YaKit: a locality based messaging system using iCon overlay
Bibliographic record
Abstract
We propose a new approach to building localized, context-driven social networking applications to allow people to communicate, interact, collaborate, and socialize in a truly innovative manner. In particular, the goal is to provide mechanisms to form communities of people who do not necessarily know each other, but are in close proximity to each other. In other words, it allows a user to communicate with unfamiliar people with whom the user does not know how to get in touch with. To achieve these goals we designed and implemented an infrastructure and an application using proximity based structures hashed over a set of clouds to distribute the user load effectively. Our infrastructure, called iCon, provides layers of APIs to separate concerns and support the key features of our approach. To evaluate this layered architecture we developed YaKit, an innovative social networking application. This locality based end-user application, which runs on workstations as well as mobile phones, allows to identify persons in close proximity and interact with them. The premise is that people are tied to the places and time periods of their life's experiences. Communicating in such a way is not only novel but also presents a wide range of capabilities and opportunities for smart web applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".