{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:26:49Z","timestamp":1742912809537,"version":"3.40.3"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031345852"},{"type":"electronic","value":"9783031345869"}],"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\/978-3-031-34586-9_16","type":"book-chapter","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T03:27:04Z","timestamp":1686367624000},"page":"229-246","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Up-Sampling Active Learning: An Activity Recognition Method for Parkinson\u2019s Disease Patients"],"prefix":"10.1007","author":[{"given":"Peng","family":"Yue","sequence":"first","affiliation":[]},{"given":"Xiang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Qi","sequence":"additional","affiliation":[]},{"given":"Po","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,11]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Jankovic, J.: Parkinson\u2019s disease: clinical features and diagnosis. 79(4), 368\u2013376 (2008)","key":"16_CR1","DOI":"10.1136\/jnnp.2007.131045"},{"doi-asserted-by":"crossref","unstructured":"Dorsey, E.R., et al.: Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. 68(5), 384\u2013386 (2007)","key":"16_CR2","DOI":"10.1212\/01.wnl.0000247740.47667.03"},{"doi-asserted-by":"crossref","unstructured":"Nutt, J.G., Gancher, S.T., Woodward, W.R.: Does an inhibitory action of levodopa contribute to motor fluctuations? 38(10), 1553\u20131553 (1988). JN","key":"16_CR3","DOI":"10.1212\/WNL.38.10.1553"},{"doi-asserted-by":"crossref","unstructured":"Merello, M., Lees, A.J.: Beginning-of-dose motor deterioration following the acute administration of levodopa and apomorphine in Parkinson\u2019s disease. 55(11), 1024\u20131026 (1992). JoN, Neurosurgery, Psychiatry","key":"16_CR4","DOI":"10.1136\/jnnp.55.11.1024"},{"doi-asserted-by":"crossref","unstructured":"Maetzler, W., Klucken, J., Horne, M.: A clinical view on the development of technology\u2010based tools in managing Parkinson\u2019s disease. JMD 31(9), 1263\u20131271 (2016)","key":"16_CR5","DOI":"10.1002\/mds.26673"},{"doi-asserted-by":"crossref","unstructured":"Albani, G., et al.: An integrated multi-sensor approach for the remote monitoring of Parkinson\u2019s disease. 19(21), 4764 (2019)","key":"16_CR6","DOI":"10.3390\/s19214764"},{"issue":"18","key":"16_CR7","doi-asserted-by":"publisher","first-page":"23317","DOI":"10.1007\/s11042-018-5640-2","volume":"77","author":"T De Pessemier","year":"2018","unstructured":"De Pessemier, T., Martens, L.: Heart rate monitoring, activity recognition, and recommendation for e-coaching. Multimed. Tools Appl. 77(18), 23317\u201323334 (2018). https:\/\/doi.org\/10.1007\/s11042-018-5640-2","journal-title":"Multimed. Tools Appl."},{"doi-asserted-by":"crossref","unstructured":"Ryder, J., Longstaff, B., Reddy, S., Estrin, D.: Ambulation: a tool for monitoring mobility patterns over time using mobile phones. In: 2009 International Conference on Computational Science and Engineering, 2009, pp. 927\u2013931. IEEE (2009)","key":"16_CR8","DOI":"10.1109\/CSE.2009.312"},{"unstructured":"Emmanouil, G., et al.: MyHealthAvatar: personalised and empovermnet health services through internet of things technologies. In: 2014 4th International Conference on Wireless Mobile Communication and Healthcare Transforming Healthcare through Innovatins in Mobile and Wireless Technologies (MOBIHEALTH), 2014, pp. 331\u2013334. IEEE (2014)","key":"16_CR9"},{"doi-asserted-by":"crossref","unstructured":"Bi, H., Perello-Nieto, M., Santos-Rodriguez, R., Flach, P.: Human activity recognition based on dynamic active learning. IEEE J. Biomed. Health Inform. 25(4), 922\u2013934 (2020). JIJoB, Informatics H","key":"16_CR10","DOI":"10.1109\/JBHI.2020.3013403"},{"doi-asserted-by":"crossref","unstructured":"Qi, J., Yang, P., Waraich, A., Deng, Z., Zhao, Y., Yang, Y.: Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: a systematic review. 87, 138\u2013153 (2018)","key":"16_CR11","DOI":"10.1016\/j.jbi.2018.09.002"},{"doi-asserted-by":"crossref","unstructured":"Zhang, M., Sawchuk, A.A.: USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 1036\u20131043 (2012)","key":"16_CR12","DOI":"10.1145\/2370216.2370438"},{"unstructured":"Twomey, N., et al.: The SPHERE challenge: activity recognition with multimodal sensor data (2016). arXiv preprint arXiv:1603.00797","key":"16_CR13"},{"doi-asserted-by":"crossref","unstructured":"Mart\u00edn, H., Bernardos, A.M., Iglesias, J., Casar, J.: Activity logging using lightweight classification techniques in mobile devices. 