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相关概念视频

Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

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The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
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Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
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Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Uniform Distribution01:19

Uniform Distribution

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The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.
Two essential properties of this distribution are
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Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

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For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.
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Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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相关实验视频

Updated: Sep 18, 2025

Observation and Analysis of Blinking Surface-enhanced Raman Scattering
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Observation and Analysis of Blinking Surface-enhanced Raman Scattering

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在随机比特序列中非零元素的长度-重量分布.

Christoph Lange1, Andreas Ahrens2, Yadu Krishnan Krishnakumar2

  • 1School of Engineering-Energy and Information, Hochschule für Technik und Wirtschaft Berlin, University of Applied Sciences, 10313 Berlin, Germany.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的基于差距的突发分析,以评估网络安全中的随机性. 晶体-Kyber显示的随机性比晶体-二少,突出显示了加密算法安全性的差异.

关键词:
爆发 爆发 爆发 爆发差距的分配差距的分配差距.差距的过程 差距的过程可能性概率概率概率.随机化的比特序列.测试 测试 测试 测试 测试

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相关实验视频

Last Updated: Sep 18, 2025

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科学领域:

  • 网络安全和数据通信
  • 信息理论和密码学.

背景情况:

  • 随机性对于安全的通信和网络安全至关重要.
  • 密码输出必须随机出现,以尽量减少信息泄露给攻击者.
  • 现有的随机性测试通常依赖于对元素分布和独立性的假设测试.

研究的目的:

  • 介绍和分析一种新的基于差距的突发分析,用于评估随机性.
  • 在随机位序中检测理想差距密度函数的偏差.
  • 为了快速验证加密输出的随机性.

主要方法:

  • 开发了一种基于基于差距的突发分析的新方法.
  • 该方法侧重于偏离理想的间隙密度函数的偏差.
  • 用于测试和验证的是Crystals加密套件 (Kyber和Dilithium).

主要成果:

  • 拟议的技术有效地验证了加密输出中的随机性.
  • 晶体-Kyber (钥匙封装/交换) 的随机性水平较低.
  • 与Kyber相比,Crystals-Dilithium (数字签名) 的随机性水平更高.

结论:

  • 基于差距的突发分析是一种有效和有效的方法,用于评估加密序列中的随机性.
  • 不同的加密算法具有不同程度的随机性.
  • 这些发现对选择安全的加密原体有影响.