Procedurally generating cities in Minecraft provides players more diverse
scenarios and could help understand and improve the design of cities in other
digital worlds and the real world. This paper presents a city generator that
was submitted as an entry to the 2023 Edition of Minecraft Settlement
Generation Competition for Minecraft. The generation procedure is composed of
six main steps, namely vegetation clearing, terrain reshaping, building layout
generation, route planning, streetlight placement, and wall construction. Three
algorithms, including a heuristic-based algorithm, an evolving layout
algorithm, and a random one are applied to generate the building layout, thus
determining where to place different redstone style buildings, and tested by
generating cities on random maps in limited time. Experimental results show
that the heuristic-based algorithm is capable of finding an acceptable building
layout faster for flat maps, while the evolving layout algorithm performs
better in evolving layout for rugged maps. A user study is conducted to compare
our generator with outstanding entries of the competition's 2022 edition using
the competition's evaluation criteria and shows that our generator performs
well in the adaptation and functionality criteria

在 Minecraft 中，通过程序生成城市为玩家提供了更多多样的场景，并有助于理解和改进其他数字世界和现实世界中城市的设计。本文介绍了一个参加 Minecraft 定居点生成比赛（2023 年版）的城市生成器。生成过程包括植被清除、地形重塑、建筑布局生成、路径规划、路灯布置和城墙建设等六个主要步骤。使用基于启发式算法、进化布局算法和随机算法等三种算法生成建筑布局，确定不同红石风格建筑的放置位置，并通过在有限时间内在随机地图上生成城市进行测试。实验结果表明，对于平坦地图，基于启发式算法可以更快地找到可接受的建筑布局，而进化布局算法在崎岖地图的进化布局方面表现更好。通过使用比赛的评估标准对我们的生成器与 2022 年比赛中优秀作品进行用户研究比较，结果显示我们的生成器在适应性和功能性标准方面表现良好。

在 Minecraft 中生成 Redstone 风格的城市

Generating Redstone Style Cities in Minecraft

We study the problem of attacking video recognition models in the black-box
setting, where the model information is unknown and the adversary can only make
queries to detect the predicted top-1 class and its probability. Compared with
the black-box attack on images, attacking videos is more challenging as the
computation cost for searching the adversarial perturbations on a video is much
higher due to its high dimensionality. To overcome this challenge, we propose a
heuristic black-box attack model that generates adversarial perturbations only
on the selected frames and regions. More specifically, a heuristic-based
algorithm is proposed to measure the importance of each frame in the video
towards generating the adversarial examples. Based on the frames' importance,
the proposed algorithm heuristically searches a subset of frames where the
generated adversarial example has strong adversarial attack ability while keeps
the perturbations lower than the given bound. Besides, to further boost the
attack efficiency, we propose to generate the perturbations only on the salient
regions of the selected frames. In this way, the generated perturbations are
sparse in both temporal and spatial domains. Experimental results of attacking
two mainstream video recognition methods on the UCF-101 dataset and the HMDB-51
dataset demonstrate that the proposed heuristic black-box adversarial attack
method can significantly reduce the computation cost and lead to more than 28\%
reduction in query numbers for the untargeted attack on both datasets.

提出了一种基于启发式算法的黑盒视频识别攻击模型，生成的对抗扰动仅作用于筛选的视频帧和区域，大大降低了计算成本和减少了查询次数，攻击效果明显。