Categorizing source codes accurately and efficiently is a challenging problem
in real-world programming education platform management. In recent years,
model-based approaches utilizing abstract syntax trees (ASTs) have been widely
applied to code classification tasks. We introduce an approach named the Sparse
Attention-based neural network for Code Classification (SACC) in this paper.
The approach involves two main steps: In the first step, source code undergoes
syntax parsing and preprocessing. The generated abstract syntax tree is split
into sequences of subtrees and then encoded using a recursive neural network to
obtain a high-dimensional representation. This step simultaneously considers
both the logical structure and lexical level information contained within the
code. In the second step, the encoded sequences of subtrees are fed into a
Transformer model that incorporates sparse attention mechanisms for the purpose
of classification. This method efficiently reduces the computational cost of
the self-attention mechanisms, thus improving the training speed while
preserving effectiveness. Our work introduces a carefully designed sparse
attention pattern that is specifically designed to meet the unique needs of
code classification tasks. This design helps reduce the influence of redundant
information and enhances the overall performance of the model. Finally, we also
deal with problems in previous related research, which include issues like
incomplete classification labels and a small dataset size. We annotated the
CodeNet dataset with algorithm-related labeling categories, which contains a
significantly large amount of data. Extensive comparative experimental results
demonstrate the effectiveness and efficiency of SACC for the code
classification tasks.

通过使用稀疏注意力机制的神经网络，我们介绍了一种名为 SACC 的方法用于代码分类任务，其中把源代码分为子树序列，并使用递归神经网络编码以及 Transformer 模型进行分类，证明了其在代码分类任务中的有效性和效率。

基于稀疏注意力的代码分类神经网络

Sparse Attention-Based Neural Networks for Code Classification

Multiscale is a hallmark feature of complex nonlinear systems. While the
simulation using the classical numerical methods is restricted by the local
\textit{Taylor} series constraints, the multiscale techniques are often limited
by finding heuristic closures. This study proposes a new method for simulating
multiscale problems using deep neural networks. By leveraging the hierarchical
learning of neural network time steppers, the method adapts time steps to
approximate dynamical system flow maps across timescales. This approach
achieves state-of-the-art performance in less computational time compared to
fixed-step neural network solvers. The proposed method is demonstrated on
several nonlinear dynamical systems, and source codes are provided for
implementation. This method has the potential to benefit multiscale analysis of
complex systems and encourage further investigation in this area.

使用深度神经网络模拟多尺度问题的新方法，通过利用神经网络时间步进器的分层学习，自适应时间步长以近似不同时间尺度上的动力学系统流动图，与固定步长神经网络求解器相比，该方法在较少的计算时间内实现了业界领先的性能。