{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T15:42:43Z","timestamp":1775576563396,"version":"3.50.1"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031505232","type":"print"},{"value":"9783031505249","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T00:00:00Z","timestamp":1703894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T00:00:00Z","timestamp":1703894400000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-50524-9_2","type":"book-chapter","created":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T15:02:28Z","timestamp":1703862148000},"page":"27-49","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Abstract Interpretation-Based Feature Importance for\u00a0Support Vector Machines"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4122-5092","authenticated-orcid":false,"given":"Abhinandan","family":"Pal","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0159-0068","authenticated-orcid":false,"given":"Francesco","family":"Ranzato","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8127-9642","authenticated-orcid":false,"given":"Caterina","family":"Urban","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6164-6169","authenticated-orcid":false,"given":"Marco","family":"Zanella","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,30]]},"reference":[{"key":"2_CR1","volume-title":"Compilers: Principles, Techniques, and Tools","author":"AV Aho","year":"2006","unstructured":"Aho, A.V., Lam, M.S., Sethi, R., Ullman, J.D.: Compilers: Principles, Techniques, and Tools, 2nd edn. Addison-Wesley Longman Publishing Co., Inc, USA (2006)","edition":"2"},{"issue":"1\u20132","key":"2_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2500000051","volume":"7","author":"A Albarghouthi","year":"2021","unstructured":"Albarghouthi, A.: Introduction to neural network verification. Found. Trends Program. Lang. 7(1\u20132), 1\u2013157 (2021). https:\/\/doi.org\/10.1561\/2500000051","journal-title":"Found. Trends Program. Lang."},{"key":"2_CR3","unstructured":"Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine Bias. ProPublica 23 (2016), https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing"},{"issue":"4","key":"2_CR4","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1111\/rssb.12377","volume":"82","author":"DW Apley","year":"2020","unstructured":"Apley, D.W., Zhu, J.: Visualizing the effects of predictor variables in black box supervised learning models. J. R. Stat. Soc. Ser. B Stat Methodol. 82(4), 1059\u20131086 (2020). https:\/\/doi.org\/10.1111\/rssb.12377","journal-title":"J. R. Stat. Soc. Ser. B Stat Methodol."},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Bhatt, U., et al.: Explainable machine learning in deployment. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* 2020, pp. 648\u2013657. ACM (2020). https:\/\/doi.org\/10.1145\/3351095.3375624","DOI":"10.1145\/3351095.3375624"},{"issue":"1","key":"2_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"key":"2_CR7","doi-asserted-by":"publisher","unstructured":"Carlini, N., Wagner, D.A.: Towards evaluating the robustness of neural networks. In: Proceedings of 38th IEEE Symposium on Security and Privacy (S & P 2017), pp. 39\u201357 (2017). https:\/\/doi.org\/10.1109\/SP.2017.49","DOI":"10.1109\/SP.2017.49"},{"key":"2_CR8","doi-asserted-by":"publisher","unstructured":"Casalicchio, G., Molnar, C., Bischl, B.: Visualizing the feature importance for black box models. In: Machine Learning and Knowledge Discovery in Databases - Proceedings of the European Conference, ECML PKDD 2018. Lecture Notes in Computer Science, vol. 11051, pp. 655\u2013670. Springer (2018). https:\/\/doi.org\/10.1007\/978-3-030-10925-7_40","DOI":"10.1007\/978-3-030-10925-7_40"},{"key":"2_CR9","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","volume":"408","author":"J Cervantes","year":"2020","unstructured":"Cervantes, J., Garcia-Lamont, F., Rodr\u00edguez-Mazahua, L., Lopez, A.: A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408, 189\u2013215 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2019.10.118","journal-title":"Neurocomputing"},{"key":"2_CR10","unstructured":"Chang, Y.W., Lin, C.J.: Feature ranking using linear SVM. In: Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008. Proceedings of Machine Learning Research, vol. 3, pp. 53\u201364. PMLR (2008), http:\/\/proceedings.mlr.press\/v3\/chang08a.html"},{"issue":"2","key":"2_CR11","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1089\/big.2016.0047","volume":"5","author":"A Chouldechova","year":"2017","unstructured":"Chouldechova, A.: Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2), 153\u2013163 (2017). https:\/\/doi.org\/10.1089\/big.2016.0047","journal-title":"Big Data"},{"key":"2_CR12","unstructured":"Cousot, P.: Principles of Abstract Interpretation. MIT Press (2021)"},{"key":"2_CR13","doi-asserted-by":"publisher","unstructured":"Cousot, P., Cousot, R.: Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints. In: Proceedings of the 4th ACM Symposium on Principles of Programming Languages (POPL 1977), pp. 238\u2013252 (1977). https:\/\/doi.org\/10.1145\/512950.512973","DOI":"10.1145\/512950.512973"},{"key":"2_CR14","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389","volume-title":"An Introduction to Support Vector Machines and Other Kernel-based Learning Methods","author":"N Cristianini","year":"2000","unstructured":"Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press (2000). https:\/\/doi.org\/10.1017\/CBO9780511801389"},{"key":"2_CR15","unstructured":"Dua, D., Graff, C.: UCI Machine Learning repository (2017). https:\/\/archive.ics.uci.edu\/ml"},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.S.: Fairness through awareness. In: Innovations in Theoretical Computer Science 2012, pp. 214\u2013226. ACM (2012). https:\/\/doi.org\/10.1145\/2090236.2090255","DOI":"10.1145\/2090236.2090255"},{"key":"2_CR17","doi-asserted-by":"publisher","unstructured":"Fish, B., Kun, J., Lelkes, \u00c1.D.: A confidence-based approach for balancing fairness and accuracy. In: Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 144\u2013152. SIAM (2016). https:\/\/doi.org\/10.1137\/1.9781611974348.17","DOI":"10.1137\/1.9781611974348.17"},{"key":"2_CR18","unstructured":"Fisher, A., Rudin, C., Dominici, F.: All models are wrong, but many are useful: learning a variable\u2019s importance by studying an entire class of prediction models simultaneously. J. Mach. Learn. Res. 20(177), 1\u201381 (2019). http:\/\/jmlr.org\/papers\/v20\/18-760.html"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189\u20131232 (2001). http:\/\/www.jstor.org\/stable\/2699986","DOI":"10.1214\/aos\/1013203451"},{"key":"2_CR20","doi-asserted-by":"publisher","unstructured":"Ghorbal, K., Goubault, E., Putot, S.: The zonotope abstract domain Taylor1+. In: Computer Aided Verification, 21st International Conference, CAV 2009. Proceedings. Lecture Notes in Computer Science, vol. 5643, pp. 627\u2013633. Springer (2009). https:\/\/doi.org\/10.1007\/978-3-642-02658-4_47","DOI":"10.1007\/978-3-642-02658-4_47"},{"key":"2_CR21","doi-asserted-by":"publisher","unstructured":"Ghosh, B., Basu, D., Meel, K.S.: Algorithmic fairness verification with graphical models. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, pp. 9539\u20139548 (2022). https:\/\/doi.org\/10.1609\/aaai.v36i9.21187","DOI":"10.1609\/aaai.v36i9.21187"},{"issue":"1","key":"2_CR22","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1080\/10618600.2014.907095","volume":"24","author":"A Goldstein","year":"2015","unstructured":"Goldstein, A., Kapelner, A., Bleich, J., Pitkin, E.: Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation. J. Comput. Graph. Stat. 24(1), 44\u201365 (2015). https:\/\/doi.org\/10.1080\/10618600.2014.907095","journal-title":"J. Comput. Graph. Stat."},{"issue":"7","key":"2_CR23","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1145\/3134599","volume":"61","author":"I Goodfellow","year":"2018","unstructured":"Goodfellow, I., McDaniel, P., Papernot, N.: Making machine learning robust against adversarial inputs. Commun. ACM 61(7), 56\u201366 (2018). https:\/\/doi.org\/10.1145\/3134599","journal-title":"Commun. ACM"},{"key":"2_CR24","unstructured":"Hechtlinger, Y.: Interpretation of prediction models using the input gradient. CoRR arXiv (2016). http:\/\/arxiv.org\/abs\/1611.07634"},{"issue":"6","key":"2_CR25","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/s11222-021-10057-z","volume":"31","author":"G Hooker","year":"2021","unstructured":"Hooker, G., Mentch, L., Zhou, S.: Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance. Stat. Comput. 