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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...
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Aggregates Classification01:29

Aggregates Classification

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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.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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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

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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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Video Experimental Relacionado

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

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Un conjunto de datos para la clasificación de código fuente escrito por humanos y generado por IA

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
Resumen
Este resumen es generado por máquina.

Un nuevo conjunto de datos de 10,000 muestras de código ayuda a detectar el código generado por IA. Este recurso ayuda a los educadores de informática a distinguir entre la programación humana y la de inteligencia artificial, mejorando la integridad académica.

Palabras clave:
ChatGPTDetecciónAprendizaje automáticoLenguajes de programaciónPrompt

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Área de la Ciencia:

  • Ciencias de la Computación
  • Inteligencia Artificial
  • Aprendizaje Automático

Sus antecedentes:

  • Las herramientas de generación de código de IA plantean desafíos para verificar la autenticidad de los estudiantes en la educación en informática.
  • Las herramientas de detección de IA genéricas existentes son insuficientes para identificar con precisión el código generado por IA debido a las especificidades del lenguaje de programación.

Objetivo del estudio:

  • Introducir un conjunto de datos especializado para el desarrollo de herramientas de detección de código de IA específicas del dominio.
  • Abordar la brecha en los recursos para la investigación de la detección de código generado por IA.

Principales métodos:

  • Creación de un conjunto de datos con 10,000 muestras de código anotadas (5000 escritas por humanos, 5000 generadas por IA).
  • Inclusión de muestras en Python, Java, C y C++.
  • Muestras generadas por IA producidas a través de la API de ChatGPT; muestras humanas obtenidas de repositorios públicos.
  • Cada muestra etiquetada por origen (humano o IA) para el entrenamiento del modelo.

Principales resultados:

  • El conjunto de datos permite el entrenamiento robusto de modelos de aprendizaje automático y aprendizaje profundo para la discriminación de la fuente del código.
  • Facilita el desarrollo de herramientas especializadas para la detección de código generado por IA.

Conclusiones:

  • El conjunto de datos especializado es crucial para avanzar en la investigación de la detección de código generado por IA.
  • La disponibilidad pública del conjunto de datos y el código experimental apoya la investigación futura y el desarrollo de herramientas.