One indicator of well-being can be the person's subjective time perception.
In our project ChronoPilot, we aim to develop a device that modulates human
subjective time perception. In this study, we present a method to automatically
assess the subjective time perception of air traffic controllers, a group often
faced with demanding conditions, using their physiological data and eleven
state-of-the-art machine learning classifiers. The physiological data consist
of photoplethysmogram, electrodermal activity, and temperature data. We find
that the support vector classifier works best with an accuracy of 79 % and
electrodermal activity provides the most descriptive biomarker. These findings
are an important step towards closing the feedback loop of our
ChronoPilot-device to automatically modulate the user's subjective time
perception. This technological advancement may promise improvements in task
management, stress reduction, and overall productivity in high-stakes
professions.

通过生理数据和机器学习分类器，我们开发了一种能够调节人的主观时间感知的设备，研究发现电皮肤活动是最具描述性的生物标志物，这项技术的进步可能在高风险职业的任务管理、压力减轻和整体生产力方面带来改进。

航空管制员主观时间感知的自动分类：多模态生理数据

Automatic Classification of Subjective Time Perception Using Multi-modal  Physiological Data of Air Traffic Controllers

Support Vector Classifier (SVC) is a well-known Machine Learning (ML) model
for linear classification problems. It can be used in conjunction with a reject
option strategy to reject instances that are hard to correctly classify and
delegate them to a specialist. This further increases the confidence of the
model. Given this, obtaining an explanation of the cause of rejection is
important to not blindly trust the obtained results. While most of the related
work has developed means to give such explanations for machine learning models,
to the best of our knowledge none have done so for when reject option is
present. We propose a logic-based approach with formal guarantees on the
correctness and minimality of explanations for linear SVCs with reject option.
We evaluate our approach by comparing it to Anchors, which is a heuristic
algorithm for generating explanations. Obtained results show that our proposed
method gives shorter explanations with reduced time cost.

通过采用逻辑推理的方法，我们提出了一种在具有拒绝选项的线性支持向量分类器中，能够得到具有正确性和最小化特性的解释的形式化保证的途径，并将其与 Anchors 算法进行了比较，结果表明我们的方法能够以较短的时间得到较短的解释。

基于逻辑的线性支持向量分类器及拒绝选项的解释

Logic-based Explanations for Linear Support Vector Classifiers with  Reject Option

This paper presents a high-performance general-purpose no-reference (NR)
image quality assessment (IQA) method based on image entropy. The image
features are extracted from two domains. In the spatial domain, the mutual
information between the color channels and the two-dimensional entropy are
calculated. In the frequency domain, the two-dimensional entropy and the mutual
information of the filtered sub-band images are computed as the feature set of
the input color image. Then, with all the extracted features, the support
vector classifier (SVC) for distortion classification and support vector
regression (SVR) are utilized for the quality prediction, to obtain the final
quality assessment score. The proposed method, which we call entropy-based
no-reference image quality assessment (ENIQA), can assess the quality of
different categories of distorted images, and has a low complexity. The
proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed
a superior performance. The experimental results confirmed that the proposed
ENIQA method has a high consistency of objective and subjective assessment on
color images, which indicates the good overall performance and generalization
ability of ENIQA. The source code is available on github
this https URL

本文提出了一种名为基于熵的无参考图像质量评估（ENIQA）的方法，该方法结合空间域和频率域的熵和互信息等特征，并使用支持向量分类器和回归进行质量预测和失真分类，能够评估各种失真图像的质量，且具有低复杂度和优秀的性能。该方法在 LIVE 和 TID2013 数据库上的实验结果表明其具有高度的客观和主观评估一致性，且有很好的泛化能力。