{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T10:06:08Z","timestamp":1777284368928,"version":"3.51.4"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031434266","type":"print"},{"value":"9783031434273","type":"electronic"}],"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\/978-3-031-43427-3_17","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:01:41Z","timestamp":1694898101000},"page":"275-292","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Advancing Fraud Detection Systems Through Online Learning"],"prefix":"10.1007","author":[{"given":"Tommaso","family":"Paladini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martino","family":"Bernasconi de Luca","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michele","family":"Carminati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario","family":"Polino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco","family":"Trov\u00f2","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefano","family":"Zanero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"17_CR1","unstructured":"Advancing fraud detection systems through online learning - supplementary materials and source code. https:\/\/github.com\/necst\/advancing_fds_code (2023)"},{"key":"17_CR2","unstructured":"AlAhmadi, B.A., Axon, L., Martinovic, I.: 99% false positives: A qualitative study of SOC analysts\u2019 perspectives on security alarms. In: Butler, K.R.B., Thomas, K. (eds.) 31st USENIX Security Symposium, USENIX Security 2022, Boston, MA, USA, August 10-12, 2022, pp. 2783\u20132800. USENIX Association (2022), https:\/\/www.usenix.org\/conference\/usenixsecurity22\/presentation\/alahmadi"},{"issue":"1","key":"17_CR3","first-page":"686","volume":"22","author":"M Aziz","year":"2021","unstructured":"Aziz, M., Kaufmann, E., Riviere, M.K.: On multi-armed bandit designs for dose-finding clinical trials. J. Mach. Learn. Res. 22(1), 686\u2013723 (2021)","journal-title":"J. Mach. Learn. Res."},{"key":"17_CR4","doi-asserted-by":"publisher","unstructured":"Bagga, S., Goyal, A., Gupta, N., Goyal, A.: Credit card fraud detection using pipeling and ensemble learning. Proc. Comput. Sci. 173, 104\u2013112 (2020). https:\/\/doi.org\/10.1016\/j.procs.2020.06.014, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050920315167, international Conference on Smart Sustainable Intelligent Computing and Applications under ICITETM2020","DOI":"10.1016\/j.procs.2020.06.014"},{"key":"17_CR5","doi-asserted-by":"publisher","unstructured":"Bahnsen, A.C., Aouada, D., Stojanovic, A., Ottersten, B.E.: Feature engineering strategies for credit card fraud detection. Expert Syst. Appl. 51, 134\u2013142 (2016). https:\/\/doi.org\/10.1016\/j.eswa.2015.12.030","DOI":"10.1016\/j.eswa.2015.12.030"},{"issue":"3","key":"17_CR6","doi-asserted-by":"publisher","first-page":"193","DOI":"10.3103\/S1060992X15030030","volume":"24","author":"AS Bekirev","year":"2015","unstructured":"Bekirev, A.S., Klimov, V.V., Kuzin, M.V., Shchukin, B.A.: Payment card fraud detection using neural network committee and clustering. Optical Memory Neural Netw. 24(3), 193\u2013200 (2015). https:\/\/doi.org\/10.3103\/S1060992X15030030","journal-title":"Optical Memory Neural Netw."},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Bernasconi, M., Martino, S., Vittori, E., Trov\u00f2, F., Restelli, M.: Dark-pool smart order routing: a combinatorial multi-armed bandit approach. In: Proceedings of the Third ACM International Conference on AI in Finance, pp. 352\u2013360 (2022)","DOI":"10.1145\/3533271.3561728"},{"key":"17_CR8","doi-asserted-by":"publisher","unstructured":"Bhattacharyya, S., Jha, S., Tharakunnel, K.K., Westland, J.C.: Data mining for credit card fraud: A comparative study. Decis. Support Syst. 50(3), 602\u2013613 (2011). https:\/\/doi.org\/10.1016\/j.dss.2010.08.008","DOI":"10.1016\/j.dss.2010.08.008"},{"key":"17_CR9","doi-asserted-by":"publisher","unstructured":"Brause, R.W., Langsdorf, T.S., Hepp, H.: Neural data mining for credit card fraud detection. In: 11th IEEE International