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相关概念视频

Interference and Decay01:16

Interference and Decay

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Forgetting is a complex cognitive phenomenon influenced by several factors, among which interference and decay are particularly prominent. These processes explain why individuals often struggle to retrieve specific information from memory, leading to lapses in recall that can be observed in everyday situations.
Interference occurs when competing memories hinder the retrieval of particular information. It can be classified into two types: proactive and retroactive interference. Proactive...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Random Error01:04

Random Error

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Forgetting01:21

Forgetting

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Forgetting is an intrinsic aspect of human memory, characterized by the gradual loss or inaccessibility of information over time. Hermann Ebbinghaus, a pioneering psychologist, extensively studied this phenomenon and formulated the forgetting curve. This curve illustrates that memory loss occurs rapidly immediately after learning and then decelerates over time. Several mechanisms contribute to forgetting, including encoding failure, storage decay, retrieval failure, and interference.
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Long-term Depression01:05

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Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
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Aliasing01:18

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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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适应性日志异常检测在云环境中的时间衰变损失.

Lelisa Adeba Jilcha1, Deuk-Hun Kim2, Jin Kwak3

  • 1ISAA Laboratory, Department of AI Convergence Network, Ajou University, Suwon 16499, Republic of Korea.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
概括

本研究引入了一种新的方法,用于在云环境中检测日志异常,使用特定域的预训练语言模型和损失与衰变因子 (LDF). 这种方法通过平衡历史数据与实时相关性来提高零射击传输性能.

关键词:
在LDF中使用LDF.适应式检测适应式检测检测异常检测异常检测云计算是云计算中的一个.日志预处理日志预处理预训练的语言模型时间衰变损失.时间依赖性时间依赖性零射击检测检测零射击的检测.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 网络安全 网络安全

背景情况:

  • 记录异常检测对于云系统的可靠性和安全性至关重要.
  • 由于数据转移,传统的序列模型在零射击传输方面遇到了困难.
  • 现有的方法往往过度强调过时的信息,并产生高的计算成本.

研究的目的:

  • 开发一个有效的日志异常检测方法,用于云计算中的零射击传输场景.
  • 为了应对分布式转移和异质日志数据集中的语义差异所带来的挑战.
  • 在动态云环境中提高异常检测模型的概括能力.

主要方法:

  • 集成一个针对特定领域的预训练语言模型 (PLM),对网络安全数据进行微调.
  • 介绍了一种具有指数时间衰变机制的新损失与衰变因子 (LDF).
  • 基于时间近距离的日志消息的动态加权,以平衡历史和实时数据.

主要成果:

  • 通过经验评估证明了跨数据集异常检测性能的大幅提升.
  • 通过特定领域的PLM,在异质数据集中改进了日志数据的表示.
  • 减轻语义差异,更好地与不断变化的云环境保持一致.

结论:

  • 拟议的方法显著改善了在异质云环境中的零射击日志异常检测.
  • 域特定PLM和LDF的组合有效地解决了传统序列模型的局限性.
  • 这种方法为快速变化的系统中的异常检测提供了更具动态性和相关性的方法.