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Blockchain-Assisted Unsupervised Learning Method for crowdsourcing Reputation Management

Computer Modeling in Engineering & Sciences 2024年第9期140卷 2297-2314页

作者： Tianyu Wang Kongyang Chen Department of Cyberspace Security Guangzhou UniversityGuangzhou51006China Institute of Artificial Intelligence Guangzhou UniversityGuangzhou51006China Center of Young Scholars Pazhou LabGuangzhou510335China

crowdsourcing holds broad applications in information acquisition and dissemination,yet encounters challenges pertaining to data quality assessment and user reputation *** mechanisms stand as crucial solutions for appraising and updating participant reputation scores,thereby elevating the quality and dependability of crowdsourced ***,these mechanisms face several challenges in traditional crowdsourcing systems:1)platform security lacks robust guarantees and may be susceptible to attacks;2)there exists a potential for large-scale privacy breaches;and 3)incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations,occasionally lacking a dedicated reputation update *** paper introduces a reputation update scheme tailored for crowdsourcing,with a focus on proficiently overseeing participant reputations and alleviating the impact of malicious activities on the sensing ***,the reputation update scheme is determined by an Empirical Cumulative distribution-based Outlier Detection method(ECOD).Our scheme embraces a blockchain-based crowdsourcing framework utilizing a homomorphic encryption method to ensure data transparency and *** of user reputation scores relies on their behavioral history,actively discouraging undesirable ***,we introduce a dynamic weight incentive mechanism that mirrors alterations in participant reputation,enabling the system to allocate incentives based on user behavior and *** scheme undergoes evaluation on 11 datasets,revealing substantial enhancements in data credibility for crowdsourcing systems and a reduction in the influence of malicious *** research not only presents a practical solution for crowdsourcing reputation management but also offers valuable insights for future research and applications,holding promise for fostering more reliable and high-quality data collection in

关键词： crowdsourcing reputation management blockchain

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When crowdsourcing Meets Data Markets:A Fair Data Value Metric for Data Trading

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Journal of Computer Science & Technology 2024年第3期39卷 671-690页

作者：刘洋溯郑臻哲吴帆陈贵海 Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China CCF ACM IEEE

Large-quantity and high-quality data is critical to the success of machine learning in diverse *** with the dilemma of data silos where data is difficult to circulate,emerging data markets attempt to break the dilemma by facilitating data exchange on the ***,on the other hand,is one of the important methods to efficiently collect large amounts of data with high-value in data *** this paper,we investigate the joint problem of efficient data acquisition and fair budget distribution across the crowdsourcing and data *** propose a new metric of data value as the uncertainty reduction of a Bayesian machine learning model by integrating the data into model *** by this data value metric,we design a mechanism called Shapley Value Mechanism with Individual Rationality(SV-IR),in which we design a greedy algorithm with a constant approximation ratio to greedily select the most cost-efficient data brokers,and a fair compensation determination rule based on the Shapley value,respecting the individual rationality *** further propose a fair reward distribution method for the data holders with various effort levels under the charge of a data *** demonstrate the fairness of the compensation determination rule and reward distribution rule by evaluating our mechanisms on two real-world *** evaluation results also show that the selection algorithm in SV-IR could approach the optimal solution,and outperforms state-of-the-art methods.

关键词： data trading crowdsourcing mechanism design Shapley value

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Accountable attribute-based authentication with fine-grained access control and its application to crowdsourcing

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Frontiers of Computer Science 2023年第1期17卷 205-218页

作者： Peng LI Junzuo LAI Yongdong WU College of Information Science and Technology Jinan UniversityGuangzhou 510632China

We introduce a new notion called accountable attribute-based authentication with fine-grained access control (AccABA), which achieves (i) fine-grained access control that prevents ineligible users from authenticating;(ii) anonymity such that no one can recognize the identity of a user;(iii) public accountability, i.e., as long as a user authenticates two different messages, the corresponding authentications will be easily identified and linked, and anyone can reveal the user’s identity without any help from a trusted third party. Then, we formalize the security requirements in terms of unforgeability, anonymity, linkability and traceability, and give a generic construction to fulfill these requirements. Based on AccABA, we further present the first attribute-based, fair, anonymous and publicly traceable crowdsourcing scheme on blockchain, which is designed to filter qualified workers to participate in tasks, and ensures the fairness of the competition between workers, and finally balances the tension between anonymity and accountability.

