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Attribution Theory00:56

Attribution Theory

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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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In social interactions, individuals frequently seek to understand the motivations and causes behind others' behaviors. This fundamental aspect of social perception, known as attribution, plays a crucial role in shaping interpersonal relationships and guiding future actions. Attribution refers to the cognitive process through which people infer the reasons behind others' behaviors, allowing them to assess character traits, intentions, and situational influences.Attribution Theory and Its...
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Concepts and Prototypes01:24

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The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
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Fineness of Cement

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The fineness of cement directly influences the rate of hydration, as the hydration begins at the surface of the cement particles. In addition to hydration, the fineness of cement is vital for various properties of concrete including workability, gypsum requirement, and long-term behavior. The fineness of cement is represented in terms of the specific surface of cement which is typically measured in square meters per kilogram, with several methods available for this determination.
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The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
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Assembly and Purification of Prototype Foamy Virus Intasomes
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Super-Fine Attributes with Crowd Prototyping.

Daniel Martinho-Corbishley, Mark S Nixon, John N Carter

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    Researchers developed super-fine attributes for more precise human descriptions in surveillance footage. This method significantly improves attribute-based re-identification and image retrieval accuracy compared to traditional labels.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Attribute-based re-identification from surveillance footage is crucial but limited by coarse, expert-defined categories.
    • Existing methods struggle with challenging images due to brittle representations and limited discriminative power.
    • A semantic gap exists between image descriptions and precise subject details.

    Purpose of the Study:

    • To discover more relevant and precise subject descriptions for improved image retrieval.
    • To bridge the semantic gap in attribute-based re-identification.
    • To introduce and evaluate a novel 'super-fine attribute' representation.

    Main Methods:

    • Introduced super-fine attributes: multi-dimensional perceptual coordinates describing integral concepts of a single trait.
    • Utilized crowd prototyping for efficient crowdsourcing of super-fine labels.
    • Re-annotated gender, age, and ethnicity traits on the PETA dataset using super-fine attributes.
    • Employed joint attribute regression with the ResNet-152 Convolutional Neural Network (CNN).

    Main Results:

    • Super-fine attributes demonstrated substantially improved ranked retrieval performance compared to conventional binary labels.
    • Achieved up to 11.2% and 14.8% mean Average Precision (mAP) improvement for gender and age, respectively.
    • Super-fine attributes for ethnicity further surpassed these gains.
    • Outperformed 35 binary attributes by 6.5% mAP in a zero-shot identification scenario.

    Conclusions:

    • Super-fine attributes offer a more precise and discriminative representation for human attribute recognition.
    • This approach significantly enhances performance in attribute-based re-identification and image retrieval tasks.
    • The proposed method effectively closes the semantic gap and improves subject identification in challenging scenarios.