Edge-assisted human-to-virtual twin connectivity scheme for human digital twin frameworks
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
The human digital twin (HDT) is a new paradigm that possesses the ability to revolutionize the current healthcare systems. With HDT, ensuring an efficient connectivity scheme between each human-virtual twin pair remains a significant problem. As the concept of HDT is new, conventional connectivity schemes cannot meet the unique requirements of HDT in terms of reliability, security and privacy. This paper thus proposes an edge-assisted connectivity scheme for HDT and adopts an integrated blockchain and federated learning techniques to ensure security and privacy. To minimize long-term average connectivity cost, we formulated the connectivity problem as a Markov decision process and adopted the deep deterministic policy gradient (DDPG) algorithm to learn the optimal connectivity policy in terms of connectivity cost. The obtained results were then compared with the conventional deep Q-network-based solution. The results show that the proposed DDPG-based connectivity solution is feasible to perform the connectivity process better by optimally allocating system resources, thus reducing the overall connectivity cost, while ensuring data security and privacy.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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