{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T18:57:10Z","timestamp":1715885830972},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T00:00:00Z","timestamp":1669075200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T00:00:00Z","timestamp":1669075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s10489-022-04261-1","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T01:11:28Z","timestamp":1669165888000},"page":"15516-15536","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Exploiting semantic-level affinities with a mask-guided network for temporal action proposal in videos"],"prefix":"10.1007","volume":"53","author":[{"given":"Yu","family":"Yang","sequence":"first","affiliation":[]},{"given":"Mengmeng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jianbiao","family":"Mei","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,22]]},"reference":[{"key":"4261_CR1","doi-asserted-by":"crossref","unstructured":"Arnab A, Dehghani M, Heigold G, Sun C, Lu\u010di\u0107 M, Schmid C (2021) Vivit: a video vision transformer. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6836\u20136846","DOI":"10.1109\/ICCV48922.2021.00676"},{"key":"4261_CR2","doi-asserted-by":"crossref","unstructured":"Bai Y, Wang Y, Tong Y, Yang Y, Liu Q, Liu J (2020) Boundary content graph neural network for temporal action proposal generation. In: European conference on computer vision. Springer, pp 121\u2013137","DOI":"10.1007\/978-3-030-58604-1_8"},{"key":"4261_CR3","unstructured":"Bertasius G, Wang H, Torresani L (2021) Is space-time attention all you need for video understanding?. In: ICML, vol 2, p 4"},{"key":"4261_CR4","doi-asserted-by":"crossref","unstructured":"Buch S, Escorcia V, Ghanem B, Fei-Fei L, Niebles JC (2017) End-to-end, single-stream temporal action detection in untrimmed videos. In: Procedings of the British machine vision conference 2017. British machine vision association, pp 93\u201393","DOI":"10.5244\/C.31.93"},{"key":"4261_CR5","doi-asserted-by":"crossref","unstructured":"Buch S, Escorcia V, Shen C, Ghanem B, Carlos Niebles J (2017) Sst: single-stream temporal action proposals. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp 2911\u20132920","DOI":"10.1109\/CVPR.2017.675"},{"key":"4261_CR6","doi-asserted-by":"crossref","unstructured":"Caba Heilbron F, Escorcia V, Ghanem B, Carlos Niebles J (2015) Activitynet: a large-scale video benchmark for human activity understanding. In: Proceedings of the ieee conference on computer vision and pattern recognition, pp 961\u2013970","DOI":"10.1109\/CVPR.2015.7298698"},{"key":"4261_CR7","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European conference on computer vision. Springer, pp 213\u2013229","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"4261_CR8","doi-asserted-by":"crossref","unstructured":"Carreira J, Zisserman A (2017) Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 6299\u20136308","DOI":"10.1109\/CVPR.2017.502"},{"key":"4261_CR9","doi-asserted-by":"crossref","unstructured":"Chen W, Chai Y, Qi M, Sun H, Pu Q, Kong J, Zheng C (2022) Bottom-up improved multistage temporal convolutional network for action segmentation. Appl Intell, pp 1\u201317","DOI":"10.1007\/s10489-022-03382-x"},{"key":"4261_CR10","doi-asserted-by":"publisher","first-page":"6869","DOI":"10.1109\/TIP.2021.3099407","volume":"30","author":"X Ding","year":"2021","unstructured":"Ding X, Wang N, Gao X, Li J, Wang X, Liu T (2021) Kfc: an efficient framework for semi-supervised temporal action localization. IEEE Trans Image Process 30:6869\u20136878","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"4261_CR11","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1007\/s10489-021-02367-6","volume":"52","author":"Z Du","year":"2022","unstructured":"Du Z, Mukaidani H (2022) Linear dynamical systems approach for human action recognition with