{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:10:42Z","timestamp":1742980242972,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":50,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811951930"},{"type":"electronic","value":"9789811951947"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-5194-7_17","type":"book-chapter","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T23:03:17Z","timestamp":1660086197000},"page":"219-239","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Probability Loop Closure Detection with\u00a0Fisher Kernel Framework for\u00a0Visual SLAM"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4694-5221","authenticated-orcid":false,"given":"Ge","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0201-0670","authenticated-orcid":false,"given":"Qian","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1009-8200","authenticated-orcid":false,"given":"Hao","family":"Dang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Arroyo, R., Alcantarilla, P.F., Bergasa, L.M., Yebes, J.J., Gamez, S.: Bidirectional loop closure detection on panoramas for visual navigation. In: Proceedings of IEEE Intelligent Vehicles Symposium (IV), pp. 1378\u20131383 (2014)","DOI":"10.1109\/IVS.2014.6856457"},{"issue":"4","key":"17_CR2","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.3390\/s21041243","volume":"21","author":"S Arshad","year":"2021","unstructured":"Arshad, S., Kim, G.W.: Role of deep learning in loop closure detection for visual and Lidar SLAM: a survey. Sensors 21(4), 1243 (2021)","journal-title":"Sensors"},{"issue":"3","key":"17_CR3","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1049\/cje.2018.03.010","volume":"27","author":"D Bai","year":"2018","unstructured":"Bai, D., Wang, C., Bo, Z., Xiaodong, Y.I., Yang, X.: CNN feature boosted SeqSLAM for real-time loop closure detection. Chin. J. Electron. 27(3), 488\u2013499 (2018)","journal-title":"Chin. J. Electron."},{"issue":"1","key":"17_CR4","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1177\/0278364917740639","volume":"37","author":"L Bampis","year":"2018","unstructured":"Bampis, L., Amanatiadis, A., Gasteratos, A.: Fast loop-closure detection using visual-word-vectors from image sequences. Int. J. Robot. Res. 37(1), 62\u201382 (2018)","journal-title":"Int. J. Robot. Res."},{"key":"17_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/11744023_32","volume-title":"Computer Vision \u2013 ECCV 2006","author":"H Bay","year":"2006","unstructured":"Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404\u2013417. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744023_32"},{"issue":"4","key":"17_CR6","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/s10514-009-9138-7","volume":"27","author":"JL Blanco","year":"2009","unstructured":"Blanco, J.L., Moreno, F.A., Gonz\u00e1lez, J.: A collection of outdoor robotic datasets with centimeter-accuracy ground truth. Auton. Robot. 27(4), 327\u2013351 (2009)","journal-title":"Auton. Robot."},{"key":"17_CR7","unstructured":"Bonarini, A., Burgard, W., Fontana, G., Matteucci, M., Sorrenti, D.G., Tardos, J.D.: RAWSEEDS: robotics advancement through web-publishing of sensorial and elaborated extensive data sets. In: Proceedings of International Conference on Intelligent Robots and Systems Workshop on Benchmarks in Robotics Research (ICIRS) (2009)"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Boureau, Y.L., Bach, F., Lecun, Y., Ponce, J.: Learning mid-level features for recognition. In: Computer Vision & Pattern Recognition, pp. 2559\u20132566 (2010)","DOI":"10.1109\/CVPR.2010.5539963"},{"issue":"10","key":"17_CR9","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1177\/0278364915620033","volume":"35","author":"M Burri","year":"2016","unstructured":"Burri, M., et al.: The EuRoC micro aerial vehicle datasets. Int. J. Robot. Res. 35(10), 1157\u20131163 (2016)","journal-title":"Int. J. Robot. Res."},{"issue":"6","key":"17_CR10","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","volume":"32","author":"C Cadena","year":"2016","unstructured":"Cadena, C., et al.: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Rob. 32(6), 1309\u20131332 (2016)","journal-title":"IEEE Trans. Rob."},{"key":"17_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1007\/978-3-642-15561-1_56","volume-title":"Computer Vision \u2013 ECCV 2010","author":"M Calonder","year":"2010","unstructured":"Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 778\u2013792. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15561-1_56"},{"issue":"21","key":"17_CR12","first-page":"1","volume":"51","author":"H Dong","year":"2021","unstructured":"Dong, H., Yang, L., Wang, X.: Robust semi-supervised support vector machines with Laplace kernel-induced correntropy loss functions. Appl. Intell. 51(21), 1\u201315 (2021)","journal-title":"Appl. Intell."},{"issue":"5","key":"17_CR13","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.1109\/TRO.2012.2197158","volume":"28","author":"D Galvez-L\u00f3pez","year":"2012","unstructured":"Galvez-L\u00f3pez, D., Tardos, J.D.: Bags of binary words for fast place recognition in image sequences. IEEE Trans. Rob. 28(5), 1188\u20131197 (2012)","journal-title":"IEEE Trans. Rob."},{"issue":"1","key":"17_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10514-015-9516-2","volume":"41","author":"X Gao","year":"2017","unstructured":"Gao, X., Zhang, T.: Unsupervised learning to detect loops using deep neural networks for visual SLAM system. Auton. Robot. 41(1), 1\u201318 (2017)","journal-title":"Auton. Robot."},{"key":"17_CR15","doi-asserted-by":"publisher","first-page":"3051","DOI":"10.1109\/LRA.2018.2849609","volume":"99","author":"E Garcia-Fidalgo","year":"2018","unstructured":"Garcia-Fidalgo, E., Ortiz, A.: iBoW-LCD: an appearance-based loop closure detection approach using incremental bags of binary words. IEEE Robot. Autom. Lett. 99, 3051\u20133057 (2018)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"17_CR16","doi-asserted-by":"publisher","first-page":"124217","DOI":"10.1109\/ACCESS.2019.2937967","volume":"7","author":"Z Ge","year":"2019","unstructured":"Ge, Z., Xiaoqiang, Y., Yangdong, Y.: Loop closure detection via maximization of mutual information. IEEE Access 7, 124217\u2013124232 (2019)","journal-title":"IEEE Access"},{"issue":"11","key":"17_CR17","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231\u20131237 (2013)","journal-title":"Int. J. Robot. Res."},{"issue":"7","key":"17_CR18","doi-asserted-by":"publisher","first-page":"1271","DOI":"10.1109\/TPAMI.2009.132","volume":"32","author":"JC Van Gemert","year":"2010","unstructured":"Van Gemert, J.C., Veenman, C.J., Smeulders, A.W., Geusebroek, J.M.: Visual word ambiguity. IEEE Trans. Pattern Anal. Mach. Intell. 32(7), 1271\u20131283 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"17_CR19","doi-asserted-by":"publisher","first-page":"2325","DOI":"10.1109\/18.720541","volume":"44","author":"RM Gray","year":"1998","unstructured":"Gray, R.M., Neuhoff, D.L.: Quantization. IEEE Trans. Inf. Theory 44(6), 2325\u20132383 (1998)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"3","key":"17_CR20","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1007\/s10846-017-0718-z","volume":"93","author":"O Guclu","year":"2019","unstructured":"Guclu, O., Can, A.B.: Fast and effective loop closure detection to improve SLAM performance. J. Intell. Rob. Syst. 93(3), 495\u2013517 (2019)","journal-title":"J. Intell. Rob. Syst."},{"key":"17_CR21","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.patcog.2018.01.018","volume":"78","author":"A Gupta","year":"2018","unstructured":"Gupta, A., Barbu, A.: Parameterized principal component analysis. Pattern Recogn. 78, 215\u2013227 (2018)","journal-title":"Pattern Recogn."},{"issue":"7","key":"17_CR22","doi-asserted-by":"publisher","first-page":"1906","DOI":"10.3390\/s20071906","volume":"20","author":"D Han","year":"2020","unstructured":"Han, D., Li, Y., Song, T., Liu, Z.: Multi-objective optimization of loop closure detection parameters for indoor 2D simultaneous localization and mapping. Sensors 20(7), 1906 (2020)","journal-title":"Sensors"},{"issue":"3","key":"17_CR23","doi-asserted-by":"publisher","first-page":"2030","DOI":"10.1109\/TII.2020.3010580","volume":"17","author":"C Haosheng","year":"2021","unstructured":"Haosheng, C., Ge, Z., Yangdong, Y.: Semantic loop closure detection with instance-level inconsistency removal in dynamic industrial scenes. IEEE Trans. Ind. Inf. 17(3), 2030\u20132040 (2021)","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"1","key":"17_CR24","first-page":"117","volume":"33","author":"J Herv\u00e9","year":"2010","unstructured":"Herv\u00e9, J., Matthijs, D., Cordelia, S.