2,530 research outputs found
Optimization in Knowledge-Intensive Crowdsourcing
We present SmartCrowd, a framework for optimizing collaborative
knowledge-intensive crowdsourcing. SmartCrowd distinguishes itself by
accounting for human factors in the process of assigning tasks to workers.
Human factors designate workers' expertise in different skills, their expected
minimum wage, and their availability. In SmartCrowd, we formulate task
assignment as an optimization problem, and rely on pre-indexing workers and
maintaining the indexes adaptively, in such a way that the task assignment
process gets optimized both qualitatively, and computation time-wise. We
present rigorous theoretical analyses of the optimization problem and propose
optimal and approximation algorithms. We finally perform extensive performance
and quality experiments using real and synthetic data to demonstrate that
adaptive indexing in SmartCrowd is necessary to achieve efficient high quality
task assignment.Comment: 12 page
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
T-Crowd: Effective Crowdsourcing for Tabular Data
Crowdsourcing employs human workers to solve computer-hard problems, such as
data cleaning, entity resolution, and sentiment analysis. When crowdsourcing
tabular data, e.g., the attribute values of an entity set, a worker's answers
on the different attributes (e.g., the nationality and age of a celebrity star)
are often treated independently. This assumption is not always true and can
lead to suboptimal crowdsourcing performance. In this paper, we present the
T-Crowd system, which takes into consideration the intricate relationships
among tasks, in order to converge faster to their true values. Particularly,
T-Crowd integrates each worker's answers on different attributes to effectively
learn his/her trustworthiness and the true data values. The attribute
relationship information is also used to guide task allocation to workers.
Finally, T-Crowd seamlessly supports categorical and continuous attributes,
which are the two main datatypes found in typical databases. Our extensive
experiments on real and synthetic datasets show that T-Crowd outperforms
state-of-the-art methods in terms of truth inference and reducing the cost of
crowdsourcing
Crowds, not Drones: Modeling Human Factors in Interactive Crowdsourcing
International audienceIn this vision paper, we propose SmartCrowd, an intelligent and adaptive crowdsourcing framework. Contrary to existing crowdsourcing systems, where the process of hiring workers (crowd), learning their skills, and evaluating the accuracy of tasks they perform are fragmented, siloed, and often ad-hoc, SmartCrowd foresees a paradigm shift in that process, considering unpredictability of human nature, namely human factors. SmartCrowd offers opportunities in making crowdsourcing intelligent through iterative interaction with the workers, and adaptively learning and improving the underlying processes. Both existing (majority of which do not require longer engagement from volatile and mostly non-recurrent workers) and next generation crowdsourcing applications (which require longer engagement from the crowd) stand to benefit from SmartCrowd. We outline the opportunities in SmartCrowd, and discuss the challenges and directions, that can potentially revolutionize the existing crowdsourcing landscape
Changing the focus: worker-centric optimization in human-in-the-loop computations
A myriad of emerging applications from simple to complex ones involve human cognizance in the computation loop. Using the wisdom of human workers, researchers have solved a variety of problems, termed as “micro-tasks” such as, captcha recognition, sentiment analysis, image categorization, query processing, as well as “complex tasks” that are often collaborative, such as, classifying craters on planetary surfaces, discovering new galaxies (Galaxyzoo), performing text translation. The current view of “humans-in-the-loop” tends to see humans as machines, robots, or low-level agents used or exploited in the service of broader computation goals. This dissertation is developed to shift the focus back to humans, and study different data analytics problems, by recognizing characteristics of the human workers, and how to incorporate those in a principled fashion inside the computation loop.
The first contribution of this dissertation is to propose an optimization framework and a real world system to personalize worker’s behavior by developing a worker model and using that to better understand and estimate task completion time. The framework judiciously frames questions and solicits worker feedback on those to update the worker model. Next, improving workers skills through peer interaction during collaborative task completion is studied. A suite of optimization problems are identified in that context considering collaborativeness between the members as it plays a major role in peer learning. Finally, “diversified” sequence of work sessions for human workers is designed to improve worker satisfaction and engagement while completing tasks
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