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
In recent years, the Internet of Things (IoT) has been an inseparable part of our lives. IoT is typically heterogeneous in nature and requires interconnection with different types of devices or “things”. Being able to secure such a distributed environment is an onerous task. The heterogeneity of IoT, along with other factors, poses a challenge when it comes to securing communication between these devices. In this paper, we propose a novel IoT trust and reputation model that employs distributed probabilistic neural networks (PNNs) to classify trustworthy nodes from malicious ones. Our model tackles the cold start problem in IoT environments by predicting ratings for newly joined devices based on their characteristics and learns over time. Processing is completely distributed and is handled by the nodes themselves. This guarantees better availability, since there is no single point of failure. Moreover, our model can accommodate the various capabilities and types of IoT devices. Unlike other proposed models in the literature, our model provides different levels of security depending on the sensitivity of the data being transmitted.
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 | 0.002 |
| 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 | 0.006 | 0.005 |
| 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