17(4), 675\u2013695 (2013)","key":"16_CR14","DOI":"10.1007\/s00779-012-0515-4"},{"doi-asserted-by":"crossref","unstructured":"Kwapisz, J.R., Weiss, G.M., Moore, S.: Activity recognition using cell phone accelerometers. 12(2), 74\u201382 (2011)","key":"16_CR15","DOI":"10.1145\/1964897.1964918"},{"key":"16_CR16","doi-asserted-by":"publisher","first-page":"16217","DOI":"10.1109\/ACCESS.2019.2894184","volume":"7","author":"H Xu","year":"2019","unstructured":"Xu, H., Pan, Y., Li, J., Nie, L., Xu, X.I.: Activity recognition method for home-based elderly care service based on random forest and activity similarity. IEEE Access 7, 16217\u201316225 (2019)","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Cook, D.J., Krishnan, N.C., Rashidi, P.J.: Activity discovery and activity recognition: a new partnership. 43 (3), 820\u2013828 (2013)","key":"16_CR17","DOI":"10.1109\/TSMCB.2012.2216873"},{"doi-asserted-by":"crossref","unstructured":"Khan, A.M., Tufail, A., Khattak, A.M., Laine, T.: Activity recognition on smartphones via sensor-fusion and kda-based svms. 10(5), 503291 (2014)","key":"16_CR18","DOI":"10.1155\/2014\/503291"},{"doi-asserted-by":"crossref","unstructured":"Ouchi, K., Doi, M.: Indoor-outdoor activity recognition by a smartphone. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 600\u2013601 (2012)","key":"16_CR19","DOI":"10.1145\/2370216.2370324"},{"doi-asserted-by":"crossref","unstructured":"Ha, S., Choi, S.: Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 381\u2013388. IEEE (2016)","key":"16_CR20","DOI":"10.1109\/IJCNN.2016.7727224"},{"doi-asserted-by":"crossref","unstructured":"Bianchi, V., Bassoli, M., Lombardo, G., Fornacciari, P., Mordonini, M., De Munari, I.: IoT wearable sensor and deep learning: an integrated approach for personalized human activity recognition in a smart home environment. 6(5), 8553\u20138562 (2019)","key":"16_CR21","DOI":"10.1109\/JIOT.2019.2920283"},{"doi-asserted-by":"crossref","unstructured":"Mutegeki, R., Han, D.S.: A CNN-LSTM approach to human activity recognition. In: 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 362\u2013366. IEEE (2020)","key":"16_CR22","DOI":"10.1109\/ICAIIC48513.2020.9065078"},{"doi-asserted-by":"crossref","unstructured":"Alawneh, L., Mohsen, B., Al-Zinati, M., Shatnawi, A., Al-Ayyoub, M.A.: Comparison of unidirectional and bidirectional LSTM networks for human activity recognition. In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 1\u20136. IEEE (2020)","key":"16_CR23","DOI":"10.1109\/PerComWorkshops48775.2020.9156264"},{"doi-asserted-by":"crossref","unstructured":"Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.J.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(6), 790\u2013808 (2012)","key":"16_CR24","DOI":"10.1109\/TSMCC.2012.2198883"},{"doi-asserted-by":"crossref","unstructured":"Thomaz, E., Essa, I., Abowd, G.D.: A practical approach for recognizing eating moments with wrist-mounted inertial sensing. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 1029\u20131040 (2015)","key":"16_CR25","DOI":"10.1145\/2750858.2807545"},{"doi-asserted-by":"crossref","unstructured":"Merck, C.A., Maher, C., Mirtchouk, M., Zheng, M., Huang, Y., Kleinberg, S.: Multimodality sensing for eating recognition. In: PervasiveHealth, pp. 130\u2013137 (2016)","key":"16_CR26","DOI":"10.4108\/eai.16-5-2016.2263281"},{"key":"16_CR27","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.pmcj.2017.06.018","volume":"41","author":"J Qi","year":"2017","unstructured":"Qi, J., Yang, P., Min, G., Amft, O., Dong, F., Xu, L.: Advanced internet of things for personalised healthcare systems: a survey. Pervasive Mob. Comput. 41, 132\u2013149 (2017)","journal-title":"Pervasive Mob. Comput."