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Register a new userUne mesure dexpertise pour le crowdsourcing
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Added by
Arnaud Martin
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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Crowdsourcing, a major economic issue, is the fact that the firm outsources internal task to the crowd. It is a form of digital subcontracting for the general public. The evaluation of the participants work quality is a major issue in crowdsourcing. Indeed, contributions must be controlled to ensure the effectiveness and relevance of the campaign. We are particularly interested in small, fast and not automatable tasks. Several methods have been proposed to solve this problem, but they are applicable when the golden truth is not always known. This work has the particularity to propose a method for calculating the degree of expertise in the presence of gold data in crowdsourcing. This method is based on the belief function theory and proposes a structuring of data using graphs. The proposed approach will be assessed and applied to the data.
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Many information sources are considered into data fusion in order to improve the decision in terms of uncertainty and imprecision. For each technique used for data fusion, the asumption on independance is usually made. We propose in this article an approach to take into acount an independance measure befor to make the combination of information in the context of the theory of belief functions.
We present LEGOEval, an open-source toolkit that enables researchers to easily evaluate dialogue systems in a few lines of code using the online crowdsource platform, Amazon Mechanical Turk. Compared to existing toolkits, LEGOEval features a flexible task design by providing a Python API that maps to commonly used React.js interface components. Researchers can personalize their evaluation procedures easily with our built-in pages as if playing with LEGO blocks. Thus, LEGOEval provides a fast, consistent method for reproducing human evaluation results. Besides the flexible task design, LEGOEval also offers an easy API to review collected data.
These last years, there were many studies on the problem of the conflict coming from information combination, especially in evidence theory. We can summarise the solutions for manage the conflict into three different approaches: first, we can try to suppress or reduce the conflict before the combination step, secondly, we can manage the conflict in order to give no influence of the conflict in the combination step, and then take into account the conflict in the decision step, thirdly, we can take into account the conflict in the combination step. The first approach is certainly the better, but not always feasible. It is difficult to say which approach is the best between the second and the third. However, the most important is the produced results in applications. We propose here a new combination rule that distributes the conflict proportionally on the element given this conflict. We compare these different combination rules on real data in Sonar imagery and Radar target classification.
Incentive mechanisms for crowdsourcing have been extensively studied under the framework of all-pay auctions. Along a distinct line, this paper proposes to use Tullock contests as an alternative tool to design incentive mechanisms for crowdsourcing. We are inspired by the conduciveness of Tullock contests to attracting user entry (yet not necessarily a higher revenue) in other domains. In this paper, we explore a new dimension in optimal Tullock contest design, by superseding the contest prize---which is fixed in conventional Tullock contests---with a prize function that is dependent on the (unknown) winners contribution, in order to maximize the crowdsourcers utility. We show that this approach leads to attractive practical advantages: (a) it is well-suited for rapid prototyping in fully distributed web agents and smartphone apps; (b) it overcomes the disincentive to participate caused by players antagonism to an increasing number of rivals. Furthermore, we optimize conventional, fixed-prize Tullock contests to construct the most superior benchmark to compare against our mechanism. Through extensive evaluations, we show that our mechanism significantly outperforms the optimal benchmark, by over three folds on the crowdsourcers utility cum profit and up to nine folds on the players social welfare.
The French collaborative Trio4CLF project aims to understand and control the cryogenic cooling of amplifiers for high power (~1 PetaWatt) and high repetition rate (1-10 Hertz) lasers. In such amplifiers, the fluid evacuates the thermal power absorbed by the solid amplifying plates. A precise knowledge of the heat exchange and thus of the turbulent fluid flow in the amplifier is requested to evaluate its impact on the laser beam quality. As a first step, a Large Eddy Simulation is carried out in air without heating to study the development of the turbulent flow. The CFD code used is TrioCFD, a code developed by the CEA. For validation purpose, the simulation is carried out in the experimental setup configuration: a closed-loop wind tunnel called TRANSAT. Two horizontal plates, separated by 0.05 m, are put in the airflow to represent the amplifier plates. Turbulent boundary layers develop from the plates edges. Numerically, the entrance flow is a homogeneous planar flow with a constant velocity at 10 m/s. The results of this Large Eddy Simulation are presented in this paper as a study of the development of the turbulent boundary layers created by the plates.
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