{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:40:16Z","timestamp":1742913616532,"version":"3.40.3"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031178481"},{"type":"electronic","value":"9783031178498"}],"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\/978-3-031-17849-8_19","type":"book-chapter","created":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T20:19:42Z","timestamp":1664309982000},"page":"234-248","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Similarity-Based Unsupervised Evaluation of\u00a0Outlier Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8273-5814","authenticated-orcid":false,"given":"Henrique O.","family":"Marques","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7713-4208","authenticated-orcid":false,"given":"Arthur","family":"Zimek","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0266-3492","authenticated-orcid":false,"given":"Ricardo J. G. B.","family":"Campello","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4068-7268","authenticated-orcid":false,"given":"J\u00f6rg","family":"Sander","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,28]]},"reference":[{"issue":"2","key":"19_CR1","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1007\/s13198-016-0551-y","volume":"8","author":"AO Adewumi","year":"2017","unstructured":"Adewumi, A.O., Akinyelu, A.A.: A survey of machine-learning and nature-inspired based credit card fraud detection techniques. Int. J. Syst. Assur. Eng. Manag. 8(2), 937\u2013953 (2017)","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Ahmad, Z., Khan, A.S., Shiang, C.W., Abdullah, J., Ahmad, F.: Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans. Emerg. Telecommun. Technol. 32(1), e4150 (2021)","DOI":"10.1002\/ett.4150"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Alaverdyan, Z., Jung, J., Bouet, R., Lartizien, C.: Regularized Siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging. Med. Image Anal. 60 (2020)","DOI":"10.1016\/j.media.2019.101618"},{"key":"19_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/3-540-45681-3_2","volume-title":"Principles of Data Mining and Knowledge Discovery","author":"F Angiulli","year":"2002","unstructured":"Angiulli, F., Pizzuti, C.: Fast outlier detection in high dimensional spaces. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS, vol. 2431, pp. 15\u201327. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-45681-3_2"},{"issue":"2","key":"19_CR5","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1109\/TKDE.2005.31","volume":"17","author":"F Angiulli","year":"2005","unstructured":"Angiulli, F., Pizzuti, C.: Outlier mining in large high-dimensional data sets. IEEE Trans. Knowl. Data Eng. 17(2), 203\u2013215 (2005)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"19_CR6","volume-title":"Outliers in Statistical Data","author":"V Barnett","year":"1994","unstructured":"Barnett, V., Lewis, T.: Outliers in Statistical Data, 3rd edn. Wiley, Hoboken (1994)","edition":"3"},{"issue":"9","key":"19_CR7","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1145\/361002.361007","volume":"18","author":"JL Bentley","year":"1975","unstructured":"Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509\u2013517 (1975)","journal-title":"Commun. ACM"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: SIGMOD Conference, pp. 93\u2013104. ACM (2000)","DOI":"10.1145\/335191.335388"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Campello, R.J.G.B., Moulavi, D., Zimek, A., Sander, J.: Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Trans. Knowl. Discov. Data 10(1), 5:1\u20135:51 (2015)","DOI":"10.1145\/2733381"},{"issue":"4","key":"19_CR10","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1007\/s10618-015-0444-8","volume":"30","author":"GO Campos","year":"2016","unstructured":"Campos, G.O., et al.: On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Min. Knowl. Discov. 30(4), 891\u2013927 (2016)","journal-title":"Data Min. Knowl. Discov."},{"key":"19_CR11","unstructured":"Cl\u00e9men\u00e7on, S., Jakubowicz, J.: Scoring anomalies: a M-estimation formulation. In: AISTATS. JMLR Workshop and Conference Proceedings, vol. 31, pp. 659\u2013667 (2013)"},{"key":"19_CR12","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Djenouri, Y., Zimek, A., Chiarandini, M.: Outlier detection in urban traffic flow distributions. In: ICDM, pp. 935\u2013940. IEEE Computer Society (2018)","DOI":"10.1109\/ICDM.2018.00114"},{"key":"19_CR14","unstructured":"Goix, N.: How to evaluate the quality of unsupervised anomaly detection algorithms? CoRR abs\/1607.01152 (2016)"},{"key":"19_CR15","unstructured":"Goix, N., Sabourin, A., Cl\u00e9men\u00e7on, S.: On anomaly ranking and excess-mass curves. In: AISTATS. JMLR Workshop and Conference Proceedings, vol. 38 (2015)"},{"issue":"4","key":"19_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0152173","volume":"11","author":"M Goldstein","year":"2016","unstructured":"Goldstein, M., Uchida, S.: