Recently, end-to-end mispronunciation detection and diagnosis (MD&D) systems
has become a popular alternative to greatly simplify the model-building process
of conventional hybrid DNN-HMM systems by representing complicated modules with
a single deep network architecture. In this paper, in order to utilize the
prior text in the end-to-end structure, we present a novel text-dependent model
which is difference with sed-mdd, the model achieves a fully end-to-end system
by aligning the audio with the phoneme sequences of the prior text inside the
model through the attention mechanism. Moreover, the prior text as input will
be a problem of imbalance between positive and negative samples in the phoneme
sequence. To alleviate this problem, we propose three simple data augmentation
methods, which effectively improve the ability of model to capture
mispronounced phonemes. We conduct experiments on L2-ARCTIC, and our best
performance improved from 49.29% to 56.08% in F-measure metric compared to the
CNN-RNN-CTC model.

本文提出了一种新颖的文本依赖模型来利用前置文本，通过注意力机制将音频与前置文本的音素序列对齐，来实现完全的端到端结构，同时提出了三种简单的数据增强方法来缓解模型捕捉错读音素的能力问题，并在 L2-ARCTIC 数据集上表现出 56.08% 的 F-measure 指标，相较 CNN-RNN-CTC 模型有明显提升。

一种使用简单数据增强技术的全面依赖文本的端到端发音错误检测和诊断方法

A Full Text-Dependent End to End Mispronunciation Detection and  Diagnosis with Easy Data Augmentation Techniques

Accurate and comprehensive measurements of a range of sustainable development
outcomes are fundamental inputs into both research and policy. We synthesize
the growing literature that uses satellite imagery to understand these
outcomes, with a focus on approaches that combine imagery with machine
learning. We quantify the paucity of ground data on key human-related outcomes
and the growing abundance and resolution (spatial, temporal, and spectral) of
satellite imagery. We then review recent machine learning approaches to
model-building in the context of scarce and noisy training data, highlighting
how this noise often leads to incorrect assessment of models' predictive
performance. We quantify recent model performance across multiple sustainable
development domains, discuss research and policy applications, explore
constraints to future progress, and highlight key research directions for the
field.

本文综合研究了使用卫星图像和机器学习了解可持续发展领域期间所面对的挑战，以及如何将二者有机结合，细致测量各类相关数据，从而量化模型表现，并探讨该研究领域的未来发展。

利用卫星图像理解和推广可持续发展

Using satellite imagery to understand and promote sustainable  development

The field of learning analytics needs to adopt a more rigorous approach for
predictive model evaluation that matches the complex practice of
model-building. In this work, we present a procedure to statistically test
hypotheses about model performance which goes beyond the state-of-the-practice
in the community to analyze both algorithms and feature extraction methods from
raw data. We apply this method to a series of algorithms and feature sets
derived from a large sample of Massive Open Online Courses (MOOCs). While a
complete comparison of all potential modeling approaches is beyond the scope of
this paper, we show that this approach reveals a large gap in dropout
prediction performance between forum-, assignment-, and clickstream-based
feature extraction methods, where the latter is significantly better than the
former two, which are in turn indistinguishable from one another. This work has
methodological implications for evaluating predictive or AI-based models of
student success, and practical implications for the design and targeting of
at-risk student models and interventions.

本文提出了一种统计测试假设模型性能的过程，该过程超越了社区中的实践状态，分析了来自大规模在线开放课程（MOOC）的一系列算法和特征集。该方法揭示了论坛、作业和点击流基于特征提取方法之间的严重差距，其中后者明显优于前两者，而前两者相互之间无法区分，并为评估学生成功的预测或基于 AI 的模型以及设计和针对处于风险中的学生模型和干预措施的实践影响提供了方法论和实践上的启示。