关键词： fine-grained access control anonymous authentication accountability fairness crowdsourcing blockchain

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Knowledge Learning With crowdsourcing:A Brief Review and Systematic Perspective

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 749-762页

作者： Jing Zhang IEEE School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Big data have the characteristics of enormous volume,high velocity,diversity,value-sparsity,and uncertainty,which lead the knowledge learning from them full of *** the emergence of crowdsourcing,versatile information can be obtained on-demand so that the wisdom of crowds is easily involved to facilitate the knowledge learning *** the past thirteen years,researchers in the AI community made great efforts to remove the obstacles in the field of learning from *** concentrated survey paper comprehensively reviews the technical progress in crowdsourcing learning from a systematic perspective that includes three dimensions of data,models,and learning *** addition to reviewing existing important work,the paper places a particular emphasis on providing some promising blueprints on each dimension as well as discussing the lessons learned from our past research work,which will light up the way for new researchers and encourage them to pursue new contributions.

关键词： crowdsourcing data fusion learning from crowds learning paradigms learning with uncertainty

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Attribute augmentation-based label integration for crowdsourcing

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Frontiers of Computer Science 2023年第5期17卷 41-51页

作者： Yao ZHANG Liangxiao JIANG Chaoqun LI School of Computer Science China University of GeosciencesWuhan 430074China School of Mathematics and Physics China University of GeosciencesWuhan 430074China

crowdsourcing provides an effective and low-cost way to collect labels from crowd *** to the lack of professional knowledge,the quality of crowdsourced labels is relatively low.A common approach to addressing this issue is to collect multiple labels for each instance from different crowd workers and then a label integration method is used to infer its true ***,to our knowledge,almost all existing label integration methods merely make use of the original attribute information and do not pay attention to the quality of the multiple noisy label set of each *** solve these issues,this paper proposes a novel three-stage label integration method called attribute augmentation-based label integration(AALI).In the first stage,we design an attribute augmentation method to enrich the original attribute *** the second stage,we develop a filter to single out reliable instances with high-quality multiple noisy label *** the third stage,we use majority voting to initialize integrated labels of reliable instances and then use cross-validation to build multiple component classifiers on reliable instances to predict all *** results on simulated and real-world crowdsourced datasets demonstrate that AALI outperforms all the other stateof-the-art competitors.

关键词： crowdsourcing label integration attribute aug-mentation instance filtering

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Blockchain-Based Secure crowdsourcing in Wireless IoT

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Journal of Communications and Information Networks 2022年第1期7卷 23-36页

作者： Daquan Feng Long Zhang Shengli Zhang Qihui Wu Xianggen Xia Guangdong Province Engineering Laboratory for Digital Creative Technology Shenzhen UniversityShenzhen 518060China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of ChinaChengdu 611731China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China Department of Electrical and Computer Engineering University of DelawareNewarkDE 19716USA

With the explosive growth of interconnected smart devices and sensors,the Internet has been entering the Internet of things(IoT)era and revolutionizing many aspects of our daily life. Meanwhile, crowdsourcing has been considered as a promising technology to realize collaborative intelligence. Therefore, more and more IoT-based crowdsourcing applications are emerged to take advantages of the widely distributed IoT devices to sense, collect, and analyze data with the aim to solve complex and nontrivial tasks. However,there exist many technical challenges to be addressed in the IoT-based crowdsourcing, such as security, privacy, and incentive provision. In this paper, we propose a blockchain-based architecture as an integrated solution to realize the secure and trustworthy crowdsourcing in wireless IoT. We first overview the challenges in the traditional crowdsourcing system. Then, we briefly introduce the background of the blockchain and smart contract, and propose a blockchain-based crowdsourcing architecture. In particular, we elaborate the utilization of smart contract on the specific phases of crowdsourcing. By deploying the smart contract instance, we confirm the proposed blockchain-based architecture is feasible.

