{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:33:50Z","timestamp":1775579630797,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T00:00:00Z","timestamp":1584403200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T00:00:00Z","timestamp":1584403200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Key R&D Program of China","award":["2018YFC0807500"],"award-info":[{"award-number":["2018YFC0807500"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61772396"],"award-info":[{"award-number":["61772396"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61472302"],"award-info":[{"award-number":["61472302"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61772392"],"award-info":[{"award-number":["61772392"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["JB170306"],"award-info":[{"award-number":["JB170306"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["JB170304"],"award-info":[{"award-number":["JB170304"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["JBF180301"],"award-info":[{"award-number":["JBF180301"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Vision","award":["201805053ZD4CG37"],"award-info":[{"award-number":["201805053ZD4CG37"]}]},{"name":"Innovation fund of Xidian University"},{"name":"Spanish project","award":["TIN74946-P"],"award-info":[{"award-number":["TIN74946-P"]}]},{"name":"CERCA programme \/ Generalitat de Catalunya"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s11263-020-01309-y","type":"journal-article","created":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T15:02:33Z","timestamp":1584457353000},"page":"2763-2780","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["CR-Net: A Deep Classification-Regression Network for Multimodal Apparent Personality Analysis"],"prefix":"10.1007","volume":"128","author":[{"given":"Yunan","family":"Li","sequence":"first","affiliation":[]},{"given":"Jun","family":"Wan","sequence":"additional","affiliation":[]},{"given":"Qiguang","family":"Miao","sequence":"additional","affiliation":[]},{"given":"Sergio","family":"Escalera","sequence":"additional","affiliation":[]},{"given":"Huijuan","family":"Fang","sequence":"additional","affiliation":[]},{"given":"Huizhou","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xiangda","family":"Qi","sequence":"additional","affiliation":[]},{"given":"Guodong","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,3,17]]},"reference":[{"issue":"1","key":"1309_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.1744-6570.1991.tb00688.x","volume":"44","author":"MR Barrick","year":"1991","unstructured":"Barrick, M. R., & Mount, M. K. (1991). The big five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 44(1), 1\u201326.","journal-title":"Personnel Psychology"},{"issue":"3","key":"1309_CR2","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1109\/TAFFC.2018.2828845","volume":"9","author":"A Basu","year":"2018","unstructured":"Basu, A., Dasgupta, A., Thyagharajan, A., Routray, A., Guha, R., & Mitra, P. (2018). A portable personality recognizer based on affective state classification using spectral fusion of features. IEEE Transactions on Affective Computing, 9(3), 330\u2013342.","journal-title":"IEEE Transactions on Affective Computing"},{"key":"1309_CR3","doi-asserted-by":"crossref","unstructured":"Bekhouche, S. E., Dornaika, F., Ouafi, A., & Taleb-Ahmed, A. (2017). Personality traits and job candidate screening via analyzing facial videos. In 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW) (pp. 1660\u20131663). IEEE.","DOI":"10.1109\/CVPRW.2017.211"},{"issue":"6942","key":"1309_CR4","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1136\/bmj.308.6942.1499","volume":"308","author":"JM Bland","year":"1994","unstructured":"Bland, J. M., & Altman, D. G. (1994a). Regression towards the mean. BMJ: British Medical Journal, 308(6942), 1499.","journal-title":"BMJ: British Medical Journal"},{"issue":"6957","key":"1309_CR5","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1136\/bmj.309.6957.780","volume":"309","author":"JM Bland","year":"1994","unstructured":"Bland, J. M., & Altman, D. G. (1994b). Statistics notes: Some examples of regression towards the mean. BMJ, 309(6957), 780.","journal-title":"BMJ"},{"issue":"8","key":"1309_CR6","doi-asserted-by":"publisher","first-page":"2209","DOI":"10.1109\/TMM.2017.2786869","volume":"20","author":"S Chen","year":"2018","unstructured":"Chen, S., Zhang, C., & Dong, M. (2018). Deep age estimation: From classification to ranking. IEEE Transactions on Multimedia, 20(8), 2209\u20132222.","journal-title":"IEEE Transactions on Multimedia"},{"key":"1309_CR7","doi-asserted-by":"crossref","unstructured":"Corr, P. J., & Matthews, G. (2009). The Cambridge handbook of personality