Automatic Discovery of Network Applications: A hybrid Approach
Bibliographic record
Abstract
I attended the Canadian AI conference between May 30, 2010 – June 2, 2010. On May 30, 2010, I jointly with Marina Sokolova from the Children Hospital of Eastern Ontario, co-chaired the Canadian AI graduate students symposium. The symposium had originally attracted about 23 submissions and had an acceptance rate of around 25%. There were about 30 participants from around Canada along with 5 professor panelists and 2 researchers from industry. The organization of this symposium both from an academic perspective and also logistics was done by myself and Marina. The symposium was a great success in terms of both the number of attendance and the quality of the work presented at the symposium. On the May 31, 2010, I presented my paper titled “Automatic Discovery of Network Applications: A hybrid Approach”. There were interesting issues raised during the Q&A period that can lead to the betterment of the work in the future including how the network packets were labeled before they were used in the classification algorithm and also about the possible applications of our work for network planning and alert correlation.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".