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Record W1529853583 · doi:10.1049/iet-com.2011.0196

Unveiling popularity of BitTorrent Darknets

2012· article· en· W1529853583 on OpenAlex
Xiao Chen, Xiaowen Chu, J. Liu

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIET Communications · 2012
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBitTorrentPopularityBitTorrent trackerUploadComputer scienceIncentiveInternet privacyComputer securityComputer networkPeer-to-peerWorld Wide WebLawArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

BitTorrent is todays most influential peer-to-peer content distribution system. Currently BitTorrent has two very different operating models: (i) public trackers, and (ii) private trackers (a.k.a. PTs, Darknets). A PT can only be accessed by its registered users, and can provide ultrahigh downloading speed because of its effective share-ratio enforcement (SRE) incentive mechanism which stimulates the users to upload contents as much as possible. Although PTs are becoming more and more popular, they receive little attention from the research literature, possibly because they are operated underground. To understand the popularity of Darknets, the authors have traced 17 PT sites, 2 public tracker sites and 1 BitTorrent search engine for over a year. The authors investigate these PT sites from several aspects and try to understand why they are so successful in terms of attracting loyal users and providing high downloading speed. The authors then analyse the SRE mechanism and ratio free system which are commonly used by PTs. Our results unveil the reason of popularity and effectiveness of PTs. These understandings are essential to the sustainable development of future BitTorrent content distribution systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.301
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it