{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T04:37:32Z","timestamp":1727239052458},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031064265"},{"type":"electronic","value":"9783031064272"}],"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"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-06427-2_10","type":"book-chapter","created":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T19:33:35Z","timestamp":1652556815000},"page":"111-123","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Investigating One-Class Classifiers to\u00a0Diagnose Alzheimer\u2019s Disease from\u00a0Handwriting"],"prefix":"10.1007","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-2911-9737","authenticated-orcid":false,"given":"Antonio","family":"Parziale","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4092-6102","authenticated-orcid":false,"given":"Antonio","family":"Della Cioppa","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2019-2826","authenticated-orcid":false,"given":"Angelo","family":"Marcelli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,15]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Ba-Karait, N.O., Shamsuddin, S.M., Sudirman, R.: Eeg signals classification using a hybrid method based on negative selection and particle swarm optimization. In: Proceedings of the 8th International Conference on Machine Learning and Data Mining in Pattern Recognition, pp. 427\u2013438 (2012)","key":"10_CR1","DOI":"10.1007\/978-3-642-31537-4_34"},{"issue":"3","key":"10_CR2","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/s00221-009-1925-z","volume":"197","author":"MP Broderick","year":"2009","unstructured":"Broderick, M.P., Van Gemmert, A.W., Shill, H.A., Stelmach, G.E.: Hypometria and bradykinesia during drawing movements in individuals with Parkinson\u2019s disease. Exp. Brain Res. 197(3), 223\u2013233 (2009)","journal-title":"Exp. Brain Res."},{"doi-asserted-by":"crossref","unstructured":"Cavaliere, F., Della Cioppa, A., Marcelli, A., Parziale, A., Senatore, R.: Parkinson\u2019s disease diagnosis: towards grammar-based explainable artificial intelligence. In: 2020 IEEE Symposium on Computers and Communications (ISCC), pp. 1\u20136 (2020)","key":"10_CR3","DOI":"10.1109\/ISCC50000.2020.9219616"},{"issue":"12","key":"10_CR4","doi-asserted-by":"publisher","first-page":"4243","DOI":"10.1109\/JBHI.2021.3101982","volume":"25","author":"ND Cilia","year":"2021","unstructured":"Cilia, N.D., D\u2019Alessandro, T., De Stefano, C., Fontanella, F., Molinara, M.: From online handwriting to synthetic images for Alzheimer\u2019s disease detection using a deep transfer learning approach. IEEE J. Biomed. Health Inform. 25(12), 4243\u20134254 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"doi-asserted-by":"publisher","unstructured":"Cilia, N.D., De Gregorio, G., De Stefano, C., Fontanella, F., Marcelli, A., Parziale, A.: Diagnosing Alzheimer\u2019s disease from on-line handwriting: a novel dataset and performance benchmarking. Eng. Appl. Artif. Intell. 111, 104822 (2022). https:\/\/doi.org\/10.1016\/j.engappai.2022.104822","key":"10_CR5","DOI":"10.1016\/j.engappai.2022.104822"},{"key":"10_CR6","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.procs.2018.10.141","volume":"141","author":"ND Cilia","year":"2018","unstructured":"Cilia, N.D., De Stefano, C., Fontanella, F., Di Freca, A.S.: An experimental protocol to support cognitive impairment diagnosis by using handwriting analysis. Procedia Comput. Sci. 141, 466\u2013471 (2018)","journal-title":"Procedia Comput. Sci."},{"key":"10_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1007\/978-3-030-30645-8_62","volume-title":"Image Analysis and Processing \u2013 ICIAP 2019","author":"ND Cilia","year":"2019","unstructured":"Cilia, N.D., De Stefano, C., Fontanella, F., Molinara, M., Scotto Di Freca, A.: Using handwriting features to characterize cognitive impairment. In: Ricci, E., Rota Bul\u00f2, S., Snoek, C., Lanz, O., Messelodi, S., Sebe, N. (eds.) ICIAP 2019. LNCS, vol. 11752, pp. 683\u2013693. