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
Clustering is a commonly used technique for multimedia data analysis and management. In this article, we propose a high-order clustering algorithm by fast search and find of density peaks (HOCFS) by extending the traditional clustering scheme by fast search and find of density peaks (CFS) algorithm from the vector space to the tensor space for multimedia data clustering. Furthermore, we propose a privacy preserving HOCFS algorithm (PPHOCFS) which improves the efficiency of the HOCFS algorithm by using the cloud computing to perform most of the clustering operations. To protect the private data in the multimedia data sets during the clustering process on the cloud, the raw data is encrypted by the Brakerski-Gentry-Vaikun-tanathan (BGV) strategy before being uploaded to the cloud for performing the HOCFS clustering algorithm efficiently. In the proposed method, the client is required to only execute the encryption/decryption operations and the cloud servers are employed to perform all the computing operations. Finally, the performance of our scheme is evaluated on two representative multimedia data sets, namely NUS-WIDE and SNAE2, in terms of clustering accuracy, execution time, and speedup in the experiments. The results demonstrate that the proposed PPHOCFS scheme can save at least 40% running time compared with HOCFS, without disclosing the private data on the cloud, making our scheme securely suitable for multimedia big data clustering.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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