{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:05:20Z","timestamp":1743044720445,"version":"3.40.3"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031358937"},{"type":"electronic","value":"9783031358944"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-35894-4_34","type":"book-chapter","created":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T23:05:33Z","timestamp":1688857533000},"page":"453-466","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLO NFPEM: A More Accurate Iris Detector"],"prefix":"10.1007","author":[{"given":"Xiangyu","family":"Ge","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianxiang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianqing","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongrui","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingyuan","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,9]]},"reference":[{"key":"34_CR1","doi-asserted-by":"publisher","unstructured":"Tong, K., Wu, Y., Zhou, F.: Recent advances in small object detection based on deep learning: A review. Image Vis. Comput. 97 (2020), https:\/\/doi.org\/10.1016\/j.imavis.2020.103910","DOI":"10.1016\/j.imavis.2020.103910"},{"key":"34_CR2","doi-asserted-by":"publisher","unstructured":"Sandler, M., Howard, A., Zhu, M., et al.: MobileNetV2: Inverted Residuals and Linear Bottlenecks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018) https:\/\/doi.org\/10.1109\/CVPR.2018.00474.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"34_CR3","unstructured":"Wong, A., Famuori, M., Shafiee, M.J., et al.: YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection. arXiv:1910.01271"},{"key":"34_CR4","unstructured":"Deng, C., Wang, M., Liu, L., et al.: Extended Feature Pyramid Network for Small Object Detection. arXiv: 2003.07021v2"},{"issue":"6","key":"34_CR5","doi-asserted-by":"publisher","first-page":"1810","DOI":"10.1007\/s11263-020-01301-6","volume":"128","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Bai, Y., Ding, M., Ghanem, B.: Multi-task generative adversarial network for detecting small objects in the wild. Int. J. Comput. Vision 128(6), 1810\u20131828 (2020). https:\/\/doi.org\/10.1007\/s11263-020-01301-6","journal-title":"Int. J. Comput. Vision"},{"key":"34_CR6","doi-asserted-by":"crossref","unstructured":"Kisantal, M., Wojna, Z.,Murawski, J., et al.: Augmentation for small object detection. (2019) arXiv: 1902.07296v1","DOI":"10.5121\/csit.2019.91713"},{"key":"34_CR7","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249\u2013259 (2018)","journal-title":"Neural Netw."},{"key":"34_CR8","doi-asserted-by":"publisher","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R., et al.: Feature Pyramid Networks for Object Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936\u2013944, 2017, https:\/\/doi.org\/10.1109\/CVPR.2017.106.","DOI":"10.1109\/CVPR.2017.106"},{"key":"34_CR9","doi-asserted-by":"publisher","unstructured":"Wang, W., Xie, E., Song, X., et al.: Efficient and Accurate Arbitrary-Shaped Text Detection With Pixel Aggregation Network. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8439\u20138448 (2019) https:\/\/doi.org\/10.1109\/ICCV.2019.00853.","DOI":"10.1109\/ICCV.2019.00853"},{"key":"34_CR10","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1109\/ICCV.2017.30","volume":"2017","author":"S Zhang","year":"2017","unstructured":"Zhang, S., Zhu, X., Lei, Z., et al.: S^3FD: single shot scale-invariant face detector. IEEE Int. Conf. Comput. Vis. (ICCV) 2017, 192\u2013201 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.30","journal-title":"IEEE Int. Conf. Comput. Vis. (ICCV)"},{"key":"34_CR11","doi-asserted-by":"publisher","unstructured":"Zhang, W., Wang, S., Thachan, S., Chen, J., Qian, Y.: Deconv R-CNN for Small Object Detection on Remote Sensing Images. In: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018, pp. 2483\u20132486, https:\/\/doi.org\/10.1109\/IGARSS. 2018.8517436.","DOI":"10.1109\/IGARSS"},{"key":"34_CR12","doi-asserted-by":"publisher","first-page":"3578","DOI":"10.1109\/CVPR.2018.00377","volume":"2018","author":"B Singh","year":"2018","unstructured":"Singh, B., Davis, L.S.: An analysis of scale invariance in object detection - SNIP. IEEE\/CVF Conf. Comput. Vis. Patt. Recogn. 2018, 3578\u20133587 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00377","journal-title":"IEEE\/CVF Conf. Comput. Vis. Patt. Recogn."