{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:55:45Z","timestamp":1761897345138,"version":"3.37.3"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,9,19]],"date-time":"2020-09-19T00:00:00Z","timestamp":1600473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,19]],"date-time":"2020-09-19T00:00:00Z","timestamp":1600473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012456","name":"National Social Science Foundation of China","doi-asserted-by":"crossref","award":["15BGL048"],"award-info":[{"award-number":["15BGL048"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"crossref","award":["191010001"],"award-info":[{"award-number":["191010001"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hubei Key Laboratory of Transportation Internet of Things","award":["2018IOT003","2020III026GX"],"award-info":[{"award-number":["2018IOT003","2020III026GX"]}]},{"DOI":"10.13039\/501100018806","name":"Science and Technology Department of Hubei Province","doi-asserted-by":"crossref","award":["2017CFA012"],"award-info":[{"award-number":["2017CFA012"]}],"id":[{"id":"10.13039\/501100018806","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s11063-020-10352-2","type":"journal-article","created":{"date-parts":[[2020,9,19]],"date-time":"2020-09-19T07:03:34Z","timestamp":1600499014000},"page":"2353-2369","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Adaptively Converting Auxiliary Attributes and Textual Embedding for Video Captioning Based on BiLSTM"],"prefix":"10.1007","volume":"52","author":[{"given":"Shuqin","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5242-0467","authenticated-orcid":false,"given":"Xian","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luo","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,19]]},"reference":[{"key":"10352_CR1","doi-asserted-by":"crossref","unstructured":"Ye M, Shen J, Lin G, Xiang T, Shao L, Hoi S.C.H. (2020) Deep learning for person re-identification: a survey and outlook. arXiv preprint arXiv:2001.04193","DOI":"10.1109\/TPAMI.2021.3054775"},{"key":"10352_CR2","doi-asserted-by":"crossref","unstructured":"Rohrbach M, Qiu W, Titov I, Thater S, Pinkal M, Schiele B (2013) Translating video content to natural language descriptions. In: Proceedings of the IEEE international conference on computer vision, pp 433\u2013440","DOI":"10.1109\/ICCV.2013.61"},{"key":"10352_CR3","doi-asserted-by":"crossref","unstructured":"Venugopalan S, Rohrbach M, Donahue J, Mooney R, Darrell T, Saenko K (2015) Sequence to sequence-video to text. In: Proceedings of the IEEE international conference on computer vision, pp 4534\u20134542","DOI":"10.1109\/ICCV.2015.515"},{"key":"10352_CR4","doi-asserted-by":"crossref","unstructured":"Zhang Z, Shi Y, Yuan C, Li B, Wang P, Hu W, Zha ZJ (2020) Object relational graph with teacher-recommended learning for video captioning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13278\u201313288","DOI":"10.1109\/CVPR42600.2020.01329"},{"key":"10352_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2920899","author":"W Zhang","year":"2019","unstructured":"Zhang W, Wang B, Ma L, Liu W (2019) Reconstruct and represent video contents for captioning via reinforcement learning. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2019.2920899","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10352_CR6","doi-asserted-by":"crossref","unstructured":"Chen Y, Wang S, Zhang W, Huang Q (2018) Less is more: picking informative frames for video captioning. In: Proceedings of the European conference on computer vision (ECCV), pp 358\u2013373","DOI":"10.1007\/978-3-030-01261-8_22"},{"key":"10352_CR7","unstructured":"Thomason J, Venugopalan S, Guadarrama S, Saenko K, Mooney R (2014) Integrating language and vision to generate natural language descriptions of videos in the wild. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: Technical Papers, pp 1218\u20131227"},{"key":"10352_CR8","first-page":"8191","volume":"33","author":"S Chen","year":"2019","unstructured":"Chen S, Jiang YG (2019) Motion guided spatial attention for video captioning. Proc AAAI Conf Artif Intell 33:8191\u20138198","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"10352_CR9","doi-asserted-by":"crossref","unstructured":"Chen Y, Zhang W, Wang S, Li L, Huang Q (2018) Saliency-based spatiotemporal attention for video captioning. In: 2018 IEEE fourth international conference on multimedia big data (BigMM), pp 1\u20138","DOI":"10.1109\/BigMM.2018.8499257"},{"key":"10352_CR10","first-page":"8167","volume":"33","author":"J Chen","year":"2019","unstructured":"Chen J, Pan