{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:58:26Z","timestamp":1740099506998,"version":"3.37.3"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030308582"},{"type":"electronic","value":"9783030308599"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30859-9_44","type":"book-chapter","created":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T08:03:26Z","timestamp":1568189006000},"page":"515-524","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Concept of Smart Medical Autonomous Distributed System for Diagnostics Based on Machine Learning Technology"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8983-3719","authenticated-orcid":false,"given":"Elena","family":"Velichko","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4416-9380","authenticated-orcid":false,"given":"Elina","family":"Nepomnyashchaya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4555-0009","authenticated-orcid":false,"given":"Maxim","family":"Baranov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1720-1813","authenticated-orcid":false,"given":"Marina A.","family":"Galeeva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0726-6613","authenticated-orcid":false,"given":"Vitalii A.","family":"Pavlov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3398-3616","authenticated-orcid":false,"given":"Sergey V.","family":"Zavjalov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0473-5007","authenticated-orcid":false,"given":"Ekaterina","family":"Savchenko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9948-7303","authenticated-orcid":false,"given":"Tatiana M.","family":"Pervunina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1809-0270","authenticated-orcid":false,"given":"Igor","family":"Govorov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2943-0883","authenticated-orcid":false,"given":"Eduard","family":"Komlichenko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,12]]},"reference":[{"issue":"6","key":"44_CR1","doi-asserted-by":"publisher","first-page":"e639","DOI":"10.1542\/peds.113.6.e639","volume":"113","author":"SA Spooner","year":"2004","unstructured":"Spooner, S.A., Gotlieb, E.M.: Telemedicine: pediatric applications. Pediatrics 113(6), e639\u2013e643 (2004)","journal-title":"Pediatrics"},{"issue":"3","key":"44_CR2","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1542\/peds.2008-2193","volume":"123","author":"WJ Gonz\u00e1lez-Espada","year":"2009","unstructured":"Gonz\u00e1lez-Espada, W.J., Hall-Barrow, J., Hall, R.W., Burke, B.L., Smith, C.E.: A Achieving success connecting academic and practicing clinicians through telemedicine. Pediatrics 123(3), 476\u2013483 (2009)","journal-title":"Pediatrics"},{"issue":"9","key":"44_CR3","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1089\/tmj.2011.0033","volume":"17","author":"GC Doolittle","year":"2011","unstructured":"Doolittle, G.C., Spaulding, A.O., Williams, A.R.: The decreasing cost of telemedicine and telehealth. Telemed. Health 17(9), 671\u2013675 (2011)","journal-title":"Telemed. Health"},{"issue":"3","key":"44_CR4","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.jpeds.2009.03.014","volume":"155","author":"R Izquierdo","year":"2009","unstructured":"Izquierdo, R., et al.: School-centered telemedicine for children with type 1 diabetes mellitus. Pediatr. 155(3), 374\u2013379 (2009)","journal-title":"Pediatr."},{"issue":"5","key":"44_CR5","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1542\/peds.2004-0335","volume":"115","author":"KM McConnochie","year":"2005","unstructured":"McConnochie, K.M., Wood, N.E., Kitzman, H.J., Herendeen, N.E., Roy, J., Roghmann, K.J.: Telemedicine reduces absence resulting from illness in urban child care: evaluation of an innovation. Pediatrics 115(5), 1273\u20131282 (2005)","journal-title":"Pediatrics"},{"issue":"3","key":"44_CR6","first-page":"3518","volume":"15","author":"M Sato","year":"2018","unstructured":"Sato, M., et al.: Application of deep learning to the classification of images from colposcopy. Oncol. Lett. 15(3), 3518\u20133523 (2018)","journal-title":"Oncol. Lett."