{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T05:09:09Z","timestamp":1776488949793,"version":"3.51.2"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031138317","type":"print"},{"value":"9783031138324","type":"electronic"}],"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\/978-3-031-13832-4_19","type":"book-chapter","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T11:32:22Z","timestamp":1660563142000},"page":"220-233","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Gait Identification Using Hip Joint Movement and Deep Machine Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6689-7944","authenticated-orcid":false,"given":"Luke","family":"Topham","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7511-3873","authenticated-orcid":false,"given":"Wasiq","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9170-0568","authenticated-orcid":false,"given":"Dhiya","family":"Al-Jumeily","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1370-5993","authenticated-orcid":false,"given":"Atif","family":"Waraich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8413-0045","authenticated-orcid":false,"given":"Abir","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,16]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","unstructured":"Nambiar, A., Bernardino, A., Nascimento, J.C.: Gait-based person re-identification\u202f: a survey. ACM Comput. Surv. 52(2) (2019). https:\/\/doi.org\/10.1145\/3243043","DOI":"10.1145\/3243043"},{"issue":"3","key":"19_CR2","doi-asserted-by":"publisher","first-page":"754","DOI":"10.31031\/rmes.2019.07.000662","volume":"7","author":"W Khan","year":"2019","unstructured":"Khan, W., Badii, A.: Pathological gait abnormality detection and segmentation by processing the hip joints motion data to support mobile gait rehabilitation. Res. Med. Eng. Sci. 7(3), 754\u2013762 (2019). https:\/\/doi.org\/10.31031\/rmes.2019.07.000662","journal-title":"Res. Med. Eng. Sci."},{"key":"19_CR3","doi-asserted-by":"publisher","unstructured":"Sheng, W., Li, X.: Multi-task learning for gait-based identity recognition and emotion recognition using attention enhanced temporal graph convolutional network. Pattern Recogn. 114 (2021). https:\/\/doi.org\/10.1016\/j.patcog.2021.107868","DOI":"10.1016\/j.patcog.2021.107868"},{"key":"19_CR4","doi-asserted-by":"publisher","unstructured":"Balazia, M., Sojka, P.: You are how you walk: uncooperative MoCap gait identification for video surveillance with incomplete and noisy data. In: IEEE International Joint Conference on Biometrics, IJCB 2017, vol. 2018, pp. 208\u2013215, January 2018. https:\/\/doi.org\/10.1109\/BTAS.2017.8272700","DOI":"10.1109\/BTAS.2017.8272700"},{"key":"19_CR5","doi-asserted-by":"publisher","unstructured":"Connor, P.C.: Comparing and combining underfoot pressure features for shod and unshod gait biometrics. In: 2015 IEEE International Symposium on Technologies for Homeland Security, HST 2015 (2015). https:\/\/doi.org\/10.1109\/THS.2015.7225338","DOI":"10.1109\/THS.2015.7225338"},{"key":"19_CR6","doi-asserted-by":"publisher","unstructured":"Vera-Rodriguez, R., Fierrez, J., Mason, J.S.D., Orteua-Garcia, J.: A novel approach of gait recognition through fusion with footstep information (2013). https:\/\/doi.org\/10.1109\/ICB.2013.6613014","DOI":"10.1109\/ICB.2013.6613014"},{"key":"19_CR7","doi-asserted-by":"publisher","unstructured":"De Carvalho, R.L., Rosa, P.F.F.: Identification system for smart homes using footstep sounds. In: IEEE International Symposium on Industrial Electronics, pp. 1639\u20131644 (2010). https:\/\/doi.org\/10.1109\/ISIE.2010.5637551","DOI":"10.1109\/ISIE.2010.5637551"},{"key":"19_CR8","doi-asserted-by":"publisher","unstructured":"Shoji, Y., Takasuka, T., Yasukawa, H.: Personal identification using footstep detection. In: International Symposium on Intelligent Signal Processing and Communication Systems, pp. 43\u201347 (2004). https:\/\/doi.org\/10.1109\/ISPACS.2004.1439012","DOI":"10.1109\/ISPACS.2004.1439012"},{"key":"19_CR9","doi-asserted-by":"publisher","first-page":"107701","DOI":"10.1016\/j.apacoust.2020.107701","volume":"173","author":"E Aydemir","year":"2021","unstructured":"Aydemir, E., Tuncer, T., Dogan, S., Unsal, M.: A novel biometric recognition method based on multi kernelled bijection octal pattern using gait sound. Appl. Acoust. 