{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T20:26:04Z","timestamp":1782505564796,"version":"3.54.5"},"publisher-location":"Cham","reference-count":50,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585914","type":"print"},{"value":"9783030585921","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58592-1_11","type":"book-chapter","created":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:34:03Z","timestamp":1604363643000},"page":"170-186","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["SACA Net: Cybersickness Assessment of Individual Viewers for VR Content via Graph-Based Symptom Relation Embedding"],"prefix":"10.1007","author":[{"given":"Sangmin","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jung Uk","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hak Gu","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seongyeop","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong Man","family":"Ro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"11_CR1","unstructured":"Methodology for the subjective assessment of the quality of television pictures. ITU-R BT.500-13 (2012)"},{"key":"11_CR2","unstructured":"Subjective methods for the assessment of stereoscopic 3dtv systems. ITU-R BT.2021 (2012)"},{"issue":"3","key":"11_CR3","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1109\/TASSP.1977.1162950","volume":"25","author":"J Allen","year":"1977","unstructured":"Allen, J.: Short term spectral analysis, synthesis, and modification by discrete Fourier transform. IEEE Trans. Acoust. Speech Signal Process. 25(3), 235\u2013238 (1977)","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"issue":"10","key":"11_CR4","first-page":"203","volume":"11","author":"D Basak","year":"2007","unstructured":"Basak, D., Pal, S., Patranabis, D.C.: Support vector regression. Neural Inf. Process. Lett. Rev. 11(10), 203\u2013224 (2007)","journal-title":"Neural Inf. Process. Lett. Rev."},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Bruck, S., Watters, P.A.: Estimating cybersickness of simulated motion using the simulator sickness questionnaire (SSQ): a controlled study. In: CIGV, pp. 486\u2013488 (2009)","DOI":"10.1109\/CGIV.2009.83"},{"issue":"3","key":"11_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3196885","volume":"15","author":"LE Buck","year":"2018","unstructured":"Buck, L.E., Young, M.K., Bodenheimer, B.: A comparison of distance estimation in HMD-based virtual environments with different HMD-based conditions. ACM Trans. Appl. Percept. (TAP) 15(3), 1\u201315 (2018)","journal-title":"ACM Trans. Appl. Percept. (TAP)"},{"issue":"5","key":"11_CR7","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MCG.2015.98","volume":"35","author":"K Carnegie","year":"2015","unstructured":"Carnegie, K., Rhee, T.: Reducing visual discomfort with HMDs using dynamic depth of field. IEEE Comput. Graph. Appl. 35(5), 34\u201341 (2015)","journal-title":"IEEE Comput. Graph. Appl."},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Y., Rohrbach, M., Yan, Z., Shuicheng, Y., Feng, J., Kalantidis, Y.: Graph-based global reasoning networks. In: CVPR, pp. 433\u2013442 (2019)","DOI":"10.1109\/CVPR.2019.00052"},{"issue":"02","key":"11_CR9","doi-asserted-by":"publisher","first-page":"1650007","DOI":"10.1142\/S0129065716500076","volume":"26","author":"SW Chuang","year":"2016","unstructured":"Chuang, S.W., Chuang, C.H., Yu, Y.H., King, J.T., Lin, C.T.: EEG alpha and gamma modulators mediate motion sickness-related spectral responses. Int. J. Neural Syst. 26(02), 1650007 (2016)","journal-title":"Int. J. Neural Syst."