{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:05:49Z","timestamp":1742958349442,"version":"3.40.3"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030723781"},{"type":"electronic","value":"9783030723798"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-72379-8_1","type":"book-chapter","created":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T19:02:45Z","timestamp":1617044565000},"page":"3-16","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Uncertainty Modeling and Deep Learning Applied to Food Image Analysis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2463-0301","authenticated-orcid":false,"given":"Eduardo","family":"Aguilar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2473-2057","authenticated-orcid":false,"given":"Bhalaji","family":"Nagarajan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9682-5888","authenticated-orcid":false,"given":"Rupali","family":"Khatun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9838-1435","authenticated-orcid":false,"given":"Marc","family":"Bola\u00f1os","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0047-5172","authenticated-orcid":false,"given":"Petia","family":"Radeva","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.jvcir.2019.03.011","volume":"60","author":"E Aguilar","year":"2019","unstructured":"Aguilar, E., Bola\u00f1os, M., Radeva, P.: Regularized uncertainty-based multi-task learning model for food analysis. J. Vis. Commun. Image Represent. 60, 360\u2013370 (2019)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"1_CR2","doi-asserted-by":"publisher","unstructured":"Aguilar, E., Radeva, P.: Class-conditional data augmentation applied to image classification. In: Vento, M., Percannella, G. (eds.) CAIP 2019. LNCS, vol. 11679, pp. 182\u2013192. Springer, Cham (2019a). https:\/\/doi.org\/10.1007\/978-3-030-29891-3_17","DOI":"10.1007\/978-3-030-29891-3_17"},{"key":"1_CR3","doi-asserted-by":"publisher","unstructured":"Aguilar, E., Radeva, P.: Food recognition by integrating local and flat classifiers. In: Morales, A., Fierrez, J., S\u00e1nchez, J.S., Ribeiro, B. (eds.) IbPRIA 2019. LNCS, vol. 11867, pp. 65\u201374. Springer, Cham (2019b). https:\/\/doi.org\/10.1007\/978-3-030-31332-6_6","DOI":"10.1007\/978-3-030-31332-6_6"},{"issue":"12","key":"1_CR4","doi-asserted-by":"publisher","first-page":"3266","DOI":"10.1109\/TMM.2018.2831627","volume":"20","author":"E Aguilar","year":"2018","unstructured":"Aguilar, E., Remeseiro, B., Bola\u00f1os, M., Radeva, P.: Grab, pay, and eat: semantic food detection for smart restaurants. IEEE Trans. Multimed. 20(12), 3266\u20133275 (2018)","journal-title":"IEEE Trans. Multimed."},{"key":"1_CR5","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.neucom.2019.06.043","volume":"361","author":"A Ali-Gombe","year":"2019","unstructured":"Ali-Gombe, A., Elyan, E.: MFC-GAN: class-imbalanced dataset classification using multiple fake class generative adversarial network. Neurocomputing 361, 212\u2013221 (2019)","journal-title":"Neurocomputing"},{"key":"1_CR6","unstructured":"Alliance, I.U.N.: National adult nutrition survey. Public Health (2019)"},{"issue":"4","key":"1_CR7","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1109\/JBHI.2014.2308928","volume":"18","author":"MM Anthimopoulos","year":"2014","unstructured":"Anthimopoulos, M.M., Gianola, L., Scarnato, L., Diem, P., Mougiakakou, S.G.: A food recognition system for diabetic patients based on an optimized bag-of-features model. IEEE J. Biomed. Health Inform. 18(4), 1261\u20131271 (2014)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"2","key":"1_CR8","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1587\/transinf.2018EDL8183","volume":"102","author":"M Anzawa","year":"2019","unstructured":"Anzawa, M., Amano, S., Yamakata, Y., Motonaga, K., Kamei, A., Aizawa, K.: Recognition of multiple food items in a single photo for use in a buffet-style restaurant. IEICE Trans. Inf. Syst. 102(2), 410\u2013414 (2019)","journal-title":"IEICE Trans. Inf. Syst."