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相关概念视频

Labeling DNA Probes03:31

Labeling DNA Probes

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DNA probes are fragments of DNA labeled with a reporter tag to enable their detection or purification. The resulting labeled DNA probes can then hybridize to target nucleic acid sequences through complementary base-pairing, and may be used to recover or identify these regions.
Radioisotopes, fluorophores, or small molecule binding partners like biotin or digoxigenin, are the most widely used reporter tags for labeling DNA probes. These labels can be attached to the probe DNA molecule via...
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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Sampling and Analysis of Animal Scent Signals
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臭味代码++:用于代码嗅觉检测的多标签数据集

Nawaf Alomari1, Amal Alazba2, Hamoud Aljamaan3,4

  • 1Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. g201931050@kfupm.edu.sa.

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此摘要是机器生成的。

这项研究引入了一个新的多标签数据集用于代码嗅觉检测,提高了软件质量分析的现实性. 数据集支持先进的检测方法,提高维护能力和重构工作.

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科学领域:

  • 软件工程 软件工程 软件工程
  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学

背景情况:

  • 代码气味意味着软件设计不佳,影响维护能力,需要准确的检测才能进行有效的重构.
  • 当前的数据集经常使用单标签分类,这并不反映现实世界项目中代码嗅觉事件的复杂,多方面的性质.

研究的目的:

  • 开发一种新的多标签数据集,用于代码嗅觉检测.
  • 整合来自开源Java项目的文本和数字特征,以实现更现实的表示.
  • 为了促进先进的研究和提高代码嗅觉检测工具的准确性.

主要方法:

  • 从103个开源Java项目中收集了代码.
  • 将代码解析成抽象语法树 (AST),并提取相关特征.
  • 使用数据清理和统一技术,对四种特定代码气味 (God Class,Data Class,Feature Envy,Long Method) 进行注释样本.

主要成果:

  • 创建了一个包含107,554个样本的数据集,具有多标签注释,增强检测现实性.
  • 获得了高的F1分数:数据类的95.89%,上帝类的94.48%,特征嫉妒的88.68%,长方法的88.87%.
  • 该数据集为评估和改进代码嗅觉检测算法提供了坚实的基础.

结论:

  • 开发的数据集对于高级代码嗅觉检测研究非常有价值,包括微调大型语言模型 (LLM).
  • 未来的工作可以扩展数据集,包括其他编程语言和额外的代码气味,增加其适用性和多样性.
  • 该资源将通过更准确的代码气味识别,有助于提高软件质量和可维护性.