Bibliographic record
Abstract
In this article, we propose an intelligent RFID (Radio Frequency Identification) checkout to facilitate access and payment, to assist visually impaired people and to improve marketing strategy. To avoid saturated queues which we used to see in conventional stores remote identification of the customer and items is used to purchase the latter. Payment is then performed by to online payment or by ATM or credit cards. In addition, to ensure high performance and smooth operation of this checkout, we thought of seven major criteria namely: · Improving marketing strategy by displaying specific advertisement for each customer depending on the history of his previous purchases. · Vocally assisting vocally the visually impaired people (Vocal Messages: welcome, total, confirmation...) · Checking the validity of products during purchase process to avoid sales of expired unhealthy products · Adding a biometric security level by using fingerprints so that the customer can confirm his identity and validate his purchase. This makes steeling others' identity cards useless. · Enabling the customer performing the transaction faster by automatic online payment and sending electronic invoice to his inbox. · Offering to the owner the possibility to check and supervise the history of transactions via internet. · Saving energy by activating the RFID reader only if a user enters or leaves.
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 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".