The social sciences have produced an impressive body of research on
determinants of fertility outcomes, or whether and when people have children.
However, the strength of these determinants and underlying theories are rarely
evaluated on their predictive ability on new data. This prevents us from
systematically comparing studies, hindering the evaluation and accumulation of
knowledge. In this paper, we present two datasets which can be used to study
the predictability of fertility outcomes in the Netherlands. One dataset is
based on the LISS panel, a longitudinal survey which includes thousands of
variables on a wide range of topics, including individual preferences and
values. The other is based on the Dutch register data which lacks attitudinal
data but includes detailed information about the life courses of millions of
Dutch residents. We provide information about the datasets and the samples, and
describe the fertility outcome of interest. We also introduce the fertility
prediction data challenge PreFer which is based on these datasets and will
start in Spring 2024. We outline the ways in which measuring the predictability
of fertility outcomes using these datasets and combining their strengths in the
data challenge can advance our understanding of fertility behaviour and
computational social science. We further provide details for participants on
how to take part in the data challenge.

本文介绍了两个数据集，用于研究荷兰的生育结果的可预测性，并提出了基于这些数据集的生育预测数据挑战，旨在推动我们对生育行为和计算社会科学的理解。

结合荷兰调查和登记数据的数据挑战，预测生育率（PreFer）

Combining the Strengths of Dutch Survey and Register Data in a Data  Challenge to Predict Fertility (PreFer)

In this article, we present our contribution to the ICPHM 2023 Data Challenge
on Industrial Systems' Health Monitoring using Vibration Analysis. For the task
of classifying sun gear faults in a gearbox, we propose a residual
Convolutional Neural Network that operates on raw three-channel time-domain
vibration signals. In conjunction with data augmentation and regularization
techniques, the proposed model yields very good results in a multi-class
classification scenario with real-world data despite its relatively small size,
i.e., with less than 30,000 trainable parameters. Even when presented with data
obtained from multiple operating conditions, the network is still capable to
accurately predict the condition of the gearbox under inspection.

本文为 ICPHM 2023 数据挑战赛关于利用振动分析进行工业系统健康监测的研究贡献，并提出了一种基于残差卷积神经网络方法的二级行星轮故障分类模型，经过数据增强和正则化技术的运用，该模型在多种不同数据条件下都表现出很好的性能。

ICPHM 2023 数据挑战赛中结合数据增强与正则化技术的 1-D 剩余卷积神经网络

1-D Residual Convolutional Neural Network coupled with Data Augmentation  and Regularization Techniques for the ICPHM 2023 Data Challenge

In this article we present an account of the state-of-the-art in acoustic
scene classification (ASC), the task of classifying environments from the
sounds they produce. Starting from a historical review of previous research in
this area, we define a general framework for ASC and present different imple-
mentations of its components. We then describe a range of different algorithms
submitted for a data challenge that was held to provide a general and fair
benchmark for ASC techniques. The dataset recorded for this purpose is
presented, along with the performance metrics that are used to evaluate the
algorithms and statistical significance tests to compare the submitted methods.
We use a baseline method that employs MFCCS, GMMS and a maximum likelihood
criterion as a benchmark, and only find sufficient evidence to conclude that
three algorithms significantly outperform it. We also evaluate the human
classification accuracy in performing a similar classification task. The best
performing algorithm achieves a mean accuracy that matches the median accuracy
obtained by humans, and common pairs of classes are misclassified by both
computers and humans. However, all acoustic scenes are correctly classified by
at least some individuals, while there are scenes that are misclassified by all
algorithms.

介绍声音场景分类中的一些当前前沿、定义一个标准框架，并提供各种不同算法、提交给数据挑战以及评估人类分类准确性的表现，最终得出三种算法显著优于基准方法，但在某些场景下算法和人类都会出现误差。