Mobile crowdsensing (MCS) has emerged as a prominent trend across various
domains. However, ensuring the quality of the sensing data submitted by mobile
users (MUs) remains a complex and challenging problem. To address this
challenge, an advanced method is required to detect low-quality sensing data
and identify malicious MUs that may disrupt the normal operations of an MCS
system. Therefore, this article proposes a prediction- and reputation-based
truth discovery (PRBTD) framework, which can separate low-quality data from
high-quality data in sensing tasks. First, we apply a correlation-focused
spatial-temporal transformer network to predict the ground truth of the input
sensing data. Then, we extract the sensing errors of the data as features based
on the prediction results to calculate the implications among the data.
Finally, we design a reputation-based truth discovery (TD) module for
identifying low-quality data with their implications. Given sensing data
submitted by MUs, PRBTD can eliminate the data with heavy noise and identify
malicious MUs with high accuracy. Extensive experimental results demonstrate
that PRBTD outperforms the existing methods in terms of identification accuracy
and data quality enhancement.

移动众感知是各领域中的一个突出趋势，然而确保移动用户提交的传感数据的质量仍然是一个复杂而具有挑战性的问题。为了解决这个问题，本文提出了一种基于预测和声誉的真实性发现框架，可以在感知任务中筛选出低质量数据并识别可能破坏系统正常运行的恶意用户。通过广泛的实验结果表明，该框架在识别准确性和数据质量提升方面优于现有方法。

能否在没有地面真实数据的情况下提高移动众感知数据的质量？

Can We Enhance the Quality of Mobile Crowdsensing Data Without Ground  Truth?

While the availability of large datasets is perceived to be a key requirement
for training deep neural networks, it is possible to train such models with
relatively little data. However, compensating for the absence of large datasets
demands a series of actions to enhance the quality of the existing samples and
to generate new ones. This paper summarizes our winning submission to the
"Data-Centric AI" competition. We discuss some of the challenges that arise
while training with a small dataset, offer a principled approach for systematic
data quality enhancement, and propose a GAN-based solution for synthesizing new
data points. Our evaluations indicate that the dataset generated by the
proposed pipeline offers 5% accuracy improvement while being significantly
smaller than the baseline.

本论文总结了在数据中心人工智能比赛中的获胜结论，提出了针对小数据集训练的质量增强方法和基于生成对抗网络的数据点生成解决方案，并指出该管道生成的数据集在比基准要小的情况下提高了 5% 的准确度。