Y, Li Y, Yao T, Chao H, Mei T (2019) Temporal deformable convolutional encoder-decoder networks for video captioning. Proc AAAI Conf Artif Intell 33:8167\u20138174","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"2","key":"10352_CR11","doi-asserted-by":"publisher","first-page":"1891","DOI":"10.1007\/s11063-019-09978-8","volume":"50","author":"J Zhang","year":"2019","unstructured":"Zhang J, Hu H (2019) Deep captioning with attention-based visual concept transfer mechanism for enriching description. Neural Process Lett 50(2):1891\u20131905","journal-title":"Neural Process Lett"},{"issue":"1","key":"10352_CR12","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s11063-018-09973-5","volume":"50","author":"P Cao","year":"2019","unstructured":"Cao P, Yang Z, Sun L, Liang Y, Yang MQ, Guan R (2019) Image captioning with bidirectional semantic attention-based guiding of long short-term memory. Neural Process Lett 50(1):103\u2013119","journal-title":"Neural Process Lett"},{"key":"10352_CR13","doi-asserted-by":"crossref","unstructured":"Wang J, Wang W, Huang Y, Wang L, Tan T (2018) M3: Multimodal memory modelling for video captioning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7512\u20137520","DOI":"10.1109\/CVPR.2018.00784"},{"key":"10352_CR14","doi-asserted-by":"crossref","unstructured":"Wu X, Li G, Cao Q, Ji Q, Lin L (2018) Interpretable video captioning via trajectory structured localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6829\u20136837","DOI":"10.1109\/CVPR.2018.00714"},{"key":"10352_CR15","doi-asserted-by":"crossref","unstructured":"Song J, Guo Z, Gao L, Liu W, Zhang D, Shen HT (2017) Hierarchical LSTM with adjusted temporal attention for video captioning. arXiv preprint arXiv:1706.01231","DOI":"10.24963\/ijcai.2017\/381"},{"key":"10352_CR16","doi-asserted-by":"crossref","unstructured":"Liu Y, Li X, Shi Z (2017) Video captioning with listwise supervision. In: Thirty-first AAAI conference on artificial intelligence, pp 4197\u20134203","DOI":"10.1609\/aaai.v31i1.11239"},{"key":"10352_CR17","doi-asserted-by":"crossref","unstructured":"Pan Y, Yao T, Li H, Mei T (2017) Video captioning with transferred semantic attributes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6504\u20136512","DOI":"10.1109\/CVPR.2017.111"},{"key":"10352_CR18","doi-asserted-by":"crossref","unstructured":"Bin Y, Yang Y, Zhou J, Huang Z, Shen HT (2017) Adaptively attending to visual attributes and linguistic knowledge for captioning. In: Proceedings of the 25th ACM international conference on multimedia, pp 1345\u20131353","DOI":"10.1145\/3123266.3123391"},{"key":"10352_CR19","doi-asserted-by":"crossref","unstructured":"Aafaq N, Akhtar N, Liu W, Gilani SZ, Mian A (2019) Spatio-temporal dynamics and semantic attribute enriched visual encoding for video captioning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 12487\u201312496","DOI":"10.1109\/CVPR.2019.01277"},{"key":"10352_CR20","unstructured":"Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473"},{"issue":"8","key":"10352_CR21","doi-asserted-by":"publisher","first-page":"1575","DOI":"10.1109\/TCYB.2014.2356200","volume":"45","author":"M Jian","year":"2014","unstructured":"Jian M, Lam KM, Dong J, Shen L (2014) Visual-patch-attention-aware saliency detection. IEEE Trans Cybernet 45(8):1575\u20131586","journal-title":"IEEE Trans Cybernet"},{"key":"10352_CR22","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.sigpro.2017.12.008","volume":"145","author":"M Zhang","year":"2018","unstructured":"Zhang M, Yang Y, Ji Y, Xie N, Shen F (2018) Recurrent attention network using spatial-temporal relations for action recognition. Signal Process 145:137\u2013145","journal-title":"Signal Process"},{"key":"10352_CR23","doi-asserted-by":"crossref","unstructured":"Zhu Y, Jiang S (2019) Attention-based densely connected LSTM for video captioning. In: Proceedings of the 27th ACM international conference on multimedia, pp 802\u2013810","DOI":"10.1145\/3343031.3350932"},{"key":"10352_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2932058","author":"J Yu","year":"2019","unstructured":"Yu J, Tan M, Zhang H, Tao D, Rui Y (2019) Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2019.2932058","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"10352_CR25","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1109\/TNNLS.2019.2908982","volume":"31","author":"J Yu","year":"2019","unstructured":"Yu J, Zhu C, Zhang J, Huang Q, Tao D (2019) Spatial pyramid-enhanced NetVLAD with weighted triplet loss for place recognition. IEEE Trans Neural Netw Learn Syst 