Bibliographic record
Abstract
Caring for elderly is a task which is facing various communities. Enabling seniors to live with dignity and security is one of the main goals in providing the care they deserve. Living in independent dwellings or in care giving facilities with minimum supervision and intervention can enable our elderly to maintain such dignity. Being able to monitor movements and activities of the elderly through various available sensing modalities are the key requirements for promoting such sense of independence. However, due to various limitations of the current sensing technology (i.e. either due to the lack of privacy, distributed and coarseness of the sensed information), the tracking information are subject to occlusions or occasional black-outs. It has been shown that the Sequential Bayesian approach can offer a suitable framework for tracking targets with the expected state-space definitions where their trajectories can follow a non-Gaussian distribution. However, the general approach requires to distribute various sample estimates of the motion in order to capture the prior distribution of the expected trajectories. This paper presents an approach which can be used as a part of such a priori distributions of sample trajectories in order to capture the predicted movements of the elderly in sequential framework. As such, it is possible to reduce the number of samples and offer a more computationally efficient approach.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".