Conference on Tools with Artificial Intelligence, ICTAI \u201999, Chicago, Illinois, USA, November 8-10, 1999, pp. 103\u2013106. IEEE Computer Society (1999). https:\/\/doi.org\/10.1109\/TAI.1999.809773","DOI":"10.1109\/TAI.1999.809773"},{"key":"17_CR10","doi-asserted-by":"publisher","unstructured":"Carcillo, F., Borgne, Y.L., Caelen, O., Bontempi, G.: An assessment of streaming active learning strategies for real-life credit card fraud detection. In: 2017 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2017, Tokyo, Japan, October 19-21, 2017, pp. 631\u2013639. IEEE (2017). https:\/\/doi.org\/10.1109\/DSAA.2017.10","DOI":"10.1109\/DSAA.2017.10"},{"key":"17_CR11","doi-asserted-by":"publisher","unstructured":"Carminati, M., Baggio, A., Maggi, F., Spagnolini, U., Zanero, S.: Fraudbuster: Temporal analysis and detection of advanced financial frauds. In: Giuffrida, C., Bardin, S., Blanc, G. (eds.) Detection of Intrusions and Malware, and Vulnerability Assessment - 15th International Conference, DIMVA 2018, Saclay, France, June 28-29, 2018, Proceedings. Lecture Notes in Computer Science, vol. 10885, pp. 211\u2013233. Springer (2018). https:\/\/doi.org\/10.1007\/978-3-319-93411-2_10","DOI":"10.1007\/978-3-319-93411-2_10"},{"key":"17_CR12","doi-asserted-by":"publisher","unstructured":"Carminati, M., Caron, R., Maggi, F., Epifani, I., Zanero, S.: Banksealer: A decision support system for online banking fraud analysis and investigation. Comput. Secur. 53, 175\u2013186 (2015). https:\/\/doi.org\/10.1016\/j.cose.2015.04.002","DOI":"10.1016\/j.cose.2015.04.002"},{"key":"17_CR13","doi-asserted-by":"publisher","unstructured":"Carminati, M., Polino, M., Continella, A., Lanzi, A., Maggi, F., Zanero, S.: Security evaluation of a banking fraud analysis system. ACM Trans. Priv. Secur. 21(3), 11:1\u201311:31 (2018). https:\/\/doi.org\/10.1145\/3178370","DOI":"10.1145\/3178370"},{"key":"17_CR14","unstructured":"Carminati, M., Santini, L., Polino, M., Zanero, S.: Evasion attacks against banking fraud detection systems. In: Egele, M., Bilge, L. (eds.) 23rd International Symposium on Research in Attacks, Intrusions and Defenses, RAID 2020, San Sebastian, Spain, October 14\u201315, 2020, pp. 285\u2013300. USENIX Association (2020), https:\/\/www.usenix.org\/conference\/raid2020\/presentation\/carminati"},{"key":"17_CR15","unstructured":"Cartella, F., Anuncia\u00e7\u00e3o, O., Funabiki, Y., Yamaguchi, D., Akishita, T., Elshocht, O.: Adversarial attacks for tabular data: Application to fraud detection and imbalanced data. In: Espinoza, H., McDermid, J.A., Huang, X., Castillo-Effen, M., Chen, X.C., Hern\u00e1ndez-Orallo, J., h\u00c9igeartaigh, S.\u00d3., Mallah, R. (eds.) Proceedings of the Workshop on Artificial Intelligence Safety 2021 (SafeAI 2021) co-located with the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021), Virtual, February 8, 2021. CEUR Workshop Proceedings, vol.\u00a02808. CEUR-WS.org (2021). https:\/\/ceur-ws.org\/Vol-2808\/Paper_4.pdf"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Cesa-Bianchi, N., Lugosi, G.: Prediction, learning, and games. Cambridge University Press (2006)","DOI":"10.1017\/CBO9780511546921"},{"key":"17_CR17","doi-asserted-by":"publisher","unstructured":"Continella, A., Carminati, M., Polino, M., Lanzi, A., Zanero, S., Maggi, F.: Prometheus: Analyzing webinject-based information stealers. J. Comput. Secur. 25(2), 117\u2013137 (2017). https:\/\/doi.org\/10.3233\/JCS-15773","DOI":"10.3233\/JCS-15773"},{"key":"17_CR18","unstructured":"European Central Bank: Seventh report on card fraud. Tech. rep. (2021). http:\/\/web.archive.org\/web\/20230521043629\/https:\/\/www.ecb.europa.eu\/pub\/cardfraud\/html\/ecb.cardfraudreport202110~cac4c418e8.en.html"},{"key":"17_CR19","doi-asserted-by":"publisher","unstructured":"Forough, J., Momtazi, S.: Ensemble of deep sequential models for credit card fraud detection. Appl. Soft Comput. 