31(2):661\u2013674","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"10352_CR26","doi-asserted-by":"publisher","first-page":"2420","DOI":"10.1109\/TIP.2018.2804218","volume":"27","author":"J Zhang","year":"2018","unstructured":"Zhang J, Yu J, Tao D (2018) Local deep-feature alignment for unsupervised dimension reduction. IEEE Trans Image Process 27(5):2420\u20132432","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"10352_CR27","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1109\/TCYB.2014.2336697","volume":"45","author":"J Yu","year":"2014","unstructured":"Yu J, Tao D, Wang M, Rui Y (2014) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybernet 45(4):767\u2013779","journal-title":"IEEE Trans Cybernet"},{"issue":"7","key":"10352_CR28","doi-asserted-by":"publisher","first-page":"3952","DOI":"10.1109\/TII.2018.2884211","volume":"15","author":"C Hong","year":"2018","unstructured":"Hong C, Yu J, Zhang J, Jin X, Lee KH (2018) Multimodal face-pose estimation with multitask manifold deep learning. IEEE Trans Ind Inform 15(7):3952\u20133961","journal-title":"IEEE Trans Ind Inform"},{"key":"10352_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3013379","author":"M Ye","year":"2020","unstructured":"Ye M, Shen J, Zhang X, Yuen PC, Chang SF (2020) Augmentation invariant and instance spreading feature for softmax embedding. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2020.3013379","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10352_CR30","doi-asserted-by":"crossref","unstructured":"Venugopalan S, Xu H, Donahue J, Rohrbach M, Mooney R, Saenko K (2014) Translating videos to natural language using deep recurrent neural networks. arXiv preprint arXiv:1412.4729","DOI":"10.3115\/v1\/N15-1173"},{"key":"10352_CR31","doi-asserted-by":"crossref","unstructured":"Yao L, Torabi A, Cho K, Ballas N, Pal C, Larochelle H, Courville A (2015) Describing videos by exploiting temporal structure. In: Proceedings of the IEEE international conference on computer vision, pp 4507\u20134515","DOI":"10.1109\/ICCV.2015.512"},{"issue":"7","key":"10352_CR32","doi-asserted-by":"publisher","first-page":"2631","DOI":"10.1109\/TCYB.2018.2831447","volume":"49","author":"Y Bin","year":"2018","unstructured":"Bin Y, Yang Y, Shen F, Xie N, Shen HT, Li X (2018) Describing video with attention-based bidirectional LSTM. IEEE Trans Cybernet 49(7):2631\u20132641","journal-title":"IEEE Trans Cybernet"},{"key":"10352_CR33","first-page":"8965","volume":"33","author":"X Wang","year":"2019","unstructured":"Wang X, Wu J, Zhang D, Su Y, Wang WY (2019) Learning to compose topic-aware mixture of experts for zero-shot video captioning. Proc AAAI Conf Artif Intell 33:8965\u20138972","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"10352_CR34","doi-asserted-by":"crossref","unstructured":"Yang L, Tang K, Yang J, Li LJ (2017) Dense captioning with joint inference and visual context. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2193\u20132202","DOI":"10.1109\/CVPR.2017.214"},{"issue":"1","key":"10352_CR35","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1109\/TII.2019.2946030","volume":"16","author":"M Ye","year":"2019","unstructured":"Ye M, Cheng Y, Lan X, Zhu H (2019) Improving night-time pedestrian retrieval with distribution alignment and contextual distance. IEEE Trans Industr Inform 16(1):615\u2013624","journal-title":"IEEE Trans Industr Inform"},{"key":"10352_CR36","doi-asserted-by":"crossref","unstructured":"Fang H, Gupta S, Iandola F, Srivastava RK, Deng L, Doll\u00e1r P, Lawrence Zitnick C (2015) From captions to visual concepts and back. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1473\u20131482","DOI":"10.1109\/CVPR.2015.7298754"},{"key":"10352_CR37","doi-asserted-by":"crossref","unstructured":"Wu Q, Shen C, Liu L, Dick A, Van Den Hengel A (2016) What value do explicit high level concepts have in vision to language problems? In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 203\u2013212","DOI":"10.1109\/CVPR.2016.29"},{"key":"10352_CR38","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1109\/TIFS.2019.2921454","volume":"15","author":"M Ye","year":"2020","unstructured":"Ye M, Lan X, Wang Z, Yuen PC (2020) Bi-directional center-constrained top-ranking for visible thermal person re-identification. IEEE Trans Inf Forens Secur 15:407\u2013419","journal-title":"IEEE Trans Inf Forens Secur"},{"issue":"12","key":"10352_CR39","doi-asserted-by":"publisher","first-page":"2553","DOI":"10.1109\/TMM.2016.2605058","volume":"18","author":"M Ye","year":"2016","unstructured":"Ye M, Liang C, Yu Y, Wang