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HCOMP 2016 : Conference on Human Computation and Crowdsourcing | |||||||||||||||
Link: http://www.humancomputation.com/2016 | |||||||||||||||
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Call For Papers | |||||||||||||||
HCOMP strongly believes in inviting, fostering, and promoting broad, interdisciplinary research on crowdsourcing and human computation. Submissions may present principles, studies, and/or applications of systems that rely on programmatic interaction with crowds, or where human perception, knowledge, reasoning, or physical activity and coordination contributes to the operation of computational systems, applications, or services. More generally, we invite submissions from the broad spectrum of related fields and application areas including (but not limited to):
* human-centered research: e.g., human-computer interaction, social computing, design, cognitive and behavioral sciences (psychology and sociology), management science, economics, policy, ethics, etc. * computation-centered research, e.g., databases, systems, information retrieval, optimization, vision, language and speech, machine learning, novel programming languages, etc. To ensure relevance, submissions are encouraged include research questions and contributions of broad interest to crowdsourcing and human computation, as well as discuss relevant open problems and prior work in the field. When evaluation is conducted entirely within a specific domain, authors are encouraged to discuss how findings might generalize to other communities and application areas using crowdsourcing and human computation. All papers must be anonymized (include no information identifying the authors or their institutions) for double-blind peer-review and formatted according to the conference's style guidelines. Accepted papers will be published in the HCOMP conference proceedings and included in the Conference's Digital Archive. HCOMP is a young but quickly growing conference, with a historical acceptance rate of 30% for full papers. Full Papers Full papers of up to 10 pages may be submitted. Full papers must represent original work, not previously published or under simultaenous peer-review for any other peer-reviewed, archival conference or journal. |
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