Dynamic framework to mining Internet of Things for multimedia services
Bibliographic record
Abstract
Abstract The rapid and unprecedented technological advancements are currently dominated by two technologies. At one hand, we witness the rise of the Internet of Things (IoT) as the next evolution of the Internet. At the other hand, we witness a vast spread of social networks that connects people together socially and opens the door for people to share and express ideas, thoughts, and information. IoT is overpopulated by a vast number of objects, millions of multimedia services, and interactions. Therefore, the search of the right object that can provide the specific multimedia service is considered as an important issue. The merge of these two technologies resulted in new paradigm called Social IoT (SIoT). The main idea in SIoT is that every object can mine IoT in search for certain multimedia service. We investigate the issue of friends' management in SIoT and propose a framework to manage friends' requests. The proposed framework employs several mechanisms to better manage friends' relationships. The proposed framework consists of friend selection, friendship removal, and an update module. It proposes a weight‐based algorithm and Naïve Bayes Classifier‐based algorithm for the selection component. Moreover, a random service allocation model is proposed to construct service‐specific network model. This model is then used in the simulation setup to examine the performance of different friends' management algorithms. The performance of the proposed framework is evaluated using simulation under different scenarios. The obtained simulation results show improvement over other strategies in terms of average degree of connections, average path length, local cluster coefficients, and throughput.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".