Expansion Properties of Topology for Networking of Information in Cloud
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.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".