},{"doi-asserted-by":"crossref","unstructured":"Peng, X., Yang, Y., Wang, X., Li, J, Qi, J., Yang, P.: Experimental analysis of artificial neural networks performance for accessing physical activity recognition in daily life. In: 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA\/BDCloud\/SocialCom\/SustainCom), pp. 1348\u20131353. IEEE (2020)","key":"16_CR28","DOI":"10.1109\/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00200"},{"doi-asserted-by":"crossref","unstructured":"Vrigkas, M., Nikou, C., Kakadiaris, I.A.: A review of human activity recognition methods. Front. Robot. AI 2, 28 (2015). JFiR, AI","key":"16_CR29","DOI":"10.3389\/frobt.2015.00028"},{"key":"16_CR30","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.pmcj.2012.07.003","volume":"10","author":"NC Krishnan","year":"2014","unstructured":"Krishnan, N.C., Cook, D.J.: Activity recognition on streaming sensor data. Pervasive Mob. Comput. 10, 138\u2013154 (2014)","journal-title":"Pervasive Mob. Comput."},{"doi-asserted-by":"crossref","unstructured":"Zhang, M., Sawchuk, A.A.: Human daily activity recognition with sparse representation using wearable sensors. 17(3), 553\u2013560 (2013). JIJOBInformatics H","key":"16_CR31","DOI":"10.1109\/JBHI.2013.2253613"},{"doi-asserted-by":"crossref","unstructured":"Basilakis, J., Lovell, N.H., Redmond, S.J., Celler, B.G.: Design of a decision-support architecture for management of remotely monitored patients. IEEE Trans. Inf. Technol. Biomed. 14(5), 1216\u20131226 (2010)","key":"16_CR32","DOI":"10.1109\/TITB.2010.2055881"},{"doi-asserted-by":"publisher","unstructured":"Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) Pervasive Computing. Pervasive 2004. LNCS, vol. 3001, pp. 1\u201317. Springer, Berlin, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-24646-6_1","key":"16_CR33","DOI":"10.1007\/978-3-540-24646-6_1"},{"doi-asserted-by":"crossref","unstructured":"Cheng, W.Y., Scotland, A., Lipsmeier, F., Kilchenmann, T., Jin, L., Schjodt-Eriksen, J., et al: Human activity recognition from sensor-based large-scale continuous monitoring of Parkinson\u2019s disease patients. In: 2017 IEEE\/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 249\u2013250 (2017)","key":"16_CR34","DOI":"10.1109\/CHASE.2017.87"},{"key":"16_CR35","doi-asserted-by":"publisher","first-page":"158","DOI":"10.3389\/fneur.2012.00158","volume":"3","author":"MV Albert","year":"2012","unstructured":"Albert, M.V., Toledo, S., Shapiro, M., Kording, K.: Using mobile phones for activity recognition in Parkinson\u2019s patients. Front. Neurol. 3, 158 (2012)","journal-title":"Front. Neurol."},{"issue":"1","key":"16_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.11","volume":"3","author":"BM Bot","year":"2016","unstructured":"Bot, B.M., et al.: The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci. Data 3(1), 1\u20139 (2016)","journal-title":"Sci. Data"},{"issue":"9","key":"16_CR37","doi-asserted-by":"publisher","first-page":"4682","DOI":"10.3390\/app12094682","volume":"12","author":"M Kazemimoghadam","year":"2022","unstructured":"Kazemimoghadam, M.: Fey NP an activity recognition framework for continuous monitoring of non-steady-state locomotion of individuals with Parkinson\u2019s disease. Appl. Sci. 12(9), 4682 (2022)","journal-title":"Appl. Sci."},{"issue":"7","key":"16_CR38","doi-asserted-by":"publisher","first-page":"10113","DOI":"10.1007\/s11042-020-10114-1","volume":"80","author":"S Kaur","year":"2020","unstructured":"Kaur, S., Aggarwal, H., Rani, R.: Diagnosis of Parkinson\u2019s disease using deep CNN with transfer learning and data augmentation. Multimed. Tools Appl. 80(7), 10113\u201310139 (2020). https:\/\/doi.org\/10.1007\/s11042-020-10114-1","journal-title":"Multimed. Tools Appl."},{"doi-asserted-by":"crossref","unstructured":"Balabka, D.: Semi-supervised learning for human activity recognition using adversarial autoencoders. In: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, pp. 685\u2013688 (2019)","key":"16_CR39","DOI":"10.1145\/3341162.3344854"},{"doi-asserted-by":"crossref","unstructured":"Ma, Y., Ghasemzadeh, H.: Labelforest: non-parametric semi-supervised learning for activity recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 01. Pp. 4520\u20134527 (2019)","key":"16_CR40","DOI":"10.1609\/aaai.v33i01.33014520"},{"doi-asserted-by":"crossref","unstructured":"Qin, X., Chen, Y., Wang, J., Yu, C.: Cross-dataset activity recognition via adaptive spatial-temporal transfer learning. 