Bibliographic record
Abstract
The Tor network gives anonymity to Internet users by relaying their traffic through the world over a variety of routers. This incurs latency, and this thesis first explores where this latency occurs. Experiments discount the latency induced by routing traffic and computational latency to determine there is a substantial component that is caused by delay in the communication path. We determine that congestion control is causing the delay. \n \nTor multiplexes multiple streams of data over a single TCP connection. This is not a wise use of TCP, and as such results in the unfair application of congestion control. We illustrate an example of this occurrence on a Tor node on the live network and also illustrate how packet dropping and reordering cause interference between the multiplexed streams. \n \nOur solution is to use a TCP-over-DTLS (Datagram Transport Layer Security) transport between routers, and give each stream of data its own TCP connection. We give our design for our proposal, and details about its implementation. Finally, we perform experiments on our implemented version to illustrate that our proposal has in fact resolved the multiplexing issues discovered in our system performance analysis. The future work gives a number of steps towards optimizing and improving our work, along with some tangential ideas that were discovered during research. \n \nAdditionally, the open-source software projects latency_proxy and libspe, which were designed for our purposes but programmed for universal applicability, are discussed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".