In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension
Generative Adversarial Network), a novel Procedural Content Generation (PCG)
algorithm that generates token-based video game levels. TOAD-GAN follows the
SinGAN architecture and can be trained using only one example. We demonstrate
its application for Super Mario Bros. levels and are able to generate new
levels of similar style in arbitrary sizes. We achieve state-of-the-art results
in modeling the patterns of the training level and provide a comparison with
different baselines under several metrics. Additionally, we present an
extension of the method that allows the user to control the generation process
of certain token structures to ensure a coherent global level layout. We
provide this tool to the community to spur further research by publishing our
source code.

本文提出了一种新的流程式内容生成算法，即 TOAD-GAN（基于令牌的单次任意维度生成对抗网络），它可以生成基于令牌的视频游戏关卡，并在超级马里奥兄弟游戏中展示了应用，其结果在多个度量标准下达到了最先进的水平，并提供了一种扩展方法以控制生成过程，为了促进进一步的研究，我们公开了源代码。

TOAD-GAN：从单个样本生成连贯的风格级别

TOAD-GAN: Coherent Style Level Generation from a Single Example

Despite a decade of active research, there is a marked lack in clone
detectors that scale to very large repositories of source code, in particular
for detecting near-miss clones where significant editing activities may take
place in the cloned code. We present SourcererCC, a token-based clone detector
that targets three clone types, and exploits an index to achieve scalability to
large inter-project repositories using a standard workstation. SourcererCC uses
an optimized inverted-index to quickly query the potential clones of a given
code block. Filtering heuristics based on token ordering are used to
significantly reduce the size of the index, the number of code-block
comparisons needed to detect the clones, as well as the number of required
token-comparisons needed to judge a potential clone.
We evaluate the scalability, execution time, recall and precision of
SourcererCC, and compare it to four publicly available and state-of-the-art
tools. To measure recall, we use two recent benchmarks, (1) a large benchmark
of real clones, BigCloneBench, and (2) a Mutation/Injection-based framework of
thousands of fine-grained artificial clones. We find SourcererCC has both high
recall and precision, and is able to scale to a large inter-project repository
(250MLOC) using a standard workstation.

本研究介绍了一种基于令牌的克隆检测器 SourcererCC，该克隆检测器可以扩展到非常大的源代码库，利用索引在标准工作站上实现可扩展性。我们评估了 SourcererCC 的可扩展性、执行时间、召回率和精度，并将其与四种公开可用的和最先进的工具进行比较。