{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:24:24Z","timestamp":1775323464963,"version":"3.50.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T00:00:00Z","timestamp":1690588800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T00:00:00Z","timestamp":1690588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"crossref","award":["PID2020-118447RA-I00"],"award-info":[{"award-number":["PID2020-118447RA-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100011596","name":"Conselleria d\u2019Educaci\u00f3, Investigaci\u00f3, Cultura i Esport","doi-asserted-by":"publisher","award":["GV\/2021\/064"],"award-info":[{"award-number":["GV\/2021\/064"]}],"id":[{"id":"10.13039\/501100011596","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100016386","name":"Conselleria d\u2019Innovaci\u00f3, Universitats, Ci\u00e8ncia i Societat Digital","doi-asserted-by":"crossref","award":["APOSTD\/2020\/256"],"award-info":[{"award-number":["APOSTD\/2020\/256"]}],"id":[{"id":"10.13039\/501100016386","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100009092","name":"Universidad de Alicante","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100009092","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Siamese Neural Networks (SNNs) constitute one of the most representative approaches for addressing Few-Shot Image Classification. These schemes comprise a set of Convolutional Neural Network (CNN) models whose weights are shared across the network, which results in fewer parameters to train and less tendency to overfit. This fact eventually leads to better convergence capabilities than standard neural models when considering scarce amounts of data. Based on a contrastive principle, the SNN scheme jointly trains these inner CNN models to map the input image data to an embedded representation that may be later exploited for the recognition process. However, in spite of their extensive use in the related literature, the representation capabilities of SNN schemes have neither been thoroughly assessed nor combined with other strategies for boosting their classification performance. Within this context, this work experimentally studies the capabilities of SNN architectures for obtaining a suitable embedded representation in scenarios with a severe data scarcity, assesses the use of train data augmentation for improving the feature learning process, introduces the use of transfer learning techniques for further exploiting the embedded representations obtained by the model, and uses test data augmentation for boosting the performance capabilities of the SNN scheme by mimicking an ensemble learning process. The results obtained with different image corpora report that the combination of the commented techniques achieves classification rates ranging from 69<jats:italic>%<\/jats:italic> to 78<jats:italic>%<\/jats:italic> with just 5 to 20 prototypes per class whereas the CNN baseline considered is unable to converge. Furthermore, upon the convergence of the baseline model with the sufficient amount of data, still the adequate use of the studied techniques improves the accuracy in figures from 4<jats:italic>%<\/jats:italic> to 9<jats:italic>%<\/jats:italic>.<\/jats:p>","DOI":"10.1007\/s11042-023-15607-3","type":"journal-article","created":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T04:01:40Z","timestamp":1690603300000},"page":"19929-19952","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["An overview of ensemble and feature learning in few-shot image classification using siamese networks"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8667-4070","authenticated-orcid":false,"given":"Jose J.","family":"Valero-Mas","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3148-6886","authenticated-orcid":false,"given":"Antonio Javier","family":"Gallego","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9199-5802","authenticated-orcid":false,"given":"Juan Ram\u00f3n","family":"Rico-Juan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,29]]},"reference":[{"issue":"12","key":"15607_CR1","doi-asserted-by":"publisher","first-page":"9321","DOI":"10.1007\/s00521-018-3844-z","volume":"31","author":"K Ahrabian","year":"2019","unstructured":"Ahrabian K, Babaali B (2019) Usage of autoencoders and siamese networks for online handwritten signature verification. Neural Comput Applic 31 (12):9321\u20139334","journal-title":"Neural Comput Applic"},{"key":"15607_CR2","doi-asserted-by":"crossref","unstructured":"Babenko A, Slesarev A, Chigorin A, Lempitsky V (2014) Neural