31(6), 82 (2021). https:\/\/doi.org\/10.1007\/s11222-021-10057-z","journal-title":"Stat. Comput."},{"issue":"11","key":"2_CR26","doi-asserted-by":"publisher","first-page":"2767","DOI":"10.1016\/j.jbankfin.2010.06.001","volume":"34","author":"AE Khandani","year":"2010","unstructured":"Khandani, A.E., Kim, A.J., Lo, A.W.: Consumer credit-risk models via machine-learning algorithms. J. Bank. Finance 34(11), 2767\u20132787 (2010). https:\/\/doi.org\/10.1016\/j.jbankfin.2010.06.001","journal-title":"J. Bank. Finance"},{"key":"2_CR27","doi-asserted-by":"publisher","unstructured":"Langenberg, P., Balda, E.R., Behboodi, A., Mathar, R.: On the robustness of support vector machines against adversarial examples. In: 13th International Conference on Signal Processing and Communication Systems, ICSPCS 2019, pp. 1\u20136. IEEE (2019). https:\/\/doi.org\/10.1109\/ICSPCS47537.2019.9008746","DOI":"10.1109\/ICSPCS47537.2019.9008746"},{"issue":"3\u20134","key":"2_CR28","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1561\/2400000035","volume":"4","author":"C Liu","year":"2021","unstructured":"Liu, C., Arnon, T., Lazarus, C., Strong, C.A., Barrett, C.W., Kochenderfer, M.J.: Algorithms for verifying deep neural networks. Found. Trends Optim. 4(3\u20134), 244\u2013404 (2021). https:\/\/doi.org\/10.1561\/2400000035","journal-title":"Found. Trends Optim."},{"key":"2_CR29","unstructured":"Lundberg, S.M., Lee, S.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, pp. 4765\u20134774 (2017). https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/8a20a8621978632d76c43dfd28b67767-Abstract.html"},{"issue":"6","key":"2_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. 54(6), 1\u201335 (2021). https:\/\/doi.org\/10.1145\/3457607","journal-title":"ACM Comput. Surv."},{"issue":"11","key":"2_CR31","doi-asserted-by":"publisher","first-page":"992","DOI":"10.3217\/jucs-008-11-0992","volume":"8","author":"F Messine","year":"2002","unstructured":"Messine, F.: Extentions of affine arithmetic: application to unconstrained global optimization. J. Univ. Comput. Sci. 8(11), 992\u20131015 (2002). https:\/\/doi.org\/10.3217\/jucs-008-11-0992","journal-title":"J. Univ. Comput. Sci."},{"key":"2_CR32","doi-asserted-by":"publisher","unstructured":"Mladenic, D., Brank, J., Grobelnik, M., Milic-Frayling, N.: Feature selection using linear classifier weights: interaction with classification models. In: SIGIR 2004: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 234\u2013241. ACM (2004). https:\/\/doi.org\/10.1145\/1008992.1009034","DOI":"10.1145\/1008992.1009034"},{"key":"2_CR33","unstructured":"Pal, A., Ranzato, F., Urban, C., Zanella, M.: Abstract Feature Importance for SVMs (2023). https:\/\/github.com\/AFI-SVM"},{"key":"2_CR34","doi-asserted-by":"publisher","unstructured":"Park, S., Byun, J., Lee, J.: Privacy-preserving fair learning of support vector machine with homomorphic encryption. In: WWW 2022: The ACM Web Conference 2022, pp. 3572\u20133583. ACM (2022). https:\/\/doi.org\/10.1145\/3485447.3512252","DOI":"10.1145\/3485447.3512252"},{"key":"2_CR35","doi-asserted-by":"publisher","unstructured":"Ranzato, F., Urban, C., Zanella, M.: Fairness-aware training of decision trees by abstract interpretation. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, CIKM2021, pp. 1508\u20131517 (2021). https:\/\/doi.org\/10.1145\/3459637.3482342","DOI":"10.1145\/3459637.3482342"},{"key":"2_CR36","doi-asserted-by":"publisher","unstructured":"Ranzato, F., Zanella, M.: Robustness verification of support vector machines. In: Proceedings of the 26th International Static Analysis Symposium (SAS 2019), pp. 271\u2013295. LNCS vol. 11822 (2019). https:\/\/doi.org\/10.1007\/978-3-030-32304-2_14","DOI":"10.1007\/978-3-030-32304-2_14"},{"key":"2_CR37","unstructured":"Ranzato, F., Zanella, M.: Saver: SVM Abstract Verifier (2019). https:\/\/github.com\/abstract-machine-learning\/saver"},{"key":"2_CR38","doi-asserted-by":"publisher","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should I trust you?