ARCANE: Algoritmo Meta-heurístico para Alocacao de Tarefas em Nuvens Veiculares
Bibliographic record
Abstract
O avanço dos Sistemas de Transporte Inteligentes (ITS) vem para auxiliar a resolver problemas de tráfego, que atualmente geram problemas socioeconômicos. Entretanto, devido a dinamicidade da rede em que os ITS atuam, a alta mobilidade dos veículos e a constante mudança de topologia faz com que o problema de alocação de recursos e tarefas se tornem ainda mais desafiador. Diante desse desafio, é proposto o ARCANE, um algoritmo meta-heurístico para alocação de tarefas em nuvens veiculares. O ARCANE é um método bio-inspirado baseado no Algoritmo do Morcego (BAT). O objetivo do ARCANE é otimizar o processo de busca para fornecer soluções subótimas no processo de alocação de recursos e tarefas em uma nuvem veicular. Quando comparado com outas soluções da literatura, o ARCANE mostrou ser efetivo em alocar tarefas, aproveitando melhor os recursos das nuvens veiculares em todos os cenários.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".