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 117\u2013128 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"J\u00e9gou, H., Douze, M., Schmid, C., Perez, P.: Aggregating local descriptors into a compact image representation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3304\u20133311, July 2010","DOI":"10.1109\/CVPR.2010.5540039"},{"key":"17_CR26","doi-asserted-by":"crossref","unstructured":"Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 1\u201310 (2008)","DOI":"10.1109\/ISMAR.2007.4538852"},{"issue":"2","key":"17_CR27","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1007\/s11042-018-6200-5","volume":"78","author":"S Ksibi","year":"2018","unstructured":"Ksibi, S., Mejdoub, M., Amar, C.B.: Deep salient-Gaussian Fisher vector encoding of the spatio-temporal trajectory structures for person re-identification. Multimed. Tools Appl. 78(2), 1583\u20131611 (2018). https:\/\/doi.org\/10.1007\/s11042-018-6200-5","journal-title":"Multimed. Tools Appl."},{"issue":"8","key":"17_CR28","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1016\/j.robot.2013.04.016","volume":"61","author":"H Kwon","year":"2013","unstructured":"Kwon, H., Yousef, K.M.A., Kak, A.C.: Building 3D visual maps of interior space with a new hierarchical sensor fusion architecture. Robot. Auton. Syst. 61(8), 749\u2013767 (2013)","journal-title":"Robot. Auton. Syst."},{"issue":"3","key":"17_CR29","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1109\/TRO.2013.2242375","volume":"29","author":"M Labbe","year":"2013","unstructured":"Labbe, M., Michaud, F.: Appearance-based loop closure detection for online large-scale and long-term operation. IEEE Trans. Rob. 29(3), 734\u2013745 (2013)","journal-title":"IEEE Trans. Rob."},{"issue":"14","key":"17_CR30","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1177\/0278364913498910","volume":"32","author":"Y Latif","year":"2013","unstructured":"Latif, Y., Cadena, C., Neira, J.: Robust loop closing over time for pose graph SLAM. Int. J. Robot. Res. 32(14), 1611\u20131626 (2013)","journal-title":"Int. J. Robot. Res."},{"key":"17_CR31","doi-asserted-by":"crossref","unstructured":"Marr, D., Hildreth, E.: Theory of edge detection. In: Proceedings of the Royal Society of London, vol. 207, pp. 187\u2013217 (1980)","DOI":"10.1098\/rspb.1980.0020"},{"key":"17_CR32","doi-asserted-by":"crossref","unstructured":"Memon, A.R., Wang, H., Hussain, A.: Loop closure detection using supervised and unsupervised deep neural networks for monocular SLAM systems. Robot. Auton. Syst. 126, 103470 (2020)","DOI":"10.1016\/j.robot.2020.103470"},{"key":"17_CR33","doi-asserted-by":"crossref","unstructured":"Mur-Artal, R., Tard\u00f3s, J.D.: Fast relocalisation and loop closing in keyframe-based slam. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 846\u2013853 (2014)","DOI":"10.1109\/ICRA.2014.6906953"},{"key":"17_CR34","doi-asserted-by":"crossref","unstructured":"Perronnin, F., Dance, C.R.: Fisher kernels on visual vocabularies for image categorization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1\u20138 (2008)","DOI":"10.1109\/CVPR.2007.383266"},{"key":"17_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1007\/11744085_36","volume-title":"Computer Vision \u2013 ECCV 2006","author":"F Perronnin","year":"2006","unstructured":"Perronnin, F., Dance, C., Csurka, G., Bressan, M.: Adapted vocabularies for generic visual categorization. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 464\u2013475. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744085_36"},{"key":"17_CR36","doi-asserted-by":"crossref","unstructured":"Perronnin, F., Yan, L., S\u00e1nchez, J., Poirier, H.: Large-scale image retrieval with compressed fisher vectors. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3384\u20133391 (2010)","DOI":"10.1109\/CVPR.2010.5540009"},{"key":"17_CR37","doi-asserted-by":"crossref","unstructured":"Pire, T., Fischer, T., Civera, J., Crist\u00f3foris, P.D., Berlles, J.J.: Stereo parallel tracking and mapping for robot localization. In: Proceedings of International Conference on Intelligent Robots and Systems (ICIRS) (2015)","DOI":"10.1109\/IROS.2015.7353546"},{"key":"17_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/11744023_34","volume-title":"Computer Vision \u2013 ECCV 2006","author":"E Rosten","year":"2006","unstructured":"Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430\u2013443. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744023_34"},{"key":"17_CR39","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to sift or SURF. In: International Conference on Computer Vision, pp. 2564\u20132571 (2012)","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"17_CR40","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.measurement.2020.108162","volume":"165","author":"B Safarinejadian","year":"2020","unstructured":"Safarinejadian, B., Mozaffari, M.: A distributed averaging-based evidential expectation-maximization algorithm for density estimation in unreliable sensor networks. Measurement 165, 108\u2013162 (2020)","journal-title":"Measurement"},{"issue":"3","key":"17_CR41","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1007\/s11263-013-0636-x","volume":"105","author":"J S\u00e1nchez","year":"2013","unstructured":"S\u00e1nchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image classification with the fisher vector: theory and practice. Int. J. Comput. Vis. 105(3), 222\u2013245 (2013)","journal-title":"Int. J. Comput. Vis."