{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T19:02:00Z","timestamp":1758394920657,"version":"3.41.2"},"reference-count":56,"publisher":"World Scientific Pub Co Pte Ltd","issue":"13","funder":[{"DOI":"10.13039\/501100005145","name":"Basic Research Program of Jiangsu Province","doi-asserted-by":"publisher","award":["61932013"],"award-info":[{"award-number":["61932013"]}],"id":[{"id":"10.13039\/501100005145","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NUPTSF","award":["NY219149","NY220189"],"award-info":[{"award-number":["NY219149","NY220189"]}]},{"name":"Natural Science Foundation of Jiangsu Province of China","award":["BK20200739"],"award-info":[{"award-number":["BK20200739"]}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["20KJB520003"],"award-info":[{"award-number":["20KJB520003"]}]},{"name":"Henan Key Laboratory of Food Safety Data Intelligence, ZZULI","award":["KF2020YB01"],"award-info":[{"award-number":["KF2020YB01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:p> This paper presents a system, AscentX, for labeled data creation and training with active supervision. The aim of system is that it makes collecting data and training models easier. We integrate semi-supervised learning and active learning as well as crowdsourcing learning paradigms. We design a web-based interface for labeling examples based on Python-Django and incorporate our proposed algorithms into it, including semi-supervised classification algorithms, semi-supervised clustering algorithms and with application to data filtering. Moreover, we provide the interfaces for future development including standard datasets attaching, algorithms and active learning models plunging. The system supports the visualization of labeling the examples and analyzing the results of algorithms, which is user-friendly for human annotator. We experimentally verify that AscentX achieves comparable performance to the state-of-the-art systems, using real-life (crawling web data) and benchmark datasets. <\/jats:p>","DOI":"10.1142\/s0218126621502443","type":"journal-article","created":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T05:16:58Z","timestamp":1617340618000},"source":"Crossref","is-referenced-by-count":1,"title":["AscentX: A System for Crowdsourcing and Annotating with Active Supervision"],"prefix":"10.1142","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7468-1991","authenticated-orcid":false,"given":"Yanchao","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Qixia District, Nanjing, Jiangsu 210023, P. R. China"},{"name":"Henan Key Laboratory of Food Safety Data Intelligence, Zhengzhou University of Light Industry, Gaoxin District, Zhengzhou, Henan 450011, P. R. 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