Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two fundamentally different kinds of statistical relational models, both of which can scale to massive datasets. The first is based on latent feature models such as tensor factorization and multiway neural networks. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. To this end, we also discuss Google's Knowledge Vault project as an example of such combination.

本文讨论了一种识别蜂窝块片段轮廓的算法。显示了OpenCV库的现成功能的不适用性。考虑了两个提出的算法。直接扫描算法在二值化图像中找到极端的白色像素，它充分适用于产品的凸形形状，但在凹形区域和产品的空腔中找不到轮廓。为了解决这个问题，提出了一种使用滑动矩阵的扫描算法，其在任何形状的产品上正常工作。

Deepfakes在良好的信仰应用中越来越受欢迎，例如在娱乐和恶意预期的操作，例如在图像和视频伪造中。主要是由后者的动机，最近已经提出了大量的浅频道探测器以识别此类内容。虽然这种探测器的性能仍然需要进一步的改进，但它们通常以简单的话进行评估，如果不是琐碎的情景。特别地，诸如转码，去噪，调整和增强的良性处理操作的影响是不充分研究。本文提出了一种更严格和系统的框架，以评估DeepFake探测器在更现实情况中的性能。它定量测量每个良性处理方法如何以及在艺术最先进的深蓝检测方法的情况下衡量如何和何种程度。通过在流行的DeepFake探测器中说明它，我们的基准测试提出了一种框架来评估探测器的稳健性，并提供有价值的洞察设计更高效的DeeFake探测器。