},{"key":"17_CR42","doi-asserted-by":"crossref","unstructured":"S\u00fcnderhauf, N., et al.: Place recognition with convnet landmarks: viewpoint-robust, condition-robust, training-free. In: Robotics: Science and Systems, p. 296 (2015)","DOI":"10.15607\/RSS.2015.XI.022"},{"issue":"4","key":"17_CR43","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.ins.2021.06.039","volume":"575","author":"N Tonellotto","year":"2021","unstructured":"Tonellotto, N., Gotta, A., Nardini, F.M., Gadler, D., Silvestri, F.: Neural network quantization in federated learning at the edge. Inf. Sci. 575(4), 417\u2013436 (2021)","journal-title":"Inf. Sci."},{"key":"17_CR44","unstructured":"Uchida, Y., Sakazawa, S.: Image retrieval with fisher vectors of binary features. In: Proceedings of IAPR Asian Conference on Pattern Recognition (ACPR), pp. 1\u201311 (2017)"},{"key":"17_CR45","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, J., Kai, Y., Lv, F., Huang, T.S., Gong, Y.: Locality-constrained linear coding for image classification. In: Computer Vision & Pattern Recognition, pp. 3360\u20133367 (2010)","DOI":"10.1109\/CVPR.2010.5540018"},{"key":"17_CR46","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.inffus.2019.10.006","volume":"56","author":"X Yan","year":"2020","unstructured":"Yan, X., Ye, Y., Qiu, X., Yu, H.: Synergetic information bottleneck for joint multi-view and ensemble clustering. Inf. Fusion 56, 15\u201327 (2020)","journal-title":"Inf. Fusion"},{"issue":"719","key":"17_CR47","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1002\/qj.3438","volume":"145","author":"Y Yang","year":"2019","unstructured":"Yang, Y., M\u00e9min, E.: Estimation of physical parameters under location uncertainty using an ensemble2-expectation-maximization algorithm. Q. J. R. Meteorol. Soc. 145(719), 418\u2013433 (2019)","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"17_CR48","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.robot.2017.09.010","volume":"98","author":"G Younes","year":"2017","unstructured":"Younes, G., Asmar, D., Shammas, E., Zelek, J.: Keyframe-based monocular SLAM: design, survey, and future directions. Robot. Auton. Syst. 98, 67\u201388 (2017)","journal-title":"Robot. Auton. Syst."},{"key":"17_CR49","doi-asserted-by":"publisher","first-page":"5122","DOI":"10.1109\/TIP.2021.3067166","volume":"30","author":"W Zhou","year":"2021","unstructured":"Zhou, W., Zhang, L., Gao, S., Lou, X.: Gradient-based feature extraction from raw Bayer pattern images. IEEE Trans. Image Process. 30, 5122\u20135137 (2021)","journal-title":"IEEE Trans. Image Process."},{"issue":"13","key":"17_CR50","doi-asserted-by":"publisher","first-page":"4499","DOI":"10.3390\/s21134499","volume":"21","author":"Z Zhu","year":"2021","unstructured":"Zhu, Z., Xu, X., Liu, X., Jiang, Y.: LFM: a lightweight LCD algorithm based on feature matching between similar key frames. Sensors 21(13), 4499 (2021)","journal-title":"Sensors"}],"container-title":["Communications in Computer and Information Science","Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-5194-7_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:16:49Z","timestamp":1710256609000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-5194-7_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811951930","9789811951947"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-5194-7_17","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"10 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPCSEE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of Pioneering Computer Scientists, Engineers and Educators","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chengdu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpcsee2022","order":10,"name":"conference_id","label":"Conference ID","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"261","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":"65","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":"26","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":"25% - 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":"5","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)"}}]}}