},{"key":"1_CR9","unstructured":"Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: ICML, pp. 1613\u20131622 (2015)"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Bosch, M., Zhu, F., Khanna, N., Boushey, C.J., Delp, E.J.: Combining global and local features for food identification in dietary assessment. In: 2011 18th IEEE International Conference on Image Processing, pp. 1789\u20131792. IEEE (2011)","DOI":"10.1109\/ICIP.2011.6115809"},{"key":"1_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1007\/978-3-319-10599-4_29","volume-title":"Computer Vision \u2013 ECCV 2014","author":"L Bossard","year":"2014","unstructured":"Bossard, L., Guillaumin, M., Van Gool, L.: Food-101 \u2013 mining discriminative components with random forests. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 446\u2013461. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10599-4_29"},{"key":"1_CR12","unstructured":"Bruno, V., Silva Resende, C.J.: A survey on automated food monitoring and dietary management systems. J. Health Med. Inform. 8(3) (2017)"},{"issue":"4","key":"1_CR13","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R., Yang, J.: PFID: Pittsburgh fast-food image dataset. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 289\u2013292. IEEE (2009)","DOI":"10.1109\/ICIP.2009.5413511"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Ciocca, G., Napoletano, P., Schettini, R.: Food recognition: a new dataset, experiments and results. IEEE J. Biomed. Health Inform. 21(3), 588\u2013598 (2017a)","DOI":"10.1109\/JBHI.2016.2636441"},{"key":"1_CR16","doi-asserted-by":"publisher","unstructured":"Ciocca, G., Napoletano, P., Schettini, R.: Learning CNN-based features for retrieval of food images. In: Battiato, S., Farinella, G.M., Leo, M., Gallo, G. (eds.) ICIAP 2017. LNCS, vol. 10590, pp. 426\u2013434. Springer, Cham (2017b). https:\/\/doi.org\/10.1007\/978-3-319-70742-6_41","DOI":"10.1007\/978-3-319-70742-6_41"},{"key":"1_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1007\/978-3-030-30645-8_55","volume-title":"Image Analysis and Processing \u2013 ICIAP 2019","author":"I Donadello","year":"2019","unstructured":"Donadello, I., Dragoni, M.: Ontology-driven food category classification in images. In: Ricci, E., Rota Bul\u00f2, S., Snoek, C., Lanz, O., Messelodi, S., Sebe, N. (eds.) ICIAP 2019. LNCS, vol. 11752, pp. 607\u2013617. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30645-8_55"},{"key":"1_CR18","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1016\/j.jand.2018.11.010","volume":"119","author":"CF El Khoury","year":"2019","unstructured":"El Khoury, C.F., Karavetian, M., Halfens, R.J., Crutzen, R., Khoja, L., Schols, J.M.: The effects of dietary mobile apps on nutritional outcomes in adults with chronic diseases: a systematic review. J. Acad. Nutr. Diet. 119, 626\u2013651 (2019)","journal-title":"J. Acad. Nutr. Diet."},{"key":"1_CR19","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: ICML, pp. 1050\u20131059 (2016)"},{"key":"1_CR20","unstructured":"Goodfellow, I., Mirza, M., Courville, A., Bengio, Y.: Multi-prediction deep Boltzmann machines. In: Advances in Neural Information Processing Systems, pp. 548\u2013556 (2013)"},{"key":"1_CR21","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Hassannejad, H., Matrella, G., Ciampolini, P., De Munari, I., Mordonini, M., Cagnoni, S.: Food image recognition using very deep convolutional networks. In: Proceedings of the 2nd International Workshop on MADiMa, pp. 41\u201349. ACM (2016)","DOI":"10.1145\/2986035.2986042"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Chen, J., Ngo, C.W.: Deep-based ingredient recognition for cooking recipe retrival. In: ACM Multimedia (2016)","DOI":"10.1145\/2964284.2964315"},{"key":"1_CR25","unstructured":"Joutou, T., Yanai, K.: A food image recognition system with multiple kernel learning. