{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:25:59Z","timestamp":1753885559237},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Popular crowdsourcing techniques mostly focus on evaluating workers' labeling quality before adjusting their weights during label aggregation. Recently, another cohort of models regard crowdsourced annotations as incomplete tensors and recover unfilled labels by tensor completion. However, mixed strategies of the two methodologies have never been comprehensively investigated, leaving them as rather independent approaches. In this work, we propose\u00a0MiSC\u00a0(\u00a0Mixed\u00a0Strategies\u00a0Crowdsourcing), a versatile framework integrating arbitrary conventional crowdsourcing and tensor completion techniques. In particular, we propose a novel iterative Tucker label aggregation algorithm that outperforms state-of-the-art methods in extensive experiments.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/193","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"1394-1400","source":"Crossref","is-referenced-by-count":1,"title":["MiSC: Mixed Strategies Crowdsourcing"],"prefix":"10.24963","author":[{"given":"Ching Yun","family":"Ko","sequence":"first","affiliation":[{"name":"The University of Hong Kong, Hong Kong"}]},{"given":"Rui","family":"Lin","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Hong Kong"}]},{"given":"Shu","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing 210023, China"}]},{"given":"Ngai","family":"Wong","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Hong Kong"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:47:29Z","timestamp":1564285649000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/193"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/193","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}