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Related Concept Videos

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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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Associative Learning01:27

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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.
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The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Related Experiment Video

Updated: Jun 23, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Auditable and Verifiable Federated Learning Based on Blockchain-Enabled Decentralization.

Aditya Pribadi Kalapaaking, Ibrahim Khalil, Xun Yi

    IEEE Transactions on Neural Networks and Learning Systems
    |June 14, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an auditable and verifiable decentralized federated learning (DFL) framework using blockchain technology. The DFL system enhances trustworthiness and reduces communication costs, despite a minor increase in processing time.

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    Area of Science:

    • * Blockchain and Machine Learning
    • * Decentralized Systems and Security

    Background:

    • * Federated learning (FL) typically relies on a central authority, creating a single point of failure and undermining privacy and security.
    • * Lack of auditability and verifiability in traditional FL architectures hinders trustworthiness and robustness.

    Purpose of the Study:

    • * To propose an auditable and verifiable decentralized federated learning (DFL) framework.
    • * To enhance transparency, accountability, and independent validation in FL processes.
    • * To address the limitations of centralized FL architectures.

    Main Methods:

    • * Development of a smart-contract-based monitoring system for DFL participants.
    • * Deployment of the monitoring system to record local training data for auditing.
    • * Utilization of blockchain nodes for model exchange, validation, and decentralized aggregation with multisignature schemes.
    • * Implementation of a consensus protocol for tamper-proof storage of the validated global model.

    Main Results:

    • * Experimental validation on CIFAR-10, F-MNIST, and MedMNIST datasets.
    • * A slight increase in time consumption as a tradeoff for enhanced auditability and verifiability.
    • * Significant reduction in communication costs by up to 95% for participants.

    Conclusions:

    • * The proposed blockchain-enabled DFL framework successfully achieves auditability and verifiability.
    • * The system enhances trustworthiness and security in decentralized federated learning.
    • * The framework offers a practical solution for secure and transparent collaborative machine learning.