173, 107701 (2021). https:\/\/doi.org\/10.1016\/j.apacoust.2020.107701","journal-title":"Appl. Acoust."},{"key":"19_CR10","unstructured":"Suutala, J., R\u00f6ning, J.: Towards the adaptive identification of walkers: automated feature selection of footsteps using distinction-sensitive LVQ. In: Proceedings of International Workshop on Processing Sensory Information for Proactive Systems (PSIPS 2004), pp. 1\u20137 (2004). http:\/\/www.ee.oulu.fi\/research\/isg\/files\/pdf\/pdf_515.pdf"},{"key":"19_CR11","unstructured":"Rodriguez, R.V., Lewis, R.P., Evans, N.W.D., Mason, J.S.D.: Optimisation of geometric and holistic feature extraction approaches for a footstep biometric verification system (2007)"},{"key":"19_CR12","doi-asserted-by":"publisher","unstructured":"Vera-Rodriguez, R., Evans, N.W.D., Lewis, R.P., Fauve, B., Mason, J.S.D.: An experimental study on the feasibility of footsteps as a biometric. In: European Signal Processing Conference, pp. 748\u2013752 (2007). https:\/\/doi.org\/10.1109\/ACCESS.2019.2939613","DOI":"10.1109\/ACCESS.2019.2939613"},{"key":"19_CR13","doi-asserted-by":"publisher","unstructured":"Geiger, J.T., Knei\u00dfl, M., Schuller, B., Rigoll, G.: Acoustic gait-based person identification using hidden Markov models. In: MAPTRAITS 2014 - Proceedings of the 1st ACM Audio\/Video Mapping Personality Traits Challenge and Workshop, Co-located with ICMI 2014, pp. 25\u201330 (2014). https:\/\/doi.org\/10.1145\/2668024.2668027","DOI":"10.1145\/2668024.2668027"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Rodriguez, R.V., Evans, N., Mason, J.S.D.: Footstep recognition. In: Encyclopedia of Biometrics, pp. 693\u2013700 (2015)","DOI":"10.1007\/978-1-4899-7488-4_41"},{"key":"19_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12311-021-01361-5","volume":"2022","author":"SF Castiglia","year":"2021","unstructured":"Castiglia, S.F., et al.: Identification of gait unbalance and fallers among subjects with cerebellar ataxia by a set of trunk acceleration-derived indices of gait. Cerebellum 2022, 1\u201313 (2021). https:\/\/doi.org\/10.1007\/s12311-021-01361-5","journal-title":"Cerebellum"},{"key":"19_CR16","doi-asserted-by":"publisher","unstructured":"Hajati, N., Rezaeizadeh, A.: A wearable pedestrian localization and gait identification system using Kalman filtered inertial data. IEEE Trans. Instrum. Meas. 70 (2021). https:\/\/doi.org\/10.1109\/TIM.2021.3073440","DOI":"10.1109\/TIM.2021.3073440"},{"key":"19_CR17","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1007\/978-3-030-01249-6_37","volume-title":"ECCV 2018","author":"T von Marcard","year":"2018","unstructured":"von Marcard, T., Henschel, R., Black, M.J., Rosenhahn, B., Pons-Moll, G.: Recovering accurate 3D human pose in the wild using IMUs and a moving camera. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 614\u2013631. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_37"},{"key":"19_CR18","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1007\/978-3-642-35395-6_30","volume-title":"IWAAL 2012","author":"D Anguita","year":"2012","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: Bravo, J., Herv\u00e1s, R., Rodr\u00edguez, M. (eds.) IWAAL 2012. LNCS, vol. 7657, pp. 216\u2013223. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-35395-6_30"},{"key":"19_CR19","doi-asserted-by":"publisher","unstructured":"De Marsico, M., Mecca, A.: A survey on gait recognition via wearable sensors. ACM Comput. Surv. 52(4) (2019). https:\/\/doi.org\/10.1145\/3340293","DOI":"10.1145\/3340293"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Topham, L., Khan, W., Al-Jumeily, D., Hussain, A.J.: Human body pose estimation for gait identification: a comprehensive survey of datasets and models. ACM Comput. Surv. (2022)","DOI":"10.1145\/3533384"},{"key":"19_CR21","unstructured":"MOTI: MOTI. MOTI (2021). http:\/\/moti.dk. Accessed 12 Apr 2022"},{"issue":"2","key":"19_CR22","doi-asserted-by":"publisher","first-page":"256","DOI":"10.12720\/ijsps.1.2.256-262","volume":"1","author":"N Ahmad","year":"2013","unstructured":"Ahmad, N., Ghazilla, R.A.R., Khairi, N.M., Kasi, V.: Reviews on various inertial measurement unit (IMU) sensor applications. Int. J. Signal Process. Syst. 1(2), 256\u2013262 (2013). https:\/\/doi.org\/10.12720\/ijsps.1.2.256-262","journal-title":"Int. J. Signal Process. Syst."