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 285\u2013288. IEEE (2009)"},{"key":"1_CR26","unstructured":"Kaur, P., Sikka, K., Wang, W., Belongie, S., Divakaran, A.: Foodx-251: a dataset for fine-grained food classification. arXiv preprint arXiv:1907.06167 (2019)"},{"key":"1_CR27","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, pp. 5574\u20135584 (2017)"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Khan, S., Hayat, M., Zamir, S.W., Shen, J., Shao, L.: Striking the right balance with uncertainty. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 103\u2013112 (2019)","DOI":"10.1109\/CVPR.2019.00019"},{"key":"1_CR29","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"1_CR30","doi-asserted-by":"crossref","unstructured":"Lee, K.-H., He, X., Zhang, L., Yang, L.: CleanNet: transfer learning for scalable image classifier training with label noise. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5447\u20135456 (2018)","DOI":"10.1109\/CVPR.2018.00571"},{"key":"1_CR31","doi-asserted-by":"publisher","unstructured":"Liu, C., Cao, Yu., Luo, Y., Chen, G., Vokkarane, V., Ma, Y.: DeepFood: deep learning-based food image recognition for computer-aided dietary assessment. In: Chang, C.K., Chiari, L., Cao, Yu., Jin, H., Mokhtari, M., Aloulou, H. (eds.) ICOST 2016. LNCS, vol. 9677, pp. 37\u201348. Springer, Cham (2016a). https:\/\/doi.org\/10.1007\/978-3-319-39601-9_4","DOI":"10.1007\/978-3-319-39601-9_4"},{"key":"1_CR32","unstructured":"Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin softmax loss for convolutional neural networks. In: ICML, vol. 2, p. 7 (2016b)"},{"key":"1_CR33","unstructured":"Louizos, C., Welling, M.: Multiplicative normalizing flows for variational Bayesian neural networks. In: ICML vol. 70, pp. 2218\u20132227. JMLR.org (2017)"},{"key":"1_CR34","unstructured":"Mariani, G., Scheidegger, F., Istrate, R., Bekas, C., Malossi, C.: Bagan: data augmentation with balancing GAN. arXiv preprint arXiv:1803.09655 (2018)"},{"key":"1_CR35","doi-asserted-by":"crossref","unstructured":"Martinel, N., Foresti, G.L., Micheloni, C.: Wide-slice residual networks for food recognition. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 567\u2013576. IEEE (2018)","DOI":"10.1109\/WACV.2018.00068"},{"key":"1_CR36","doi-asserted-by":"crossref","unstructured":"Matsuda, Y., Hoashi, H., Yanai, K.: Recognition of multiple-food images by detecting candidate regions. In: 2012 IEEE International Conference on Multimedia and Expo, pp. 25\u201330. IEEE (2012)","DOI":"10.1109\/ICME.2012.157"},{"key":"1_CR37","doi-asserted-by":"crossref","unstructured":"Meyers, A., et al.: Im2calories: towards an automated mobile vision food diary. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1233\u20131241 (2015)","DOI":"10.1109\/ICCV.2015.146"},{"key":"1_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/978-3-319-73600-6_12","volume-title":"MultiMedia Modeling","author":"Z-Y Ming","year":"2018","unstructured":"Ming, Z.-Y., Chen, J., Cao, Yu., Forde, C., Ngo, C.-W., Chua, T.S.: Food photo recognition for dietary tracking: system and experiment. In: Schoeffmann, K., et al. (eds.) MMM 2018. LNCS, vol. 10705, pp. 129\u2013141. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-73600-6_12"},{"key":"1_CR39","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)"},{"key":"1_CR40","unstructured":"Molchanov, D., Ashukha, A., Vetrov, D.: Variational dropout sparsifies deep neural networks. In: ICML, vol. 70, pp. 2498\u20132507. JMLR.org (2017)"},{"key":"1_CR41","doi-asserted-by":"crossref","unstructured":"Nag, N., Pandey, V., Jain, R.: Health multimedia: lifestyle recommendations based on diverse observations. In: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, pp. 99\u2013106. ACM (2017)","DOI":"10.1145\/3078971.3080545"},{"key":"1_CR42","unstructured":"Nielsen, C., Okoniewski, M.: GAN data augmentation through active learning inspired sample acquisition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 