Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Cause and Effect01:53

Cause and Effect

10.9K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
10.9K
Factorial Design02:01

Factorial Design

13.0K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.0K
Confirmation Biases01:31

Confirmation Biases

5.5K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
5.5K
Factors Affecting Perception01:25

Factors Affecting Perception

1.5K
Perception is influenced by perceptual set, context, motivation, and emotion. Perceptual set, or perceptual expectancy, refers to the tendency to perceive things in a particular way, influenced by previous experiences and expectations. This phenomenon affects the interpretation of stimuli, creating a set of mental tendencies and assumptions that impact sensory perceptions of sound, taste, touch, and sight.
An illustrative example of a perceptual set is the scenario where an airline pilot told...
1.5K
Nonconscious Mimicry01:13

Nonconscious Mimicry

4.5K
Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
4.5K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.0K
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...
6.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Human-Like Multimodal Fake News Detection via Reflective Summarization and Large-Small Model Collaboration.

IEEE transactions on neural networks and learning systems·2026
Same author

Mapping PFAS Exceedance Risk in China's Surface Water: A Machine Learning Approach Informed by Source Distribution.

Environmental science & technology·2026
Same author

Single-Cell RNA Sequencing of Lung Tissue in a Rat Model of Acute Respiratory Distress Syndrome.

Journal of visualized experiments : JoVE·2026
Same author

Hybrid graph attention learning with pseudo-label guided adaptive evolution.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Attribute-Topology Cross-Frequency Aligned Graph Neural Networks for Homophilic and Heterophilic Graphs in Node Classification.

IEEE transactions on neural networks and learning systems·2026
Same author

Quadruplet Augmentation With Attribute and Structure Invariance for Online Continual Learning.

IEEE transactions on pattern analysis and machine intelligence·2026

相关实验视频

Updated: Jun 12, 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

2.6K

模式感知基于学习的决定性因素发现,用于多模式假新闻检测.

Boyue Wang, Guangchao Wu, Xiaoyan Li

    IEEE transactions on neural networks and learning systems
    |September 20, 2024
    PubMed
    概括
    此摘要是机器生成的。

    新的MoPeD模型通过分析文本和图像特征来有效检测假新闻,解决当前方法的局限性. 它通过关注模式异质性和发现关键决定性因素来增强虚假新闻的检测能力.

    更多相关视频

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    7.5K
    The Deese-Roediger-McDermott DRM Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory
    07:26

    The Deese-Roediger-McDermott DRM Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory

    Published on: January 31, 2017

    37.9K

    相关实验视频

    Last Updated: Jun 12, 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

    2.6K
    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    7.5K
    The Deese-Roediger-McDermott DRM Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory
    07:26

    The Deese-Roediger-McDermott DRM Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory

    Published on: January 31, 2017

    37.9K

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 信息科学 信息科学 信息科学

    背景情况:

    • 假新闻的传播对公众安全和社会论构成重大风险.
    • 现有的多模式假新闻检测方法往往忽视模式异质性,限制其识别关键决定性信息的能力.
    • 需要先进的模型,能够有效地处理虚假新闻文章中的各种信息.

    研究的目的:

    • 提出一种新的模型,一种基于感知学习的定性因素发现 (MoPeD) 的模式,用于增强假新闻检测.
    • 通过关注模式异质性和提取决定性信息来解决现有方法的局限性.
    • 提高虚假新闻检测系统的准确性和稳定性.

    主要方法:

    • MoPeD模型集成了一个双编码模块,结合了对比性语言图像预训 (CLIP) 编码器和模态特定编码器.
    • 采用多层跨模式融合模块来处理模式异质性,并理解文本和图像之间的隐含含义.
    • 一个模式感知学习模块动态强调基于跨模式内容异质性得分的决定性特征.

    主要成果:

    • 在三个公共数据集上的实验评估表明,MoPeD模型的优越性超过了最先进的假新闻检测方法.
    • 该模型有效地从单模和多模特征中提取决定因素.
    • 通过考虑模式特定和跨模式信息,MoPeD在识别假新闻方面表现得更好.

    结论:

    • 拟议的MoPeD模型通过有效解决模式异质性,在假新闻检测方面取得了重大进展.
    • 模式感知学习方法允许对决定性特征进行适应性强调,从而实现更准确的检测.
    • MoPeD提供了一个强大的框架,用于发现假新闻中的决定性因素,优于现有的方法.