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Record W1709547319

Mosh: an interactive remote shell for mobile clients

2012· article· en· W1709547319 on OpenAlexaff
Keith Winstein, Hari Balakrishnan

Bibliographic record

VenueDSpace@MIT (Massachusetts Institute of Technology) · 2012
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersNational Science Foundation
KeywordsComputer scienceLatency (audio)Operating systemTerminal (telecommunication)Synchronization (alternating current)Protocol (science)Keystroke loggingComputer networkEmbedded systemChannel (broadcasting)Telecommunications
DOInot available

Abstract

fetched live from OpenAlex

Mosh (mobile shell) is a remote terminal application that supports intermittent connectivity, allows roaming, and speculatively and safely echoes user keystrokes for better interactive response over high-latency paths. Mosh is built on the State Synchronization Protocol (SSP), a new UDP-based protocol that securely synchronizes client and server state, even across changes of the client’s IP address. Mosh uses SSP to synchronize a character-cell terminal emulator, maintaining terminal state at both client and server to predictively echo keystrokes. Our evaluation analyzed keystroke traces from six different users covering a period of 40 hours of real-world usage. Mosh was able to immediately display the effects of 70% of the user keystrokes. Over a commercial EV-DO (3G) network, median keystroke response latency with Mosh was less than 5 ms, compared with 503 ms for SSH. Mosh is free software, available from http://mosh.mit.edu. It was downloaded more than 15,000 times in the first week of its release.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: Software
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.005

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 head0.012
GPT teacher head0.260
Teacher spread0.247 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreSoftware

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".

Quick stats

Citations36
Published2012
Admission routes1
Has abstractyes

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