Lumen Loading : The effects of beating process on tensile index
Bibliographic record
Abstract
Recently, access to multimedia data has become much easier due to rapid growth of the internet. Everyone could access these data, and use them for personal or commercial purposes, for these reason copyright problems appeared. Digital watermarking techniques are used to protect the copyrights of multimedia data by embedding secret information in the host media, for example, embedding in images, audios or videos. Many watermarking techniques have been proposed in the literature to solve the copyright violation problems, but most of these techniques failed to satisfy both imperceptibility and robustness requirements. In this thesis, color image watermarking technique is proposed. The proposed technique involves three main stages, which are, pre-processing, embedding, and extraction. In the pre-processing stage, a host image is converted from RGB to YCbCr color space to preserve imperceptibility and robustness, then, Cb component is extracted and partitioned into four quadrants. Finally, canny edges detection algorithm is applied on all quadrants to choose the best quadrant which contains the highest number of edges. Subsequently, in the embedding stage, DCT is applied on the selected quadrant to produce four DCT sub-bands namely LL, HL, LH, and HH. A watermark is embedded in the LL sub-band to obtain a maximum level of robustness. In the extracting stage, DCT is used again to decompose the selected quadrant of watermarked image, and finally the watermark image is extracted. To prove the efficiency of proposed technique, five types of attacks is applied on watermarked image namely, Gaussian noise, Salt & Pepper Noise, Poisson, Speckle and Cropping. The experiments results have shown that the proposed technique successfully withstood against all the mentioned attacks, and at the same time preserved the watermarked image quality
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 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".