Domain-specific data is the crux of the successful transfer of machine learning systems from
benchmarks to real life. In simple problems such as image classification, crowdsourcing has …

IoT devices provide users with great convenience in smart homes. However, the
interdependent behaviors across devices may yield unexpected interactions. To analyze the …

Data preparation--the process of discovering, integrating, transforming, cleaning, and
annotating data--is one of the oldest, hardest, yet inevitable data management problems …

In this paper, we present a comprehensive study that evaluates six state-of-the-art sentiment
analysis tools on five public datasets, based on the quality of predictive results in the …

Data annotation has been a key boost for the artificial intelligence. However, difficult tasks
such as fine-grained classification need lots of labeled data to train a feasible model. On the …

Massive open online courses (MOOCs) require coursewares with different types of course
resources recommended to learners based on their learning situations to meet personalized …

Crowdworkers silently enable much of today's AI-based products, with several online
platforms offering a myriad of data labelling and content moderation tasks through …

Many Natural Language Processing (NLP) systems use annotated corpora for training and
evaluation. However, labeled data is often costly to obtain and scaling annotation projects is …

Crowdsourcing platforms use various truth discovery algorithms to aggregate annotations
from multiple labelers. In an online setting, however, the main challenge is to decide …

In recent years, many crowdsourcing platforms have emerged, using the resources of
recruited workers to perform diverse outsourcing tasks, where the video analytics attracts …