论文标题
推进图形代码要求的视觉规范
Advancing Visual Specification of Code Requirements for Graphs
论文作者
论文摘要
人文学科的研究人员是正在探索大数据世界的众多人之一。他们已经开始使用Python或R及其相应库等编程语言来操纵大型数据集并发现全新的见解。仍然存在的主要障碍之一是将这些数据的可视化纳入其项目中。可视化库也很难学习如何使用，即使对于那些接受正规培训的人也是如此。然而，这些可视化对于识别主题和将结果传达给其他研究人员，而且是公众至关重要。本文着重于使用机器学习生成数据的有意义的可视化。我们允许用户在视觉上指定其代码要求，以降低人文科学研究人员学习如何编程可视化的障碍。我们使用混合模型，结合神经网络和光学特征识别来生成代码以创建可视化。
Researchers in the humanities are among the many who are now exploring the world of big data. They have begun to use programming languages like Python or R and their corresponding libraries to manipulate large data sets and discover brand new insights. One of the major hurdles that still exists is incorporating visualizations of this data into their projects. Visualization libraries can be difficult to learn how to use, even for those with formal training. Yet these visualizations are crucial for recognizing themes and communicating results to not only other researchers, but also the general public. This paper focuses on producing meaningful visualizations of data using machine learning. We allow the user to visually specify their code requirements in order to lower the barrier for humanities researchers to learn how to program visualizations. We use a hybrid model, combining a neural network and optical character recognition to generate the code to create the visualization.
