The objective of this study is to characterize inequality in infrastructure
quality across urban areas. While a growing of body of literature has
recognized the importance of characterizing infrastructure inequality in cities
and provided quantified metrics to inform urban development plans, the majority
of the existing approaches focus primarily on measuring the quantity of
infrastructure, assuming that more infrastructure is better. Also, the existing
research focuses primarily on index-based approaches in which the status of
infrastructure provision in urban areas is determined based on assumed
subjective weights. The focus on infrastructure quantity and use of indices
obtained from subjective weights has hindered the ability to properly examine
infrastructure inequality as it pertains to urban inequality and environmental
justice considerations. Recognizing this gap, we propose a machine
learning-based approach in which infrastructure features that shape
environmental hazard exposure are identified and we use the weights obtained by
the model to calculate an infrastructure quality provision for spatial areas of
cities and accordingly, quantify the extent of inequality in infrastructure
quality. The implementation of the model in five metropolitan areas in the U.S.
demonstrates the capability of the proposed approach in characterizing
inequality in infrastructure quality and capturing city-specific differences in
the weights of infrastructure features. The results also show that areas in
which low-income populations reside have lower infrastructure quality
provision, suggesting the lower infrastructure quality provision as a
determinant of urban disparities. Accordingly, the proposed approach can be
effectively used to inform integrated urban design strategies to promote
infrastructure equity and environmental justice based on data-driven and
machine intelligence-based insights.

本研究旨在揭示城市地区基础设施质量的不平等特征，并提出了一种基于机器学习的方法来计算基础设施质量的不平等程度，以此为基础推动数据驱动与机器智能为依据的综合城市设计策略，促进基础设施公平和环境正义。

基于机器学习的城市基础设施质量供给不平等的特征化研究

Beyond Quantities: Machine Learning-based Characterization of Inequality  in Infrastructure Quality Provision in Cities

Changes and advances in information technology have played an important role
in the development of intelligent vehicle systems in recent years. Driver
fatigue and distracted driving are important factors in traffic accidents.
Thus, onboard monitoring of driving behavior has become a crucial component of
advanced driver assistance systems for intelligent vehicles. In this article,
we present techniques for simultaneously detecting fatigue and distracted
driving behaviors using vision-based and machine learning-based approaches. In
driving fatigue detection, we use facial alignment networks to identify facial
feature points in the images, and calculate the distance of the facial feature
points to detect the opening and closing of the eyes and mouth. Furthermore, we
use a convolutional neural network (CNN) based on the MobileNet architecture to
identify various distracted driving behaviors. Experiments are performed on a
PC based setup with a webcam and results are demonstrated using public datasets
as well as custom datasets created for training and testing. Compared to
previous approaches, we build our own datasets and provide better results in
terms of accuracy and computation time.

通过使用基于视觉和基于机器学习的方法，同时检测疲劳和注意力分散的行为，以提高智能车辆系统的驾驶行为监测能力并获得更准确和时间更短的结果。

使用机器学习改进驾驶员疲劳和注意力分散的自动检测

Improving automatic detection of driver fatigue and distraction using  machine learning

The proliferation of fake news has become a significant concern in recent
times due to its potential to spread misinformation and manipulate public
opinion. In this paper, we present a comprehensive study on the detection of
fake news in Brazilian Portuguese, focusing on journalistic-type news. We
propose a machine learning-based approach that leverages natural language
processing techniques, including TF-IDF and Word2Vec, to extract features from
textual data. We evaluate the performance of various classification algorithms,
such as logistic regression, support vector machine, random forest, AdaBoost,
and LightGBM, on a dataset containing both true and fake news articles. The
proposed approach achieves a high level of accuracy and F1-Score, demonstrating
its effectiveness in identifying fake news. Additionally, we develop a
user-friendly web platform, FAKENEWSBR.COM, to facilitate the verification of
news articles' veracity. Our platform provides real-time analysis, allowing
users to assess the likelihood of news articles being fake. Through empirical
analysis and comparative studies, we demonstrate the potential of our approach
to contribute to the fight against the spread of fake news and promote more
informed media consumption.

在这篇论文中，我们提供了一项关于检测巴西葡萄牙语伪新闻的综合研究，专注于新闻报道类型。我们提出了一种基于机器学习的方法，利用自然语言处理技术，包括 TF-IDF 和 Word2Vec，从文本数据中提取特征。我们评估了各种分类算法（如逻辑回归、支持向量机、随机森林、AdaBoost 和 LightGBM）在包含真实和伪新闻文章的数据集上的性能。所提出的方法达到了很高的准确性和 F1-Score，证明了其识别伪新闻的有效性。此外，我们还开发了一个易于使用的名为 FAKENEWSBR.COM 的网络平台，以促进新闻文章真实性的验证。我们的平台提供实时分析，使用户能够评估新闻文章的伪造可能性。通过实证分析和比较研究，我们展示了我们的方法对抗伪新闻传播和促进更加明智的媒体消费的潜力。