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

Masking and Demasking Agents01:19

Masking and Demasking Agents

2.7K
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.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.7K
Reinforcement01:23

Reinforcement

341
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:
341
Observational Learning01:12

Observational Learning

312
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...
312
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

149
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...
149
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.4K
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.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.4K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.9K
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...
1.9K

You might also read

Related Articles

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

Sort by
Same author

Advancing workpiece dimension measurement: Integrating AI-based edge detection with machine vision and coordinate measuring systems.

PloS one·2026
Same author

An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images.

Diagnostics (Basel, Switzerland)·2026
Same author

New Gait Representation Maps for Enhanced Recognition in Clinical Gait Analysis.

Bioengineering (Basel, Switzerland)·2025
Same author

Human pose estimation in physiotherapy fitness exercise correction using novel transfer learning approach.

PeerJ. Computer science·2025
Same author

Imbalanced Power Spectral Generation for Respiratory Rate and Uncertainty Estimations Based on Photoplethysmography Signal.

Sensors (Basel, Switzerland)·2025
Same author

Improved Confidence-Interval Estimations Using Uncertainty Measure and Weighted Feature Decisions for Cuff-Less Blood-Pressure Measurements.

Bioengineering (Basel, Switzerland)·2025

Related Experiment Video

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

4.1K

A graph attention network-based multi-agent reinforcement learning framework for robust detection of smart contract

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
Summary

This study introduces a novel multi-agent Reinforcement Learning (MARL) approach with Hierarchical Graph Attention Networks (HGAT) to detect smart contract vulnerabilities. The method significantly improves accuracy in identifying various threats on blockchain platforms.

Keywords:
Blockchain vulnerability detectionDecentralized applicationGraph attention networksHierarchical reinforcement learningPredictive analyticsSmart contract security

Related Experiment Videos

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

4.1K

Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Smart contracts automate agreements on blockchains, but detecting vulnerabilities is difficult due to complex state interdependencies.
  • Existing methods struggle with the intricate nature of smart contract interactions and semantic ambiguities.

Purpose of the Study:

  • To develop a novel approach for identifying smart contract vulnerabilities using multi-agent Reinforcement Learning (MARL).
  • To enhance the detection of complex vulnerabilities arising from inter-contract state interactions.

Main Methods:

  • Integration of a Hierarchical Graph Attention Network (HGAT) within a Multi-Agent Actor-Critic framework.
  • Decomposition of vulnerability detection into high-level (historical interactions) and low-level (structured actions) policies.
  • Modeling smart contract interactions as multistep reasoning paths to navigate transaction sequences.

Main Results:

  • Achieved 93.8% accuracy and 89.8% F1 score for reentrancy attacks.
  • Demonstrated strong performance in detecting front running (88.9% accuracy), denial-of-service (91.2% accuracy), and unchecked low-level vulnerabilities (91.6% accuracy).
  • Outperformed existing approaches across multiple smart contract vulnerability categories.

Conclusions:

  • The proposed MARL framework effectively navigates complex transaction sequences and resolves semantic ambiguities.
  • This novel approach offers significant improvements in detecting diverse smart contract vulnerabilities, enhancing blockchain security.