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Record W2091795249 · doi:10.4018/jssoe.2012070102

Refactoring Flash Embedding Methods

2012· article· en· W2091795249 on OpenAlex

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

VenueInternational Journal of Systems and Service-Oriented Engineering · 2012
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsJavaScriptAjaxComputer scienceRich Internet applicationFlash (photography)Unobtrusive JavaScriptMarkup languageEmbeddingWeb applicationHTML5ActionScriptProgramming languageWorld Wide WebXMLArtificial intelligence

Abstract

fetched live from OpenAlex

Flash and Ajax are currently two popular Rich Internet Application (RIA) technologies, integrating Flash and Ajax will further enhance Internet users’ experiences. To communicate Flash written in ActionScript with Ajax written in JavaScript, the first step is to embed Flash content into a web page. Two methods can be used: markup-based Flash embedding methods and JavaScript-based Flash embedding methods. However, the drawbacks of markup-based Flash embedding methods make JavaScript-based Flash embedding methods a better solution. To automatically convert markup-based Flash embedding methods into a JavaScript-based method, this paper presents a refactoring tool, called FlashembedRT, to assist programmers with the transformation. This tool refactors the five different markup-based Flash embedding methods to the JavaScript-based Flash embedding method called flashembed.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.677
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.022
GPT teacher head0.328
Teacher spread0.307 · 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