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相关概念视频

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
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Learning Disabilities01:25

Learning Disabilities

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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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相关实验视频

Updated: Sep 14, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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VDCRL:通过监督的对比代码表示学习来检测漏洞.

Xinghang Lv1, Jianming Fu1, Yu Nie1

  • 1The Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, 430000, Hubei, China.

Neural networks : the official journal of the International Neural Network Society
|July 21, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了VDCRL,这是一个新的代码漏洞检测框架,可以提高概括性. VDCRL使用监督对比学习和数据增强来增强跨不同数据集的软件安全性.

关键词:
代码增强增强 代码增强相反的学习学习.概括能力 概括能力软件安全 软件安全漏洞检测 发现漏洞的检测

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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:

  • 对于代码漏洞检测的深度学习模型表现有希望,但在将其推广到新数据集时遇到了困难.
  • 现有的方法往往过度适应训练数据,导致在看不见的代码上显著降低性能.
  • 改进的概括对于自动化漏洞检测的有效现实应用至关重要.

研究的目的:

  • 提出VDCRL,这是一个新的代码漏洞检测框架.
  • 提高深度学习模型在识别软件漏洞方面的概括能力.
  • 在合成和现实数据集上实现卓越的检测性能.

主要方法:

  • VDCRL使用监督的对比代码表示学习.
  • 基于输入和基于特征空间的数据增强产生了多样化的代码样本.
  • 功能融合编码器 (SAFE) 集成了源代码和组装指令功能.
  • 用于漏洞检测的是一种双向门式反复单元 (BGRU) 模型.

主要成果:

  • 与最先进的方法相比,VDCRL显示了显著改善的泛化能力.
  • 该框架在真实数据集上实现了优越的漏洞检测性能.
  • 功能融合和监督对比学习是提高性能的关键因素.

结论:

  • VDCRL提供了一个强大的解决方案,用于代码漏洞检测,并增强了泛化.
  • 拟议的框架有效地解决了当前深度学习方法的局限性.
  • 通过改进的漏洞检测,VDCRL在确保软件安全方面取得了重大进展.