8,168 research outputs found
Tuning the Diversity of Open-Ended Responses from the Crowd
Crowdsourcing can solve problems that current fully automated systems cannot.
Its effectiveness depends on the reliability, accuracy, and speed of the crowd
workers that drive it. These objectives are frequently at odds with one
another. For instance, how much time should workers be given to discover and
propose new solutions versus deliberate over those currently proposed? How do
we determine if discovering a new answer is appropriate at all? And how do we
manage workers who lack the expertise or attention needed to provide useful
input to a given task? We present a mechanism that uses distinct payoffs for
three possible worker actions---propose,vote, or abstain---to provide workers
with the necessary incentives to guarantee an effective (or even optimal)
balance between searching for new answers, assessing those currently available,
and, when they have insufficient expertise or insight for the task at hand,
abstaining. We provide a novel game theoretic analysis for this mechanism and
test it experimentally on an image---labeling problem and show that it allows a
system to reliably control the balance betweendiscovering new answers and
converging to existing ones
A Stochastic Team Formation Approach for Collaborative Mobile Crowdsourcing
Mobile Crowdsourcing (MCS) is the generalized act of outsourcing sensing
tasks, traditionally performed by employees or contractors, to a large group of
smart-phone users by means of an open call. With the increasing complexity of
the crowdsourcing applications, requesters find it essential to harness the
power of collaboration among the workers by forming teams of skilled workers
satisfying their complex tasks' requirements. This type of MCS is called
Collaborative MCS (CMCS). Previous CMCS approaches have mainly focused only on
the aspect of team skills maximization. Other team formation studies on social
networks (SNs) have only focused on social relationship maximization. In this
paper, we present a hybrid approach where requesters are able to hire a team
that, not only has the required expertise, but also is socially connected and
can accomplish tasks collaboratively. Because team formation in CMCS is proven
to be NP-hard, we develop a stochastic algorithm that exploit workers knowledge
about their SN neighbors and asks a designated leader to recruit a suitable
team. The proposed algorithm is inspired from the optimal stopping strategies
and uses the odds-algorithm to compute its output. Experimental results show
that, compared to the benchmark exponential optimal solution, the proposed
approach reduces computation time and produces reasonable performance results.Comment: This paper is accepted for publication in 2019 31st International
Conference on Microelectronics (ICM
Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media
Social media is often viewed as a sensor into various societal events such as
disease outbreaks, protests, and elections. We describe the use of social media
as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our
approach detects a broad range of cyber-attacks (e.g., distributed denial of
service (DDOS) attacks, data breaches, and account hijacking) in an
unsupervised manner using just a limited fixed set of seed event triggers. A
new query expansion strategy based on convolutional kernels and dependency
parses helps model reporting structure and aids in identifying key event
characteristics. Through a large-scale analysis over Twitter, we demonstrate
that our approach consistently identifies and encodes events, outperforming
existing methods.Comment: 13 single column pages, 5 figures, submitted to KDD 201
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