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30645-8_62"},{"unstructured":"Cohen, G., Hilario, M., Sax, H., Hugonnet, S., Pellegrini, C., Geissbuhler, A.: An application of one-class support vector machines to nosocomial infection detection. In: MEDINFO 2004, pp. 716\u2013720. IOS Press (2004)","key":"10_CR8"},{"doi-asserted-by":"publisher","unstructured":"De Gregorio, G., Desiato, D., Marcelli, A., Polese, G.: A multi classifier approach for supporting Alzheimer\u2019s diagnosis based on handwriting analysis. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12661, pp. 559\u2013574. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68763-2_43","key":"10_CR9","DOI":"10.1007\/978-3-030-68763-2_43"},{"key":"10_CR10","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.patrec.2018.05.013","volume":"121","author":"C De Stefano","year":"2019","unstructured":"De Stefano, C., Fontanella, F., Impedovo, D., Pirlo, G., di Freca, A.S.: Handwriting analysis to support neurodegenerative diseases diagnosis: a review. Pattern Recogn. Lett. 121, 37\u201345 (2019)","journal-title":"Pattern Recogn. Lett."},{"key":"10_CR11","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.artmed.2016.01.004","volume":"67","author":"P Drot\u00e1r","year":"2016","unstructured":"Drot\u00e1r, P., Mekyska, J., Rektorov\u00e1, I., Masarov\u00e1, L., Sm\u00e9kal, Z., Faundez-Zanuy, M.: Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson\u2019s disease. Artif. Intell. Med. 67, 39\u201346 (2016)","journal-title":"Artif. Intell. Med."},{"unstructured":"Forrest, S., Perelson, A.S., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy, pp. 202\u2013212 (1994)","key":"10_CR12"},{"unstructured":"Gautier, S., Rosa-Neto, P., Morais, J.a., Webster, C.: World Alzheimer Report 2021: Journey through the diagnosis of dementia. ADI, London, UK (2021)","key":"10_CR13"},{"doi-asserted-by":"crossref","unstructured":"Gonzalez, F., Dasgupta, D., Kozma, R.: Combining negative selection and classification techniques for anomaly detection. In: Proceedings of the 2002 Congress on Evolutionary Computation. CEC 2002, vol. 1, pp. 705\u2013710 (2002)","key":"10_CR14","DOI":"10.1109\/CEC.2002.1007012"},{"issue":"10","key":"10_CR15","doi-asserted-by":"publisher","first-page":"3849","DOI":"10.1109\/TMAG.2011.2158520","volume":"47","author":"L Guo","year":"2011","unstructured":"Guo, L., Zhao, L., Wu, Y., Li, Y., Xu, G., Yan, Q.: Tumor detection in MR images using one-class immune feature weighted SVMs. IEEE Trans. Magn. 47(10), 3849\u20133852 (2011)","journal-title":"IEEE Trans. Magn."},{"unstructured":"Gupta, K.D., Dasgupta, D.: Negative selection algorithm research and applications in the last decade: a review (2021)","key":"10_CR16"},{"issue":"2","key":"10_CR17","doi-asserted-by":"publisher","first-page":"22","DOI":"10.5430\/air.v4n2p22","volume":"4","author":"SH Huang","year":"2015","unstructured":"Huang, S.H.: Supervised feature selection: a tutorial. Artif. Intell. Res. 4(2), 22\u201337 (2015)","journal-title":"Artif. Intell. Res."},{"issue":"10","key":"10_CR18","doi-asserted-by":"publisher","first-page":"247","DOI":"10.3390\/info9100247","volume":"9","author":"D Impedovo","year":"2018","unstructured":"Impedovo, D., Pirlo, G., Vessio, G.: Dynamic handwriting analysis for supporting earlier Parkinson\u2019s disease diagnosis. Information 9(10), 247 (2018)","journal-title":"Information"},{"issue":"4","key":"10_CR19","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1136\/jnnp.2007.131045","volume":"79","author":"J Jankovic","year":"2008","unstructured":"Jankovic, J.: Parkinson\u2019s disease: clinical features and diagnosis. J. Neurol. Neurosur. Psychiatry 79(4), 368\u2013376 (2008)","journal-title":"J. Neurol. Neurosur. Psychiatry"},{"issue":"10","key":"10_CR20","doi-asserted-by":"publisher","first-page":"1390","DOI":"10.1016\/j.ins.2008.12.015","volume":"179","author":"Z Ji","year":"2009","unstructured":"Ji, Z., Dasgupta, D.: V-detector: an efficient negative selection algorithm with \u201cprobably adequate\u2019\u2019 detector coverage. Inf. Sci. 179(10), 1390\u20131406 (2009)","journal-title":"Inf. Sci."