Framework and literature analysis for crowdsourcing's answer aggregation

This paper presents a classification framework and a systematic analysis of literature on answer aggregation techniques for the most popular and important type of crowdsourcing, i.e., micro-task crowdsourcing. In doing so, we analyzed research articles since 2006 and developed four classification taxonomies. First, we provided a classification framework based on the algorithmic characteristics of answer aggregation techniques. Second, we outlined the statistical and probabilistic foundations used by different types of algorithms and micro-tasks. Third, we provided a matrix catalog of the data characteristics for which an answer aggregation algorithm is designed. Fourth, a matrix catalog of the commonly used evaluation metrics for each type of micro-task was presented. This paper represents the first systematic literature analysis and classification of the answer aggregation techniques for micro-task crowdsourcing

Task assignment in microtask crowdsourcing platforms using learning automata

Conventional microtask crowdsourcing platforms rely on a random task distribution strategy and repeatedly assign tasks to workers. This strategy known as repeated labelling suffers from two shortcomings of high cost and low accuracy as a result of making random distributions. To overcome such shortcomings researchers have introduced task assignment as a substitute strategy. In this strategy, an algorithm selectively chooses suitable tasks for an online worker. Hence, task assignment has gained attentions from researchers to reduce the cost of microtasking whiling increasing its accuracy. However, the existing algorithms on task assignment suffer from four shortcomings as: (i) human intervention, (ii) reliance on a rough estimation of ground truth, (iii) reliance on workers' dynamic capabilities and (iv) lack of ability in dealing with sparsity. To overcome these shortcomings this paper proposes a new task assignment algorithm known as LEarning Automata based Task assignment (LEATask), that works based on the similarities of workers in performance. This algorithm has two stages of exploration and exploitation. In exploration stage, first a number of workers are hired to learn their reliability. Then, LEATask clusters the hired workers using a given clustering algorithm, and for each cluster generates learning automata. Later, the clusters of workers along with their attached learning automata will be used in exploitation stage. Exploitation stage initially assigns a number of tasks to a newly arrived worker to learn the worker's reliability. Then, LEATask identifies the cluster of worker. Based on the cluster that worker resides in and the attached learning automata, the next tasks will be assigned to the new worker. LEATask has been empirically evaluated using several real datasets and compared against the baseline and novel algorithms, in terms of root mean square error. The comparisons indicates LEATask consistently is showing better or comparable performance

Overlapping Community Detection Combining Topological Potential and Trust Value of Nodes

Part 4: Social ComputingInternational audienceAiming at the problems of existing algorithms, such as instability, neglecting interaction between nodes and neglecting attributes of node, an overlapping community discovery algorithm combining topological potential and trust value of nodes was proposed. Firstly, the importance of nodes is calculated according to topological potential and the trust value of the node, and then K core nodes are selected. In final, the final division of communities are finished by using the extended modularity and core nodes. Experimental results on LFR network datasets and three real network datasets, verify the efficiency of the proposed OCDTT algorithm