psychology, chap. MethodsofPersonalityAssessment (pp. 110\u2013126). Cambridge: Cambridge University Press.","DOI":"10.1017\/CBO9780511596544"},{"key":"1309_CR8","doi-asserted-by":"publisher","unstructured":"Correa, J. A. M., Abadi, M. K., Sebe, N., & Patras, I. (2018). Amigos: A dataset for affect, personality and mood research on individuals and groups. IEEE Transactions on Affective Computing. https:\/\/doi.org\/10.1109\/TAFFC.2018.2884461.","DOI":"10.1109\/TAFFC.2018.2884461"},{"key":"1309_CR9","unstructured":"Escalante, H. J., Kaya, H., Salah, A. A., Escalera, S., Gucluturk, Y., Guclu, U., et\u00a0al. (2018). Explaining first impressions: Modeling, recognizing, and explaining apparent personality from videos. arXiv preprint arXiv:1802.00745."},{"key":"1309_CR10","doi-asserted-by":"crossref","unstructured":"Escalante, H. J., Ponce-L\u00f3pez, V., Wan, J., Riegler, M. A., Chen, B., Clap\u00e9s, A., et al. (2016). Chalearn joint contest on multimedia challenges beyond visual analysis: An overview. In ICPR (pp. 67\u201373).","DOI":"10.1109\/ICPR.2016.7899609"},{"key":"1309_CR11","doi-asserted-by":"crossref","unstructured":"Eyben, F., W\u00f6llmer, M., & Schuller, B. (2010). Opensmile: The munich versatile and fast open-source audio feature extractor. In Proceedings of the 18th ACM international conference on multimedia (pp. 1459\u20131462). ACM.","DOI":"10.1145\/1873951.1874246"},{"key":"1309_CR12","doi-asserted-by":"crossref","unstructured":"Gao, B. B., Zhou, H. Y., Wu, J., & Geng, X. (2018). Age estimation using expectation of label distribution learning. In IJCAI (pp. 712\u2013718).","DOI":"10.24963\/ijcai.2018\/99"},{"issue":"1","key":"1309_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3\u201342.","journal-title":"Machine Learning"},{"issue":"3","key":"1309_CR14","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/TAFFC.2017.2751469","volume":"9","author":"Y G\u00fc\u00e7l\u00fct\u00fcrk","year":"2018","unstructured":"G\u00fc\u00e7l\u00fct\u00fcrk, Y., G\u00fc\u00e7l\u00fc, U., Baro, X., Escalante, H. J., Guyon, I., Escalera, S., et al. (2018). Multimodal first impression analysis with deep residual networks. IEEE Transactions on Affective Computing, 9(3), 316\u2013329.","journal-title":"IEEE Transactions on Affective Computing"},{"key":"1309_CR15","doi-asserted-by":"crossref","unstructured":"G\u00fc\u00e7l\u00fct\u00fcrk, Y., G\u00fc\u00e7l\u00fc, U., van Gerven, M. A., & van Lier, R. (2016a). Deep impression: Audiovisual deep residual networks for multimodal apparent personality trait recognition. In European conference on computer vision (pp. 349\u2013358). Berlin: Springer.","DOI":"10.1007\/978-3-319-49409-8_28"},{"key":"1309_CR16","doi-asserted-by":"crossref","unstructured":"G\u00fcrp\u0131nar, F., Kaya, H., & Salah, A. A. (2016b) Combining deep facial and ambient features for first impression estimation. In European conference on computer vision (pp. 372\u2013385). Berlin: Springer.","DOI":"10.1007\/978-3-319-49409-8_30"},{"key":"1309_CR17","doi-asserted-by":"crossref","unstructured":"G\u00fcrpinar, F., Kaya, H., & Salah, A. A. (2016) Multimodal fusion of audio, scene, and face features for first impression estimation. In 2016 23rd International conference on pattern recognition (ICPR) (pp. 43\u201348). IEEE.","DOI":"10.1109\/ICPR.2016.7899605"},{"key":"1309_CR18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In CVPR (pp. 770\u2013778).","DOI":"10.1109\/CVPR.2016.90"},{"issue":"8","key":"1309_CR19","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735\u20131780.","journal-title":"Neural Computation"},{"key":"1309_CR20","doi-asserted-by":"crossref","unstructured":"Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2004). Extreme learning machine: A new learning scheme of feedforward neural networks. In Proceedings of the 2004 IEEE international joint conference on neural networks (vol.\u00a02, pp. 985\u2013990). IEEE.","DOI":"10.1109\/IJCNN.2004.1380068"},{"key":"1309_CR21","doi-asserted-by":"crossref","unstructured":"Huang, S., & Ramanan, D. (2017). Expecting the unexpected: Training detectors for unusual pedestrians with adversarial imposters. In IEEE conference on computer vision and pattern recognition (CVPR) (vol.\u00a01).","DOI":"10.1109\/CVPR.2017.496"},{"issue":"1","key":"1309_CR22","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2013","unstructured":"Ji, S., Xu, W., Yang, M., & Yu, K. (2013). 3d convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1), 221\u2013231.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1309_CR23","unstructured":"Johnson, J., Alahi, A., & Fei-Fei, L. (2016). Perceptual losses for real-time style transfer and super-resolution. In European conference on computer vision (pp. 694\u2013711). Berlin: Springer."