Novel lightweight multicasting protocol for thin-client systems
Bibliographic record
Abstract
Thin-client computing is being adopted by many industries to bring services to users through a unique platform over wide area networks (WAN). To improve thin-client communication sessions, some authors have proposed using the thin-client protocol with enhanced image compression techniques while others have proposed using drawing commands instead. To take full advantage of the capabilities of thin-client systems, an advance communication protocol is needed. In this paper, a lightweight multicasting protocol for thin-client systems, Thin- Cast, is proposed where the Quality of Service (QoS) and Quality of Experience (QoE) perceived by a user are maintained while meeting the latency constraints. ThinCast establishes a peer-to-peer (P2P) overlay network using the underlying Internet protocol (IP) infrastructure. A central server explicitly requests a peer to forward a payload to a list of defined neighboring peers. While using ThinCast, server costs are reduced since the required throughput to achieve the same quality of service is lowered. On average, the throughput decreased by 11% under different simulation scenarios. The decrease in throughput values allows the server to temporarily increase the packet generation rate, thereby increasing the quality of the received video by the users.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".