A semantic approach for accessible services delivery in a smart environment
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.
Bibliographic record
Abstract
Traditional technology-push approaches fail to overcome user adaptability and user acceptability issues in heterogeneous environments. It becomes crucial to adopt a user-centric approach, both from methodological and technological points of view. In this paper we present a novel approach to provide the user with accessible services in a smart environment. This approach is based on detection of user limitation capabilities ('handicap situations') in a smart assistive environment. It is built upon a formalism based on Description Logic (DL), named Semantic Matching Framework (SMF). The architecture of SMF is designed in such a way that Human-Environment Interaction (HEI) is generated online to identify and compensate for the handicap situation occurring in the course of activities of daily living. It was implemented using semantic web technologies and integrated into a demonstrator, which has been used to validate the concept in laboratory conditions. This paper includes the time response and the scalability analysis of SMF.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it