Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
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

Related Experiment Video

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

A Recommendation System for Trigger-Action Programming Rules via Graph Contrastive Learning.

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
Summary

This study introduces GCL4TAP, a new system for recommending trigger-action programming (TAP) rules for Internet of Things (IoT) devices. GCL4TAP effectively models user-device relationships and collaborative user information to improve rule automation.

Keywords:
Internet of Thingsgraph contrastive learningrule recommendationtrigger–action programming

More Related Videos

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

Related Experiment Videos

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

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Trigger-Action Programming (TAP) automates Internet of Things (IoT) devices through user-defined rules.
  • The increasing number of IoT devices makes manual rule creation complex and time-consuming.
  • Existing TAP recommendation systems overlook user-rule associations and collaborative user information.

Purpose of the Study:

  • To propose GCL4TAP, a novel graph contrastive learning-based recommendation system for TAP rules.
  • To address the limitations of existing systems by incorporating cross-user rule relationships and user similarities.
  • To enhance the efficiency and accuracy of automated rule discovery for IoT devices.

Main Methods:

  • Developed DATA2DIV, a data partitioning method to represent cross-user rule relationships in a user-rule bipartite graph.
  • Constructed a user-user graph to capture user similarities based on owned device categories and quantities.
  • Utilized graph contrastive learning to generate low-dimensional vector representations for users and rules.

Main Results:

  • GCL4TAP demonstrated superior performance compared to state-of-the-art methods in extensive experiments.
  • The system effectively models collaborative information among users for improved rule recommendations.
  • Experimental results on a real-world smart home dataset validate the proposed approach.

Conclusions:

  • GCL4TAP offers a significant advancement in recommendation systems for trigger-action programming.
  • The graph contrastive learning approach effectively captures complex relationships in IoT automation scenarios.
  • The proposed system enhances the user experience by simplifying the automation of diverse IoT devices.