Bibliographic record
Abstract
Semantic hiding is the technology of using semantic knowledge to embed secret information into text carrier. Among the many methods of semantic hiding, "synonym substitution" is paid more and more attention by semantic hiding. The main idea of this method is to hide the secret information by replacing synonyms in text so as to retain its original meaning as much as possible. In order to effectively restore hidden information, we need to find the synonym replacement location as accurately as possible, so it is very important to recognize the collocation of words. So far, however, there is no effective way to identify and match Natural Language Processing, that is, it is very difficult to tell exactly whether or not the words in the text have been replaced.In this paper, a hidden reduction method based on collocation is proposed. By analyzing the characteristics of synonyms and their collocation, this paper treats their relation as the relation between the pairs of samples in statistical sense. According to the nature of the statistic, we design several decision features to identify the collocations. At the same time, we introduce the form of point mutual information in the information theory as a feature to use the independence of quantifier pairs. In order to recognize word collocation effectively, this paper combines these features, and uses genetic algorithm to get the recognition degree of each feature. Then, a replacement recognition system based on immune abnormality mechanism is designed. Synonyms for collocation are regarded as "normal", while substitutions are regarded as "anomalies"". The experimental samples are generated by semantic hidden software TLEX. To better render the restore process, we rewrote the TLEX to add the key selection module.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.008 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".