A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PLoS ONE 11(4), 1\u201331 (2016)","journal-title":"PLoS ONE"},{"key":"19_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-0-387-84858-7","edition":"2"},{"key":"19_CR18","unstructured":"Hautam\u00e4ki, V., K\u00e4rkk\u00e4inen, I., Fr\u00e4nti, P.: Outlier detection using k-nearest neighbour graph. In: ICPR (3), pp. 430\u2013433. IEEE Computer Society (2004)"},{"key":"19_CR19","unstructured":"He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: NIPS, pp. 507\u2013514 (2005)"},{"issue":"3","key":"19_CR20","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/s00357-019-9312-3","volume":"36","author":"F Iglesias","year":"2019","unstructured":"Iglesias, F., Zseby, T., Ferreira, D.C., Zimek, A.: MDCGen: multidimensional dataset generator for clustering. J. Classif. 36(3), 599\u2013618 (2019)","journal-title":"J. Classif."},{"issue":"9","key":"19_CR21","doi-asserted-by":"publisher","first-page":"2096","DOI":"10.1109\/TPAMI.2019.2912970","volume":"42","author":"F Iglesias","year":"2020","unstructured":"Iglesias, F., Zseby, T., Zimek, A.: Absolute cluster validity. IEEE Trans. Pattern Anal. Mach. Intell. 42(9), 2096\u20132112 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR22","volume-title":"Algorithms for Clustering Data","author":"AK Jain","year":"1988","unstructured":"Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Hoboken (1988)"},{"issue":"2","key":"19_CR23","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1007\/s10115-015-0851-6","volume":"47","author":"PA Jaskowiak","year":"2016","unstructured":"Jaskowiak, P.A., Moulavi, D., Furtado, A.C.S., Campello, R.J.G.B., Zimek, A., Sander, J.: On strategies for building effective ensembles of relative clustering validity criteria. Knowl. Inf. Syst. 47(2), 329\u2013354 (2016)","journal-title":"Knowl. Inf. Syst."},{"key":"19_CR24","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/11731139_68","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"W Jin","year":"2006","unstructured":"Jin, W., Tung, A.K.H., Han, J., Wang, W.: Ranking outliers using symmetric neighborhood relationship. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 577\u2013593. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11731139_68"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Kriegel, H., Kr\u00f6ger, P., Schubert, E., Zimek, A.: LoOP: local outlier probabilities. In: CIKM, pp. 1649\u20131652. ACM (2009)","DOI":"10.1145\/1645953.1646195"},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Kriegel, H., Kr\u00f6ger, P., Schubert, E., Zimek, A.: Interpreting and unifying outlier scores. In: SDM, pp. 13\u201324. SIAM\/Omnipress (2011)","DOI":"10.1137\/1.9781611972818.2"},{"key":"19_CR27","doi-asserted-by":"crossref","unstructured":"Kriegel, H., Schubert, M., Zimek, A.: Angle-based outlier detection in high-dimensional data. In: KDD, pp. 444\u2013452. ACM (2008)","DOI":"10.1145\/1401890.1401946"},{"key":"19_CR28","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/978-3-540-73499-4_6","volume-title":"Machine Learning and Data Mining in Pattern Recognition","author":"LJ Latecki","year":"2007","unstructured":"Latecki, L.J., Lazarevic, A., Pokrajac, D.: Outlier detection with kernel density functions. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 61\u201375. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-73499-4_6"},{"key":"19_CR29","doi-asserted-by":"crossref","unstructured":"Marques, H.O., Campello, R.J.G.B., Sander, J., Zimek, A.: Internal evaluation of unsupervised outlier detection. ACM Trans. Knowl. Discov. Data 14(4), 47:1\u201347:42 (2020)","DOI":"10.1145\/3394053"},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Marques, H.O., Campello, R.J.G.B., Zimek, A., Sander, J.: On the internal evaluation of unsupervised outlier detection. In: SSDBM, pp. 7:1\u20137:12. ACM (2015)","DOI":"10.1145\/2791347.2791352"},{"key":"19_CR31","doi-asserted-by":"crossref","unstructured":"Rakthanmanon, T., et al.: Searching and mining trillions of time series subsequences under dynamic time warping. In: KDD, pp. 262\u2013270 (2012)","DOI":"10.1145\/2339530.2339576"},{"key":"19_CR32","doi-asserted-by":"crossref","unstructured":"Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: SIGMOD Conference, pp. 427\u2013438. ACM (2000)","DOI":"10.1145\/335191.335437"},{"issue":"8","key":"19_CR33","doi-asserted-by":"publisher","first-page":"2491","DOI":"10.3390\/s18082491","volume":"18","author":"DT Ramotsoela","year":"2018","unstructured":"Ramotsoela, D.T., Abu-Mahfouz, A.M., Hancke, G.P.: A survey of anomaly detection in industrial wireless sensor networks with critical water system infrastructure as a case study. Sensors 18(8), 2491 (2018)","journal-title":"Sensors"},{"key":"19_CR34","unstructured":"Ruff, L., et al.: Deep semi-supervised anomaly detection. In: ICLR (2020)"},{"issue":"3","key":"19_CR35","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1109\/TSMC.1983.6313167","volume":"13","author":"A Sanfeliu","year":"1983","unstructured":"Sanfeliu, A., Fu, K.: A distance measure between attributed relational graphs for pattern recognition. IEEE Trans. Syst. Man Cybern. 