众包中基于CIDA和PI-Cosine的双向质量控制策略  

Bidirectional Quality Control Strategies Based on CIDA and PI-cosine in Crowdsourcing

作　　者：刘庆菊 潘庆先 童向荣 于嵩 潘亚楠 LIU Qingju;PAN Qingxian;TONG Xiangrong;YU Song;PAN Yanan(School of Computer and Control Engineering,Yantai University,Yantai,Shandong 264005,China)

机构地区：[1]烟台大学计算机与控制工程学院,山东烟台264005

出　　处：《计算机科学》2023年第10期282-290,共9页Computer Science

基　　金：国家自然科学基金(60903098,61502140,61572418,61472095,62072392);黑龙江自然科学基金(LH2020F023);山东省本科教学改革研究重点项目(Z2022327)。

摘　　要：随着移动智能终端的普及,众包采集大规模感知数据变得越来越容易。众包工人的自私性使得他们想通过最少的努力获得最多的报酬,甚至互相勾结、随意提交众包数据,导致众包任务完成质量不高。文中提出了一种基于陪审团的质量控制策略,该机制解决了数据验证问题。针对降低众包质量的行为,在判断是否存在垃圾邮件员工和共谋组织后,使用社区影响力检测算法(CIDA)来检测出共谋团伙领导者及其所在组织,最后使用改进的相似性检测算法(PI-Cosine)筛查垃圾邮件员工。从这两个方面来提高众包数据质量。实验结果表明,所提方法在accuracy和F1-score衡量指标上相比Cosine相似度检测算法提高了12.3%。With the popularity of mobile smart terminals,crowdsourcing to collect large-scale perceptual data becomes easier and easier.The selfishness of crowdworkers makes them want to get the most pay with the least effort,and even collude with each other and submit crowdsourced data arbitrarily,resulting in poor quality of crowdsourced task completion.This paper proposes a jury-based quality control strategy,a mechanism that solves the data validation problem.To address the behaviors that degrade the quality of crowdsourcing,this paper uses the proposed community influence detection algorithm(CIDA)to detect conspiracy leaders and their organizations after determining the presence of spam employees and conspiracy organizations,and finally uses an improved similarity detection algorithm(PI-Cosine)to screen out for spam employees.These two aspects are used to improve the quality of crowdsourcing data.Experiments show that the proposed method improves the accuracy of 12.3%over Cosine similarity detection algorithm in accuracy and F1-score measures.

关 键 词：众包 质量控制 CIDA算法 PI-Cosine相似性检测 垃圾邮件 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]