dual-stream deep features. Appl Intell 52(1):452\u2013470","journal-title":"Appl Intell"},{"key":"4261_CR12","doi-asserted-by":"crossref","unstructured":"Duke B, Ahmed A, Wolf C, Aarabi P, Taylor GW (2021) Sstvos: sparse spatiotemporal transformers for video object segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5912\u20135921","DOI":"10.1109\/CVPR46437.2021.00585"},{"key":"4261_CR13","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Fan H, Malik J, He K (2019) Slowfast networks for video recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6202\u20136211","DOI":"10.1109\/ICCV.2019.00630"},{"key":"4261_CR14","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1933\u20131941","DOI":"10.1109\/CVPR.2016.213"},{"key":"4261_CR15","doi-asserted-by":"crossref","unstructured":"Gao J, Chen K, Nevatia R (2018) Ctap: complementary temporal action proposal generation. In: Proceedings of the European conference on computer vision (ECCV), pp 68\u201383","DOI":"10.1007\/978-3-030-01216-8_5"},{"key":"4261_CR16","doi-asserted-by":"crossref","unstructured":"Gao J, Shi Z, Wang G, Li J, Yuan Y, Ge S, Zhou X (2020) Accurate temporal action proposal generation with relation-aware pyramid network. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34, pp 10810\u201310817","DOI":"10.1609\/aaai.v34i07.6711"},{"key":"4261_CR17","doi-asserted-by":"crossref","unstructured":"Gao J, Yang Z, Chen K, Sun C, Nevatia R (2017) Turn tap: temporal unit regression network for temporal action proposals. In: Proceedings of the IEEE international conference on computer vision, pp 3628\u20133636","DOI":"10.1109\/ICCV.2017.392"},{"key":"4261_CR18","first-page":"107","volume":"107477","author":"L Gao","year":"2020","unstructured":"Gao L, Li T, Song J, Zhao Z, Shen HT (2020) Play and rewind: context-aware video temporal action proposals. Pattern Recogn 107477:107","journal-title":"Pattern Recogn"},{"key":"4261_CR19","doi-asserted-by":"crossref","unstructured":"Gao Y, Liu X, Li J, Fang Z, Jiang X, Huq KMS (2022) Lft-net: local feature transformer network for point clouds analysis. IEEE transactions on intelligent transportation systems","DOI":"10.1109\/TITS.2022.3140355"},{"issue":"10","key":"4261_CR20","doi-asserted-by":"publisher","first-page":"7043","DOI":"10.1007\/s10489-021-02195-8","volume":"51","author":"G Jiang","year":"2021","unstructured":"Jiang G, Jiang X, Fang Z, Chen S (2021) An efficient attention module for 3d convolutional neural networks in action recognition. Appl Intell 51(10):7043\u20137057","journal-title":"Appl Intell"},{"key":"4261_CR21","unstructured":"Jiang YG, Liu J, Zamir AR, Toderici G, Laptev I, Shah M, Sukthankar R (2014) Thumos challenge: action recognition with a large number of classes"},{"key":"4261_CR22","doi-asserted-by":"crossref","unstructured":"Lin C, Li J, Wang Y, Tai Y, Luo D, Cui Z, Wang C, Li J, Huang F, Ji R (2020) Fast learning of temporal action proposal via dense boundary generator. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 11499\u201311506","DOI":"10.1609\/aaai.v34i07.6815"},{"key":"4261_CR23","doi-asserted-by":"crossref","unstructured":"Lin T, Liu X, Li X, Ding E, Wen S (2019) Bmn: boundary-matching network for temporal action proposal generation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3889\u20133898","DOI":"10.1109\/ICCV.2019.00399"},{"key":"4261_CR24","doi-asserted-by":"crossref","unstructured":"Lin T, Zhao X, Shou Z (2017) Single shot temporal action detection. In: Proceedings of the 25th ACM international conference on Multimedia, pp 988\u2013996","DOI":"10.1145\/3123266.3123343"},{"key":"4261_CR25","doi-asserted-by":"crossref","unstructured":"Lin T, Zhao X, Su H, Wang C, Yang M (2018) Bsn: boundary sensitive network for temporal action proposal generation. