SenseFace: A sensor network overlay for social networks
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
Sensor devices can capture and process physical phenomena and transmit sensory data to remote locations without any human intervention. One noticeable multidisciplinary advancement in recent sensor technology has resulted in wearable sensor devices that can monitor different daily activities of a human body by forming a body sensor network (BSN). Based on the event, the captured physical data might be very important and must reach interested entities or individuals, if deemed necessary. This requires a framework to deliver the captured sensory data to one's community of interest (COI), which can also be regarded as one's social network. The social network encompasses one's family members, friends, colleagues at work or business, government, and all those that a person communicates with for either a short or long period of time. In this paper, we propose a framework that creates an overlay network to create a secure communication passage for dynamic sensory data communication among users belonging to the same COI. We consider a real life scenario of a 4-tier network consisting of a BSN, cellular network, Intenet and overlay network to deliver sensory data from the BSN to the overlay network. We then introduce our initial open source service oriented prototype of the framework, called SenseFace. SenseFace is suitable for capturing the sensory data from a user's BSN, processing and storing the sensory data in his/her personal gateway, which is a mobile device, sending the data to a remote Internet gateway and finally disseminating the sensory data intelligently to a list of his/her social networks. Finally, we share the experiences we have gathered after the initial phase of the implementation.
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 | 0.000 | 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 it