13(3), 353\u2013362 (1983)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"19_CR36","doi-asserted-by":"crossref","unstructured":"Schubert, E., Wojdanowski, R., Zimek, A., Kriegel, H.: On evaluation of outlier rankings and outlier scores. In: SDM, pp. 1047\u20131058. SIAM\/Omnipress (2012)","DOI":"10.1137\/1.9781611972825.90"},{"key":"19_CR37","doi-asserted-by":"crossref","unstructured":"Schubert, E., Zimek, A., Kriegel, H.: Generalized outlier detection with flexible kernel density estimates. In: SDM, pp. 542\u2013550. SIAM (2014)","DOI":"10.1137\/1.9781611973440.63"},{"issue":"1","key":"19_CR38","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/s10618-012-0300-z","volume":"28","author":"E Schubert","year":"2014","unstructured":"Schubert, E., Zimek, A., Kriegel, H.: Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data Min. Knowl. Discov. 28(1), 190\u2013237 (2014)","journal-title":"Data Min. Knowl. Discov."},{"key":"19_CR39","doi-asserted-by":"crossref","unstructured":"Swersky, L., Marques, H.O., Sander, J., Campello, R.J.G.B., Zimek, A.: On the evaluation of outlier detection and one-class classification methods. In: DSAA (2016)","DOI":"10.1109\/DSAA.2016.8"},{"key":"19_CR40","unstructured":"Tan, P., Steinbach, M.S., Karpatne, A., Kumar, V.: Introduction to Data Mining, 2nd edn. Pearson (2019)"},{"key":"19_CR41","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/3-540-47887-6_53","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"J Tang","year":"2002","unstructured":"Tang, J., Chen, Z., Fu, A.W., Cheung, D.W.: Enhancing effectiveness of outlier detections for low density patterns. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 535\u2013548. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-47887-6_53"},{"issue":"4","key":"19_CR42","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1002\/sam.10080","volume":"3","author":"L Vendramin","year":"2010","unstructured":"Vendramin, L., Campello, R.J.G.B., Hruschka, E.R.: Relative clustering validity criteria: a comparative overview. Stat. Anal. Data Min. 3(4), 209\u2013235 (2010)","journal-title":"Stat. Anal. Data Min."},{"key":"19_CR43","doi-asserted-by":"crossref","unstructured":"Vendramin, L., Jaskowiak, P.A., Campello, R.J.G.B.: On the combination of relative clustering validity criteria. In: SSDBM, pp. 4:1\u20134:12. ACM (2013)","DOI":"10.1145\/2484838.2484844"},{"issue":"4","key":"19_CR44","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"19_CR45","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1007\/978-3-642-01307-2_84","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"K Zhang","year":"2009","unstructured":"Zhang, K., Hutter, M., Jin, H.: A new local distance-based outlier detection approach for scattered real-world data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 813\u2013822. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-01307-2_84"},{"issue":"1","key":"19_CR46","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1145\/2594473.2594476","volume":"15","author":"A Zimek","year":"2013","unstructured":"Zimek, A., Campello, R.J.G.B., Sander, J.: Ensembles for unsupervised outlier detection: challenges and research questions a position paper. SIGKDD Explor. 15(1), 11\u201322 (2013)","journal-title":"SIGKDD Explor."},{"key":"19_CR47","doi-asserted-by":"crossref","unstructured":"Zimek, A., Filzmoser, P.: There and back again: outlier detection between statistical reasoning and data mining algorithms. WIREs Data Min. Knowl. Discov. 8(6) (2018)","DOI":"10.1002\/widm.1280"}],"container-title":["Lecture Notes in Computer Science","Similarity Search and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17849-8_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T23:05:49Z","timestamp":1664319949000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17849-8_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031178481","9783031178498"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17849-8_19","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":"28 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SISAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Similarity Search and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bologna","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":"5 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sisap2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.sisap.org\/2022\/","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34","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":"15","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":"8","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":"44% - 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","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)"}}]}}