{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T09:04:54Z","timestamp":1776935094899,"version":"3.51.2"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031263866","type":"print"},{"value":"9783031263873","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\/978-3-031-26387-3_13","type":"book-chapter","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T15:03:10Z","timestamp":1678978990000},"page":"209-224","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["R2-AD2: Detecting Anomalies by\u00a0Analysing the\u00a0Raw Gradient"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1787-4102","authenticated-orcid":false,"given":"Jan-Philipp","family":"Schulze","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7901-7168","authenticated-orcid":false,"given":"Philip","family":"Sperl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3139-2954","authenticated-orcid":false,"given":"Ana","family":"R\u0103du\u021boiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carla","family":"Sagebiel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9337-7506","authenticated-orcid":false,"given":"Konstantin","family":"B\u00f6ttinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"13_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/978-3-030-20893-6_39","volume-title":"Computer Vision \u2013 ACCV 2018","author":"S Akcay","year":"2019","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622\u2013637. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20893-6_39"},{"key":"13_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1007\/978-3-030-46150-8_13","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"L Beggel","year":"2020","unstructured":"Beggel, L., Pfeiffer, M., Bischl, B.: Robust anomaly detection in images using adversarial autoencoders. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds.) ECML PKDD 2019. LNCS (LNAI), vol. 11906, pp. 206\u2013222. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46150-8_13"},{"key":"13_CR3","unstructured":"Bergman, L., Hoshen, Y.: Classification-based anomaly detection for general data. In: International Conference on Learning Representations (2020). https:\/\/openreview.net\/forum?id=H1lK_lBtvS"},{"issue":"3","key":"13_CR4","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/S0168-1699(99)00046-0","volume":"24","author":"JA Blackard","year":"1999","unstructured":"Blackard, J.A., Dean, D.J.: Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables. Comput. Electron. Agric. 24(3), 131\u2013151 (1999). https:\/\/doi.org\/10.1016\/S0168-1699(99)00046-0","journal-title":"Comput. Electron. Agric."},{"key":"13_CR5","doi-asserted-by":"publisher","unstructured":"Borghesi, A., Bartolini, A., Lombardi, M., Milano, M., Benini, L.: Anomaly detection using autoencoders in high performance computing systems. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 9428\u20139433 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33019428","DOI":"10.1609\/aaai.v33i01.33019428"},{"key":"13_CR6","unstructured":"Chalapathy, R., Menon, A.K., Chawla, S.: Anomaly detection using one-class neural networks. arXiv:1802.06360 [cs, stat] (2019). http:\/\/arxiv.org\/abs\/1802.06360, arXiv: 1802.06360"},{"key":"13_CR7","unstructured":"Dhaliwal, J., Shintre, S.: Gradient similarity: an explainable approach to detect adversarial attacks against deep learning. arXiv:1806.10707 [cs] (2018). http:\/\/arxiv.org\/abs\/1806.10707, arXiv: 1806.10707"},{"key":"13_CR8","unstructured":"Golan, I., El-Yaniv, R.: Deep anomaly detection using geometric transformations. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc. (2018), https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/5e62d03aec0d17facfc5355dd90d441c-Paper.pdf"},{"key":"13_CR9","unstructured":"Goyal, S., Raghunathan, A., Jain, M., Simhadri, H.V., Jain, P.: DROCC: deep robust one-class classification. In: Proceedings of the 37th International Conference on Machine Learning, pp. 3711\u20133721. PMLR (2020).https:\/\/proceedings.mlr.press\/v119\/goyal20c.html, iSSN: 2640-3498"},{"key":"13_CR10","doi-asserted-by":"publisher","unstructured":"Habibi Lashkari, A., Kaur, G., Rahali, A.: DIDarknet: a contemporary approach to detect and characterize the darknet traffic using deep image learning. In: 2020 the 10th International Conference on Communication and Network Security, ICCNS 2020, pp. 1\u201313. Association for Computing Machinery, New York (2020). https:\/\/doi.org\/10.1145\/3442520.3442521","DOI":"10.1145\/3442520.3442521"},{"key":"13_CR11","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017). https:\/\/openreview.net\/forum?id=Hkg4TI9xl"},{"key":"13_CR12","unstructured":"Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=HyxCxhRcY7"},{"issue":"8","key":"13_CR13","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). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"13_CR14","unstructured":"Huang, C., Ye, F., Zhao, P., Zhang, Y., Wang, Y.F., Tian, Q.: ESAD: end-to-end deep semi-supervised anomaly detection. In: The 32nd British Machine Vision Conference (2021). https:\/\/www.bmvc2021-virtualconference.com\/conference\/papers\/paper_0329.html"},{"key":"13_CR15","unstructured":"Huang, R., Geng, A., Li, Y.: On the importance of gradients for detecting distributional shifts in the wild. arXiv:2110.00218 [cs] (2021). http:\/\/arxiv.org\/abs\/2110.00218, arXiv: 2110.00218"},{"key":"13_CR16","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, pp. 448\u2013456. PMLR (2015). https:\/\/proceedings.mlr.press\/v37\/ioffe15.html, iSSN: 1938-7228"},{"key":"13_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1007\/978-3-030-58589-1_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"G Kwon","year":"2020","unstructured":"Kwon, G., Prabhushankar, M., Temel, D., AlRegib, G.: Backpropagated gradient representations for anomaly detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12366, pp. 206\u2013226. