132600","journal-title":"IEEE Trans Circ Sys Video Technol"},{"issue":"3","key":"1953_CR55","first-page":"17","volume":"9","author":"Z Wang","year":"2015","unstructured":"Wang Z, Ye J (2015) Querying discriminative and representative samples for batch mode active learning. ACM Trans Knowl Discovery Data (TKDD) 9(3):17","journal-title":"ACM Trans Knowl Discovery Data (TKDD)"},{"issue":"1","key":"1953_CR56","first-page":"51","volume":"6","author":"Y Wu","year":"2020","unstructured":"Wu Y, Wu Q, Dey N, Sherratt S (2020) Learning models for semantic classification of insufficient plantar pressure images. Int J Interact Multimed Artificial Intell 6(1):51\u201361","journal-title":"Int J Interact Multimed Artificial Intell"},{"issue":"1","key":"1953_CR57","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/TPAMI.2016.2539965","volume":"39","author":"C Xiong","year":"2017","unstructured":"Xiong C, Johnson DM, Corso JJ (2017) Active clustering with model-based uncertainty reduction. IEEE Trans Pattern Anal Machine Intell 39(1):5\u201317","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"key":"1953_CR58","doi-asserted-by":"crossref","unstructured":"Xu Z, Yu K, Tresp V, Xu X, Wang J (2003) Representative sampling for text classification using support vector machines. In: European conference on information retrieval. Springer, pp 393\u2013407","DOI":"10.1007\/3-540-36618-0_28"},{"issue":"2","key":"1953_CR59","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s11263-014-0781-x","volume":"113","author":"Y Yang","year":"2015","unstructured":"Yang Y, Ma Z, Nie F, Chang X, Hauptmann AG (2015) Multi-class active learning by uncertainty sampling with diversity maximization. Int J Comput Vis 113(2):113\u2013127","journal-title":"Int J Comput Vis"},{"key":"1953_CR60","doi-asserted-by":"crossref","unstructured":"Yin C, Qian B, Cao S, Li X, Wei J, Zheng Q, Davidson I (2017) Deep similarity-based batch mode active learning with exploration-exploitation. In: 2017 IEEE international conference on data mining (ICDM). IEEE, pp 575\u2013584","DOI":"10.1109\/ICDM.2017.67"},{"key":"1953_CR61","doi-asserted-by":"crossref","unstructured":"Yoo D, Kweon IS (2019) Learning loss for active learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 93\u2013102","DOI":"10.1109\/CVPR.2019.00018"},{"key":"1953_CR62","doi-asserted-by":"crossref","unstructured":"Zhang Y, Lease M, Wallace BC (2017) Active discriminative text representation learning. In: Thirty-first AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.10962"},{"key":"1953_CR63","unstructured":"Zuluaga M, Sergent G, Krause A, P\u00fcschel M (2013) Active learning for multi-objective optimization. In: International conference on machine learning, pp 462\u2013470"}],"updated-by":[{"DOI":"10.1007\/s10489-020-02146-9","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2020,12,29]],"date-time":"2020-12-29T00:00:00Z","timestamp":1609200000000}}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01953-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-020-01953-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01953-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T02:02:13Z","timestamp":1669600933000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-020-01953-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,14]]},"references-count":63,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["1953"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-01953-4","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s10489-020-02146-9","asserted-by":"object"}]},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,14]]},"assertion":[{"value":"16 September 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2020","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2020","order":3,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":4,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":5,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s10489-020-02146-9","URL":"https:\/\/doi.org\/10.1007\/s10489-020-02146-9","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}}]}}