Bibliographic record
Abstract
After the success of the previous 1 st edition (1 st International Workshop on Human-Centric Smart Environments for Health and Well-being IHSH'2018) held at Médéa, Algeria on 24 th -25 th November 2018, and the 2 nd edition was held at Boumerdès, Algeria on 9 th -10 th February 2021, the new 2022 edition is promoted to the rank of a conference entitled "The 2022 International Conference on Human-centric Smart environments for Health and well-being (IHSH'2022)".It will be organized by the University of Quebec at Rimouski (UQAR, QC, Canada) in collaboration with the University of Sciences and Technology Houari Boumediene (Algiers, Algeria).It will be held in Hybrid at Lévis, Canada (QC) on October 26 th -28 th , 2022.Smart environment for health and well-being may be defined as "a world that is richly and invisibly interwoven with smart devices (sensors, actuators, displays, etc.) that are continuously working to make humans' lives more comfortable and to improve their health and well-being".The aim of the conference IHSH'2022 is to contribute to the scientific knowledge of building smart environments for health and well-being.This conference will provide an interesting multi-disciplinary collaborative forum for the active community of academics, researchers, and industrials from computer science, information technology, electrical engineering, biomedical engineering, and telecommunication.The conference invites authors to present original works describing research results, theoretical, practical, or industrial solutions (prototype, formal modeling, augmented reality, machine learning, big data, web & internet of things, system theory, optimization, robotics, etc.) and discussing innovative ideas that have potentials to build human-centric smart environments for health and well-being.The conference will include plenary talks and oral/poster sessions.IHSH'2022 is Technically Co-Sponsored by IEEE and is planned as a hybrid event: some sessions in-person to be held in Lévis, Canada (QC) and some over the Internet.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.867 | 0.808 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".