4,074 research outputs found
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
Exploring Cyberbullying and Other Toxic Behavior in Team Competition Online Games
In this work we explore cyberbullying and other toxic behavior in team
competition online games. Using a dataset of over 10 million player reports on
1.46 million toxic players along with corresponding crowdsourced decisions, we
test several hypotheses drawn from theories explaining toxic behavior. Besides
providing large-scale, empirical based understanding of toxic behavior, our
work can be used as a basis for building systems to detect, prevent, and
counter-act toxic behavior.Comment: CHI'1
Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
Participatory sensing is a powerful paradigm which takes advantage of
smartphones to collect and analyze data beyond the scale of what was previously
possible. Given that participatory sensing systems rely completely on the
users' willingness to submit up-to-date and accurate information, it is
paramount to effectively incentivize users' active and reliable participation.
In this paper, we survey existing literature on incentive mechanisms for
participatory sensing systems. In particular, we present a taxonomy of existing
incentive mechanisms for participatory sensing systems, which are subsequently
discussed in depth by comparing and contrasting different approaches. Finally,
we discuss an agenda of open research challenges in incentivizing users in
participatory sensing.Comment: Updated version, 4/25/201
10 simple rules to create a serious game, illustrated with examples from structural biology
Serious scientific games are games whose purpose is not only fun. In the
field of science, the serious goals include crucial activities for scientists:
outreach, teaching and research. The number of serious games is increasing
rapidly, in particular citizen science games, games that allow people to
produce and/or analyze scientific data. Interestingly, it is possible to build
a set of rules providing a guideline to create or improve serious games. We
present arguments gathered from our own experience ( Phylo , DocMolecules ,
HiRE-RNA contest and Pangu) as well as examples from the growing literature on
scientific serious games
Social Machinery and Intelligence
Social machines are systems formed by technical and human elements interacting in a
structured manner. The use of digital platforms as mediators allows large numbers of human participants to join such mechanisms, creating systems where interconnected digital and human components operate as a single machine capable of highly sophisticated behaviour. Under certain conditions, such systems can be described as autonomous and goal-driven agents. Many examples of modern Artificial Intelligence (AI) can be regarded as instances of this class of mechanisms. We argue that this type of autonomous social machines has provided a new paradigm for the design of intelligent systems marking a new phase in the field of AI. The consequences of this observation range from methodological, philosophical to ethical. On the one side, it emphasises the role of Human-Computer Interaction in the design of intelligent systems, while on the other side it draws attention to both the risks for a human being and those for a society relying on mechanisms that are not necessarily controllable. The difficulty by companies in regulating the spread of misinformation, as well as those by authorities to protect task-workers managed by a software infrastructure, could be just some of the effects of this technological paradigm
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