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 …

The labels used to train machine learning (ML) models are of paramount importance.
Typically for ML classification tasks, datasets contain hard labels, yet learning using soft …

The popularity of the Android platform in smartphones and other Internet-of-Things devices
has resulted in the explosive of malware attacks against it. Malware presents a serious …

Crowdsourcing services provide a fast, efficient, and cost-effective way to obtain large
labeled data for supervised learning. Unfortunately, the quality of crowdsourced labels …

Background Accurate classification of sites of interest on prostate-specific membrane
antigen (PSMA) positron emission tomography (PET) images is an important diagnostic …

The rawly collected training data often comes with separate noisy labels collected from
multiple imperfect annotators (eg, via crowdsourcing). A typical way of using these separate …

Crowdsourcing provides an effective and low-cost way to collect labels from crowd workers.
Due to the lack of professional knowledge, the quality of crowdsourced labels is relatively …

In crowdsourcing scenarios, we can hire crowd workers to label crowdsourced tasks and
then use label integration algorithms to infer the integrated label for each instance in the …

Electroencephalogram (EEG) classification has attracted great attention in recent years, and
many models have been presented for this task. Nevertheless, EEG data vary from subject to …

In crowdsourcing scenarios, we can obtain each instance's multiple noisy label set from
different workers and then use a ground truth inference algorithm to infer its integrated label …