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Record W2027332812 · doi:10.1115/msec2007-31177

Agent-Based Integration of Collaborative Mobile Environment and Smartboard

2007· article· en· W2027332812 on OpenAlexaff
Abdou Al-Rahim Kadri, Mohammad Mozaffari Kermani, Hamada Ghenniwa, Weiming Shen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Agent-Based Network Management
Canadian institutionsNational Research Council CanadaWestern University
Fundersnot available
KeywordsComputer scienceInteroperabilityUbiquitous computingFocus (optics)Human–computer interactionMobile deviceContext-aware pervasive systemsMobile computingSmart environmentWorld Wide WebComputer networkInternet of Things

Abstract

fetched live from OpenAlex

Ubiquitous computing is a new emerging computing style which adapts various computing devices throughout our living and working spaces. These devices include mobile devices (such as PDAs, smartphones), traditional desktop, smart-board and so on. Mobile devices coordinate with each other and network services seamlessly, yet they are often inactive and even when in use they are most of the time disconnected from the network, which severely limits their use for ubiquitous interaction with the environment’s entities. In this paper we study the integration of Collaborative Mobile Environment (CoMoE) and interactive devices such as smart-board to overcome the pervious problems. The focus of this work is on the development of the integration architectural framework. This architectural framework is based on Coordinated Intelligent Rational Agent (CIR-Agent) model, which supports sophisticated real-time interaction among agents. Also we discuss the notion of personal assistant agents that allows the physical integration and interoperability of entities in an environment and in the same time provides assistance in achieving user’s goals.

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 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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.853
Threshold uncertainty score0.440

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 communication0.0000.000
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 head0.009
GPT teacher head0.227
Teacher spread0.217 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreMethods

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

Citations0
Published2007
Admission routes1
Has abstractyes

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