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相关概念视频

Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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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.
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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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相关实验视频

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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基于区块链的深度学习可靠模型评估框架及其在移动对象细分中的应用.

Rui Jiang1, Jiatao Li1, Weifeng Bu1

  • 1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
概括

本研究引入了一个基于区块链的框架,以提高深度学习模型评估的可靠性. 它通过确保安全的数据共享,培训和防改评估结果来解决传统方法中的安全漏洞.

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 区块链技术 区块链技术

背景情况:

  • 传统的深度学习模型评估面临着重要的可信性问题,包括不安全的数据处理,培训漏洞和容易被改的结果.
  • 集中评估过程缺乏透明度,容易被操纵,阻碍了可靠的模型评估.

研究的目的:

  • 提出和验证一个基于区块链的新框架,用于安全和可信的深度学习模型评估.
  • 通过解决数据安全,访问控制和结果改防护等问题,提高模型评估的完整性.

主要方法:

  • 一个分层的框架,包括访问控制,安全存储 (IPFS和区块链),分散的培训和基于区块链的评估.
  • 实现基于属性和基于角色的访问控制,使用智能合约来实现细粒度的安全性.
  • 使用IPFS进行资源存储,使用区块链进行不可变的记录保存,以及用于自动评估和评分的智能合约.

主要成果:

  • 拟议的框架成功地展示了其在基于深度学习的运动对象细分方面的功能.
  • 验证证实了存储战略的有效性和基于区块链的评估系统的整体可信度.
  • 智能合约可以实现自动评估和安全,防改的分数上传.

结论:

关键词:
区块链区块链区块链区块链区块链计算机视觉 计算机视觉深度学习是一种深度学习.模型评价模型评价移动物体的细分 移动物体的细分

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  • 基于区块链的模型评估框架显著提高了深度学习模型评估的可靠性和安全性.
  • 该系统有效地减轻了传统的漏洞,为模型评估提供了分散和透明的方法.
  • 该框架为深度学习应用程序的安全资源共享,培训和评估提供了强大的解决方案.