Probabilistic survival analysis models seek to estimate the distribution of the future occurrence (time) of an event given a set of covariates. In recent years, these models have preferred nonparametric specifications that avoid directly estimating survival distributions via discretization. Specifically, they estimate the probability of an individual event at fixed times or the time of an event at fixed probabilities (quantiles), using supervised learning. Borrowing ideas from the quantile regression literature, we propose a parametric survival analysis method based on the Asymmetric Laplace Distribution (ALD). This distribution allows for closed-form calculation of popular event summaries such as mean, median, mode, variation, and quantiles. The model is optimized by maximum likelihood to learn, at the individual level, the parameters (location, scale, and asymmetry) of the ALD distribution. Extensive results on synthetic and real-world data demonstrate that the proposed method outperforms parametric and nonparametric approaches in terms of accuracy, discrimination and calibration.

There is thereby an urgent need but it is still a significant challenge to solve long Chinese t ext sentiment. BERT-based pre-trained language model (PLM) has been demonstrated to be the state-of-the-art approach for sentiment analysis. However, BERT can only process 510 tokens at a time, limiting the accuracy of sentiment analysis for long texts. Meanwhile, existing long text truncation methods for this BERT deficiency perform still weak in capture core sentiments. Aiming to better solve long text sentiment analysis, we propose a BERT-based fusion model. Firstly, we elaborately devise a new truncation method to gain four types of embeddings and resample the dataset to alter the imbalanced distribution of labels. Secondly, N BERT-based models are leveraged to joint learn the above four embeddings. Thirdly, we adopt a BiGRU network to fuse N BERT-based models and further utilizes attention mechanism to obtain effective core sentiments of the long Chinese texts. In the end, we employ three ensemble algorithms to optimize our model, improving Micro and Macro F1 by 1.68% and 1.53% respectively.

Massive Online Open Courses (MOOC) have popularized educational opportunities for students all over the world, while immensely high dropout is becoming a central challenge nowadays.Most researches predict course dropout labels through analyzing the student engagement data.However, these models have high structural complexity with high time cost and cannot provide in-depth insights into why a student is likely to drop out.We devise a lightweight pipeline to simplify the MOOC dropout problem, grasp the core features to make student behaviours interpretable at the model and instance level, visualize the changing trend of predicted label probability estimation with feature values for longitudinally interpreting the sample student behaviours.Based on qualitative insights and quantitative analysis, our main findings are that shorter videos and instructors speak fast are more engaging.Most students complete MOOC learning with a rapid speed, while a few students who watch the video slowly have a higher completion rate.When the frequency of fast-forwarding increases while the percentage of videos watching decreases, the likelihood to drop this course raises.In the end, our pipeline achieves 69.52% AUC, 0.744 R-squared and 0.553 R-squared with 0.982s inference time on the 20238 sample student data.

The outbreak of the COVID-19 pandemic arises enormous attention to online education then knowledge tracking is an increasingly crucial task with its vigorous development.However, the surge of student historical interactions and the lack of prior knowledge is engendering a sequence of issues, such as the decrease in prediction accuracy while the increase in training time.Simultaneously, most existing approaches fail to provide in-depth insights into why a student is likely to answer the question incorrectly and what affects the knowledge state of the student.To address those issues, we propose a multi-head self-attention model named MAKT for dynamic knowledge tracing, which makes the prediction results interpretable at the model and instance level.The customized multi-head self-attention layer has high training efficiency owing to its parallelization capability and spends about 6 seconds in each epoch on a single GPU.We further visualize the attention weights of MAKT and student knowledge acquisition tracking, finding that not all historical interactions are equally important but the recent interactions profoundly establish the knowledge state of students.In the end, extensive experiments on three datasets demonstrate the robustness and superiorities of MAKT, improving ACC by 1.14 % and AUC by 1.20 % on average.