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01225-0_1"},{"issue":"1","key":"4261_CR26","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/TCSVT.2021.3075607","volume":"32","author":"Y Liu","year":"2021","unstructured":"Liu Y, Chen J, Chen X, Deng B, Huang J, Hua XS (2021) Centerness-aware network for temporal action proposal. IEEE Trans Circuits Syst Video Technol 32(1):5\u201316","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"4261_CR27","doi-asserted-by":"crossref","unstructured":"Liu Y, Ma L, Zhang Y, Liu W, Chang SF (2019) Multi-granularity generator for temporal action proposal. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3604\u20133613","DOI":"10.1109\/CVPR.2019.00372"},{"key":"4261_CR28","doi-asserted-by":"crossref","unstructured":"Mao J, Xue Y, Niu M, Bai H, Feng J, Liang X, Xu H, Xu C (2021) Voxel transformer for 3d object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3164\u20133173","DOI":"10.1109\/ICCV48922.2021.00315"},{"key":"4261_CR29","doi-asserted-by":"crossref","unstructured":"Neimark D, Bar O, Zohar M, Asselmann D (2021) Video transformer network. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3163\u20133172","DOI":"10.1109\/ICCVW54120.2021.00355"},{"key":"4261_CR30","first-page":"194","volume":"105590","author":"F P\u00e9rez-Hern\u00e1ndez","year":"2020","unstructured":"P\u00e9rez-Hern\u00e1ndez F., Tabik S, Lamas A, Olmos R, Fujita H, Herrera F (2020) Object detection binary classifiers methodology based on deep learning to identify small objects handled similarly: application in video surveillance. Knowl-Based Syst 105590:194","journal-title":"Knowl-Based Syst"},{"key":"4261_CR31","doi-asserted-by":"crossref","unstructured":"Qing Z, Su H, Gan W, Wang D, Wu W, Wang X, Qiao Y, Yan J, Gao C, Sang N (2021) Temporal context aggregation network for temporal action proposal refinement. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 485\u2013494","DOI":"10.1109\/CVPR46437.2021.00055"},{"key":"4261_CR32","doi-asserted-by":"crossref","unstructured":"Shou Z, Wang D, Chang SF (2016) Temporal action localization in untrimmed videos via multi-stage cnns. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1049\u20131058","DOI":"10.1109\/CVPR.2016.119"},{"key":"4261_CR33","unstructured":"Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos advances in neural information processing systems, vol 27"},{"key":"4261_CR34","doi-asserted-by":"crossref","unstructured":"Su H, Gan W, Wu W, Qiao Y, Yan J (2021) Bsn++: complementary boundary regressor with scale-balanced relation modeling for temporal action proposal generation. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 2602\u20132610","DOI":"10.1609\/aaai.v35i3.16363"},{"key":"4261_CR35","doi-asserted-by":"crossref","unstructured":"Tan J, Tang J, Wang L, Wu G (2021) Relaxed transformer decoders for direct action proposal generation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 13526\u201313535","DOI":"10.1109\/ICCV48922.2021.01327"},{"key":"4261_CR36","doi-asserted-by":"crossref","unstructured":"Tian F, Gao Y, Fang Z, Fang Y, Gu J, Fujita H, Hwang JN (2021) Depth estimation using a self-supervised network based on cross-layer feature fusion and the quadtree constraint IEEE transactions on circuits and systems for video technology","DOI":"10.1109\/TCSVT.2021.3080928"},{"key":"4261_CR37","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need advances in neural information processing systems, vol 30"},{"key":"4261_CR38","doi-asserted-by":"crossref","unstructured":"Wang L, Xiong Y, Lin D, Van Gool L (2017) Untrimmednets for weakly supervised action recognition and detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4325\u20134334","DOI":"10.1109\/CVPR.2017.678"},{"key":"4261_CR39","doi-asserted-by":"crossref","unstructured":"Wang L, Xiong Y, Wang Z, Qiao Y, Lin D, Tang X, Gool LV (2016) Temporal segment networks: towards good practices for deep action recognition. In: