Smart environments using near-field communication and HTML5
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
Home health care and home automation increasingly allows more seniors to maintain independence, and remain longer in their own homes. Similarly, a post-surgical patient may be discharged from a medical facility to their house, which electronically facilitates their recuperation and promotes recovery. Smart environments are making the task of providing assistive technology in the home easier and more affordable. Near-field communication (NFC) has become popular in recent years. Increasing uptake of NFC-enabled smartphones has opened a new avenue to facilitate creation of a smart environment without the need for significant infrastructure. HTML5 is the latest version of the hypertext markup language, with unique code that enables access to advanced features on a smartphone. Proprietary apps can potentially be inconvenient and inconsistent and may even decrease uptake of the technology. In this paper, we propose a new methodology to enable NFC tags and NFC smartphones in conjunction with HTML5 backbone code, to be used for smart environments in home health care applications without the need for specific applications to be installed on the smartphone. Results show significant promise with just the built in phone software with use of NFC and HTML5 for various applications of smart environments. In many common tasks in a smart environment that increase patient safety, NFC tags can be not only informative, but an integral component of the system by triggering specific HTML5 code to provide appropriate responses - without the need to install specialized apps as long as the NFC is enabled in the mobile device.
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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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