3(4), 1\u201325 (2019)","key":"16_CR41","DOI":"10.1145\/3369818"},{"issue":"4","key":"16_CR42","first-page":"1","volume":"3","author":"J Wang","year":"2019","unstructured":"Wang, J., Zheng, V.W., Chen, Y., Huang, M.: Deep transfer learning for cross-domain activity recognition. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(4), 1\u201325 (2019)","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"doi-asserted-by":"crossref","unstructured":"Khan MAAH, Roy N, Misra A: Scaling human activity recognition via deep learning-based domain adaptation. In: 2018 IEEE international conference on pervasive computing and communications (PerCom),. IEEE, pp 1\u20139 (2018)","key":"16_CR43","DOI":"10.1109\/PERCOM.2018.8444585"},{"doi-asserted-by":"crossref","unstructured":"Stikic, M., Van Laerhoven, K., Schiele, B.: Exploring semi-supervised and active learning for activity recognition. In: 2008 12th IEEE International Symposium on Wearable Computers, pp 81\u201388. IEEE (2008)","key":"16_CR44","DOI":"10.1109\/ISWC.2008.4911590"},{"doi-asserted-by":"crossref","unstructured":"Liu, R., Chen, T., Huang, L.: Research on human activity recognition based on active learning. In: 2010 International Conference on Machine Learning and Cybernetics, pp. 285\u2013290. IEEE (2010)","key":"16_CR45","DOI":"10.1109\/ICMLC.2010.5581050"},{"unstructured":"Diethe, T., Twomey, N., Flach, P.A.: Active transfer learning for activity recognition. In: ESANN (2016)","key":"16_CR46"},{"doi-asserted-by":"crossref","unstructured":"Hoque, E., Stankovic, J.: AALO: activity recognition in smart homes using active learning in the presence of overlapped activities. In: 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, 2012, pp. 139\u2013146. IEEE (2012)","key":"16_CR47","DOI":"10.4108\/icst.pervasivehealth.2012.248600"},{"doi-asserted-by":"crossref","unstructured":"Hossain, H.S., Al Haiz Khan, M.A., Roy, N.: DeActive: scaling activity recognition with active deep learning. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2(2), 1\u201323 (2018)","key":"16_CR48","DOI":"10.1145\/3214269"},{"doi-asserted-by":"crossref","unstructured":"Wang, D., Shang, Y.: A new active labeling method for deep learning. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 112\u2013119. IEEE (2014)","key":"16_CR49","DOI":"10.1109\/IJCNN.2014.6889457"},{"unstructured":"Zhou, S., Chen, Q., Wang, X.: Active deep networks for semi-supervised sentiment classification. In: Coling 2010: Posters, pp. 1515\u20131523 (2010)","key":"16_CR50"},{"unstructured":"Ke, G., et al.: Lightgbm: a highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30, 3146\u20133154 (2017)","key":"16_CR51"},{"doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","key":"16_CR52","DOI":"10.1145\/2939672.2939785"},{"doi-asserted-by":"crossref","unstructured":"Joshi, A.J., Porikli, F., Papanikolopoulos, N.: Multi-class active learning for image classification. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2372\u20132379. IEEE (2009)","key":"16_CR53","DOI":"10.1109\/CVPR.2009.5206627"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Pervasive Computing Technologies for Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34586-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T03:32:15Z","timestamp":1686367935000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34586-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031345852","9783031345869"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34586-9_16","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"11 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pervasive Computing Technologies for Healthcare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thessaloniki","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"12 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ph2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pervasivehealth.eai-conferences.org\/2022\/","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":"Confy Plus","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"120","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":"45","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":"38% - 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":"2.5","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","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)"}}]}}