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Record W4241415638 · doi:10.1108/ijpcc-02-2021-177

Guest editorial

2021· editorial· en· W4241415638 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 Pervasive Computing and Communications · 2021
Typeeditorial
Languageen
FieldComputer Science
TopicInternet of Things and AI
Canadian institutionsBrandon University
FundersNational University of Singapore
KeywordsComputer scienceComputer graphics (images)Human–computer interaction

Abstract

fetched live from OpenAlex

Special issue on next generation of pervasive computing and IoT systemsThe pervasive computing and internet of things (IoT) communities examine similar problems and face similar challenges.Some might argue that pervasive computing focuses more on HCI issues, while IoT focuses more on connecting the devices, yet both communities share largely overlapping technical interests and goals.[AQ2]Both are interested in issues beyond just technology, such as privacy, security and ethics and both are pursuing similar use cases.Ebling, thus, encourages the two communities to join forces and work together to achieve common goals.Today, the infrastructure needed to support pervasive computing and the IoT faces unprecedented challenges as entirely new classes of applications and systems emerge.For example, pervasive systems designed to augment human cognition with tasks such as face recognition must operate at "superhuman speeds," delivering insights to help with human decision-making within very strict and narrow time limits.Similarly, the emergence of pervasive video analytics demands the processing of very large volumes of video data in near-real-time.In general, the field of pervasive computing is rapidly changing in the face of major advances in sensing, data processing techniques and wearable computing.The articles collected in this special issue:The IoT-based smart irrigation system designed with various sensors to collect farm field data and stored all the data in the cloud for scheduling the irrigation (Mannar Mannan J et al.); Proposed novel feature selection approach in combination with the machinelearning algorithm which can early predict the chronic disease with utmost accuracy (Hegde, S et al.);The purpose of this paper is to provide performance analysis for four-state tandem open queue network and a governing equation is formulated with the help of a transition diagram (Priya, B et al.);The novel approach in this paper is used to study the hybrid ABC-DT classifier and compare the performance against three well-known classifiers such as PSO-KM, SVM and K-NN (Jesuretnam, J.B et al.);The reticulum perception is that the methods which examine and determine the scheme of contact on unearths toward the number of dangerous and perchance fateful interchanges occurring toward the system (Sreeram, G et al.);The security levels of the proposed model are compared with the existing models and provide a better performance using the Key Distribution Centre (Anbu Malar et al.);

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.007
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0070.003
Research integrity0.0000.002
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 head0.013
GPT teacher head0.317
Teacher spread0.304 · 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