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Stereotype Content Model02:16

Stereotype Content Model

14.0K
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...
14.0K
Stereotype Threat and Self-fulfilling Prophecies02:09

Stereotype Threat and Self-fulfilling Prophecies

37.5K
When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...
37.5K
Labeling Emotion01:20

Labeling Emotion

113
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
113
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

107
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
107
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

42
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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相关实验视频

Updated: Jun 9, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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用metaheuristic优化机器学习分类器来识别内部威胁的情感分类.

Djordje Mladenovic1, Milos Antonijevic2, Luka Jovanovic2

  • 1ICT College of vocational studies, Belgrade, Belgrade, 11000, Serbia.

Scientific reports
|October 29, 2024
PubMed
概括

这项研究通过使用自然语言处理和机器学习来提高组织安全性,以检测内部威胁,如数据泄露. 该方法侧重于恶意行动的背景,以改善可适应的威胁检测.

关键词:
在 AdaBoost 中使用 AdaBoost.超参数优化超参数优化内部威胁是内部威胁.自然语言处理自然语言处理.在XGBoost中使用.

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相关实验视频

Last Updated: Jun 9, 2025

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科学领域:

  • 网络安全 网络安全
  • 数据科学数据科学数据科学
  • 人工智能的人工智能

背景情况:

  • 内幕威胁带来了重大风险,包括勒索软件,数据泄露和勒索.
  • 传统的安全指标 (位置,访问时间) 往往不足以检测复杂的内部威胁.
  • 新兴的自然语言处理 (NLP) 和机器学习 (ML) 为威胁检测提供了新的途径.

研究的目的:

  • 开发和评估基于NLP和ML的方法来检测内部威胁.
  • 专注于恶意行动的情绪和背景,以便更强大的威胁识别.
  • 为增强检测引入基于频率反向文档频率 (TF-IDF) 的术语方法.

主要方法:

  • 使用电子邮件,HTTP和文件内容数据进行了六次实验.
  • 使用自然语言处理 (NLP) 技术与机器学习 (ML) 分类器 (XGBoost,AdaBoost).
  • 实现了TF-IDF方法和当代优化器,包括修改的红狐优化算法,用于超参数调整.

主要成果:

  • 拟议的方法在模拟场景中显示出值得称赞的结果.
  • 对情绪和背景的关注在识别恶意行为方面被证明是有效的.
  • TF-IDF方法提供了一个强大的,可适应和可维护的检测解决方案.

结论:

  • 在打击内部威胁方面,NLP和ML技术,特别是侧重于背景和情绪,是有效的.
  • TF-IDF方法与高级优化相结合,提高了威胁检测系统的适应性和可维护性.
  • 该研究提供了一个有希望的框架,用于改善组织安全,防止内部风险.