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58589-1_13"},{"key":"13_CR18","doi-asserted-by":"publisher","unstructured":"Kwon, G., Prabhushankar, M., Temel, D., AlRegib, G.: Novelty detection through model-based characterization of neural networks. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 3179\u20133183 (2020). https:\/\/doi.org\/10.1109\/ICIP40778.2020.9190706, iSSN: 2381-8549","DOI":"10.1109\/ICIP40778.2020.9190706"},{"issue":"11","key":"13_CR19","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"13_CR20","doi-asserted-by":"publisher","unstructured":"Lee, J., AlRegib, G.: Open-set recognition with gradient-based representations. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 469\u2013473 (2021). https:\/\/doi.org\/10.1109\/ICIP42928.2021.9506430, iSSN: 2381-8549","DOI":"10.1109\/ICIP42928.2021.9506430"},{"key":"13_CR21","unstructured":"Lee, J., Prabhushankar, M., AlRegib, G.: Gradient-based adversarial and out-of-distribution detection. In: International Conference on Machine Learning (ICML) Workshop on New Frontiers in Adversarial Machine Learning (2022)"},{"key":"13_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1007\/978-3-030-30490-4_56","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Text and Time Series","author":"D Li","year":"2019","unstructured":"Li, D., Chen, D., Jin, B., Shi, L., Goh, J., Ng, S.-K.: MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks. In: Tetko, I.V., K\u016frkov\u00e1, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11730, pp. 703\u2013716. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30490-4_56"},{"key":"13_CR23","unstructured":"Lust, J., Condurache, A.P.: GraN: an efficient gradient-norm based detector for adversarial and misclassified examples. In: ESANN 2020, p. 6 (2020)"},{"key":"13_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/978-3-319-46298-1_30","volume-title":"Network and System Security","author":"MSI Mamun","year":"2016","unstructured":"Mamun, M.S.I., Rathore, M.A., Lashkari, A.H., Stakhanova, N., Ghorbani, A.A.: Detecting malicious URLs using lexical analysis. In: Chen, J., Piuri, V., Su, C., Yung, M. (eds.) NSS 2016. LNCS, vol. 9955, pp. 467\u2013482. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46298-1_30"},{"key":"13_CR25","doi-asserted-by":"publisher","unstructured":"MontazeriShatoori, M., Davidson, L., Kaur, G., Lashkari, A.H.: Detection of DoH tunnels using time-series classification of encrypted traffic. In: The 5th IEEE Cyber Science and Technology Congress, pp. 63\u201370 (2020). https:\/\/doi.org\/10.1109\/DASC-PICom-CBDCom-CyberSciTech49142.2020.00026","DOI":"10.1109\/DASC-PICom-CBDCom-CyberSciTech49142.2020.00026"},{"key":"13_CR26","doi-asserted-by":"publisher","unstructured":"Pang, G., Cao, L., Chen, L., Liu, H.: Learning Representations of ultrahigh-dimensional data for random distance-based outlier detection. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, pp. 2041\u20132050. Association for Computing Machinery, New York (2018). https:\/\/doi.org\/10.1145\/3219819.3220042","DOI":"10.1145\/3219819.3220042"},{"key":"13_CR27","doi-asserted-by":"publisher","unstructured":"Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. 54(2), 38:1\u201338:38 (2021). https:\/\/doi.org\/10.1145\/3439950","DOI":"10.1145\/3439950"},{"key":"13_CR28","doi-asserted-by":"publisher","unstructured":"Pang, G., Shen, C., van den Hengel, A.: Deep anomaly detection with deviation networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, pp. 353\u2013362. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3292500.3330871","DOI":"10.1145\/3292500.3330871"},{"key":"13_CR29","doi-asserted-by":"publisher","unstructured":"Pozzolo, A.D., Caelen, O., Johnson, R.A., Bontempi, G.: Calibrating probability with undersampling for unbalanced classification. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 159\u2013166 (2015). https:\/\/doi.org\/10.1109\/SSCI.2015.33","DOI":"10.1109\/SSCI.2015.33"},{"key":"13_CR30","doi-asserted-by":"publisher","unstructured":"Ruff, L., et al.: A unifying review of deep and shallow anomaly detection. In: Proceedings of the IEEE, pp. 1\u201340 (2021). https:\/\/doi.org\/10.1109\/JPROC.2021.3052449","DOI":"10.1109\/JPROC.2021.3052449"},{"key":"13_CR31","unstructured":"Ruff, L., et al.: Deep semi-supervised anomaly detection. In: International Conference on Learning Representations (2020). https:\/\/openreview.net\/forum?id=HkgH0TEYwH"},{"key":"13_CR32","unstructured":"Salehi, M., Mirzaei, H., Hendrycks, D., Li, Y., Rohban, M.H., Sabokrou, M.: A unified survey on anomaly, novelty, open-set, and out-of-distribution detection: solutions and future challenges. arXiv:2110.14051 [cs] (2021). http:\/\/arxiv.org\/abs\/2110.14051, arXiv: 2110.14051"},{"key":"13_CR33","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.media.2019.01.010","volume":"54","author":"T Schlegl","year":"2019","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30\u201344 (2019). https:\/\/doi.org\/10.1016\/j.media.2019.01.010","journal-title":"Med. Image Anal."