Application of human-computer interaction system based on machine learning algorithm in artistic visual communication
Bibliographic record
Abstract
Abstract After entering the 21st century, with the development of science and technology represented by artificial intelligence, the content of art design and creation is increasingly rich. Its intelligence, interactivity, and digital content expression make the relationship between science and technology, art and people more close, and also bring new opportunities for the development of interactive art. Among them, human-computer interaction is more and more widely used. With the promotion of this kind of technology, interactive art is no longer based on auditory sensory experience, but on the in-depth study of human nature and comprehensive senses. On this basis, human-computer interaction technology is introduced, thus forming a comprehensive visual art form, which communicates and expresses through strong interaction, initiative and emotion. Based on machine learning technology and human-computer visual interaction technology, this paper explores the art visual communication module based on human-computer interaction system. By analyzing the audience's line of sight and visual differences, we can get the audience satisfaction, so as to improve the expression of the work. Firstly, this paper proposes the design of human-computer interaction visual structure, and introduces the basic structure of art visual communication system, the simulation of art visual scene, the process and evaluation index of human-computer interaction recognition, visual recognition effect, etc. based on the needs of visual interaction. Finally, it analyzes the common forms of human-computer interaction in art visual communication, the reconstruction of thinking mode of art visual communication and the development direction of art visual communication. This paper believes that human-computer interaction technology plays a leading role in the development of the art field. It has a variety of functions, display methods and values. By studying machine learning algorithm and human-computer interaction system, this paper applies it to the field of artistic vision, thus promoting the development of artistic visual communication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".