\u201d: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135\u20131144. ACM (2016). https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"key":"2_CR39","unstructured":"Ribeiro, M.T.C.: Local Interpretable Model-agnostic Explanations (LIME) (2016). https:\/\/lime-ml.readthedocs.io"},{"key":"2_CR40","unstructured":"Roh, Y., Lee, K., Whang, S., Suh, C.: Fr-train: a mutual information-based approach to fair and robust training. In: Proceedings of the 37th International Conference on Machine Learning (ICML 2020). Proceedings of Machine Learning Research, vol. 119, pp. 8147\u20138157. PMLR (2020). http:\/\/proceedings.mlr.press\/v119\/roh20a.html"},{"key":"2_CR41","unstructured":"Ruoss, A., Balunovic, M., Fischer, M., Vechev, M.T.: Learning certified individually fair representations. In: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems (NeurIPS 2020) (2020). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/55d491cf951b1b920900684d71419282-Abstract.html"},{"key":"2_CR42","first-page":"307","volume-title":"Contributions to the Theory of Games II","author":"LS Shapley","year":"1953","unstructured":"Shapley, L.S.: A value for n-person games. In: Kuhn, H.W., Tucker, A.W. (eds.) Contributions to the Theory of Games II, pp. 307\u2013317. Princeton University Press, Princeton (1953)"},{"issue":"11","key":"2_CR43","doi-asserted-by":"publisher","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","volume":"32","author":"E Tjoa","year":"2021","unstructured":"Tjoa, E., Guan, C.: A survey on explainable artificial intelligence (XAI): toward medical XAI. IEEE Trans. Neural Networks Learn. Syst. 32(11), 4793\u20134813 (2021). https:\/\/doi.org\/10.1109\/TNNLS.2020.3027314","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"OOPSLA","key":"2_CR44","doi-asserted-by":"publisher","first-page":"185:1","DOI":"10.1145\/3428253","volume":"4","author":"C Urban","year":"2020","unstructured":"Urban, C., Christakis, M., W\u00fcstholz, V., Zhang, F.: Perfectly parallel fairness certification of neural networks. Proc. ACM Program. Lang. 4(OOPSLA), 185:1-185:30 (2020). https:\/\/doi.org\/10.1145\/3428253","journal-title":"Proc. ACM Program. Lang."},{"key":"2_CR45","unstructured":"Urban, C., Min\u00e9, A.: A review of formal methods applied to machine learning. CoRR arXiv (2021). https:\/\/arxiv.org\/abs\/2104.02466"},{"key":"2_CR46","doi-asserted-by":"publisher","unstructured":"Verma, S., Rubin, J.: Fairness definitions explained. In: Proceedings of the International Workshop on Software Fairness, FairWare@ICSE 2018, pp. 1\u20137. ACM (2018). https:\/\/doi.org\/10.1145\/3194770.3194776","DOI":"10.1145\/3194770.3194776"},{"key":"2_CR47","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.neucom.2014.08.081","volume":"160","author":"H Xiao","year":"2015","unstructured":"Xiao, H., Biggio, B., Nelson, B., Xiao, H., Eckert, C., Roli, F.: Support vector machines under adversarial label contamination. Neurocomputing 160, 53\u201362 (2015). https:\/\/doi.org\/10.1016\/j.neucom.2014.08.081","journal-title":"Neurocomputing"},{"key":"2_CR48","unstructured":"Yurochkin, M., Bower, A., Sun, Y.: Training individually fair ML models with sensitive subspace robustness. In: Proceedings of the 8th International Conference on Learning Representations, ICLR 2020 (2020). https:\/\/openreview.net\/forum?id=B1gdkxHFDH"}],"container-title":["Lecture Notes in Computer Science","Verification, Model Checking, and Abstract Interpretation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-50524-9_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T00:11:06Z","timestamp":1704240666000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-50524-9_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,30]]},"ISBN":["9783031505232","9783031505249"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-50524-9_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,30]]},"assertion":[{"value":"30 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"VMCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Verification, Model Checking, and Abstract Interpretation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 January 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 January 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"vmcai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/popl24.sigplan.org\/home\/VMCAI-2024","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":"74","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":"30","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":"41% - 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":"6","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)"}}]}}