The issue of missing data poses a great challenge on boosting performance and
application of deep learning models in the {\em Knowledge Tracing} (KT)
problem. However, there has been the lack of understanding on the issue in the
literature. %are not sufficient studies tackling this problem. In this work, to
address this challenge, we adopt a subject-based training method to split and
impute data by student IDs instead of row number splitting which we call
non-subject based training. The benefit of subject-based training can retain
the complete sequence for each student and hence achieve efficient training.
Further, we leverage two existing deep generative frameworks, namely
variational Autoencoders (VAE) and Longitudinal Variational Autoencoders (LVAE)
frameworks and build LSTM kernels into them to form LSTM-VAE and LSTM LVAE
(noted as VAE and LVAE for simplicity) models to generate quality data. In
LVAE, a Gaussian Process (GP) model is trained to disentangle the correlation
between the subject (i.e., student) descriptor information (e.g., age, gender)
and the latent space. The paper finally compare the model performance between
training the original data and training the data imputed with generated data
from non-subject based model VAE-NS and subject-based training models (i.e.,
VAE and LVAE). We demonstrate that the generated data from LSTM-VAE and
LSTM-LVAE can boost the original model performance by about 50%. Moreover, the
original model just needs 10% more student data to surpass the original
performance if the prediction model is small and 50\% more data if the
prediction model is large with our proposed frameworks.

采用基于主题的训练方法和深度生成模型建立 LSTM-VAE 和 LSTM LVAE 模型来生成缺失数据，并将使用生成数据训练的模型与原始数据训练的模型进行比较，结果表明使用 LSTM-VAE 和 LSTM-LVAE 生成的数据能够提高原始模型的性能约 50%。

使用 LSTM 变分自编码器框架通过基于主题的培训填补知识追踪数据

Imputing Knowledge Tracing Data with Subject-Based Training via LSTM Variational Autoencoders Frameworks

The detection of anomalous executions is valuable for reducing potential
hazards in assistive manipulation. Multimodal sensory signals can be helpful
for detecting a wide range of anomalies. However, the fusion of
high-dimensional and heterogeneous modalities is a challenging problem. We
introduce a long short-term memory based variational autoencoder (LSTM-VAE)
that fuses signals and reconstructs their expected distribution. We also
introduce an LSTM-VAE-based detector using a reconstruction-based anomaly score
and a state-based threshold. For evaluations with 1,555 robot-assisted feeding
executions including 12 representative types of anomalies, our detector had a
higher area under the receiver operating characteristic curve (AUC) of 0.8710
than 5 other baseline detectors from the literature. We also show the
multimodal fusion through the LSTM-VAE is effective by comparing our detector
with 17 raw sensory signals versus 4 hand-engineered features.

本研究使用多模态传感信号来检测辅助操纵中的异常执行，提出了一种基于 LSTM-VAE 的异常检测器，评估结果显示该检测器在执行过程中比其他 5 种基准检测器表现更佳。