European conference on computer vision. Springer, pp 20\u201336","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"4261_CR40","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"4261_CR41","doi-asserted-by":"crossref","unstructured":"Wang X, Shi J, Fujita H, Zhao Y (2021) Aggregate attention module for fine-grained image classification. J Ambient Intell Humanized Comput, pp 1\u201311","DOI":"10.1007\/s12652-021-03599-7"},{"key":"4261_CR42","doi-asserted-by":"crossref","unstructured":"Wang Y, Long M, Wang J, Yu PS (2017) Spatiotemporal pyramid network for video action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1529\u20131538","DOI":"10.1109\/CVPR.2017.226"},{"key":"4261_CR43","doi-asserted-by":"crossref","unstructured":"Wang Y, Xu Z, Wang X, Shen C, Cheng B, Shen H, Xia H (2021) End-to-end video instance segmentation with transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8741\u20138750","DOI":"10.1109\/CVPR46437.2021.00863"},{"key":"4261_CR44","first-page":"108","volume":"107405","author":"Y Wu","year":"2021","unstructured":"Wu Y, Jiang X, Fang Z, Gao Y, Fujita H (2021) Multi-modal 3d object detection by 2d-guided precision anchor proposal and multi-layer fusion. Appl Soft Comput 107405:108","journal-title":"Appl Soft Comput"},{"key":"4261_CR45","first-page":"129","volume":"108725","author":"K Xia","year":"2022","unstructured":"Xia K, Wang L, Zhou S, Hua G, Tang W (2022) Dual relation network for temporal action localization. Pattern Recogn 108725:129","journal-title":"Pattern Recogn"},{"key":"4261_CR46","unstructured":"Xiong Y, Wang L, Wang Z, Zhang B, Song H, Li W, Lin D, Qiao Y, Van Gool L, Tang X (2016) Cuhk & ethz & siat submission to activitynet challenge 2016. arXiv:1608.00797"},{"key":"4261_CR47","doi-asserted-by":"publisher","first-page":"3081","DOI":"10.1109\/TIP.2022.3163544","volume":"31","author":"J Xu","year":"2022","unstructured":"Xu J, Chen G, Zhou N, Zheng WS, Lu J (2022) Probabilistic temporal modeling for unintentional action localization. IEEE Trans Image Process 31:3081\u20133094","journal-title":"IEEE Trans Image Process"},{"key":"4261_CR48","doi-asserted-by":"crossref","unstructured":"Xu M, Zhao C, Rojas DS, Thabet A, Ghanem B (2020) G-tad: sub-graph localization for temporal action detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10156\u201310165","DOI":"10.1109\/CVPR42600.2020.01017"},{"key":"4261_CR49","doi-asserted-by":"crossref","unstructured":"Yan B, Peng H, Fu J, Wang D, Lu H (2021) Learning spatio-temporal transformer for visual tracking. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10448\u201310457","DOI":"10.1109\/ICCV48922.2021.01028"},{"key":"4261_CR50","doi-asserted-by":"publisher","first-page":"8535","DOI":"10.1109\/TIP.2020.3016486","volume":"29","author":"L Yang","year":"2020","unstructured":"Yang L, Peng H, Zhang D, Fu J, Han J (2020) Revisiting anchor mechanisms for temporal action localization. IEEE Trans Image Process 29:8535\u20138548","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"4261_CR51","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1007\/s10489-018-1347-3","volume":"49","author":"G Yao","year":"2019","unstructured":"Yao G, Lei T, Zhong J, Jiang P (2019) Learning multi-temporal-scale deep information for action recognition. Appl Intell 49(6):2017\u20132029","journal-title":"Appl Intell"},{"key":"4261_CR52","doi-asserted-by":"publisher","first-page":"108708","DOI":"10.1016\/j.patcog.2022.108708","volume":"129","author":"Y Yao","year":"2022","unstructured":"Yao Y, Jiang X, Fujita H, Fang Z (2022) A sparse graph wavelet convolution neural network for video-based person re-identification. Pattern Recogn 129:108708","journal-title":"Pattern Recogn"},{"key":"4261_CR53","first-page":"17283","volume":"33","author":"M Zaheer","year":"2020","unstructured":"Zaheer M, Guruganesh G, Dubey KA, Ainslie J, Alberti C, Ontanon S, Pham P, Ravula A, Wang Q, Yang L et al (2020) Big bird: transformers for longer sequences. Adv Neural Inf Process Syst 33:17283\u201317297","journal-title":"Adv Neural Inf Process Syst"},{"key":"4261_CR54","doi-asserted-by":"crossref","unstructured":"Zeng R, Huang W, Tan M, Rong Y, Zhao P, Huang J, Gan C (2019) Graph convolutional networks for temporal action localization. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 7094\u20137103","DOI":"10.1109\/ICCV.2019.00719"},{"key":"4261_CR55","doi-asserted-by":"crossref","unstructured":"Zeng R, Huang W, Tan M, Rong Y, Zhao P, Huang J, Gan C (2021) Graph convolutional module for temporal action localization in videos. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2021.3090167"},{"key":"4261_CR56","doi-asserted-by":"crossref","unstructured":"Zhai Y, Wang L, Tang W, Zhang Q, Yuan J, Hua G (2020) Two-stream consensus network for weakly-supervised temporal action localization. In: European conference on computer vision. Springer, pp 37\u201354","DOI":"10.1007\/978-3-030-58539-6_3"},{"key":"4261_CR57","doi-asserted-by":"crossref","unstructured":"Zhao P, Xie L, Ju C, Zhang Y, Wang Y, Tian Q (2020) Bottom-up temporal action localization with mutual regularization. In: European conference on computer vision. Springer, pp 539\u2013555","DOI":"10.1007\/978-3-030-58598-3_32"},{"key":"4261_CR58","doi-asserted-by":"crossref","unstructured":"Zhao Y, Xiong Y, Wang L, Wu Z, Tang X, Lin D (2017) Temporal action detection with structured segment networks. In: Proceedings of the IEEE international conference on computer vision, pp 2914\u20132923","DOI":"10.1109\/ICCV.2017.317"},{"key":"4261_CR59","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/s11263-019-01211-2","volume":"128","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, Xiong Y, Wang L, Wu Z, Tao X, Lin D (2020) Temporal action detection with structured segment networks. Int J Comput Vis 128:74\u201395","journal-title":"Int J Comput Vis"},{"key":"4261_CR60","doi-asserted-by":"crossref","unstructured":"Zhao Y, Zhang H, Gao Z, Guan W, Nie J, Liu A, Wang M, Chen S (2022) A temporal-aware relation and attention network for temporal action localization. IEEE Trans Image Process","DOI":"10.1109\/TIP.2022.3182866"},{"key":"4261_CR61","doi-asserted-by":"publisher","first-page":"4363","DOI":"10.1109\/TMM.2020.3042077","volume":"23","author":"Y Zhou","year":"2020","unstructured":"Zhou Y, Wang R, Li H, Kung SY (2020) Temporal action localization using long short-term dependency. IEEE Trans Multimedia 23:4363\u20134375","journal-title":"IEEE Trans Multimedia"},{"key":"4261_CR62","first-page":"213","volume":"106671","author":"K Zhu","year":"2021","unstructured":"Zhu K, Jiang X, Fang Z, Gao Y, Fujita H, Hwang JN (2021) Photometric transfer for direct visual odometry. Knowl-Based Syst 106671:213","journal-title":"Knowl-Based Syst"},{"key":"4261_CR63","doi-asserted-by":"crossref","unstructured":"Zhu Z, Tang W, Wang L, Zheng N, Hua G (2021) Enriching local and global contexts for temporal action localization. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 13516\u201313525","DOI":"10.1109\/ICCV48922.2021.01326"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04261-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04261-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04261-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T04:00:40Z","timestamp":1685592040000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04261-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,22]]},"references-count":63,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["4261"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04261-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,22]]},"assertion":[{"value":"10 October 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}