Z, Leng Q, Xiao C, Hu R (2016) Person reidentification via ranking aggregation of similarity pulling and dissimilarity pushing. IEEE Trans Multimedia 18(12):2553\u20132566","journal-title":"IEEE Trans Multimedia"},{"key":"10352_CR40","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.patcog.2017.10.009","volume":"77","author":"MA Carbonneau","year":"2018","unstructured":"Carbonneau MA, Cheplygina V, Granger E, Gagnon G (2018) Multiple instance learning: a survey of problem characteristics and applications. Pattern Recognit 77:329\u2013353","journal-title":"Pattern Recognit"},{"key":"10352_CR41","doi-asserted-by":"crossref","unstructured":"You Q, Jin H, Wang Z, Fang C, Luo J (2016) Image captioning with semantic attention. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4651\u20134659","DOI":"10.1109\/CVPR.2016.503"},{"issue":"12","key":"10352_CR42","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1093\/bioinformatics\/btw252","volume":"32","author":"OZ Kraus","year":"2016","unstructured":"Kraus OZ, Ba JL, Frey BJ (2016) Classifying and segmenting microscopy images with deep multiple instance learning. Bioinformatics 32(12):52\u201359","journal-title":"Bioinformatics"},{"key":"10352_CR43","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Zitnick CL (2014) Common objects in context. In: European conference on computer vision, Microsoft coco, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"10352_CR44","unstructured":"Zhang C, Platt JC, Viola PA (2006) Multiple instance boosting for object detection. In: Proceedings of the conference neural information processing systems, pp 1417\u20131424"},{"key":"10352_CR45","doi-asserted-by":"crossref","unstructured":"Ye M, Shen J (2020) Probabilistic structural latent representation for unsupervised embedding. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5457\u20135466","DOI":"10.1109\/CVPR42600.2020.00550"},{"key":"10352_CR46","unstructured":"Chen DL, Dolan WB (2011) Collecting highly parallel data for paraphrase evaluation. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, vol 1, pp 190\u2013200"},{"key":"10352_CR47","doi-asserted-by":"crossref","unstructured":"Xu J, Mei T, Yao T, Rui Y (2016) MSR-VTT: a large video description dataset for bridging video and language. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5288\u20135296","DOI":"10.1109\/CVPR.2016.571"},{"key":"10352_CR48","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"10352_CR49","doi-asserted-by":"crossref","unstructured":"Mahajan D, Girshick R, Ramanathan V, He K, Paluri M, Li Y, van der Maaten L (2018) Exploring the limits of weakly supervised pretraining. In: Proceedings of the European conference on computer vision (ECCV), pp 181\u2013196","DOI":"10.1007\/978-3-030-01216-8_12"},{"key":"10352_CR50","doi-asserted-by":"crossref","unstructured":"Papineni K, Roukos S, Ward T, Zhu WJ (2002) BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting on association for computational linguistics, pp 311\u2013318","DOI":"10.3115\/1073083.1073135"},{"key":"10352_CR51","unstructured":"Banerjee S, Lavie A (2005) METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL workshop on intrinsic and extrinsic evaluation measures for machine translation and\/or summarization, pp 65\u201372"},{"key":"10352_CR52","doi-asserted-by":"crossref","unstructured":"Vedantam R, Lawrence Zitnick C, Parikh D (2015) Cider: consensus-based image description evaluation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4566\u20134575","DOI":"10.1109\/CVPR.2015.7299087"},{"key":"10352_CR53","unstructured":"Lin C-Y (2004) Rouge: a package for automatic evaluation of summaries. Text summarization branches out, pp 74\u201381"},{"key":"10352_CR54","unstructured":"Chen X, Fang H, Lin TY, Vedantam R, Gupta S, Doll\u00e1r P, Zitnick CL (2015) Microsoft coco captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10352-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-020-10352-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10352-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T06:33:05Z","timestamp":1668839585000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-020-10352-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,19]]},"references-count":54,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["10352"],"URL":"https:\/\/doi.org\/10.1007\/s11063-020-10352-2","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2020,9,19]]},"assertion":[{"value":"8 September 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2020","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}