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相关概念视频

Leaky Scanning02:28

Leaky Scanning

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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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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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Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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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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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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相关实验视频

Updated: May 1, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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一种改进的软件源代码漏洞检测方法:多功能选和集成采样模型的组合.

Xin He1, Asiya1, Daoqi Han1

  • 1College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了软件漏洞检测的新模型,提高了准确性并缩短了培训时间. 多特征选和综合采样模型 (MFISM) 通过解决数据集不平衡,提高软件安全性.

关键词:
这是一个双LSTM.抽象语法树 (AST) 是一个抽象语法树.综合过量采样过量采样多功能查多功能查源代码漏洞检测 源代码漏洞检测

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科学领域:

  • 计算机科学 计算机科学
  • 软件工程 软件工程 软件工程
  • 网络安全 网络安全

背景情况:

  • 软件漏洞检测对于安全至关重要.
  • 现有的方法在不平衡的数据集和漫长的培训中扎.
  • 需要高效准确的漏洞检测模型.

研究的目的:

  • 引入多特征选和综合采样模型 (MFISM) 以加强软件漏洞检测.
  • 提高源代码中识别漏洞的效率和准确性.
  • 为了克服现有模型中的阶级不平衡和长时间培训的挑战.

主要方法:

  • 使用抽象语法树 (AST) 进行特征提取.
  • 应用变异分析 (ANOVA) 和特征选择技术.
  • 实现了数据平衡的综合过量采样和异常结果检测.
  • 采用双向长期短期记忆 (Bi-LSTM) 网络进行分类.

主要成果:

  • 与DeepBalance相比,MFISM提高了F1的得分约10%.
  • 将模型培训时间缩短到2-3小时.
  • 在源代码漏洞检测方面表现出卓越的性能.

结论:

  • MFISM有效地提高了漏洞检测的准确性和效率.
  • 该模型成功地解决了阶级不平衡,并减少了培训时间.
  • MFISM为安全软件开发提供了卓越的解决方案.