},{"key":"44_CR7","doi-asserted-by":"publisher","first-page":"33910","DOI":"10.1109\/ACCESS.2018.2839338","volume":"6","author":"K Fernandes","year":"2018","unstructured":"Fernandes, K., Cardoso, J.S., Fernandes, J.: Automated methods for the decision support of cervical cancer screening using digital colposcopies. IEEE Access 6, 33910\u201333927 (2018)","journal-title":"IEEE Access"},{"key":"44_CR8","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.1109\/ACCESS.2015.2461602","volume":"3","author":"A Gupta","year":"2015","unstructured":"Gupta, A., Jha, R.K.: A survey of 5G network: architecture and emerging technologies. IEEE Access 3, 1206\u20131232 (2015)","journal-title":"IEEE Access"},{"key":"44_CR9","doi-asserted-by":"publisher","first-page":"9860","DOI":"10.1109\/ACCESS.2018.2890558","volume":"7","author":"A Ometov","year":"2019","unstructured":"Ometov, A., Moltchanov, D., Komarov, M., Volvenko, S.V., Koucheryavy, Y.: Packet level performance assessment of mmWave backhauling technology for 3GPP NR systems. IEEE Access 7, 9860\u20139871 (2019)","journal-title":"IEEE Access"},{"issue":"2","key":"44_CR10","doi-asserted-by":"publisher","first-page":"1973","DOI":"10.1109\/TVT.2018.2887343","volume":"68","author":"Margarita Gapeyenko","year":"2019","unstructured":"Gapeyenko, M., et al.: On the degree of multi-connectivity in 5G millimeter-wave cellular urban deployments. IEEE Trans. Veh. Technol. 68(2), 1973\u20131978 (2019)","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"44_CR11","doi-asserted-by":"crossref","unstructured":"Sadovaya, Y., Gelgor, A.: Synthesis of signals with a low-level of out-of-band emission and peak-to-average power ratio. In: 2018 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), St. Petersburg, pp. 103\u2013106 (2018)","DOI":"10.1109\/EExPolytech.2018.8564428"},{"key":"44_CR12","doi-asserted-by":"crossref","unstructured":"Gorbunov, S., Rashich A.: BER performance of SEFDM signals in LTE fading channels. In: 2018 41st International Conference on Telecommunications and Signal Processing (TSP), pp. 1\u20134 (2018)","DOI":"10.1109\/TSP.2018.8441462"},{"issue":"8","key":"44_CR13","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/MCOM.2016.7537178","volume":"54","author":"S Andreev","year":"2016","unstructured":"Andreev, S., et al.: Exploring synergy between communications, caching, and computing in 5G-grade deployments. IEEE Commun. Mag. 54(8), 60\u201369 (2016)","journal-title":"IEEE Commun. Mag."},{"key":"44_CR14","doi-asserted-by":"crossref","unstructured":"Varga, J., Hilt, A., Rotter, C., J\u00e1r\u00f3, G.: Providing ultra-reliable low latency services for 5G with unattended datacenters. In: 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP), Budapest, pp. 1\u20134 (2018)","DOI":"10.1109\/CSNDSP.2018.8471756"},{"key":"44_CR15","doi-asserted-by":"crossref","unstructured":"Schmoll, R., Pandi, S., Braun, P.J., Fitzek, F.H.P.: Demonstration of VR\/AR offloading to Mobile Edge Cloud for low latency 5G gaming application. In: 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, pp. 1\u20133 (2018)","DOI":"10.1109\/CCNC.2018.8319323"},{"key":"44_CR16","doi-asserted-by":"crossref","unstructured":"Ovsyannikova, A.S., Zavjalov, S.V., Volvenko, S.V.: On the joint use of turbo codes and optimal signals with increased symbol rate. In: 2018 10th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), Moscow, Russia, 1\u20134 (2018)","DOI":"10.1109\/ICUMT.2018.8631243"},{"key":"44_CR17","doi-asserted-by":"crossref","unstructured":"Gelgor, A., Gorlov, A.: A performance of coded modulation based on optimal Faster-than-Nyquist signals. In: 2017 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Istanbul, pp. 1\u20135 (2017)","DOI":"10.1109\/BlackSeaCom.2017.8277678"},{"key":"44_CR18","doi-asserted-by":"crossref","unstructured":"Rashich, A., Urvantsev, A.: Pulse-shaped multicarrier signals with nonorthogonal frequency spacing. In: 2018 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Batumi, pp. 1\u20135 (2018)","DOI":"10.1109\/BlackSeaCom.2018.8433714"},{"issue":"5","key":"44_CR19","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.1109\/TMTT.2019.2901667","volume":"67","author":"H Ghannam","year":"2019","unstructured":"Ghannam, H., Nopchinda, D., Gavell, M., Zirath, H., Darwazeh, I.: Experimental demonstration of spectrally efficient frequency division multiplexing transmissions at E-Band. IEEE Trans. Microw. Theory Tech. 67(5), 1911\u20131923 (2019)","journal-title":"IEEE Trans. Microw. Theory Tech."},{"issue":"1","key":"44_CR20","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol, E.J.: High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25(1), 44 (2019)","journal-title":"Nat. Med."