},{"key":"1309_CR24","doi-asserted-by":"crossref","unstructured":"Kaya, H., G\u00fcrpinar, F., & Salah, A. A. (2017). Multi-modal score fusion and decision trees for explainable automatic job candidate screening from video CVS. In CVPR workshops (pp. 1651\u20131659).","DOI":"10.1109\/CVPRW.2017.210"},{"key":"1309_CR25","unstructured":"Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980."},{"key":"1309_CR26","unstructured":"Kiros, R., Zhu, Y., Salakhutdinov, R. R., Zemel, R., Urtasun, R., Torralba, A., et al. (2015). Skip-thought vectors. In Advances in neural information processing systems (pp. 3294\u20133302)."},{"key":"1309_CR27","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1146\/annurev-clinpsy-032210-104540","volume":"7","author":"DN Klein","year":"2011","unstructured":"Klein, D. N., Kotov, R., & Bufferd, S. J. (2011). Personality and depression: Explanatory models and review of the evidence. Annual Review of Clinical Psychology, 7, 269\u2013295.","journal-title":"Annual Review of Clinical Psychology"},{"key":"1309_CR28","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097\u20131105)."},{"key":"1309_CR29","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., et al. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4681\u20134690).","DOI":"10.1109\/CVPR.2017.19"},{"key":"1309_CR30","unstructured":"Li, Y., Miao, Q., Tian, K., Fan, Y., Xu, X., Li, R., et al. (2016). Large-scale gesture recognition with a fusion of rgb-d data based on the c3d model. In 2016 23rd International Conference on Pattern Recognition (ICPR) (pp. 25\u201330). IEEE."},{"issue":"10","key":"1309_CR31","doi-asserted-by":"publisher","first-page":"2956","DOI":"10.1109\/TCSVT.2017.2749509","volume":"28","author":"Y Li","year":"2017","unstructured":"Li, Y., Miao, Q., Tian, K., Fan, Y., Xu, X., Li, R., et al. (2017). Large-scale gesture recognition with a fusion of rgb-d data based on saliency theory and c3d model. IEEE Transactions on Circuits and Systems for Video Technology, 28(10), 2956\u20132964.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"1309_CR32","unstructured":"Mairesse, F., & Walker, M. (2007). Personage: Personality generation for dialogue. In Proceedings of the 45th annual meeting of the association of computational linguistics (pp. 496\u2013503)."},{"key":"1309_CR33","doi-asserted-by":"crossref","unstructured":"Mohammadi, G., & Vinciarelli, A. (2015). Automatic personality perception: Prediction of trait attribution based on prosodic features extended abstract. In 2015 International conference on affective computing and intelligent interaction (ACII) (pp. 484\u2013490). IEEE.","DOI":"10.1109\/ACII.2015.7344614"},{"key":"1309_CR34","doi-asserted-by":"crossref","unstructured":"Naim, I., Tanveer, M. I., Gildea, D., & Hoque, M.E. (2015). Automated prediction and analysis of job interview performance: The role of what you say and how you say it. In 2015 11th IEEE international conference and workshops on automatic face and gesture recognition (FG) (vol.\u00a01, pp. 1\u20136). IEEE.","DOI":"10.1109\/FG.2015.7163127"},{"key":"1309_CR35","doi-asserted-by":"crossref","unstructured":"Niu, Z., Zhou, M., Wang, L., Gao, X., & Hua, G. (2016). Ordinal regression with multiple output CNN for age estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4920\u20134928).","DOI":"10.1109\/CVPR.2016.532"},{"issue":"6","key":"1309_CR36","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1037\/h0040291","volume":"66","author":"WT Norman","year":"1963","unstructured":"Norman, W. T. (1963). Toward an adequate taxonomy of personality attributes: Replicated factor structure in peer nomination personality ratings. The Journal of Abnormal and Social Psychology, 66(6), 574.","journal-title":"The Journal of Abnormal and Social Psychology"},{"key":"1309_CR37","doi-asserted-by":"crossref","unstructured":"Parkhi, O. M., Vedaldi, A., Zisserman, A., et\u00a0al. (2015). Deep face recognition. In British machine vision conference (Vol.\u00a01, p.\u00a06).","DOI":"10.5244\/C.29.41"},{"key":"1309_CR38","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., et al. (2017). Automatic differentiation in pytorch."},{"issue":"6","key":"1309_CR39","doi-asserted-by":"publisher","first-page":"1296","DOI":"10.1037\/0022-3514.77.6.1296","volume":"77","author":"JW Pennebaker","year":"1999","unstructured":"Pennebaker, J. W., & King, L. A. (1999). Linguistic styles: Language use as an individual difference. Journal of Personality and Social Psychology, 77(6), 1296.","journal-title":"Journal of Personality and Social Psychology"},{"key":"1309_CR40","doi-asserted-by":"crossref","unstructured":"Polzehl, T., Moller, S., & Metze, F. (2010). Automatically assessing personality from speech. In 2010 IEEE fourth international conference on semantic computing (ICSC) (pp. 134\u2013140). IEEE.","DOI":"10.1109\/ICSC.2010.41"},{"key":"1309_CR41","unstructured":"Ponce-L\u00f3pez, V., Chen, B., Oliu, M., Corneanu, C., Clap\u00e9s, A., Guyon, I., et al. (2016). Chalearn lap 2016: First round challenge on first impressions-dataset and results. In European conference on computer vision (pp. 400\u2013418). Berlin: Springer."