Hybrid P2P-Based Architecture for Remote Software Utilization
Bibliographic record
Abstract
The world presents us with two contradictory situations.Firstly, most people encounter the problem of lacking necessary hardware and/or software resources.Secondly, some individuals or organizations possess these resources but fail to utilize them to their full potential.The proposed solution is to provide underutilized resources to those who require them.However, it is important to ensure that there is no illegal copying or pirating of software.Instead, the owners of these resources can offer their services by processing data remotely.The Peer-to-peer (P2P) paradigm is used primarily for collaborative systems over a network.This article proposes a Hybrid P2P-Based collaborative architecture that employs HTTP transport to provide data remote-treatment services.In our peer-to-peer (P2P) solution, one peer provides a service, such as a software application capable of processing specific data, while another peer actively searches for this software and sends its own data to be processed by it.In this scenario, the second peer can utilize the software without encountering piracy issues, given that the first peer executes the software locally and adheres to the appropriate license.By employing HTTP for communication, it enables collaborative interaction among heterogeneous peer platforms.This system enables individuals without essential hardware and/or software resources to leverage the resources provided by others.The simulation results conducted using PeerSim simulator, are encouraging, indicating that the proposed architecture can serve as a reliable solution for collaboration between peers.The outcomes of the simulation demonstrate a significant level of satisfaction across the essential metrics we defined to assess the effectiveness of our solution.particularly in terms of responsiveness to requests, resulting in approximately 80% satisfaction and 20% dissatisfaction rates for requests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".