We study the problem of knowledge tracing (KT) where the goal is to trace the students' knowledge mastery over time so as to make predictions on their future performance. Owing to the good representation capacity of deep neural networks (DNNs), recent advances on KT have increasingly concentrated on exploring DNNs to improve the performance of KT. However, we empirically reveal that the DNNs based KT models may run the risk of overfitting, especially on small datasets, leading to limited generalization. In this paper, by leveraging the current advances in adversarial training (AT), we propose an efficient AT based KT method (ATKT) to enhance KT model's generalization and thus push the limit of KT. Specifically, we first construct adversarial perturbations and add them on the original interaction embeddings as adversarial examples. The original and adversarial examples are further used to jointly train the KT model, forcing it is not only to be robust to the adversarial examples, but also to enhance the generalization over the original ones. To better implement AT, we then present an efficient attentive-LSTM model as KT backbone, where the key is a proposed knowledge hidden state attention module that adaptively aggregates information from previous knowledge hidden states while simultaneously highlighting the importance of current knowledge hidden state to make a more accurate prediction. Extensive experiments on four public benchmark datasets demonstrate that our ATKT achieves new state-of-the-art performance. Code is available at: https://github.com/xiaopengguo/ATKT. CCS CONCEPTS• Applied computing → Computer-assisted instruction; Learning management systems.
Mild cognitive impairment (MCI) is the early stage of Alzheimer’s disease (AD). Automatic diagnosis of MCI by magnetic resonance imaging (MRI) images has been the focus of research in recent years. Furthermore, deep learning models based on 2D view and 3D view have been widely used in the diagnosis of MCI. The deep learning architecture can capture anatomical changes in the brain from MRI scans to extract the underlying features of brain disease. In this paper, we propose a multi-view based multi-model (MVMM) learning framework, which effectively combines the local information of 2D images with the global information of 3D images. First, we select some 2D slices from MRI images and extract the features representing 2D local information. Then, we combine them with the features representing 3D global information learned from 3D images to train the MVMM learning framework. We evaluate our model on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed model can effectively recognize MCI through MRI images (accuracy of 87.50% for MCI/HC and accuracy of 83.18% for MCI/AD).
The effective entity recognition method can quickly and accurately identify the citation entity to facilitate citation comparison, thereby reducing the occurrence of academic fraud and other behaviors. But there is no very effective way to solve this problem till now. In recent years, neural network models for named entity recognition (NER) have shown better performances on general domain datasets. After the multi-feature citation dataset is created, the article proposes contextual multi-feature embedding (CMFE) method for word embedding which use multi-feature to enhance semantic and use CNN to get multi-level feature. Based on CMFE, a multi-feature semantic fusion model (MFSFM) is proposed. It designs the multi-convolution kernel mixed residual CNN module to obtain local attention information and enhance the sensitivity of the entity boundary information. The BiLSTM and LSTM is used for timing learning. The experimental results of Chinese citation datasets and Chinese-English mixed citation datasets show that CMFE can better represent semantics, and MFSFM can perform citation entity recognition well. Finally, the experimental results of CONLL2003 dataset show that it is general on NER.
For keeping in line with the international trend, English major students not only need to exercise their own listening, speaking, reading, writing, translation ability, but also need to have a full understanding of British and American culture to carry out targeted exchanges according to the actual needs. According to big data analysis, in the basic English teaching, the presentation of British and American cultural content can help students understand the basic cultural characteristics of British and American areas to cultivate students’ understanding of British and American culture. Based on the characteristics of basic English teaching, this paper discusses the cultivation methods of students’ British and American cultural cognition.
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