},{"issue":"22","key":"44_CR21","doi-asserted-by":"publisher","first-page":"2353","DOI":"10.1001\/jama.2016.17438","volume":"316","author":"S Jha","year":"2016","unstructured":"Jha, S., Topol, E.J.: Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA 316(22), 2353\u20132354 (2016)","journal-title":"JAMA"},{"key":"44_CR22","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva, A., et al.: A guide to deep learning in healthcare. Nat. Med. 25, 24\u201329 (2019)","journal-title":"Nat. Med."},{"key":"44_CR23","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122\u20131131 (2018)","journal-title":"Cell"},{"key":"44_CR24","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"44_CR25","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"44_CR26","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Neural Information Processing Systems (NIPS) (2015)"},{"key":"44_CR27","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real \u2013 time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"44_CR28","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2017.690"},{"key":"44_CR29","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement, arXiv preprint \n                      arXiv:1804.02767\n                      \n                     (2018)"},{"key":"44_CR30","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., et al.: Focal loss for dense object detection, arXiv preprint \n                      arXiv:1708.02002\n                      \n                     (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"44_CR31","first-page":"21","volume-title":"ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, pp. 21\u201337. Springer, Cham (2016)"},{"key":"44_CR32","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"44_CR33","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, pp. 1520\u20131528 (2015)","DOI":"10.1109\/ICCV.2015.178"},{"issue":"12","key":"44_CR34","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"44_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, William M., Frangi, Alejandro F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). \n                      https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"44_CR36","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 6230\u20136239 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"issue":"4","key":"44_CR37","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L Chen","year":"2018","unstructured":"Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"44_CR38","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"44_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: Context encoding for semantic segmentation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, pp. 7151\u20137160 (2018)","DOI":"10.1109\/CVPR.2018.00747"},{"key":"44_CR40","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint \n                      arXiv:1409.1556\n                      \n                     (2014)"},{"key":"44_CR41","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, 25, pp. 1097\u20131105. Curran Associates Inc. (2012)"},{"key":"44_CR42","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"44_CR43","doi-asserted-by":"crossref","unstructured":"Musakulova, Z., Mirkin, E., Savchenko, E.: Synthesis of the backpropagation error algorithm for a multilayer neural network with nonlinear synaptic inputs. In: 2018 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech), pp. 131\u2013135 (2018)","DOI":"10.1109\/EExPolytech.2018.8564433"}],"container-title":["Lecture Notes in Computer Science","Internet of Things, Smart Spaces, and Next Generation Networks and Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30859-9_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T08:09:30Z","timestamp":1568189370000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30859-9_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030308582","9783030308599"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30859-9_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"12 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NEW2AN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Next Generation Wired\/Wireless Networking","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"St. Petersburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"new2an2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/rusmart.e-werest.org\/2019.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EDAS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"144","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}