},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Corbillon, X., De Simone, F., Simon, G.: 360-degree video head movement dataset. In: Proceedings of the 8th ACM on Multimedia Systems Conference, pp. 199\u2013204 (2017)","DOI":"10.1145\/3083187.3083215"},{"key":"11_CR11","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.displa.2016.07.002","volume":"44","author":"MS Dennison","year":"2016","unstructured":"Dennison, M.S., Wisti, A.Z., D\u2019Zmura, M.: Use of physiological signals to predict cybersickness. Displays 44, 42\u201352 (2016)","journal-title":"Displays"},{"issue":"1\u20132","key":"11_CR12","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/S0165-1838(97)00090-8","volume":"67","author":"I Doweck","year":"1997","unstructured":"Doweck, I., et al.: Alterations in R-R variability associated with experimental motion sickness. J. Auton. Nerv. Syst. 67(1\u20132), 31\u201337 (1997)","journal-title":"J. Auton. Nerv. Syst."},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Egan, D., Brennan, S., Barrett, J., Qiao, Y., Timmerer, C., Murray, N.: An evaluation of heart rate and electrodermal activity as an objective QoE evaluation method for immersive virtual reality environments. In: QoMEX, pp. 1\u20136 (2016)","DOI":"10.1109\/QoMEX.2016.7498964"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Freina, L., Ott, M.: A literature review on immersive virtual reality in education: state of the art and perspectives. In: eLSE, vol. 1, p. 133. \u201cCarol I\u201d National Defence University (2015)","DOI":"10.12753\/2066-026X-15-020"},{"issue":"2","key":"11_CR15","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1097\/01.sla.0000151982.85062.80","volume":"241","author":"AG Gallagher","year":"2005","unstructured":"Gallagher, A.G., et al.: Virtual reality simulation for the operating room: proficiency-based training as a paradigm shift in surgical skills training. Ann. Surg. 241(2), 364 (2005)","journal-title":"Ann. Surg."},{"issue":"2","key":"11_CR16","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1002\/bjs.4407","volume":"91","author":"TP Grantcharov","year":"2004","unstructured":"Grantcharov, T.P., Kristiansen, V.B., Bendix, J., Bardram, L., Rosenberg, J., Funch-Jensen, P.: Randomized clinical trial of virtual reality simulation for laparoscopic skills training. Br. J. Surg. 91(2), 146\u2013150 (2004)","journal-title":"Br. J. Surg."},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Jeong, D.K., Yoo, S., Jang, Y.: VR sickness measurement with EEG using DNN algorithm. In: VRST, p. 134 (2018)","DOI":"10.1145\/3281505.3283387"},{"issue":"3","key":"11_CR18","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1207\/s15327108ijap0303_3","volume":"3","author":"RS Kennedy","year":"1993","unstructured":"Kennedy, R.S., Lane, N.E., Berbaum, K.S., Lilienthal, M.G.: Simulator sickness questionnaire: an enhanced method for quantifying simulator sickness. Int. J. Aviat. Psychol. 3(3), 203\u2013220 (1993)","journal-title":"Int. J. Aviat. Psychol."},{"issue":"1","key":"11_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-17765-5","volume":"8","author":"AY Kim","year":"2018","unstructured":"Kim, A.Y., et al.: Automatic detection of major depressive disorder using electrodermal activity. Sci. Rep. 8(1), 1\u20139 (2018)","journal-title":"Sci. Rep."},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Kim, H.G., Baddar, W.J., Lim, H., Jeong, H., Ro, Y.M.: Measurement of exceptional motion in VR video contents for VR sickness assessment using deep convolutional autoencoder. In: VRST, p. 36 (2017)","DOI":"10.1145\/3139131.3139137"},{"issue":"4","key":"11_CR21","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.1109\/TIP.2018.2880509","volume":"28","author":"HG Kim","year":"2018","unstructured":"Kim, H.G., Lim, H.T., Lee, S., Ro, Y.M.: VRSA net: VR sickness assessment considering exceptional motion for 360 VR video. IEEE Trans. Image Process. 