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相关概念视频

Classification of Systems-I01:26

Classification of Systems-I

616
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
616
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

5.5K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
5.5K
Aggregates Classification01:29

Aggregates Classification

1.1K
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.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.1K
Classification of Systems-II01:31

Classification of Systems-II

522
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,
522
Methods of Documentation I: Source-Oriented Records01:18

Methods of Documentation I: Source-Oriented Records

1.7K
Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:
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How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

45.8K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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相关实验视频

Updated: Feb 19, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

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一个用于人类编写和人工智能生成代码源代码分类的数据集.

Ghizlane Boukili1, Said El Garouani1, Jamal Riffi1

  • 1LISAC Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University, Fez, 30003, Morocco.

Data in brief
|February 18, 2026
PubMed
概括
此摘要是机器生成的。

一个由10,000个代码样本组成的新数据集有助于检测人工智能生成的代码. 这个资源有助于计算机科学教育工作者区分人类和人工智能编程,提高学术完整性.

关键词:
聊天GPT 聊天GPT 聊天检测 检测 检测 检测 检测机器学习是机器学习.编程语言的编程语言.快速地提醒了他们.

相关实验视频

Last Updated: Feb 19, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

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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
  • 机器学习 机器学习

背景情况:

  • 人工智能代码生成工具在计算机科学教育中对验证学生真实性提出了挑战.
  • 由于编程语言的特殊性,现有的通用AI检测工具不足以准确识别AI生成的代码.

研究的目的:

  • 引入专门的数据集,用于开发特定领域的AI代码检测工具.
  • 解决研究人工智能生成代码检测的资源缺口.

主要方法:

  • 创建一个数据集,包含1万个注释代码样本 (5000个由人类编写,5000个由人工智能生成).
  • 包含跨 Python,Java,C 和 C++ 的样本.
  • 通过ChatGPT API生成的人工智能生成的样本;来自公共存储库的人类样本.
  • 每个样本按原产地 (人类或人工智能) 标记,用于模型训练.

主要成果:

  • 该数据集能够对机器学习和深度学习模型进行强有力的训练,以进行代码源区别.
  • 促进开发专门的工具来检测人工智能生成的代码.

结论:

  • 该专业数据集对于推进人工智能生成代码检测研究至关重要.
  • 数据集和实验代码的公开可用性支持进一步的学术研究和工具开发.