Self-organised map and trust-aware-based quality of service prediction for reliable services selection in distributed computing environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The distributed computing environment allows to provide the outsourced computing services in addition to web-services for IoT and mobile technologies. An emerging research topic is the QoS and security indicator prediction to achieve a reliable service selection that meets user requirements. Collaborative filtering technique is one of the most widely used model in service selection. It is based on similarity computation between users or services. But the main drawback of this method is the lack of data to compute an effective similarity value. Furthermore, malicious users give false feedback which influences the accuracy of prediction. In this work, we propose a novel similarity evaluation model based on self-organisation map to address the problem of data lack and the robust index computation to detect the untrustworthy users. The proposed approach uses a K-means-based average evaluation to determine the tenderness of the data and an offline build-up model to increase computational efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it