},{"key":"34_CR13","unstructured":"Singh, B., Najibi & L.y S. Davis.: SNIPER: Efficient Multi-Scale Training. In: NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems, Dec. 2018, pp. 9333\u20139343 (2018)"},{"key":"34_CR14","first-page":"328","volume-title":"\u201cSAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection\u201d, 15th European Conference of Computer Vision","author":"Y Kim","year":"2018","unstructured":"Kim, Y., Kang, B., Kim, D.: \u201cSAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection\u201d, 15th European Conference of Computer Vision, pp. 328\u2013343. Munich, Germany (2018)"},{"key":"34_CR15","doi-asserted-by":"publisher","first-page":"3039","DOI":"10.1109\/CVPR.2017.324","volume":"2017","author":"X Wang","year":"2017","unstructured":"Wang, X., Shrivastava, A., Gupta, A.: A-fast-RCNN: hard positive generation via adversary for object detection. IEEE Conf. Comput. Vis. Patt. Recogn. (CVPR) 2017, 3039\u20133048 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.324","journal-title":"IEEE Conf. Comput. Vis. Patt. Recogn. (CVPR)"},{"key":"34_CR16","unstructured":"Grel, T.: Region of interest pooling explained. https:\/\/deepsense.ai\/region-of-interest-pooling-explained\/ (2017)"},{"issue":"3","key":"34_CR17","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1109\/TITS.2018.2838132","volume":"20","author":"X Hu","year":"2019","unstructured":"Hu, X., Xu, X., Xiao, Y., et al.: SINet: A scale-insensitive convolutional neural network for fast vehicle detection. IEEE Trans. Intell. Transp. Syst. 20(3), 1010\u20131019 (2019). https:\/\/doi.org\/10.1109\/TITS.2018.2838132","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"34_CR18","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1109\/CVPR.2016.91","volume":"2016","author":"J Redmon","year":"2016","unstructured":"Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. IEEE Conf. Comput. Vis. Patt. Recogn. (CVPR) 2016, 779\u2013788 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.91","journal-title":"IEEE Conf. Comput. Vis. Patt. Recogn. (CVPR)"},{"issue":"2","key":"34_CR19","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1109\/TIT.2018.2854560","volume":"65","author":"M Soltanolkotabi","year":"2019","unstructured":"Soltanolkotabi, M., Javanmard, A., Lee, J.D.: Theoretical insights into the optimization landscape of over-parameterized shallow neural networks. IEEE Trans. Inf. Theor. 65(2), 742\u2013769 (2019). https:\/\/doi.org\/10.1109\/TIT.2018.2854560","journal-title":"IEEE Trans. Inf. Theor."},{"key":"34_CR20","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.1109\/LSP.2020.3025128","volume":"27","author":"S Chen","year":"2020","unstructured":"Chen, S., Li, Z., Tang, Z.: Relation R-CNN: a graph based relation-aware network for object detection. IEEE Signal Process. Lett. 27, 1680\u20131684 (2020). https:\/\/doi.org\/10.1109\/LSP.2020.3025128","journal-title":"IEEE Signal Process. Lett."},{"key":"34_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1007\/978-3-030-01240-3_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Tang","year":"2018","unstructured":"Tang, X., Du, D.K., He, Z., Liu, J.: PyramidBox: A Context-Assisted Single Shot Face Detector. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 812\u2013828. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01240-3_49"},{"key":"34_CR22","unstructured":"Gregor, K., Danihelka, I., Graves, A., et al.: DRAW: A Recurrent Neural Network for Image Generation. In: ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine Learning, vol. 37, pp. 1462\u20131471 (2015)"},{"key":"34_CR23","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A., Beaufays, F.: Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition. Comput. Sci. 338\u2013342, (2014)","DOI":"10.21437\/Interspeech.2014-80"},{"key":"34_CR24","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.patrec.2019.03.009","volume":"137","author":"Y Xi","year":"2020","unstructured":"Xi, Y., Zheng, J., He, X., et al.: Beyond context: exploring semantic similarity for small object detection in crowded scenes. Patt. Recogn. Lett. 137, 53\u201360 (2020). https:\/\/doi.org\/10.1016\/j.patrec.2019.03.009","journal-title":"Patt. Recogn. Lett."