Theory and practice of AI-based interactivity enhancement strategies in digital media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AI technology can accurately capture and feedback user emotions in digital media interaction to realize precise interaction.In this paper, we design an AI emotion interactivity enhancement model based on multimodal fusion, and apply the neural network model of Bi-GRU and dual attention mechanism to fuse the long and short-term emotion classification results of the tested samples at the decision level to obtain the final emotion classification results.Then the weight coefficient vector of each sentiment category is calculated based on the sentiment classification confusion matrix of the classifier, which is used as the a priori knowledge for multimodal sentiment analysis for decision fusion.The performance is examined on the MOSI dataset and the AI-based interaction design strategy in digital media is proposed.Analyzing the interaction design effect, the interaction design applying the model of this paper has better user experience sense, emotional arousal, pleasure level, and emotional feedback effect in subjectivity evaluation than the control group, and 75% of the experimental subjects think that the feedback-adjusted digital media has a better pleasure level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it