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  • Open Access


    P&T-Inf: A Result Inference Method for Context-Sensitive Tasks in Crowdsourcing

    Zhifang Liao1, Hao Gu1, Shichao Zhang1, Ronghui Mo1, Yan Zhang2,*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 599-618, 2023, DOI:10.32604/iasc.2023.036794

    Abstract Context-Sensitive Task (CST) is a complex task type in crowdsourcing, such as handwriting recognition, route plan, and audio transcription. The current result inference algorithms can perform well in simple crowdsourcing tasks, but cannot obtain high-quality inference results for CSTs. The conventional method to solve CSTs is to divide a CST into multiple independent simple subtasks for crowdsourcing, but this method ignores the context correlation among subtasks and reduces the quality of result inference. To solve this problem, we propose a result inference algorithm based on the Partially ordered set and Tree augmented naive Bayes Infer (P&T-Inf) for CSTs. Firstly, we… More >

  • Open Access


    Distributed Trusted Computing for Blockchain-Based Crowdsourcing

    Yihuai Liang, Yan Li, Byeong-Seok Shin*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2825-2842, 2021, DOI:10.32604/cmc.2021.016682

    Abstract A centralized trusted execution environment (TEE) has been extensively studied to provide secure and trusted computing. However, a TEE might become a throughput bottleneck if it is used to evaluate data quality when collecting large-scale data in a crowdsourcing system. It may also have security problems compromised by attackers. Here, we propose a scheme, named dTEE, for building a platform for providing distributed trusted computing by leveraging TEEs. The platform is used as an infrastructure of trusted computations for blockchain-based crowdsourcing systems, especially to securely evaluate data quality and manage remuneration: these operations are handled by a TEE group. First,… More >

  • Open Access


    Research on Crowdsourcing Price Game Model in Crowd Sensing

    Weijin Jiang1,2, Xiaoliang Liu1,2,*, Dejia Shi1, Junpeng Chen1,2, Yongxia Sun1,2, Liang Guo3

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1769-1784, 2021, DOI:10.32604/cmc.2021.016609

    Abstract Crowd-Sensing is an innovative data acquisition method that combines the perception of mobile devices with the idea of crowdsourcing. It is a new application mode under the development of the Internet of Things. The perceptual data that mobile users can provide is limited. Multiple crowdsourcing parties will share this limited data, but the cost that the crowdsourcing party can pay is limited, and enough mobile users are needed to complete the perceptual task, making the group wisdom is really played. In this process, there is bound to be a game between the crowds and the mobile users. Most of the… More >

  • Open Access


    A Crowdsourcing Recommendation that Considers the Influence of Workers

    Zhifang Liao1, Xin Xu1, Peng Lan1, Liu Yang1, Yan Zhang2, Xiaoping Fan3,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1379-1396, 2021, DOI:10.32604/cmc.2020.011995

    Abstract In the context of the continuous development of the Internet, crowdsourcing has received continuous attention as a new cooperation model based on the relationship between enterprises, the public and society. Among them, a reasonably designed recommendation algorithm can recommend a batch of suitable workers for crowdsourcing tasks to improve the final task completion quality. Therefore, this paper proposes a crowdsourcing recommendation framework based on workers’ influence (CRBI). This crowdsourcing framework completes the entire process design from task distribution, worker recommendation, and result return through processes such as worker behavior analysis, task characteristics construction, and cost optimization. In this paper, a… More >

  • Open Access


    Strength in numbers: Crowdsourcing the most relevant literature in pediatric cardiology

    Joseph J. Knadler1, Daniel J. Penny1, Tyler H. Harris2, Gary D. Webb3, Antonio G. Cabrera1,4, William B. Kyle1

    Congenital Heart Disease, Vol.13, No.5, pp. 794-798, 2018, DOI:10.1111/chd.12669

    Abstract Objective: The growing body of medical literature in pediatric cardiology has made it increasingly difficult for individual providers to stay abreast of the most current, meaningful articles to help guide practice. Crowdsourcing represents a collaborative process of obtaining information from a large group of individuals, typically from an online or web‐based community, and could serve a potential mechanism to pool indi‐ vidual efforts to combat this issue. This study aimed to utilize crowdsourcing as a novel way to generate a list of the most relevant, current publications in congenital heart disease, utilizing input from an international group of professionals in… More >

  • Open Access


    A Review of Data Cleaning Methods for Web Information System

    Jinlin Wang1, Xing Wang1, *, Yuchen Yang1, Hongli Zhang1, Binxing Fang1

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1053-1075, 2020, DOI:10.32604/cmc.2020.08675

    Abstract Web information system (WIS) is frequently-used and indispensable in daily social life. WIS provides information services in many scenarios, such as electronic commerce, communities, and edutainment. Data cleaning plays an essential role in various WIS scenarios to improve the quality of data service. In this paper, we present a review of the state-of-the-art methods for data cleaning in WIS. According to the characteristics of data cleaning, we extract the critical elements of WIS, such as interactive objects, application scenarios, and core technology, to classify the existing works. Then, after elaborating and analyzing each category, we summarize the descriptions and challenges… More >

  • Open Access


    Crowdsourcing-Based Framework for Teaching Quality Evaluation and Feedback Using Linguistic 2-Tuple

    Tiejun Wang1, Tao Wu1,*, Amir Homayoon Ashrafzadeh2, Jia He1

    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 81-96, 2018, DOI:10.32604/cmc.2018.03259

    Abstract Crowdsourcing is widely used in various fields to collect goods and services from large participants. Evaluating teaching quality by collecting feedback from experts or students after class is not only delayed but also not accurate. In this paper, we present a crowdsourcing-based framework to evaluate teaching quality in the classroom using a weighted average operator to aggregate information from students’ questionnaires described by linguistic 2-tuple terms. Then we define crowd grade based on similarity degree to distinguish contribution from different students and minimize the abnormal students’ impact on the evaluation. The crowd grade would be updated at the end of… More >

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