},{"key":"1309_CR42","doi-asserted-by":"crossref","unstructured":"Rothe, R., Timofte, R., & Van\u00a0Gool, L. (2015). Dex: Deep expectation of apparent age from a single image. In Proceedings of the IEEE international conference on computer vision workshops (pp. 10\u201315).","DOI":"10.1109\/ICCVW.2015.41"},{"key":"1309_CR43","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556."},{"key":"1309_CR44","unstructured":"Subramaniam, A., Patel, V., Mishra, A., Balasubramanian, P., & Mittal, A. (2016). Bi-modal first impressions recognition using temporally ordered deep audio and stochastic visual features. In European conference on computer vision (pp. 337\u2013348). Berlin: Springer."},{"issue":"11","key":"1309_CR45","doi-asserted-by":"publisher","first-page":"2610","DOI":"10.1109\/TPAMI.2017.2779808","volume":"40","author":"Z Tan","year":"2018","unstructured":"Tan, Z., Wan, J., Lei, Z., Zhi, R., Guo, G., & Li, S. Z. (2018). Efficient group-n encoding and decoding for facial age estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(11), 2610\u20132623.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1309_CR46","doi-asserted-by":"crossref","unstructured":"Ventura, C., Masip, D., & Lapedriza, A. (2017). Interpreting CNN models for apparent personality trait regression. In 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW) (pp. 1705\u20131713). IEEE.","DOI":"10.1109\/CVPRW.2017.217"},{"key":"1309_CR47","unstructured":"Vo, N. N., Liu, S., He, X., & Xu, G. (2018). Multimodal mixture density boosting network for personality mining. In Pacific-Asia conference on knowledge discovery and data mining (pp. 644\u2013655). Berlin: Springer."},{"key":"1309_CR48","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Dong, C., & Change\u00a0Loy, C. (2018). Recovering realistic texture in image super-resolution by deep spatial feature transform. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 606\u2013615).","DOI":"10.1109\/CVPR.2018.00070"},{"issue":"3","key":"1309_CR49","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1109\/TAFFC.2017.2762299","volume":"9","author":"XS Wei","year":"2018","unstructured":"Wei, X. S., Zhang, C. L., Zhang, H., & Wu, J. (2018). Deep bimodal regression of apparent personality traits from short video sequences. IEEE Transactions on Affective Computing, 9(3), 303\u2013315.","journal-title":"IEEE Transactions on Affective Computing"},{"issue":"4","key":"1309_CR50","doi-asserted-by":"publisher","first-page":"2255","DOI":"10.1109\/JSYST.2014.2342375","volume":"11","author":"F Xia","year":"2017","unstructured":"Xia, F., Asabere, N. Y., Liu, H., Chen, Z., & Wang, W. (2017). Socially aware conference participant recommendation with personality traits. IEEE Systems Journal, 11(4), 2255\u20132266.","journal-title":"IEEE Systems Journal"},{"key":"1309_CR51","unstructured":"Zhang, C. L., Zhang, H., Wei, X. S., & Wu, J. (2016). Deep bimodal regression for apparent personality analysis. In European conference on computer vision (pp. 311\u2013324). Berlin: Springer."},{"issue":"10","key":"1309_CR52","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10), 1499\u20131503.","journal-title":"IEEE Signal Processing Letters"},{"issue":"3","key":"1309_CR53","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1109\/TAFFC.2017.2786207","volume":"9","author":"G Zhao","year":"2018","unstructured":"Zhao, G., Ge, Y., Shen, B., Wei, X., & Wang, H. (2018). Emotion analysis for personality inference from eeg signals. IEEE Transactions on Affective Computing, 9(3), 362\u2013371.","journal-title":"IEEE Transactions on Affective Computing"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-020-01309-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11263-020-01309-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-020-01309-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T00:48:22Z","timestamp":1615942102000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11263-020-01309-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,17]]},"references-count":53,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1309"],"URL":"https:\/\/doi.org\/10.1007\/s11263-020-01309-y","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,17]]},"assertion":[{"value":"14 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}