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Understanding Deception01:14

Understanding Deception

142
Deception is a pervasive aspect of human communication. Empirical studies have shown that most individuals engage in some form of deceit on a daily basis, with approximately 20% of social exchanges involving deceptive elements. Lying follows a developmental trajectory, peaking during adolescence and declining with age, possibly due to the maturation of cognitive control and social accountability.Cognitive and Social Factors in Deception DetectionDespite its prevalence, accurately detecting...
142
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

150
The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
150
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.0K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.8K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.8K
Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
950
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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相关实验视频

Updated: Jan 8, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.2K

基于超图的对比学习,用于增强欺诈检测和发现欺诈行为.

Qinhong Wang1, Yiming Shen2, Husheng Dong1

  • 1School of Computer Engineering, Suzhou Polytechnic University, Suzhou, China.

Frontiers in artificial intelligence
|December 12, 2025
PubMed
概括

本研究介绍了基于超图的对比学习网络 (HCLNet),用于检测复杂的欺诈行为. HCLNet有效地识别了传统方法错过的复杂,高级欺诈模式,提高了数字安全性.

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 网络安全 网络安全

背景情况:

  • 数字平台面临着使用多跳攻击的复杂欺诈.
  • 传统的图形神经网络 (GNN) 由于同类性,标签不平衡和噪音而面临复杂的欺诈模式.
  • 现有的方法无法捕捉欺诈网络中的高阶关系结构.

研究的目的:

  • 开发一个新的框架,基于超图的对比学习网络 (HCLNet),用于检测伪装欺诈.
  • 克服传统的GNN在捕捉复杂,高级欺诈模式方面的局限性.
  • 提高数字生态系统中欺诈检测系统的准确性和稳定性.

主要方法:

  • 多关系超图融合以模拟集团间的欺诈集团.
  • 多头门式超图集成用于多种模式捕获和特征平衡.
  • 阶层式双视图对比学习,具有特征掩盖和拓脱落,用于自我监督的歧视.

主要成果:

  • 与基线方法相比,HCLNet在真实世界数据集上表现出卓越的性能.
  • 在用于欺诈检测的关键评估指标中观察到显著的改善.
  • 该模型有效地揭示了欺诈性和良性实体之间的明显分离模式.
关键词:
相反的学习学习学习.发现欺诈 发现欺诈有门的超图形卷积卷积.超边缘水平的超边缘水平.多关系的融合融合.

相关实验视频

Last Updated: Jan 8, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.2K

结论:

  • HCLNet提供了一种强大的新方法来打击不断发展的伪装欺诈策略.
  • 该框架模拟复杂关系的能力提高了检测能力.
  • 这项研究有助于在数字环境中更强大的欺诈检测.