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相关概念视频

Classification of Signals01:30

Classification of Signals

925
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
925
Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.7K
Detection of Black Holes01:10

Detection of Black Holes

2.3K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.3K
Stereotype Content Model02:16

Stereotype Content Model

14.9K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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相关实验视频

Updated: Sep 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

650

多模式仇恨言论检测:一种用于多语言文本和图像的新型深度学习框架.

Furqan Khan Saddozai1, Sahar K Badri2, Daniyal Alghazzawi2

  • 1Gomal Research Institute of Computing, Faculty of Computing, Gomal University, D.I.Khan, KP, Pakistan.

PeerJ. Computer science
|June 26, 2025
PubMed
概括

这项研究引入了一个深度学习框架,用于检测乌尔都语和英语推文中的多式仇恨言论. 该模型有效地使用文本和图像对仇恨言论进行分类,优于其他方法.

关键词:
这就是BiLSTM.深度学习是一种深度学习.有效网B1 有效网B1仇恨言论就是一种仇恨言论.图片 图片 图片 图片 图片多语言的多语言.多式联络是多式联络.乌尔都语 - 英语

相关实验视频

Last Updated: Sep 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

650

科学领域:

  • 自然语言处理自然语言处理.
  • 计算机视觉 计算机视觉
  • 社交媒体分析 社交媒体分析

背景情况:

  • 社交媒体使广泛的意见分享成为可能,但也促进了仇恨言论.
  • 在低资源语言中检测多式仇恨言论存在重大挑战.

研究的目的:

  • 开发和评估一个深度学习框架,用于对乌尔都语-英语推文中的多式仇恨言论进行分类.
  • 引入一个新的数据集,MMHS11K,用于多模式多语言仇恨言论检测.

主要方法:

  • 采用了一个深度学习框架,集成双向长期短期记忆 (BiLSTM) 和EfficientNetB1.
  • 一个早期的融合策略结合了文本和图像特征进行分类.
  • 使用了一组手动注释的数据集,包含11000条多式联络推文 (MMHS11K).

主要成果:

  • 在BiLSTM+EfficientNetB1模型中,乌尔都语推文的F1得分为81.2%,英语推文的F1得分为75.5%.
  • 拟议的多式联运方法的表现优于单式联运和基线多式联运方法.

结论:

  • 开发的框架有效地解决了多语言和多模式仇恨言论检测方面的挑战.
  • 这项研究为未来打击网上仇恨言论的进展提供了坚实的基础.