},{"key":"34_CR25","doi-asserted-by":"publisher","unstructured":"Xing, C., Liang, X., Bao, Z.: A Small Object Detection Solution by Using Super- Resolution Recovery. In: 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), 2019, pp. 313\u2013316, https:\/\/doi.org\/10.1109\/ICCSNT47585.2019.8962422","DOI":"10.1109\/ICCSNT47585.2019.8962422"},{"issue":"6","key":"34_CR26","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1016\/j.cja.2020.02.024","volume":"33","author":"Y Li","year":"2020","unstructured":"Li, Y., Dong, H., Li, H., et al.: Multi-block SSD based on small object detection for UAV railway scene surveillance. Chin. J. Aeronaut. 33(6), 1747\u20131755 (2020)","journal-title":"Chin. J. Aeronaut."},{"key":"34_CR27","doi-asserted-by":"publisher","first-page":"1951","DOI":"10.1109\/CVPR.2017.211","volume":"2017","author":"J Li","year":"2017","unstructured":"Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., Yan, S.: Perceptual generative adversarial networks for small object detection. IEEE Conf. Comput. Vis. Patt. Recogn. (CVPR) 2017, 1951\u20131959 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.211","journal-title":"IEEE Conf. Comput. Vis. Patt. Recogn. (CVPR)"},{"issue":"6","key":"34_CR28","doi-asserted-by":"publisher","first-page":"1758","DOI":"10.1109\/TCSVT.2019.2905881","volume":"30","author":"X Liang","year":"2020","unstructured":"Liang, X., Zhang, J., Zhuo, L., et al.: Small object detection in unmanned aerial vehicle images using feature fusion and scaling-based single shot detector with spatial context analysis. IEEE Trans. Circuits Syst. Video Technol. 30(6), 1758\u20131770 (2020). https:\/\/doi.org\/10.1109\/TCSVT.2019.2905881","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"34_CR29","doi-asserted-by":"publisher","unstructured":"Fang, P., Shi, Y.: Small Object Detection Using Context Information Fusion in Faster R-CNN. In: 2018 IEEE 4th International Conference on Computer and Communications (ICCC), 2018, pp. 1537\u20131540, https:\/\/doi.org\/10.1109\/Comp Comm.2018.8780579","DOI":"10.1109\/Comp"},{"key":"34_CR30","doi-asserted-by":"publisher","unstructured":"Bosquet, B., Mucientes, M., Brea, V.M.: STDnet: Exploiting high resolution feature maps for small object detection. Eng. Appl. Artif. Intell. 91, (2020) https:\/\/doi.org\/10.1016\/j.engappai.2020.103615","DOI":"10.1016\/j.engappai.2020.103615"},{"key":"34_CR31","doi-asserted-by":"publisher","first-page":"6517","DOI":"10.1109\/CVPR.2017.690","volume":"2017","author":"J Redmon","year":"2017","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. IEEE Conf. Comput. Vis. Patt. Recogn. (CVPR) 2017, 6517\u20136525 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.690","journal-title":"IEEE Conf. Comput. Vis. Patt. Recogn. (CVPR)"},{"key":"34_CR32","unstructured":"Wang, C., Zhu, Y., Liu, Y., et al.: Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework (2019) arXiv:1901.11195"},{"issue":"4","key":"34_CR33","doi-asserted-by":"publisher","first-page":"842","DOI":"10.3390\/s19040842","volume":"19","author":"Y Lee","year":"2019","unstructured":"Lee, Y., Kim, K., Hoang, T., et al.: Deep Residual CNN-based ocular recognition based on rough pupil detection in the images by NIR camera sensor. Sensors 19(4), 842\u2013872 (2019). https:\/\/doi.org\/10.3390\/s19040842","journal-title":"Sensors"},{"key":"34_CR34","unstructured":"Minaee, S., Abdolrashidi, A.: DeepIris: Iris Recognition Using A Deep Learning Approach (2019) arXiv:1907.09380"},{"key":"34_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/EHB47216.2019.8969984","volume":"2019","author":"C Miron","year":"2019","unstructured":"Miron, C., Pasarica, A., Bozomitu, R.G., et al.: Efficient pupil detection with a convolutional neural network. E-Health Bioeng. Conf. (EHB) 2019, 1\u20134 (2019). https:\/\/doi.org\/10.1109\/EHB47216.2019.8969984","journal-title":"E-Health Bioeng. Conf. (EHB)"},{"key":"34_CR36","unstructured":"Feng, C., Sun, Y., Li, X.: Iris R-CNN: Accurate Iris Segmentation in Non-cooperative Environment (2019) arXiv:1903.10140"},{"key":"34_CR37","doi-asserted-by":"publisher","first-page":"2296","DOI":"10.1109\/CVPRW.2019.00283","volume":"2019","author":"H Proen\u00e7a","year":"2019","unstructured":"Proen\u00e7a, H., Neves, J.C.: Segmentation-less and non-holistic deep-learning frameworks for iris recognition. IEEE\/CVF Conf. Comput. Vis. Patt. Recogn. Workshops (CVPRW) 2019, 2296\u20132305 (2019). https:\/\/doi.org\/10.1109\/CVPRW.2019.00283","journal-title":"IEEE\/CVF Conf. Comput. Vis. Patt. Recogn. Workshops (CVPRW)"},{"key":"34_CR38","doi-asserted-by":"publisher","unstructured":"Trokielewicz, M., Czajka, A., Maciejewicz, P.: Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation. Vol. 94, Feb. 2020, https:\/\/doi.org\/10.1016\/j.imavis.2019.103866","DOI":"10.1016\/j.imavis.2019.103866"},{"key":"34_CR39","unstructured":"Gangwar, A., Joshi, A., Joshi, P., et al.: DeepIrisNet2: Learning Deep-IrisCodes from Scratch for Segmentation-Robust Visible Wavelength and Near Infrared Iris Recognition (2019) arXiv:1902.05390"},{"key":"34_CR40","unstructured":"Hassan, B., Ahmed, R., Hassan, T., et al.: SIP-SegNet: A Deep Convolutional Encoder-Decoder Network for Joint Semantic Segmentation and Extraction of Sclera, Iris and Pupil based on Periocular Region Suppression (2020) arXiv:2003.00825"},{"key":"34_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/IWOBI.2018.8464133","volume":"2018","author":"P Rot","year":"2018","unstructured":"Rot, P., Emer\u0161i\u010d, \u017d, Struc, V., Peer, P.: Deep multi-class eye segmentation for ocular biometrics. IEEE Int. Work Conf. Bioinspired Intell. (IWOBI) 2018, 1\u20138 (2018). https:\/\/doi.org\/10.1109\/IWOBI.2018.8464133","journal-title":"IEEE Int. Work Conf. Bioinspired Intell. (IWOBI)"},{"key":"34_CR42","unstructured":"Wolfgang, F., Thiago, S., Gjergji, K., et al.: PupilNet: Convolutional Neural Networks for Robust Pupil Detection (2020) arXiv:1601.04902"},{"key":"34_CR43","doi-asserted-by":"publisher","unstructured":"Han, S.Y., Kim, Y., Lee, S.H., Cho, N.I.: Pupil Center Detection Based on the UNet for the User Interaction in VR and AR Environments. In: 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 2019, pp. 958\u2013959, https:\/\/doi.org\/10.1109\/VR.2019.8798027","DOI":"10.1109\/VR.2019.8798027"},{"key":"34_CR44","doi-asserted-by":"publisher","unstructured":"He, F., Han, Y., Wang, H., et al.: Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network. J. Electron. Imag. 26(2), (2017) https:\/\/doi.org\/10.1117\/1.JEI.26.2.023005","DOI":"10.1117\/1.JEI.26.2.023005"},{"key":"34_CR45","unstructured":"Joseph, R., Ali, F.: YOLOv3: An Incremental Improvement (2018) arXiv:1804.02767"},{"key":"34_CR46","doi-asserted-by":"publisher","unstructured":"Wu, D., Lv, S., Jiang, M., et al: Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments. Comput. Electron. Agricul. 178, 2020, https:\/\/doi.org\/10.1016\/j.compag.2020.105742","DOI":"10.1016\/j.compag.2020.105742"},{"issue":"4","key":"34_CR47","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1007\/s00371-020-01831-7","volume":"37","author":"W Chen","year":"2020","unstructured":"Chen, W., Huang, H., Peng, S., Zhou, C., Zhang, C.: YOLO-face: a real-time face detector. Vis. Comput. 37(4), 805\u2013813 (2020). https:\/\/doi.org\/10.1007\/s00371-020-01831-7","journal-title":"Vis. Comput."},{"issue":"8","key":"34_CR48","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1109\/TPAMI.2009.66","volume":"32","author":"H Proenca","year":"2010","unstructured":"Proenca, H., Filipe, S., Santos, R., Oliveira, J., Alexandre, L.A.: The UBIRIS.v2: A database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Trans. Patt. Anal. Mach. Intell. 32(8), 1529\u20131535 (2010). https:\/\/doi.org\/10.1109\/TPAMI.2009.66","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in HCI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-35894-4_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,3]],"date-time":"2023-09-03T12:04:59Z","timestamp":1693742699000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-35894-4_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031358937","9783031358944"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-35894-4_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Copenhagen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Denmark","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.hci.international\/","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":"CMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7472","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":"1578","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":"396","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":"21% - 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":"2","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":"2","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}