A BiLSTM Combining WRELM-Based Method For Online TCP State Prediction
Why this work is in the frame
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Bibliographic record
Abstract
Round-trip time (RTT) and throughput are two of the most important parameters that in networks with transmission control protocol (TCP). Deep learning-based time-series forecasting methods such as long-short term memory (LSTM) have recently been widely applied in TCP state prediction due to their strong pattern recognition and accurate prediction ability. However, the practical network environment can be dynamic and may deviate from the situations in which the deep model has been trained, resulting in deteriorated predictions. Furthermore, online retraining of the deep model to adapt to current working environment is usually unfeasible due to the nature of heavy computational complexity. In this paper, we propose a method which can online rectify the TCP predictions with a very small computational overhead (time consumption) by combining Bidirectional LSTM (BiLSTM) with the weighted regularized extreme learning machine (WRELM). Experiments show that the proposed method can greatly increase the online prediction accuracy of TCP states especially when the knowledge of the trained deep model diverges from the conditions of its original working environment.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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