Secure Distributed Human Computation

Crowdsourcing cognitive reappraisal: How distributed human computation can provide expedient and anonymous emotional support

PsycEXTRA Dataset ◽

10.1037/e537902012-314 ◽

2011 ◽

Author(s):

R. R. Morris

Keyword(s):

Emotional Support ◽

Cognitive Reappraisal ◽

Human Computation

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Report on the Sixth AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2018)

AI Magazine ◽

10.1609/aimag.v39i4.2839 ◽

2018 ◽

Vol 39 (4) ◽

pp. 19-22 ◽

Cited By ~ 1

Author(s):

Yiling Chen ◽

Gabriella Kazai

Keyword(s):

Human Computation ◽

University Of Zurich ◽

The University

The Association for the Advancement of Artificial Intelligence’s Sixth AAAI Conference on Human Computation and Crowdsourcing was held on the campus of the University of Zurich in Zurich, Switzerland from 5–8 July 2018. This report, based on the preface to the HCOMP-18 proceedings and program, summarizes the event.

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The Dot-Guessing Game: A ‘Fruit Fly’ for Human Computation Research

SSRN Electronic Journal ◽

10.2139/ssrn.1600372 ◽

2010 ◽

Cited By ~ 4

Author(s):

John J. Horton

Keyword(s):

Fruit Fly ◽

Human Computation

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A Human/Computer Learning Network to Improve Biodiversity Conservation and Research

AI Magazine ◽

10.1609/aimag.v34i1.2431 ◽

2012 ◽

Vol 34 (1) ◽

pp. 10 ◽

Cited By ~ 20

Author(s):

Steve Kelling ◽

Jeff Gerbracht ◽

Daniel Fink ◽

Carl Lagoze ◽

Weng-Keen Wong ◽

...

Keyword(s):

Artificial Intelligence ◽

Feedback Loop ◽

Human Computation ◽

Computational Power ◽

Learning Networks ◽

Learning Network ◽

Computer Learning ◽

The Individual ◽

Citizen Science Project

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. In this paper we explore how Human-Computer Learning Networks can leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.

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