Opposite Fuzzy Sets with Applications in Image Processing
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
Diverse forms of the concept of opposition are already existent in philosophy, linguistics, psychology and physics. The inter- play between entities and opposite entities is apparently fundamental for balance maintenance in almost a universal manner. However, it seems that we have failed to incorporate oppositional thinking in en- gineering, mathematics and computer science. Especially, the set theory in general, and fuzzy set theory in particular, do not offer a formal framework to incorporate opposition in inference engines. Considering sets along with their opposites can establish a new com- puting scheme with a wide range of applications. In this work, pre- liminary definitions for opposite fuzzy sets will be established. The underlaying idea of opposition-based computing is the simultaneous consideration of guess and opposite guess, and estimate and opposite estimate, in order to accelerate learning, search and optimization. To demonstrate the applicability and usefulness of opposite fuzzy sets, a new image segmentation algorithm will be proposed as well. Keywords— Fuzzy sets, opposition, opposite fuzzy sets, antonym, antonymy, complement
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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