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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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An alternator converts mechanical energy into electrical energy that varies sinusoidally, resulting in AC current. Meanwhile, a DC generator converts mechanical energy into electrical energy, which are DC pulses with the same polarity. The construction of a DC generator is similar to that of an alternator, except that the pair of slip rings is replaced by a single split ring, also called a commutator. The commutator functions like a periodic rotary switch; it changes the contacts with the...
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Transformer-Based Deep Neural Language Modeling for Construct-Specific Automatic Item Generation.

Björn E Hommel1,2, Franz-Josef M Wollang3, Veronika Kotova4

  • 1Department of Work and Organizational Psychology, Institute of Psychology - Wilhelm Wundt, Leipzig University, Neumarkt 9-19, Leipzig, 04109, Germany. bjoern.hommel@uni-leipzig.de.

Psychometrika
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This summary is machine-generated.

Deep neural networks can now generate personality test items. This automatic item generation method shows promising psychometric properties, matching human-authored items.

Keywords:
automatic item generationdeep learninglanguage modelingnatural language processingneural networks

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Area of Science:

  • Psychological assessment
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Traditional automatic item generation (AIG) is limited for non-cognitive constructs.
  • Recurrent neural networks advanced item generation but lacked domain specificity.
  • Recent progress in natural language processing (NLP) enables construct-specific AIG.

Purpose of the Study:

  • To propose and demonstrate a method for generating construct-specific non-cognitive items using NLP.
  • To leverage pre-trained causal transformer models for targeted item creation.
  • To evaluate the psychometric properties of automatically generated items.

Main Methods:

  • Fine-tuning pre-trained causal transformer models.
  • Employing implicit parameterization and conditional generation.
  • Comparing validity of human- and machine-authored items using empirical data.

Main Results:

  • Approximately two-thirds of generated items exhibited good psychometric properties (factor loadings > .40).
  • One-third of automatically generated items demonstrated psychometric properties equivalent to established human-authored items.
  • The study validates the practical application of deep neural networks for non-cognitive AIG.

Conclusions:

  • Deep neural networks, specifically causal transformers, are effective for non-cognitive automatic item generation.
  • The proposed method allows for the creation of construct-specific items with high validity.
  • This research highlights the potential of AI in psychological assessment and test development.