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Record W4416640425 · doi:10.1016/j.procs.2025.10.171

Preface

2025· article· en· W4416640425 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

VenueProcedia Computer Science · 2025
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsAcadia University
Fundersnot available
KeywordsPublicityDomain (mathematical analysis)WishTrack (disk drive)Technical reportSpecial Interest Group

Abstract

fetched live from OpenAlex

We warmly welcome you to Istanbul, Türkiye and to the 16th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-2025). With the help and support of the technical committees we have put together an exciting technical program for this years’ EUSPN conference. We hope you enjoy the program and have fruitful interactions and discussions with researchers and practitioners gathering here from around the world. EUSPN is a leading international conference for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all Ubiquitous Systems and Pervasive Networks related areas. EUSPN 2025 is held in Istanbul, Türkiye, October 28-30. In addition to the keynote and technical sessions, we have workshops dealing with more specific aspects on EUSPN topics accompanied the main conference. These workshops had their own organizing committees and refereeing process. EUSPN 2025 received 132 papers from the authors representing many continents and countries. The papers were submitted to different tracks wherein each track has a separate technical program committee. Each submitted paper was reviewed by 2 to 4 domain experts in their respective tracks. Based on these reviews, we accepted 43 papers making an acceptance rate of 32.57%. It is our wish and hope that the people participating in the conference will find common ground on which they can learn from each other, and that the conference engenders future fruitful scientific activities. We wish to thank the General Chairs, the Advisory Committee, the Workshops’ Chair, the Organizers of the Workshops, the Local Arrangements Chairs, the Publicity Chairs, the Technical Program Committee of all tracks, the participants, and, most importantly, the researchers who submitted the articles which appear here. We look forward to hearing productive and interesting discussions during the EUSPN 2025 conference. We wish you a pleasant stay and an enjoyable time in Istanbul, Türkiye!

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: Methods · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0030.001
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 head0.011
GPT teacher head0.259
Teacher spread0.248 · 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