},{"key":"10_CR21","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/B978-0-12-803468-2.00011-4","volume-title":"Applied Computing in Medicine and Health","author":"A Lasisi","year":"2016","unstructured":"Lasisi, A., Ghazali, R., Herawan, T.: Chapter 11 - application of real-valued negative selection algorithm to improve medical diagnosis. In: Al-Jumeily, D., Hussain, A., Mallucci, C., Oliver, C. (eds.) Applied Computing in Medicine and Health, pp. 231\u2013243. Morgan Kaufmann, Boston (2016)"},{"issue":"5","key":"10_CR22","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/s12264-017-0174-6","volume":"33","author":"W Le","year":"2017","unstructured":"Le, W., Dong, J., Li, S., Korczyn, A.D.: Can biomarkers help the early diagnosis of Parkinson\u2019s disease? Neurosci. Bull. 33(5), 535\u2013542 (2017)","journal-title":"Neurosci. Bull."},{"issue":"2","key":"10_CR23","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s12264-019-00433-1","volume":"36","author":"T Li","year":"2020","unstructured":"Li, T., Le, W.: Biomarkers for Parkinson\u2019s disease: how good are they? Neurosci. Bull. 36(2), 183\u2013194 (2020)","journal-title":"Neurosci. Bull."},{"issue":"1","key":"10_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2133360.2133363","volume":"6","author":"FT Liu","year":"2012","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation-based anomaly detection. ACM Trans. Knowl. Discovery Data (TKDD) 6(1), 1\u201339 (2012)","journal-title":"ACM Trans. Knowl. Discovery Data (TKDD)"},{"key":"10_CR25","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1038\/s41582-020-0377-8","volume":"16","author":"MA Myszczynska","year":"2020","unstructured":"Myszczynska, M.A., et al.: Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat. Rev. Neurol. 16, 440\u2013456 (2020)","journal-title":"Nat. Rev. Neurol."},{"key":"10_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101984","volume":"111","author":"A Parziale","year":"2021","unstructured":"Parziale, A., Senatore, R., Della Cioppa, A., Marcelli, A.: Cartesian genetic programming for diagnosis of Parkinson disease through handwriting analysis: performance vs interpretability issues. Artif. Intell. Med. 111, 101984 (2021)","journal-title":"Artif. Intell. Med."},{"doi-asserted-by":"crossref","unstructured":"Parziale, A., Della Cioppa, A., Senatore, R., Marcelli, A.: A decision tree for automatic diagnosis of Parkinson\u2019s disease from offline drawing samples: experiments and findings. In: Ricci, E., et al. (eds.) Image Analysis and Processing - ICIAP 2019, pp. 196\u2013206 (2019)","key":"10_CR27","DOI":"10.1007\/978-3-030-30642-7_18"},{"doi-asserted-by":"crossref","unstructured":"Parziale, A., Senatore, R., Marcelli, A.: Exploring speed-accuracy tradeoff in reaching movements: a neurocomputational model. Neural Comput. Appl. 32, 13377\u201313403 (2020)","key":"10_CR28","DOI":"10.1007\/s00521-019-04690-z"},{"issue":"8","key":"10_CR29","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226\u20131238 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Pereira, C.R., Weber, S.A.T., Hook, C., Rosa, G.H., Papa, J.P.: Deep learning-aided Parkinson\u2019s disease diagnosis from handwritten dynamics. In: 2016 29th Conference on Graphics, Patterns and Images, pp. 340\u2013346, October 2016","key":"10_CR30","DOI":"10.1109\/SIBGRAPI.2016.054"},{"doi-asserted-by":"crossref","unstructured":"Pereira, C.R., et al.: A new computer vision-based approach to aid the diagnosis of Parkinson\u2019s disease. Comput. Methods Programs Biomed. 