},{"key":"13_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1007\/978-3-030-88418-5_27","volume-title":"Computer Security \u2013 ESORICS 2021","author":"J-P Schulze","year":"2021","unstructured":"Schulze, J.-P., Sperl, P., B\u00f6ttinger, K.: DA3G: detecting adversarial attacks by analysing gradients. In: Bertino, E., Shulman, H., Waidner, M. (eds.) ESORICS 2021. LNCS, vol. 12972, pp. 563\u2013583. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-88418-5_27"},{"key":"13_CR35","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: ICISSP, pp. 108\u2013116 (2018)","DOI":"10.5220\/0006639801080116"},{"key":"13_CR36","unstructured":"Sohn, K., Li, C.L., Yoon, J., Jin, M., Pfister, T.: Learning and evaluating representations for deep one-class classification. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=HCSgyPUfeDj"},{"key":"13_CR37","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-030-67661-2_5","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"P Sperl","year":"2021","unstructured":"Sperl, P., Schulze, J.-P., B\u00f6ttinger, K.: Activation anomaly analysis. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12458, pp. 69\u201384. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-67661-2_5"},{"key":"13_CR38","unstructured":"Sun, J., et al.: Gradient-based novelty detection boosted by self-supervised binary classification. arXiv:2112.09815 [cs] (2021). http:\/\/arxiv.org\/abs\/2112.09815, arXiv: 2112.09815"},{"key":"13_CR39","doi-asserted-by":"publisher","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1\u20136 (2009). https:\/\/doi.org\/10.1109\/CISDA.2009.5356528, iSSN: 2329-6275","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"13_CR40","unstructured":"Vu, H.S., Ueta, D., Hashimoto, K., Maeno, K., Pranata, S., Shen, S.M.: Anomaly detection with adversarial dual autoencoders. arXiv:1902.06924 [cs] (2019). http:\/\/arxiv.org\/abs\/1902.06924, arXiv: 1902.06924"},{"key":"13_CR41","doi-asserted-by":"publisher","unstructured":"Wilcoxon, F.: Individual comparisons by ranking methods. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics: Methodology and Distribution, Springer Series in Statistics, pp. 196\u2013202. Springer, New York (1992). https:\/\/doi.org\/10.1007\/978-1-4612-4380-9_16","DOI":"10.1007\/978-1-4612-4380-9_16"},{"issue":"06","key":"13_CR42","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1142\/S0218001493000698","volume":"07","author":"KS Woods","year":"1993","unstructured":"Woods, K.S.: Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammography. Int. J. Pattern Recogn. Artif. Intell. 07(06), 1417\u20131436 (1993). https:\/\/doi.org\/10.1142\/S0218001493000698","journal-title":"Int. J. Pattern Recogn. Artif. Intell."},{"key":"13_CR43","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv:1708.07747 [cs, stat] (2017). http:\/\/arxiv.org\/abs\/1708.07747, arXiv: 1708.07747"},{"key":"13_CR44","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/978-3-030-29911-8_50","volume-title":"PRICAI 2019: Trends in Artificial Intelligence","author":"Y Yamanaka","year":"2019","unstructured":"Yamanaka, Y., Iwata, T., Takahashi, H., Yamada, M., Kanai, S.: Autoencoding binary classifiers for supervised anomaly detection. In: Nayak, A.C., Sharma, A. (eds.) PRICAI 2019. LNCS (LNAI), vol. 11671, pp. 647\u2013659. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29911-8_50"},{"key":"13_CR45","doi-asserted-by":"publisher","unstructured":"Ye, Z., Chen, Y., Zheng, H.: Understanding the effect of bias in deep anomaly detection. In: Zhou, Z.H. (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization , IJCAI-2021, pp. 3314\u20133320 (2021). https:\/\/doi.org\/10.24963\/ijcai.2021\/456","DOI":"10.24963\/ijcai.2021\/456"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26387-3_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T15:06:27Z","timestamp":1678979187000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26387-3_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031263866","9783031263873"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26387-3_13","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 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Data-driven AD reveals data points that differ from the training data distribution. Underrepresented groups in the training data may cause a bias in the detection results. In example of the census data set, which we analysed during our evaluation, e.g. the origin of the citizens could be used for the anomaly decision leading to ethical implications. To this end, we encourage users of R2-AD2 and AD in general to thoroughly evaluate potential biases in the data.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Implications"}},{"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":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.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":"1060","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":"236","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":"22% - 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":"3-4","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":"3-4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17 demo track papers have been accepted from 28 submissions","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)"}}]}}