},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Victorino, M.N., Jiang, X. Menon, C.: Wearable technologies and force myography for healthcare. In: Wearable Technology in Medicine and Health Care, pp. 135\u2013152. Elsevier Inc. (2018)","DOI":"10.1016\/B978-0-12-811810-8.00007-5"},{"key":"19_CR24","volume-title":"Statistics","author":"RS Witte","year":"2021","unstructured":"Witte, R.S., Witte, J.S.: Statistics, 11th edn. Wiley, New York (2021)","edition":"11"},{"key":"19_CR25","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1016\/j.neucom.2021.10.126","volume":"491","author":"N Kleanthous","year":"2022","unstructured":"Kleanthous, N., Hussain, A.J., Khan, W., Sneddon, J., Al-Shamma\u2019a, A., Liatsis, P.: \u201cA survey of machine learning approaches in animal behavior. Neurocomputing 491, 442\u2013463 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2021.10.126","journal-title":"Neurocomputing"},{"key":"19_CR26","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.patrec.2020.09.011","volume":"140","author":"N Kleanthous","year":"2020","unstructured":"Kleanthous, N., Hussain, A.J., Khan, W., Liatsis, P.: A new machine learning based approach to predict Freezing of Gait. Pattern Recogn. Lett. 140, 119\u2013126 (2020). https:\/\/doi.org\/10.1016\/j.patrec.2020.09.011","journal-title":"Pattern Recogn. Lett."},{"issue":"8","key":"19_CR27","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"19_CR28","doi-asserted-by":"publisher","first-page":"1270","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu, Y., Si, X., Hu, C., Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31, 1270 (2019)","journal-title":"Neural Comput."},{"issue":"8","key":"19_CR29","doi-asserted-by":"publisher","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","volume":"53","author":"G Van Houdt","year":"2020","unstructured":"Van Houdt, G., Mosquera, C., N\u00e1poles, G.: A review on the long short-term memory model. Artif. Intell. Rev. 53(8), 5929\u20135955 (2020). https:\/\/doi.org\/10.1007\/s10462-020-09838-1","journal-title":"Artif. Intell. Rev."},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Pisner, D.A., Schnyer, D.M.: Support vector machine. In: Machine Learning: Methods and Applications to Brain Disorders, pp. 101\u2013121. Elsevier Inc. (2019)","DOI":"10.1016\/B978-0-12-815739-8.00006-7"},{"issue":"12","key":"19_CR31","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1038\/nbt1206-1565","volume":"24","author":"WS Noble","year":"2006","unstructured":"Noble, W.S.: What is a support vector machine? Nat. Biotechnol. 24(12), 1565\u20131567 (2006)","journal-title":"Nat. Biotechnol."},{"issue":"8","key":"19_CR32","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1016\/j.patcog.2011.01.017","volume":"44","author":"M Galar","year":"2011","unstructured":"Galar, M., Fern\u00e1ndez, A., Barrenechea, E., Bustince, H., Herrera, F.: An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recognit. 44(8), 1761\u20131776 (2011). https:\/\/doi.org\/10.1016\/j.patcog.2011.01.017","journal-title":"Pattern Recognit."},{"key":"19_CR33","doi-asserted-by":"publisher","first-page":"105507","DOI":"10.1016\/j.compag.2020.105507","volume":"174","author":"M Koklu","year":"2020","unstructured":"Koklu, M., Ozkan, I.A.: Multiclass classification of dry beans using computer vision and machine learning techniques. Comput. Electron. Agric. 174, 105507 (2020)","journal-title":"Comput. Electron. Agric."},{"issue":"2","key":"19_CR34","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/nbt1386","volume":"26","author":"A Krogh","year":"2008","unstructured":"Krogh, A.: What are artificial neural networks? Nat. Biotechnol. 26(2), 195\u2013197 (2008). https:\/\/doi.org\/10.1038\/nbt1386","journal-title":"Nat. Biotechnol."