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Record W2076538271 · doi:10.1145/2702613.2732840

Atypical Visual Display for Monitoring Multiple CCTV Feeds

2015· article· en· W2076538271 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsComputer scienceWorkloadHuman–computer interactionCognitionKey (lock)Visual searchComputer securityArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

Despite advances in surveillance technologies, security and command and control (C2) centers still rely strongly on human operators to detect critical events. Human factors-such as cognitive workload and limited attentional capacity-have been shown to affect operators' ability to detect critical incidents. The current standard surveillance environment comprises a large screen layout that simultaneously displays multiple camera feeds. Although having access to all sources of information at once seems intuitively appealing, there is ample evidence to suggest that it can, in fact, lead to poor detection performance. We propose a design solution that is based on principles grounded in cognitive psychology and user experience design. One key objective is to test empirically whether an atypical design pattern that is consistent with serial cognitive processes induces better performance than the current standard surveillance environment. Three variations of the alternative display pattern will be tested by comparing their effects on detection performance within a surveillance microworld.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.239

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 science0.0000.000
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 head0.068
GPT teacher head0.362
Teacher spread0.294 · 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

Quick stats

Citations8
Published2015
Admission routes1
Has abstractyes

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