Preliminary Experience and Impact of Experiment-focused Teaching Approach in a Computer Architecture Course in Computer Science
Bibliographic record
Abstract
Rahman extensively conducted research at the National Institutes of Health (NIH), USA for almost six years as a Research Scientist.He significantly contributed to research and development of the image processing, classification, and retrieval methods extensively used in the NLM's Open-i Search Engine for biomedical literature.Dr. Rahman has good expertise in the fields of Computer Vision, Image Processing, Information Retrieval, Machine Learning, and Data Mining and their application to retrieval of biomedical images from large collections.Since joining Morgan, Dr. Rahman also has been actively involved in basic educational and instructional research by infusing several interactive and active learning techniques in classroom to teach introductory programming courses with a goal to improve the retention rate in the CS department.Dr. Rahman has published a book, two book chapters and around seventy articles in peer-reviewed journals and conference proceedings, such as IEEE Transaction on Information Technology in Biomedicine, Computerized Medical Imaging and Graphics, etc. and presented his works in numerous conferences and workshops, such as ICPR, CBMS, CLEF, CIVR, HISB, SPIE, BIBE, IEEE FIE, etc. His current research is focusing on Crowdsourcing and Deep learning techniques and their application in medical fields, especially for retrieval and diagnostic purposes.Pursuing continuous financial support is an integral part of Dr.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".