109\u2013112 (2019)"},{"key":"1_CR43","unstructured":"Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: ICML, vol. 70, pp. 2642\u20132651. JMLR.org (2017)"},{"key":"1_CR44","doi-asserted-by":"crossref","unstructured":"Sahoo, D., et al.: FoodAI: food image recognition via deep learning for smart food logging (2019)","DOI":"10.1145\/3292500.3330734"},{"key":"1_CR45","unstructured":"Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. In: Advances in Neural Information Processing Systems, pp. 3179\u20133189 (2018)"},{"key":"1_CR46","doi-asserted-by":"crossref","unstructured":"Shaham, T.R., Dekel, T., Michaeli, T.: SinGAN: learning a generative model from a single natural image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4570\u20134580 (2019)","DOI":"10.1109\/ICCV.2019.00467"},{"key":"1_CR47","doi-asserted-by":"publisher","first-page":"35370","DOI":"10.1109\/ACCESS.2019.2904519","volume":"7","author":"MA Subhi","year":"2019","unstructured":"Subhi, M.A., Ali, S.H., Mohammed, M.A.: Vision-based approaches for automatic food recognition and dietary assessment: a survey. IEEE Access 7, 35370\u201335381 (2019)","journal-title":"IEEE Access"},{"key":"1_CR48","doi-asserted-by":"crossref","unstructured":"Tanno, R., Okamoto, K., Yanai, K.: DeepFoodCam: a DCNN-based real-time mobile food recognition system. In: Proceedings of the 2nd International Workshop on MADiMa, p. 89. ACM (2016)","DOI":"10.1145\/2986035.2986044"},{"key":"1_CR49","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, J., Ngo, C.-W., Chua, T.-S., Zuo, W., Ming, Z.: Mixed dish recognition through multi-label learning. In: Proceedings of the 11th Workshop on Multimedia for Cooking and Eating Activities, CEA 2019, pp. 1\u201380. Association for Computing Machinery, New York (2019)","DOI":"10.1145\/3326458.3326929"},{"key":"1_CR50","doi-asserted-by":"crossref","unstructured":"Wu, H., Merler, M., Uceda-Sosa, R., Smith, J.R.: Learning to make better mistakes: semantics-aware visual food recognition. In: Proceedings of the 24th ACM International Conference on Multimedia, pp. 172\u2013176. ACM (2016)","DOI":"10.1145\/2964284.2967205"},{"key":"1_CR51","doi-asserted-by":"crossref","unstructured":"Yanai, K., Kawano, Y.: Food image recognition using deep convolutional network with pre-training and fine-tuning. In: 2015 IEEE International Conference on Multimedia And Expo Workshops (ICMEW), pp. 1\u20136. IEEE (2015)","DOI":"10.1109\/ICMEW.2015.7169816"},{"key":"1_CR52","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Communications in Computer and Information Science","Biomedical Engineering Systems and Technologies"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-72379-8_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T19:24:47Z","timestamp":1617045887000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-72379-8_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030723781","9783030723798"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72379-8_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIOSTEC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Joint Conference on Biomedical Engineering Systems and Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Valetta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malta","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 February 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 February 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"biostec2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.biostec.org\/?y=2020","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":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"363","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":"29","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":"8% - 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":"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":"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)"}}]}}