关键词： crowdsourcing blockchain wireless IoT security privacy incentive

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

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Intelligent Automation & Soft Computing 2023年第7期37卷 599-618页

作者： Zhifang Liao Hao Gu Shichao Zhang Ronghui Mo Yan Zhang School of Computer Science and Engineering Central South UniversityChangsha410075China Glasgow Caledonian University GlasgowG40BAUK

Context-Sensitive Task(CST)is a complex task type in crowdsourc-ing,such as handwriting recognition,route plan,and audio *** current result inference algorithms can perform well in simple crowd-sourcing tasks,but cannot obtain high-quality inference results for *** 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 *** 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 ***,we screen the candidate results of context-sensitive tasks based on the partially ordered *** there are parallel candidate sets,the conditional mutual information among subtasks containing context infor-mation in external knowledge(such as Google n-gram corpus,American Contemporary English corpus,etc.)will be *** with the tree augmented naive(TAN)Bayes model,the maximum weighted spanning tree is used to model the dependencies among subtasks in each *** collect two crowdsourcing datasets of handwriting recognition tasks and audio transcription tasks from the real crowdsourcing *** experimental results show that our approach improves the quality of result inference in CSTs and reduces the time cost compared with the latest methods.

关键词： crowdsourcing result inference tree augmented naive Bayes context-sensitive

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crowdsourcing aggregation with deep Bayesian learning

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Science China(Information Sciences) 2021年第3期64卷 46-56页

作者： Shao-Yuan LI Sheng-Jun HUANG Songcan CHEN College of Computer Science and Technology College of Artificial IntelligenceNanjing University of Aeronautics and Astronautics State Key Laboratory for Novel Software Technology Nanjing University

In this study, we consider a crowdsourcing classification problem in which labeling information from crowds is aggregated to infer latent true labels. We propose a fully Bayesian deep generative crowdsourcing model(Bayes DGC), which combines the strength of deep neural networks(DNNs) on automatic representation learning and the interpretable probabilistic structure encoding of probabilistic graphical models. The model comprises a DNN classifier as a prior for the true labels and a probabilistic model for the annotation generation process. The DNN classifier and annotation generation process share the latent true label variables. To address the inference challenge, we developed a natural-gradient stochastic variational inference, which combines variational message passing for conjugate parameters and stochastic gradient descent for DNN and learns the distribution of latent true labels and workers’ confusion matrix via end-to-end training. We illustrated the effectiveness of the proposed model using empirical results on 22 real-world datasets.

关键词： crowdsourcing classification fully Bayesian deep generative models natural gradient stochastic variational inference

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crowdsourcing label quality: a theoretical analysis

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Science China(Information Sciences) 2015年第11期58卷 113-124页

作者： WANG Wei ZHOU Zhi-Hua National Key Laboratory for Novel Software Technology Nanjing University

crowdsourcing has been an effective and efficient paradigm for providing labels for large-scale unlabeled data. In the past few years, many methods have been developed for inferring labels from the crowd,but few theoretical analyses have been presented to support this popular human-machine interaction *** this paper, we theoretically study the quality of labels inferred from crowd workers by majority voting and provide an analysis of label quality that shows that the label error rate decreases exponentially with the number of workers selected for each task. We also study the problem of eliminating low-quality workers from the crowd,and provide a conservative condition for eliminating low-quality workers without eliminating any non-low-quality worker with high probability. We also provide an aggressive condition for eliminating all low-quality workers with high probability.

关键词： unlabeled data crowdsourcing majority voting label quality worker selection

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

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Computers, Materials & Continua 2018年第10期57卷 81-96页

作者： Tiejun Wang Tao Wu Amir Homayoon Ashrafzadeh Jia He Department of Computer Science Chengdu University of Information and TechnologyChengdu610025China. CSIT Department School of ScienceRMIT UniversityMelbourne3058Australia.

crowdsourcing is widely used in various fields to collect goods and services from large *** teaching quality by collecting feedback from experts or students after class is not only delayed but also not *** 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 *** we define crowd grade based on similarity degree to distinguish contribution from different students and minimize the abnormal students’impact on the *** crowd grade would be updated at the end of each feedback so it can guarantee the evaluation ***,a simulated case is shown to illustrate how to apply this framework to assess teaching quality in the ***,we developed a prototype and carried out some experiments on a series of real questionnaires and two sets of modified *** results show that teachers can locate the weak points of teaching and furthermore to identify the abnormal students to improve the teaching ***,our approach provides a strong tolerance for the abnormal student to make the evaluation more accurate.

关键词： Teaching quality evaluation crowdsourcing linguistic 2-tuple group decision making

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