codes for image retrieval. In: European conference on computer vision (ECCV), Springer, pp 584\u2013599","DOI":"10.1007\/978-3-319-10590-1_38"},{"issue":"4","key":"15607_CR3","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1145\/2766959","volume":"34","author":"S Bell","year":"2015","unstructured":"Bell S, Bala K (2015) Learning visual similarity for product design with convolutional neural networks. ACM Trans Graph (TOG) 34(4):98","journal-title":"ACM Trans Graph (TOG)"},{"issue":"8","key":"15607_CR4","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798\u20131828","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"15607_CR5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"15607_CR6","doi-asserted-by":"publisher","unstructured":"Brigato L, Iocchi L (2021) A close look at deep learning with small data. In: 25Th international conference on pattern recognition (ICPR), pp 2490\u20132497. https:\/\/doi.org\/10.1109\/ICPR48806.2021.9412492","DOI":"10.1109\/ICPR48806.2021.9412492"},{"key":"15607_CR7","doi-asserted-by":"crossref","unstructured":"Bromley J, Guyon I, LeCun Y, S\u00e4ckinger E, Shah R (1994) Signature verification using a \u201csiamese\u201d time delay neural network. In: Advances in neural information processing systems (NIPS), pp 737\u2013744","DOI":"10.1142\/9789812797926_0003"},{"issue":"2","key":"15607_CR8","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1007\/s00500-019-03976-7","volume":"24","author":"J Calvo-Zaragoza","year":"2019","unstructured":"Calvo-Zaragoza J, Rico-Juan JR, Gallego AJ (2019) Ensemble classification from deep predictions with test data augmentation. Soft Comput 24 (2):1423\u20131433. 10.1007\/s00500-019-03976-7","journal-title":"Soft Comput"},{"key":"15607_CR9","unstructured":"Calvo-Zaragoza J, Valero-Mas JJ, Pertusa A (2017) End-to-end optical music recognition using neural networks. In: Proc. of ISMIR, Suzhou, China, pp 472\u2014-477"},{"issue":"1","key":"15607_CR10","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana R (1997) Multitask learning. Mach Learn 28(1):41\u201375","journal-title":"Mach Learn"},{"key":"15607_CR11","doi-asserted-by":"crossref","unstructured":"Chatfield K, Simonyan K, Vedaldi A, Zisserman A (2014) Return of the devil in the details: Delving deep into convolutional nets CoRR abs\/1405.3531","DOI":"10.5244\/C.28.6"},{"key":"15607_CR12","unstructured":"Cogswell M, Ahmed F, Girshick RB, Zitnick L, Batra D (2016) Reducing overfitting in deep networks by decorrelating representations. In: 4Th international conference on learning representations, ICLR"},{"key":"15607_CR13","doi-asserted-by":"crossref","unstructured":"Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV (2019) Autoaugment: learning augmentation strategies from data. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 113\u2013123","DOI":"10.1109\/CVPR.2019.00020"},{"key":"15607_CR14","doi-asserted-by":"publisher","first-page":"3336","DOI":"10.1109\/TIP.2019.2959254","volume":"29","author":"D Das","year":"2019","unstructured":"Das D, Lee CG (2019) A two-stage approach to few-shot learning for image recognition. IEEE Trans Image Process 29:3336\u20133350","journal-title":"IEEE Trans Image Process"},{"key":"15607_CR15","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"key":"15607_CR16","unstructured":"Dozat T (2016) Incorporating nesterov momentum into adam OpenReview"},{"key":"15607_CR17","unstructured":"Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd Edition Wiley"},{"issue":"108","key":"15607_CR18","first-page":"356","volume":"122","author":"AJ Gallego","year":"2022","unstructured":"Gallego AJ, Rico-Juan JR, Valero-Mas JJ (2022) Efficient k-nearest neighbor search based on clustering and adaptive k values. Pattern Recogn 122 (108):356","journal-title":"Pattern Recogn"},{"key":"15607_CR19","doi-asserted-by":"crossref","unstructured":"Hadsell R, Chopra S, LeCun Y (2006) Dimensionality reduction by learning an invariant mapping. In: IEEE Computer society conference on computer vision and pattern recognition (CVPR), vol. 2, IEEE, pp 1735\u20131742","DOI":"10.1109\/CVPR.2006.100"},{"key":"15607_CR20","doi-asserted-by":"crossref","unstructured":"Hoffer E, Ailon N (2015) Deep metric learning using triplet network. In: International workshop on similarity-based pattern recognition, Springer, pp 84\u201392","DOI":"10.1007\/978-3-319-24261-3_7"},{"issue":"5","key":"15607_CR21","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/34.291440","volume":"16","author":"J Hull","year":"1994","unstructured":"Hull J (1994) A database for handwritten text recognition research. IEEE Trans Pattern Anal Mach Intell (TPAMI) 16(5):550\u2013554","journal-title":"IEEE Trans Pattern Anal Mach Intell (TPAMI)"},{"key":"15607_CR22","unstructured":"Jadon S (2020)"},{"issue":"47","key":"15607_CR23","doi-asserted-by":"publisher","first-page":"35,109","DOI":"10.1007\/s11042-020-08857-y","volume":"79","author":"AB Jagtap","year":"2020","unstructured":"Jagtap AB, Sawat DD, Hegadi RS, Hegadi RS (2020) Verification of genuine and forged offline signatures using siamese neural network (snn). Multimed Tools Appl 79(47):35,109\u201335,123","journal-title":"Multimed Tools Appl"},{"issue":"5","key":"15607_CR24","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1007\/s00521-017-3158-6","volume":"29","author":"F Jiang","year":"2018","unstructured":"Jiang F, Grigorev A, Rho S, Tian Z, Fu Y, Jifara W, Adil K, Liu S (2018) Medical image semantic segmentation based on deep learning. Neural Comput Appl 29(5):1257\u20131265","journal-title":"Neural Comput Appl"},{"key":"15607_CR25","doi-asserted-by":"crossref","unstructured":"Jing L, Tian Y (2020) Self-supervised visual feature learning with deep neural networks: A survey IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"15607_CR26","unstructured":"Kingma DP, Ba J (2014)"},{"key":"15607_CR27","unstructured":"Koch G, Zemel R, Salakhutdinov R (2015) Siamese neural networks for one-shot image recognition. In: International conference on machine learning (ICML) - deep learning workshop, vol. 2, pp 1126\u20131135"},{"key":"15607_CR28","volume-title":"Learning multiple layers of features from tiny images","author":"A Krizhevsky","year":"2009","unstructured":"Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. Tech. rep., Citeseer"},{"key":"15607_CR29","doi-asserted-by":"crossref","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, vol. 86, pp 2278\u20132324","DOI":"10.1109\/5.726791"},{"key":"15607_CR30","unstructured":"Lee JM, Kang Ds (2021) Improved method for learning data imbalance in gender classification model using da-fsl. Multimed Tools Appl 1\u201319"},{"key":"15607_CR31","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.neucom.2020.04.040","volume":"406","author":"X Li","year":"2020","unstructured":"Li X, Yu L, Fu CW, Fang M, Heng PA (2020) Revisiting metric learning for few-shot image classification. Neurocomputing 406:49\u201358","journal-title":"Neurocomputing"},{"key":"15607_CR32","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Guti\u00e9rrez JC, Valero-Mas JJ, Castellanos FJ, Calvo-Zaragoza J Barney Smith EH, Pal U (eds) (2021) Data augmentation for end-to-end optical music recognition. Springer, Cham","DOI":"10.1007\/978-3-030-86198-8_5"},{"key":"15607_CR33","doi-asserted-by":"crossref","unstructured":"Medela A, Picon A, Saratxaga CL, Belar O, Cabez\u00f3n V, Cicchi R, Bilbao R, Glover B (2019) Few shot learning in histopathological images: reducing the need of labeled data on biological datasets. In: IEEE 16Th international symposium on biomedical imaging (ISBI), IEEE, pp 1860\u20131864","DOI":"10.1109\/ISBI.2019.8759182"},{"key":"15607_CR34","volume-title":"Machine Learning","author":"TM Mitchell","year":"1997","unstructured":"Mitchell TM (1997) Machine Learning. McGraw-Hill, Inc"},{"issue":"23","key":"15607_CR35","doi-asserted-by":"publisher","first-page":"8578","DOI":"10.3390\/app10238578","volume":"10","author":"L Nanni","year":"2020","unstructured":"Nanni L, Brahnam S, Lumini A, Maguolo G (2020) Animal sound classification using dissimilarity spaces. Appl Sci 10(23):8578","journal-title":"Appl Sci"},{"key":"15607_CR36","doi-asserted-by":"crossref","unstructured":"O\u2019Mahony N, Campbell S, Carvalho A, Harapanahalli S, Hernandez GV, Krpalkova L, Riordan D, Walsh J (2019) Deep learning vs. traditional computer vision. In: Science and information conference, Springer, pp 128\u2013144","DOI":"10.1007\/978-3-030-17795-9_10"},{"key":"15607_CR37","doi-asserted-by":"publisher","first-page":"53,296","DOI":"10.1109\/ACCESS.2019.2911850","volume":"7","author":"C Pan","year":"2019","unstructured":"Pan C, Huang J, Gong J, Yuan X (2019) Few-shot transfer learning for text classification with lightweight word embedding based models. IEEE Access 7:53,296\u201353,304","journal-title":"IEEE Access"},{"issue":"10","key":"15607_CR38","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359. 