Pseudocode in a scholarly paper provides a concise way to express the
algorithms implemented therein. Pseudocode can also be thought of as an
intermediary representation that helps bridge the gap between programming
languages and natural languages. Having access to a large collection of
pseudocode can provide various benefits ranging from enhancing algorithmic
understanding, facilitating further algorithmic design, to empowering NLP or
computer vision based models for tasks such as automated code generation and
optical character recognition (OCR). We have created a large pseudocode
collection by extracting nearly 320,000 pseudocode examples from arXiv papers.
This process involved scanning over $2.2$ million scholarly papers, with 1,000
of them being manually inspected and labeled. Our approach encompasses an
extraction mechanism tailored to optimize the coverage and a validation
mechanism based on random sampling to check its accuracy and reliability, given
the inherent heterogeneity of the collection. In addition, we offer insights
into common pseudocode structures, supported by clustering and statistical
analyses. Notably, these analyses indicate an exponential-like growth in the
usage of pseudocodes, highlighting their increasing significance.

通过从 arXiv 论文中提取近 320,000 个伪代码示例，我们创建了一个庞大的伪代码集合。我们的方法包括一个定制化的提取机制和一个基于随机抽样的验证机制，以优化覆盖范围并检查准确性和可靠性。同时，通过聚类和统计分析，我们提供了对常见伪代码结构的洞察，表明伪代码的使用呈现出指数型增长，凸显其不断增加的重要性。

自动提取伪代码的扩展规模

Scaling Automatic Extraction of Pseudocode

Citation prediction of scholarly papers is of great significance in guiding
funding allocations, recruitment decisions, and rewards. However, little is
known about how citation patterns evolve over time. By exploring the inherent
involution property in scholarly paper citation, we introduce the Paper
Potential Index (PPI) model based on four factors: inherent quality of
scholarly paper, scholarly paper impact decaying over time, early citations,
and early citers' impact. In addition, by analyzing factors that drive citation
growth, we propose a multi-feature model for impact prediction. Experimental
results demonstrate that the two models improve the accuracy in predicting
scholarly paper citations. Compared to the multi-feature model, the PPI model
yields superior predictive performance in terms of range-normalized RMSE. The
PPI model better interprets the changes in citation, without the need to adjust
parameters. Compared to the PPI model, the multi-feature model performs better
prediction in terms of Mean Absolute Percentage Error and Accuracy; however,
their predictive performance is more dependent on the parameter adjustment.

研究了学术论文引用格局随时间演变的特点，提出了基于 “学术论文固有质量”、“论文引用的时效性”、“早期引用” 和 “早期引用的影响力” 四个因素的学术论文潜力指数模型 (PPI)，并通过分析影响论文引用增长的因素，提出了多特征的影响力预测模型，实验证明这两种模型的预测准确性得到了改善。其中，相对于多特征模型，PPI 模型的预测性能更好，且不需要调整参数，因此更好的解释了引用的变化。而多特征模型在平均绝对百分误差和准确性方面表现更好，但它们的预测效果更依赖于参数调整。