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相关概念视频

Masking and Demasking Agents01:19

Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
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Reinforcement01:23

Reinforcement

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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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相关实验视频

Updated: Sep 11, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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一个基于网络的多代理强化学习框架,用于对智能合约漏洞的强有力的检测.

Philip Kwaku Adjei1, Qin Zhiguang1, Isaac Amankona Obiri2

  • 1School of Information and Software Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China.

Scientific reports
|August 14, 2025
PubMed
概括

本研究介绍了一种新的多代理强化学习 (MARL) 方法,使用层次图表注意网络 (HGAT) 来检测智能合约的漏洞. 该方法显著提高了识别区块链平台上的各种威胁的准确性.

关键词:
区块链漏洞检测 区块链漏洞检测分散的应用程序去中心化.图表注意力网络的图表.层次化的强化学习学习.预测分析是一种预测分析.智能合约的安全性 智能合约的安全性

相关实验视频

Last Updated: Sep 11, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 智能合约自动化了区块链上的协议,但由于复杂的状态相互依赖,检测漏洞是很困难的.
  • 现有的方法与智能合约交互和语义模两可的复杂性作斗争.

研究的目的:

  • 开发一种新的方法来识别智能合约的漏洞,使用多代理强化学习 (MARL).
  • 加强从合同国家间互动中产生的复杂漏洞的检测.

主要方法:

  • 在多代理主角-关键框架内集成层次图注意网络 (HGAT).
  • 将漏洞检测分解为高级 (历史交互) 和低级 (结构化行动) 政策.
  • 建模智能合约交互作为多步推理路径来导航交易序列.

主要成果:

  • 实现了93.8%的准确性和89.8%的F1评分,用于重新入境攻击.
  • 在检测前端运行 (88.9%准确率),拒绝服务 (91.2%准确率) 和未检查的低级漏洞 (91.6%准确率) 中表现出强的表现.
  • 在多个智能合约漏洞类别中表现优于现有的方法.

结论:

  • 拟议的MARL框架有效地导航复杂的交易序列,并解决语义模两可.
  • 这种新的方法在检测各种智能合约漏洞方面提供了显著的改进,提高了区块链安全性.