99, 106883 (2021). https:\/\/doi.org\/10.1016\/j.asoc.2020.106883","DOI":"10.1016\/j.asoc.2020.106883"},{"issue":"1\u20132","key":"17_CR20","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1006\/game.1999.0738","volume":"29","author":"Y Freund","year":"1999","unstructured":"Freund, Y., Schapire, R.E.: Adaptive game playing using multiplicative weights. Games Econom. Behav. 29(1\u20132), 79\u2013103 (1999)","journal-title":"Games Econom. Behav."},{"issue":"4","key":"17_CR21","first-page":"1503","volume":"13","author":"F Itoo","year":"2021","unstructured":"Itoo, F., Singh, S., et al.: Comparison and analysis of logistic regression, na\u00efve bayes and knn machine learning algorithms for credit card fraud detection. Int. J. Inf. Technol. 13(4), 1503\u20131511 (2021)","journal-title":"Int. J. Inf. Technol."},{"key":"17_CR22","doi-asserted-by":"publisher","unstructured":"Kim, E., et al.: Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning. Expert Syst. Appl. 128, 214\u2013224 (2019). https:\/\/doi.org\/10.1016\/j.eswa.2019.03.042","DOI":"10.1016\/j.eswa.2019.03.042"},{"key":"17_CR23","unstructured":"KPMG: Global Banking Fraud Survey. (2019). https:\/\/assets.kpmg\/content\/dam\/kpmg\/xx\/pdf\/2019\/05\/global-banking-fraud-survey.pdf"},{"key":"17_CR24","doi-asserted-by":"publisher","unstructured":"Labanca, D., Primerano, L., Markland-Montgomery, M., Polino, M., Carminati, M., Zanero, S.: Amaretto: An active learning framework for money laundering detection. IEEE Access 10, 41720\u201341739 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3167699","DOI":"10.1109\/ACCESS.2022.3167699"},{"key":"17_CR25","doi-asserted-by":"publisher","unstructured":"Marchal, S., Szyller, S.: Detecting organized ecommerce fraud using scalable categorical clustering. In: Balenson, D. (ed.) Proceedings of the 35th Annual Computer Security Applications Conference, ACSAC 2019, San Juan, PR, USA, December 09-13, 2019. pp. 215\u2013228. ACM (2019). https:\/\/doi.org\/10.1145\/3359789.3359810","DOI":"10.1145\/3359789.3359810"},{"key":"17_CR26","unstructured":"Ministero dell\u2019Economia e della Finanza: Rapporto statistico sulle frodi con le carte di pagamento. Tech. rep. (2021). https:\/\/www.dt.mef.gov.it\/export\/sites\/sitodt\/modules\/documenti_it\/antifrode_mezzi_pagamento\/antifrode_mezzi_pagamento\/Rapporto-statistico-sulle-frodi-con-le-carte-di-pagamento-edizione-2021.pdf"},{"key":"17_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2022.103663","volume":"305","author":"A Nuara","year":"2022","unstructured":"Nuara, A., Trov\u00f2, F., Gatti, N., Restelli, M.: Online joint bid\/daily budget optimization of internet advertising campaigns. Artif. Intell. 305, 103663 (2022)","journal-title":"Artif. Intell."},{"key":"17_CR28","unstructured":"Patidar, R., Sharma, L., et al.: Credit card fraud detection using neural network. Int. J. Soft Comput. Eng. (IJSCE) 1, 32\u201338 (2011)"},{"key":"17_CR29","doi-asserted-by":"publisher","unstructured":"Pozzolo, A.D., Boracchi, G., Caelen, O., Alippi, C., Bontempi, G.: Credit card fraud detection and concept-drift adaptation with delayed supervised information. In: 2015 International Joint Conference on Neural Networks, IJCNN 2015, Killarney, Ireland, July 12-17, 2015. pp.\u00a01\u20138. IEEE (2015). https:\/\/doi.org\/10.1109\/IJCNN.2015.7280527","DOI":"10.1109\/IJCNN.2015.7280527"},{"key":"17_CR30","doi-asserted-by":"publisher","unstructured":"Pozzolo, A.D., Boracchi, G., Caelen, O., Alippi, C., Bontempi, G.: Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Trans. Neural Networks Learn. Syst. 29(8), 3784\u20133797 (2018). https:\/\/doi.org\/10.1109\/TNNLS.2017.2736643","DOI":"10.1109\/TNNLS.2017.2736643"},{"key":"17_CR31","doi-asserted-by":"publisher","unstructured":"Randhawa, K., Loo, C.K., Seera, M., Lim, C.P., Nandi, A.K.: Credit card fraud detection using adaboost and majority voting. IEEE Access 6, 14277\u201314284 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2806420","DOI":"10.1109\/ACCESS.2018.2806420"},{"key":"17_CR32","unstructured":"Sahin, Y., Duman, E.: Detecting credit card fraud by decision trees and support vector machines. In: World Congress on Engineering 2012. July 4\u20136, 2012. London, UK. vol. 2188, pp. 442\u2013447. International Association of Engineers (2010)"},{"key":"17_CR33","doi-asserted-by":"crossref","unstructured":"Seeja, K., Zareapoor, M.: Fraudminer: A novel credit card fraud detection model based on frequent itemset mining. Sci. World J. 2014, 252797 (2014)","DOI":"10.1155\/2014\/252797"},{"key":"17_CR34","doi-asserted-by":"publisher","unstructured":"Sohony, I., Pratap, R., Nambiar, U.: Ensemble learning for credit card fraud detection. In: Ranu, S., Ganguly, N., Ramakrishnan, R., Sarawagi, S., Roy, S. (eds.) Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, COMAD\/CODS 2018, Goa, India, January 11-13, 2018, pp. 289\u2013294. ACM (2018). https:\/\/doi.org\/10.1145\/3152494.3156815","DOI":"10.1145\/3152494.3156815"},{"key":"17_CR35","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.ijar.2018.04.006","volume":"98","author":"F Trov\u00f2","year":"2018","unstructured":"Trov\u00f2, F., Paladino, S., Restelli, M., Gatti, N.: Improving multi-armed bandit algorithms in online pricing settings. Int. J. Approx. Reason. 98, 196\u2013235 (2018)","journal-title":"Int. J. Approx. Reason."},{"key":"17_CR36","doi-asserted-by":"publisher","unstructured":"Whitrow, C., Hand, D.J., Juszczak, P., Weston, D.J., Adams, N.M.: Transaction aggregation as a strategy for credit card fraud detection. Data Min. Knowl. Discov. 18(1), 30\u201355 (2009). https:\/\/doi.org\/10.1007\/s10618-008-0116-z","DOI":"10.1007\/s10618-008-0116-z"},{"key":"17_CR37","doi-asserted-by":"publisher","unstructured":"Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S., Jiang, C.: Random forest for credit card fraud detection. In: 15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018, Zhuhai, China, March 27-29, 2018, pp.\u00a01\u20136. IEEE (2018). https:\/\/doi.org\/10.1109\/ICNSC.2018.8361343","DOI":"10.1109\/ICNSC.2018.8361343"},{"key":"17_CR38","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Tong, J., Wang, Z., Gao, F.: Customer transaction fraud detection using xgboost model. In: 2020 International Conference on Computer Engineering and Application (ICCEA), pp. 554\u2013558 (2020). https:\/\/doi.org\/10.1109\/ICCEA50009.2020.00122","DOI":"10.1109\/ICCEA50009.2020.00122"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43427-3_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:03:53Z","timestamp":1694898233000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43427-3_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434266","9783031434273"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43427-3_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Machine learning models have become increasingly ubiquitous in decision-making processes across various industries, especially financial fraud detection ones. However, the ethical implications of these models have come under scrutiny due to the potential for bias. Focusing on our work, if the base models are biased, any approach built upon them may also be biased. This is especially concerning when the models are used in sensitive areas such as fraud detection. On the other hand, since we do not explicitly exploit transaction features, we may not introduce further bias directly. However, it is important to note that the data used to train the models may still contain hidden biases that could influence the model\u2019s predictions. Therefore, it is essential to ensure that the data sets used to train the models are diverse and representative of the population to minimize bias and prevent harm to vulnerable groups, as stated by the guidelines by the EU on AI methods ().","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Issues"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","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":"196","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":"24% - 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":"3.63","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":"4.5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}