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Record W2565279987 · doi:10.1109/mcc.2016.130

A Tensor-Based Big Service Framework for Enhanced Living Environments

2016· article· en· W2565279987 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

VenueIEEE Cloud Computing · 2016
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCloud computingComputer scienceUploadQuality of serviceTensor (intrinsic definition)Plane (geometry)Service (business)Loose couplingHuman–computer interactionData scienceDistributed computingComputer securityWorld Wide WebTelecommunicationsOperating system

Abstract

fetched live from OpenAlex

The rapid advances of information, computing, and communication technologies have led to significant enhancements to human living environments. Enhanced living environments (ELEs) encompass the coupling of information technologies (cyber), intelligent devices (physical), and human society (social) for enhanced quality of life. Together, these spaces are referred to as cyber-physical-social systems (CPSSs). For CPSSs to provide high-quality services, improved service frameworks are needed. The framework presented in this article includes a sensing plane, cloud plane, and application plane. In the sensing plane, the relationship of objects in every local CPSS is represented by a local tensor, which is cleaned and uploaded to the cloud plane. In the cloud plane, a global tensor is constructed by integrating all local tensors together. Next, the application plane provides the corresponding high-quality services. A case study using a typical CPSS smart home illustrates a simple application of the proposed service framework.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.931

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 science0.0010.000
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 head0.041
GPT teacher head0.270
Teacher spread0.228 · 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