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相关概念视频

Self-Awareness and Its Effects01:21

Self-Awareness and Its Effects

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Self-awareness is a psychological state in which the individual becomes the focal point of their attention. This inward focus transforms the self into an object of contemplation and assessment, influencing how individuals perceive their actions and their alignment with personal and societal standards.Triggers and Contexts for Self-AwarenessSelf-awareness can be activated by external stimuli that make individuals visually or audibly aware of themselves, such as mirrors, cameras, or recordings.
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Altered states of consciousness represent significant deviations from one's normal mental state. These deviations can range from subtle changes in awareness to profound transformations in perception, thought processes, and sensory experiences. Altered states of consciousness can be triggered by various factors, including drug use, meditation, hypnosis, illness, or even intense fatigue.
The ingestion of substances like stimulants or hallucinogens leads to chemical alterations in the brain...
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The concept of subconscious awareness refers to the processing of information below the level of conscious thought, which significantly influences both behaviors and decisions. It is also known as waking subconscious awareness. This complex level of cognition operates without the direct awareness of the individual, facilitating rapid and simultaneous handling of multiple information streams.
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High-Level and Low-Level Awareness01:19

High-Level and Low-Level Awareness

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Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Investigating the Neural Mechanisms of Aware and Unaware Fear Memory with fMRI
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学习频率感知图表欺诈检测检测

Wei Zhao1, Hao Chen2

  • 1Department of High-tech Business and Entrepreneurship, Faculty of Behavioural, Management and Social Sciences, IEMS, University of Twente, The Netherlands; Faculty of Business Administration, Turiba University, Riga, Latvia.

Neural networks : the official journal of the International Neural Network Society
|January 23, 2026
PubMed
概括

本研究引入了频率感知图形神经网络 (F-GNN),通过分析频率域来改善图形欺诈的检测. F-GNN有效地处理不平衡的数据和混合连接,优于现有方法.

关键词:
频率脱的频率脱是什么图形欺诈检测和检测欺诈行为图表神经网络的神经网络邻居聚合 邻居聚合

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科学领域:

  • 图形神经网络的神经网络
  • 机器学习 机器学习
  • 数据挖掘 数据挖掘

背景情况:

  • 图形欺诈检测 (GFD) 对在线系统至关重要,但面临着诸如极端标签不平衡和混合同恋/异恋连接等挑战.
  • 现有的GFD方法经常修改图形结构或抑制异构邻居,导致偏差和可扩展性问题.

研究的目的:

  • 为改进GFD提出一种新的频率感知图神经网络 (F-GNN).
  • 通过采用频域视角来解决现有GNN在处理标签不平衡和异构性方面的局限性.

主要方法:

  • 开发了F-GNN来解图频域中的节点表示.
  • 实现了节点适应性光谱门,以强调高频组件.
  • 引入了一个欺诈意识的代表融合机制,以减轻标签不平衡.

主要成果:

  • 在四个基准数据集 (Yelp,亚马逊,T-Finance,T-Social) 上,F-GNN始终优于基于GNN的最新欺诈检测方法.
  • 在监督和半监督环境中达到高性能指标,包括高达99.81%的AUC和96.65%的F1-Macro.
  • 证明了对基于结构的启发式模型的频率意识建模的有效性.

结论:

  • 频率感知建模为GFD提供了一个原则性的方法,克服了基于结构的方法的局限性.
  • F-GNN提供了一个强大的,可扩展的解决方案,用于检测复杂图形数据中的欺诈行为.
  • 拟议的方法有效地处理了GFD固有的挑战,为更可靠的在线系统铺平了道路.