28(4), 1646\u20131660 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, W., Ahn, S., Kim, J., Lee, S.: Virtual reality sickness predictor: analysis of visual-vestibular conflict and VR contents. In: QoMEX, pp. 1\u20136 (2018)","DOI":"10.1109\/QoMEX.2018.8463413"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, W., Oh, H., Lee, S., Lee, S.: A deep cybersickness predictor based on brain signal analysis for virtual reality contents. In: ICCV, pp. 10580\u201310589 (2019)","DOI":"10.1109\/ICCV.2019.01068"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Kim, K., Lee, S., Kim, H.G., Park, M., Ro, Y.M.: Deep objective assessment model based on spatio-temporal perception of 360-degree video for VR sickness prediction. In: ICIP, pp. 3192\u20133196 (2019)","DOI":"10.1109\/ICIP.2019.8803257"},{"issue":"5","key":"11_CR25","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1111\/j.1469-8986.2005.00349.x","volume":"42","author":"YY Kim","year":"2005","unstructured":"Kim, Y.Y., Kim, H.J., Kim, E.N., Ko, H.D., Kim, H.T.: Characteristic changes in the physiological components of cybersickness. Psychophysiology 42(5), 616\u2013625 (2005)","journal-title":"Psychophysiology"},{"key":"11_CR26","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"11_CR27","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Lee, S., et al.: Physiological fusion net: quantifying individual VR sickness with content stimulus and physiological response. In: ICIP, pp. 440\u2013444 (2019)","DOI":"10.1109\/ICIP.2019.8802983"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Li, Y., Ouyang, W., Zhou, B., Wang, K., Wang, X.: Scene graph generation from objects, phrases and region captions. In: ICCV, pp. 1261\u20131270 (2017)","DOI":"10.1109\/ICCV.2017.142"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Lin, C.T., Chuang, S.W., Chen, Y.C., Ko, L.W., Liang, S.F., Jung, T.P.: EEG effects of motion sickness induced in a dynamic virtual reality environment. In: EMBC, pp. 3872\u20133875 (2007)","DOI":"10.1109\/IEMBS.2007.4353178"},{"issue":"10","key":"11_CR31","doi-asserted-by":"publisher","first-page":"1689","DOI":"10.1109\/TNNLS.2013.2275003","volume":"24","author":"CT Lin","year":"2013","unstructured":"Lin, C.T., Tsai, S.F., Ko, L.W.: EEG-based learning system for online motion sickness level estimation in a dynamic vehicle environment. IEEE Trans. Neural Netw. Learn. Syst. 24(10), 1689\u20131700 (2013)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"11_CR32","doi-asserted-by":"crossref","unstructured":"Mawalid, M.A., Khoirunnisa, A.Z., Purnomo, M.H., Wibawa, A.D.: Classification of EEG signal for detecting cybersickness through time domain feature extraction using Na\u00efve Bayes. In: CENIM, pp. 29\u201334 (2018)","DOI":"10.1109\/CENIM.2018.8711320"},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Meehan, M., Insko, B., Whitton, M., Brooks, Jr., F.P.: Physiological measures of presence in stressful virtual environments. In: TOG, pp. 645\u2013652 (2002)","DOI":"10.1145\/566570.566630"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Naqvi, S.A.A., Badruddin, N., Malik, A.S., Hazabbah, W., Abdullah, B.: Does 3D produce more symptoms of visually induced motion sickness? In: EMBC, pp. 6405\u20136408 (2013)","DOI":"10.1109\/EMBC.2013.6611020"},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: ICCV, pp. 1520\u20131528 (2015)","DOI":"10.1109\/ICCV.2015.178"},{"issue":"4","key":"11_CR36","doi-asserted-by":"publisher","first-page":"1594","DOI":"10.1109\/TVCG.2018.2793560","volume":"24","author":"N Padmanaban","year":"2018","unstructured":"Padmanaban, N., Ruban, T., Sitzmann, V., Norcia, A.M., Wetzstein, G.: Towards a machine-learning approach for sickness prediction in 360 stereoscopic videos. IEEE Trans. Visual. Comput. Graph. 24(4), 1594\u20131603 (2018)","journal-title":"IEEE Trans. Visual. Comput. Graph."},{"key":"11_CR37","first-page":"170","volume":"3","author":"ES Pane","year":"2018","unstructured":"Pane, E.S., Khoirunnisaa, A.Z., Wibawa, A.D., Purnomo, M.H.: Identifying severity level of cybersickness from EEG signals using CN2 rule induction algorithm. ICIIBMS 3, 170\u2013176 (2018)","journal-title":"ICIIBMS"},{"key":"11_CR38","unstructured":"Patrao, B., Pedro, S., Menezes, P.: How to deal with motion sickness in virtual reality. Sciences and Technologies of Interaction, 2015 22 nd, pp. 40\u201346 (2015)"},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Qi, M., Li, W., Yang, Z., Wang, Y., Luo, J.: Attentive relational networks for mapping images to scene graphs. In: CVPR, pp. 3957\u20133966 (2019)","DOI":"10.1109\/CVPR.2019.00408"},{"issue":"11","key":"11_CR40","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1177\/014107687807101109","volume":"71","author":"JT Reason","year":"1978","unstructured":"Reason, J.T.: Motion sickness adaptation: a neural mismatch model. J. Roy. Soc. Med. 71(11), 819\u2013829 (1978)","journal-title":"J. Roy. Soc. Med."