Expansion Properties of Topology for Networking of Information in Cloud
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
Toward the progress in the era of globalization and ubiquity of sensors and devices, sharing and dissemination of information dominate todays networks. Content-centric networking, cloud services, and open connectivity form the main ingredients of the future Internet architecture. With the problem of information overload, the networking paradigm of cloud computing can benefit from transitioning to a network of information in which information is the main token of communication instead of physical address. Available methods may not be efficient in exploiting the semantics of information for content dissemination. Considering a content-centric approach, we intend to tackle this problem by using the expander graphs for an enhanced network coding scheme that takes an opportunistic strategy to utilize the spectral characteristics of the network topology to achieve a better solvability and reliability and lowering the processing cost for the entire system. By simulation and analytical evaluation, we compare our proposed method with an epidemic network coding based approach. Our evaluation examines the performance of our clustering method in the presence of different random topology models as well as examining the impact on the network coding technique.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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