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Label augmented and weighted majority voting for crowdsourcing
Crowdsourcing provides an efficient way to obtain multiple noisy labels from different crowd
workers for each unlabeled instance. Label integration methods are designed to infer the …
workers for each unlabeled instance. Label integration methods are designed to infer the …
Competition and Collaboration in Crowdsourcing Communities: What happens when peers evaluate each other?
Crowdsourcing has evolved as an organizational approach to distributed problem solving
and innovation. As contests are embedded in online communities and evaluation rights are …
and innovation. As contests are embedded in online communities and evaluation rights are …
Improving data and model quality in crowdsourcing using co-training-based noise correction
Crowdsourcing makes it much faster and cheaper to obtain labels for a large amount of data
used in supervised learning. In the crowdsourcing scenario, an integrated label is inferred …
used in supervised learning. In the crowdsourcing scenario, an integrated label is inferred …
Adversarial learning from crowds
Learning from Crowds (LFC) seeks to induce a high-quality classifier from training instances,
which are linked to a range of possible noisy annotations from crowdsourcing workers under …
which are linked to a range of possible noisy annotations from crowdsourcing workers under …
Efficient Privacy-Preserving Truth Discovery and Copy Detection in Crowdsourcing
Researchers continue to focus on privacy-preserving truth discovery and achieve certain
results with the increasingly popular trend of privacy protection. However, the common …
results with the increasingly popular trend of privacy protection. However, the common …
Resilient participant selection under vulnerability induced colluding attacks for crowdsourcing
This paper investigates the problem of participant selection considering colluding attacks for
crowdsourcing. Compared with existing work, a practical vulnerability-induced colluding …
crowdsourcing. Compared with existing work, a practical vulnerability-induced colluding …
[PDF][PDF] Black-Box Data Poisoning Attacks on Crowdsourcing.
Understanding the vulnerability of label aggregation against data poisoning attacks is key to
ensuring data quality in crowdsourced label collection. State-of-the-art attack mechanisms …
ensuring data quality in crowdsourced label collection. State-of-the-art attack mechanisms …
[HTML][HTML] An error consistency based approach to answer aggregation in open-ended crowdsourcing
Crowdsourcing plays a vital role in today's AI industry. However, existing crowdsourcing
research mainly focuses on those simple tasks that are often formulated as label …
research mainly focuses on those simple tasks that are often formulated as label …
Three-way decision-based label integration for crowdsourcing
In crowdsourcing learning, label integration is often used to infer instances' integrated labels
from their multiple noisy labels. However, almost all existing label integration algorithms …
from their multiple noisy labels. However, almost all existing label integration algorithms …
Large Scale Anonymous Collusion and its detection in crowdsourcing
Crowdsourcing aims to aggregate collective intelligence from independent work of
individuals using online platforms. However, some participants disregard platform …
individuals using online platforms. However, some participants disregard platform …