136, 79\u201388 (2016)","key":"10_CR31","DOI":"10.1016\/j.cmpb.2016.08.005"},{"issue":"7","key":"10_CR32","doi-asserted-by":"publisher","first-page":"4625","DOI":"10.1109\/TIM.2020.2983531","volume":"69","author":"RE Precup","year":"2020","unstructured":"Precup, R.E., Teban, T.A., Albu, A., Borlea, A.B., Zamfirache, I.A., Petriu, E.M.: Evolving fuzzy models for prosthetic hand myoelectric-based control. IEEE Trans. Instrum. Meas. 69(7), 4625\u20134636 (2020)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10_CR33","volume-title":"World Alzheimer Report 2015: The Global Impact of Dementia","author":"M Prince","year":"2015","unstructured":"Prince, M., Wimo, A., Guercet, M., Ali, G.C., Wu, Y.T., Prina, M.: World Alzheimer Report 2015: The Global Impact of Dementia. ADI, London, UK (2015)"},{"unstructured":"Sch\u00f6lkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C., et al.: Support vector method for novelty detection. In: NIPS, vol. 12, pp. 582\u2013588. Citeseer (1999)","key":"10_CR34"},{"doi-asserted-by":"crossref","unstructured":"Senatore, R., Marcelli, A.: A neural scheme for procedural motor learning of handwriting. In: International Conference on Frontiers on Handwriting Recognition, pp. 659\u2013664. Springer (2012)","key":"10_CR35","DOI":"10.1109\/ICFHR.2012.160"},{"doi-asserted-by":"crossref","unstructured":"Senatore, R., Marcelli, A.: A paradigm for emulating the early learning stage of handwriting: performance comparison between healthy controls and Parkinson\u2019s disease patients in drawing loop shapes. Hum. Mov. Sci. 65, 89\u2013101 (2019)","key":"10_CR36","DOI":"10.1016\/j.humov.2018.04.007"},{"issue":"1s","key":"10_CR37","first-page":"1","volume":"16","author":"M Tanveer","year":"2020","unstructured":"Tanveer, M., et al.: Machine learning techniques for the diagnosis of Alzheimer\u2019s disease: a review. ACM Trans. Multimedia Comput. Commun. Appl. 16(1s), 1\u201335 (2020)","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."},{"issue":"1","key":"10_CR38","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1006\/exnr.1997.6507","volume":"146","author":"HL Teulings","year":"1997","unstructured":"Teulings, H.L., Contreras-Vidal, J.L., Stelmach, G.E., Adler, C.H.: Parkinsonism reduces coordination of fingers, wrist, and arm in fine motor control. Exp. Neurol. 146(1), 159\u2013170 (1997)","journal-title":"Exp. Neurol."},{"issue":"2\u20133","key":"10_CR39","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/0167-9457(91)90010-U","volume":"10","author":"HL Teulings","year":"1991","unstructured":"Teulings, H.L., Stelmach, G.E.: Control of stroke size, peak acceleration, and stroke duration in parkinsonian handwriting. Human Mov. Sci. 10(2\u20133), 315\u2013334 (1991)","journal-title":"Human Mov. Sci."},{"doi-asserted-by":"crossref","unstructured":"Van Gemmert, A., Adler, C.H., Stelmach, G.: Parkinson\u2019s disease patients undershoot target size in handwriting and similar tasks. J. Neurol. Neurosur. Psychiatry 74(11), 1502\u20131508 (2003)","key":"10_CR40","DOI":"10.1136\/jnnp.74.11.1502"},{"doi-asserted-by":"crossref","unstructured":"Vessio, G.: Dynamic handwriting analysis for neurodegenerative disease assessment: a literary review. Appl. Sci. 9(21), 4666 (2019)","key":"10_CR41","DOI":"10.3390\/app9214666"},{"unstructured":"Zhang, J., Ma, K.K., Er, M.H., Chong, V.: Tumor segmentation from magnetic resonance imaging by learning via one-class support vector machine. In: International Workshop on Advanced Image Technology, pp. 207\u2013211 (2004)","key":"10_CR42"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06427-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T20:34:17Z","timestamp":1727210057000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06427-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031064265","9783031064272"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06427-2_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lecce","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"23 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2021.org\/","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":"Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","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":"168","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":"55% - 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":"4","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)"}}]}}