Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Associative Learning01:27

Associative Learning

308
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
308
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.0K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.0K
Observational Learning01:12

Observational Learning

145
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...
145
Heuristics01:21

Heuristics

80
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
80
Purposive Learning01:22

Purposive Learning

104
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
104
Fixed Action Patterns01:06

Fixed Action Patterns

15.9K
A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
15.9K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

An Adaptive Octile JPS and Fuzzy-DWA Fused Path Planning Algorithm for Indoor Home Environments.

Sensors (Basel, Switzerland)·2026
Same author

Identification of Key Osteoarthritis-Associated Genes Based on DNA Methylation.

International journal of molecular sciences·2026
Same author

LBMNet: a hybrid multi-scale CNN-Mamba framework for enhanced 3D stroke lesion segmentation in MRI.

Frontiers in medicine·2026
Same author

An Enhanced Hybrid Astar Path Planning Algorithm Using Guided Search and Corridor Constraints.

Sensors (Basel, Switzerland)·2026
Same author

Dual-Stream STGCN with Motion-Aware Grouping for Rehabilitation Action Quality Assessment.

Sensors (Basel, Switzerland)·2026
Same author

MSRLNet: A Multi-Source Fusion and Feedback Network for EEG Feature Recognition in ADHD.

Brain sciences·2025

相关实验视频

Updated: Jun 11, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.8K

通过图形对比学习来实现触发动作编程规则的推系统.

Zhejun Kuang1,2,3, Xingbo Xiong1,2,3, Gang Wu4

  • 1College of Computer Science and Technology, Changchun University, Changchun 130022, China.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
概括

这项研究介绍了GCL4TAP,这是一个新的系统,用于为物联网 (IoT) 设备推触发动作编程 (TAP) 规则. GCL4TAP有效地建模用户与设备之间的关系和协作用户信息,以改善规则自动化.

关键词:
物联网的物联网,就是物联网.图表对比的学习学习.规则建议规则建议触发 行动编程 行动编程

更多相关视频

Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm
12:12

Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm

Published on: May 14, 2014

10.6K
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.2K

相关实验视频

Last Updated: Jun 11, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.8K
Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm
12:12

Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm

Published on: May 14, 2014

10.6K
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.2K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 人与计算机的交互

背景情况:

  • 触发动作编程 (TAP) 通过用户定义的规则自动化物联网 (IoT) 设备.
  • 越来越多的物联网设备使手动规则的创建变得复杂和耗时.
  • 现有的TAP推系统忽视了用户规则协会和协作用户信息.

研究的目的:

  • 为TAP规则提出GCL4TAP,这是一个基于学习的推系统,用于TAP规则.
  • 通过结合跨用户规则关系和用户相似性来解决现有系统的局限性.
  • 为了提高物联网设备自动规则发现的效率和准确性.

主要方法:

  • 开发了DATA2DIV,这是一种数据分区方法,用于在用户规则双边图中表示跨用户规则关系.
  • 构建了一个用户-用户图表,以根据所拥有的设备类别和数量捕捉用户相似之处.
  • 利用图形对比学习来为用户和规则生成低维向量表示.

主要成果:

  • 与最先进的方法相比,GCL4TAP在广泛的实验中表现出更高的性能.
  • 该系统有效地模拟用户之间的协作信息,以改进规则建议.
  • 在现实世界智能家居数据集上的实验结果验证了拟议的方法.

结论:

  • GCL4TAP为触发行动编程的推系统提供了显著的进步.
  • 图形对比学习方法有效地捕捉了物联网自动化场景中的复杂关系.
  • 拟议的系统通过简化各种物联网设备的自动化来增强用户体验.