10.1109\/TKDE.2009.191","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"15607_CR39","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: IEEE Computer society conference on computer vision and pattern recognition (CVPR), pp 815\u2013823","DOI":"10.1109\/CVPR.2015.7298682"},{"issue":"1","key":"15607_CR40","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):60","journal-title":"J Big Data"},{"key":"15607_CR41","doi-asserted-by":"crossref","unstructured":"Simon C, Koniusz P, Nock R, Harandi M (2020) Adaptive subspaces for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 4136\u20134145","DOI":"10.1109\/CVPR42600.2020.00419"},{"key":"15607_CR42","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. In: Advances in neural information processing systems, pp 4077\u20134087"},{"key":"15607_CR43","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr PH, Hospedales TM (2018) Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1199\u20131208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"15607_CR44","doi-asserted-by":"crossref","unstructured":"Szeliski R (2010) Computer vision: algorithms and applications. Springer Science & Business Media","DOI":"10.1007\/978-1-84882-935-0"},{"key":"15607_CR45","doi-asserted-by":"crossref","unstructured":"Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1701\u20131708","DOI":"10.1109\/CVPR.2014.220"},{"key":"15607_CR46","doi-asserted-by":"crossref","unstructured":"Taylor L, Nitschke G (2018) Improving deep learning with generic data augmentation. In: 2018 IEEE Symposium series on computational intelligence (SSCI), IEEE, pp 1542\u20131547","DOI":"10.1109\/SSCI.2018.8628742"},{"issue":"2","key":"15607_CR47","first-page":"26","volume":"4","author":"T Tieleman","year":"2012","unstructured":"Tieleman T, Hinton G (2012) Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Netw Mach Learn 4(2):26\u201331","journal-title":"COURSERA: Neural Netw Mach Learn"},{"key":"15607_CR48","unstructured":"Vapnik Vn (1998) Statistical learning theory, 1 edn Wiley"},{"key":"15607_CR49","first-page":"3630","volume":"29","author":"O Vinyals","year":"2016","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D, et al. (2016) Matching networks for one shot learning. Adv Neural Inf Process Syst 29:3630\u20133638","journal-title":"Adv Neural Inf Process Syst"},{"key":"15607_CR50","doi-asserted-by":"crossref","unstructured":"Wang Y, Yao Q, Kwok J, Ni LM (2019) Generalizing from a few examples: a survey on few-shot learning","DOI":"10.1145\/3386252"},{"key":"15607_CR51","doi-asserted-by":"crossref","unstructured":"Wang J, Zhu Z, Li J, Li J (2018) Attention based siamese networks for few-shot learning. In: IEEE 9Th international conference on software engineering and service science (ICSESS), IEEE, pp 551\u2013554","DOI":"10.1109\/ICSESS.2018.8663732"},{"key":"15607_CR52","unstructured":"Xiao H, Rasul K, Vollgraf R (2017)"},{"issue":"107","key":"15607_CR53","first-page":"205","volume":"102","author":"L Xie","year":"2020","unstructured":"Xie L, Lee F, Liu L, Kotani K, Chen Q (2020) Scene recognition: a comprehensive survey. Pattern Recogn 102(107):205","journal-title":"Pattern Recogn"},{"key":"15607_CR54","unstructured":"Zeiler MD (2012)"},{"issue":"3","key":"15607_CR55","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1145\/3446776","volume":"64","author":"C Zhang","year":"2021","unstructured":"Zhang C, Bengio S, Hardt M, Recht B, Vinyals O (2021) Understanding deep learning (still) requires rethinking generalization. Commun ACM 64 (3):107\u2013115","journal-title":"Commun ACM"},{"key":"15607_CR56","unstructured":"Zheng L, Zhao Y, Wang S, Wang J, Tian Q (2016) Good practice in CNN feature transfer. CoRR abs\/1604.00133"},{"key":"15607_CR57","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.neucom.2017.01.043","volume":"238","author":"C Zhu","year":"2017","unstructured":"Zhu C, Peng Y (2017) Discriminative latent semantic feature learning for pedestrian detection. Neurocomputing 238:126\u2013138","journal-title":"Neurocomputing"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15607-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15607-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15607-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T10:24:22Z","timestamp":1707992662000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15607-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,29]]},"references-count":57,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["15607"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15607-3","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,29]]},"assertion":[{"value":"21 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}