给我一篇关于软件工程的外文文献

Sure, here is an article on software engineering that you may find helpful: "The Future of Software Engineering" by Barry Boehm, a well-known software engineer and professor at the University of Southern California. The article discusses the evolution of software engineering and the challenges and opportunities that lie ahead for the field. You can easily find this article by searching for its title or author on a scholarly search engine such as Google Scholar.

相关问题

请给我一篇关于地名消歧的外文文献

以下是一篇关于地名消歧的外文文献：

Title: Resolving Geographic Name Ambiguity in Textual Data: A Review of Approaches and Challenges
Authors: Qingyu Zhang, Xiaojun Chen, and Peter Athanas
Published in: Transactions in GIS, Vol. 22, Issue 4, pp. 878-906, August 2018

Abstract: Geographic names are frequently used in textual data, and their ambiguity is a major challenge for geographic information retrieval and text mining. This paper provides a comprehensive review of approaches for resolving geographic name ambiguity in textual data. We first introduce the challenges of geographic name ambiguity and the characteristics of geographic names in textual data. We then review the major approaches for resolving geographic name ambiguity, including rule-based methods, statistical methods, machine learning methods, and hybrid methods. We also discuss the challenges and limitations of these approaches, such as the lack of training data, the difficulty of handling multi-word names, and the need for domain-specific knowledge. Finally, we identify some promising research directions for resolving geographic name ambiguity, such as using deep learning methods and incorporating spatial context information.

Keywords: geographic name ambiguity, textual data, geographic information retrieval, text mining, rule-based methods, statistical methods, machine learning methods, hybrid methods, deep learning, spatial context.

给我一篇计算机科学相关的外文文献

计算机科学是一个涉及计算机硬件和软件的广泛领域，它涉及到从简单的算法到复杂的网络构架以及多媒体和图形学。本文献中，我们将研究《基于半监督学习的实时图像分类》，这是一篇有关计算机科学的外文文献。文章的作者详细介绍了基于半监督学习的实时图像分类方法，并介绍了它的实现过程。文章展示了通过在现有数据集上训练深度卷积神经网络，可以实现快速、准确的图像分类。此外，作者还使用了数据增强和特征选择方法，以提高模型的预测性能。最后，结果表明，该研究的方法可以实现有效的图像分类。