RAIMA: a Framework for the Design and Analysis of Self-Adaptive Egocentric Social Networks
Bibliographic record
Abstract
An emerging research area in pervasive computing is the inference of social context in order to facilitate and mediate communications among collocated people. Understanding users' needs through information reasoning and leveraging principles of social networks plays an important role in the emergence of innovative computer-mediated social networks. This thesis introduces a generic social networking framework for the design, analysis and visualization of opportunistic social networks. The proposed framework is capable of analyzing social similarities in order to provide decision support to users in the form of ego-centric social graphs. Using opportunistic data networks, a distributed inference model is introduced to provide multi-criteria attribute matching in an ad hoc computing environment. Enhancing communications protocols to deal with real-time analysis of dynamic data, generation of spontaneous semantics, and introducing efficient social visualization techniques are salient goals of this research. Efficient pattern matching algorithms in mobile ad hoc networks can have significant benefits in generating real-time context and eliminate the need for a centralized arbiter. In our research, we demonstrate a generic and customizable software architecture for achieving efficient pattern matching in mobile ad hoc networks. In this research we present a novel design for the development of a generic matching engine that is customizable to changing social scenarios. We show how customizable semantics can play an important role in decision-making, selection of a desired attribute, and notifying users with messages in a volatile mobile network.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".