},{"issue":"2","key":"19_CR35","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1109\/TPAMI.2007.1182","volume":"30","author":"H Samet","year":"2008","unstructured":"Samet, H.: K-nearest neighbor finding using MaxNearestDist. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 243\u2013252 (2008). https:\/\/doi.org\/10.1109\/TPAMI.2007.1182","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR36","doi-asserted-by":"publisher","unstructured":"Jiang, L., Cai, Z., Wang, D., Jiang, S.: Survey of improving K-nearest-neighbor for classification. In: Proceedings of Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, vol. 1, pp. 679\u2013683 (2007). https:\/\/doi.org\/10.1109\/FSKD.2007.552","DOI":"10.1109\/FSKD.2007.552"},{"key":"19_CR37","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/BF00153759","volume":"6","author":"DW Aha","year":"1991","unstructured":"Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6, 37\u201366 (1991)","journal-title":"Mach. Learn."},{"issue":"5","key":"19_CR38","first-page":"605","volume":"3","author":"SB Imandoust","year":"2013","unstructured":"Imandoust, S.B., Bolandraftar, M.: Application of K-Nearest Neighbor (KNN) approach for predicting economic events: theoretical background. Int. J. Eng. Res. Appl. 3(5), 605\u2013610 (2013)","journal-title":"Int. J. Eng. Res. Appl."},{"issue":"1","key":"19_CR39","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1007\/s11042-020-09425-0","volume":"80","author":"M Gruosso","year":"2020","unstructured":"Gruosso, M., Capece, N., Erra, U.: Human segmentation in surveillance video with deep learning. Multimedia Tools Appl. 80(1), 1175\u20131199 (2020). https:\/\/doi.org\/10.1007\/s11042-020-09425-0","journal-title":"Multimedia Tools Appl."},{"key":"19_CR40","doi-asserted-by":"publisher","first-page":"34366","DOI":"10.1109\/ACCESS.2021.3059170","volume":"9","author":"MT Bhatti","year":"2021","unstructured":"Bhatti, M.T., Khan, M.G., Aslam, M., Fiaz, M.J.: Weapon detection in real-time CCTV videos using deep learning. IEEE Access 9, 34366\u201334382 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3059170","journal-title":"IEEE Access"},{"key":"19_CR41","doi-asserted-by":"publisher","unstructured":"Gadaleta, M., Merelli, L., Rossi, M.: Human authentication from ankle motion data using convolutional neural networks (2016). https:\/\/doi.org\/10.1109\/SSP.2016.7551815","DOI":"10.1109\/SSP.2016.7551815"},{"key":"19_CR42","doi-asserted-by":"publisher","unstructured":"San-Segundo, R., Echeverry-Correa, J.D., Salamea-Palacios, C., Lebai Lutfi, S., Pardo, J.M.: I-vector analysis for Gait-based Person Identification using smartphone inertial signals. Pervasive Mob. Comput. 38, 140\u2013153 (2017). https:\/\/doi.org\/10.1016\/j.pmcj.2016.09.007","DOI":"10.1016\/j.pmcj.2016.09.007"},{"key":"19_CR43","doi-asserted-by":"publisher","unstructured":"De Marsico, M., De Pasquale, D., Mecca, A.: Embedded accelerometer signal normalization for cross-device gait recognition. Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik, vol. P-260 (2016). https:\/\/doi.org\/10.1109\/BIOSIG.2016.7736920","DOI":"10.1109\/BIOSIG.2016.7736920"}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Methodologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13832-4_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T14:05:45Z","timestamp":1710338745000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13832-4_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031138317","9783031138324"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13832-4_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","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":"7 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2022\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"IC-ICC-CN","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"449","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":"209","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":"47% - 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":"2.5","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)"}}]}}