},{"issue":"2","key":"11_CR41","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/s10055-016-0285-9","volume":"20","author":"L Rebenitsch","year":"2016","unstructured":"Rebenitsch, L., Owen, C.: Review on cybersickness in applications and visual displays. Virtual Reality 20(2), 101\u2013125 (2016). https:\/\/doi.org\/10.1007\/s10055-016-0285-9","journal-title":"Virtual Reality"},{"key":"11_CR42","doi-asserted-by":"publisher","first-page":"258","DOI":"10.3389\/fpubh.2017.00258","volume":"5","author":"F Shaffer","year":"2017","unstructured":"Shaffer, F., Ginsberg, J.: An overview of heart rate variability metrics and norms. Front. Publ. Health 5, 258 (2017)","journal-title":"Front. Publ. Health"},{"key":"11_CR43","doi-asserted-by":"crossref","unstructured":"Singla, A., Fremerey, S., Robitza, W., Raake, A.: Measuring and comparing QoE and simulator sickness of omnidirectional videos in different head mounted displays. In: QoMEX, pp. 1\u20136 (2017)","DOI":"10.1109\/QoMEX.2017.7965658"},{"key":"11_CR44","unstructured":"Tiiro, A.: Effect of visual realism on cybersickness in virtual reality. University of Oulu (2018)"},{"key":"11_CR45","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/978-981-10-8204-7_5","volume-title":"Innovations in Electronics and Communication Engineering","author":"KP Wagh","year":"2019","unstructured":"Wagh, K.P., Vasanth, K.: Electroencephalograph (EEG) based emotion recognition system: a review. In: Saini, H.S., Singh, R.K., Patel, V.M., Santhi, K., Ranganayakulu, S.V. (eds.) Innovations in Electronics and Communication Engineering. LNNS, vol. 33, pp. 37\u201359. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-10-8204-7_5"},{"key":"11_CR46","doi-asserted-by":"publisher","first-page":"158","DOI":"10.3389\/fpsyg.2019.00158","volume":"10","author":"S Weech","year":"2019","unstructured":"Weech, S., Kenny, S., Barnett-Cowan, M.: Presence and cybersickness in virtual reality are negatively related: a review. Front. Psychol. 10, 158 (2019)","journal-title":"Front. Psychol."},{"key":"11_CR47","doi-asserted-by":"crossref","unstructured":"Wei, C.S., Ko, L.W., Chuang, S.W., Jung, T.P., Lin, C.T.: EEG-based evaluation system for motion sickness estimation. In: NER, pp. 100\u2013103 (2011)","DOI":"10.1109\/NER.2011.5910498"},{"key":"11_CR48","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.entcom.2018.02.003","volume":"26","author":"S Wibirama","year":"2018","unstructured":"Wibirama, S., Nugroho, H.A., Hamamoto, K.: Depth gaze and ECG based frequency dynamics during motion sickness in stereoscopic 3D movie. Entertainment Comput. 26, 117\u2013127 (2018)","journal-title":"Entertainment Comput."},{"key":"11_CR49","unstructured":"Woo, S., Kim, D., Cho, D., Kweon, I.S.: Linknet: relational embedding for scene graph. In: NIPS, pp. 560\u2013570 (2018)"},{"key":"11_CR50","unstructured":"Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: NIPS, pp. 802\u2013810 (2015)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58592-1_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:24:32Z","timestamp":1730593472000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58592-1_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585914","9783030585921"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58592-1_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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":"7","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}