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相关概念视频

Bootstrapping01:24

Bootstrapping

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Sample Size Calculation01:19

Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Convenience Sampling Method00:55

Convenience Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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相关实验视频

Updated: Sep 13, 2025

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
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Published on: September 8, 2023

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基于跨域功能增强的密码猜测方法用于小样本.

Cheng Liu1,2,3, Junrong Li4, Xiheng Liu4

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Entropy (Basel, Switzerland)
|July 29, 2025
PubMed
概括

本研究介绍了一种新的小样本密码猜测技术,使用概率无上下文语法 (PCFG) 来克服数据限制. 该方法增强了跨域的功能,提高了密码猜测精度高达10.52%.

关键词:
通过猜测密码来猜测密码.概率学语法 无上下文语法类似性计算的计算.小小的样本,小小的样本.

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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 信息安全 信息安全

背景情况:

  • 猜测密码对于帐户保护和入侵检测至关重要.
  • 传统模型需要大量的数据集,但隐私法规限制了数据访问.
  • 这对研究人员来说是一个挑战,他们需要从小组中猜测密码.

研究的目的:

  • 开发一个小样本的密码猜测技术,增强跨领域的功能.
  • 解决隐私受限制的环境中传统模式的局限性.
  • 用有限的数据提高密码猜测的效率和准确性.

主要方法:

  • 使用概率上下文自由语法 (PCFG) 分析密码集,以导出结构和碎片概率.
  • 生成密码集结构向量用于使用等号相似性进行相似性比较.
  • 通过修改训练集结构向量,增强了小样本密码集功能.

主要成果:

  • 小和大密码集之间的相似度测量对于超过150个样本的集是可靠的.
  • 泄露和目标密码集之间的更高相似性与增加的命中率有关.
  • 拟议的特征增强方法提高了小样本集的命中率,高达10.52%.

结论:

  • 开发的技术有效地解决了小样本密码猜测的挑战.
  • 它提供了一个可行的解决方案,而不需要对目标密码集的预先了解.
  • 该方法在数据有限的场景中增强了安全研究能力.