Correlated multi-dimensional qos metrics for trust evaluation within web services
Bibliographic record
Abstract
Trust and reputation techniques have offered favorable solutions to the web service selection problem. In distributed systems, service consumers identify pools of service providers that offer similar functionalities. Therefore, the selection task is mostly influenced by the non-functional requirements of the consumers captured by a varied number of QoS metrics. In this paper, we present a QoS-aware trust model that leverages the correlation information among various QoS metrics. We compute the trustworthiness of web services based on probability theory by exploiting two statistical distributions, namely, Dirichlet and generalized Dirichlet, which represent the distributions of the outcomes of multi-dimensional correlated QoS metrics. We employ the Dirichlet and generalized Dirichlet when the QoS metrics are positively or negatively correlated, respectively. Experimental results endorse the advantageous capability of our model in capturing the correlation among QoS metrics and estimating the trustworthiness and reputation of service providers.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".