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相关概念视频

Structural Classification of Joints01:20

Structural Classification of Joints

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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在使用节点和结构嵌入的社交网络中检测机器人.

Ashkan Dehghan1, Kinga Siuta1, Agata Skorupka1

  • 1Toronto Metropolitan University, Toronto, ON Canada.

Journal of big data
|July 24, 2023
PubMed
概括
此摘要是机器生成的。

在社交媒体上识别机器人账户是一项挑战. 这项研究表明,使用结构嵌入式分析本地社交网络结构对机器人检测比传统方法更有效.

关键词:
检测机器人的机器人节点和结构嵌入的嵌入.社交网络 社交网络

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科学领域:

  • 计算机科学 计算机科学
  • 社交网络分析 社交网络分析
  • 机器学习 机器学习

背景情况:

  • 像Twitter这样的社交网络允许匿名交互,促进模仿真实用户的机器人帐户.
  • 机器人检测通常依赖于来自推特的用户个人资料元数据和自然语言处理 (NLP) 功能.
  • 从底层的社交网络结构中衍生出来的特征在机器人检测中不太被探索.

研究的目的:

  • 调查网络结构特征对机器人检测的有效性.
  • 将经典嵌入技术与结构嵌入算法进行比较,用于识别Twitter机器人.
  • 突出围绕机器人帐户的本地网络结构的预测能力.

主要方法:

  • 探索了两类嵌入算法:经典的 (基于近距离的) 和结构的 (局部邻里结构).
  • 应用这些算法从Twitter的社交网络数据中提取特征.
  • 评估了用于机器人分类的提取特征的预测性能.

主要成果:

  • 来自结构嵌入的特性在机器人检测中显示出更高的预测能力.
  • 与结构嵌入相比,古典嵌入技术的有效性较低.
  • 围绕机器人账户的本地社交网络结构是识别的宝贵来源.

结论:

  • 结构性嵌入提供了一种更有效的方法,用于利用社交网络数据进行机器人检测.
  • 了解本地网络模式对于提高自动化机器人识别系统的准确性至关重要.
  • 对基于网络的功能进行进一步的研究可以提高机器人检测模型的稳定性.