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Record W2594639334

D-ForenRIA: a distributed tool to reconstruct user sessions for rich internet applications

2016· article· en· W2594639334 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

VenueComputer Science and Software Engineering · 2016
Typearticle
Languageen
FieldComputer Science
TopicWeb Data Mining and Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAjaxComputer scienceJavaScriptSession (web analytics)Rich Internet applicationScalabilityThe InternetWorld Wide WebWeb applicationHuman–computer interactionDatabase
DOInot available

Abstract

fetched live from OpenAlex

Rich Internet Applications (RIAs) which use JavaScript and Ajax have become the norm for modern Web applications. However with RIA, the reconstruction of user-interactions from recorded HTTP logs is a new and challenging problem. We present D-ForenRIA a distributed tool for session-reconstruction for RIAs. D-ForenRIA provides detailed information about user actions including DOM elements involved and user-inputs provided. D-ForenRIA incorporates novel techniques to order candidate user-interactions based on DOM features and knowledge acquired during session reconstruction. In addition, using several browsers concurrently makes the system scalable for real-world use. The results of our evaluation on several RIAs show that D-ForenRIA can efficiently reconstruct use-sessions in practice.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.980
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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