Rising Star in Dependability Award
Bibliographic record
Abstract
Starting from 2020, a new award called Rising Star in Dependability Award is presented annually at the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) to recognize a junior researcher, from academia or industry, who demonstrates outstanding potential for creative ideas and innovative research in the field of dependable and resilient computer systems and networks. The award is jointly sponsored by the IEEE TC on Dependable Computing and Fault Tolerance (TCFT) and IFIP Working Group 10.4 on Dependable Computing and Fault Tolerance (WG 10.4). To be eligible, a candidate must have graduated no more than 10 years before the nomination deadline (considering the year as a reference). Career disruptions or delays (e.g. Parental leaves) that may have been experienced by the candidates are taken into consideration by the selection committee. A candidate may be nominated a maximum of two times. Previous recipients of the Award are not eligible. Self nominations and nominations by the Rising Star award committee members are not allowed The Rising star in dependability award is selected by an Award Committee appointed by the IEEE TCFT Chair, the IFIP WG 10.4 Chair and the current DSN PC chairs. The award takes the form of a plaque presented to the award recipient at the conference. The award recipient is required to attend DSN to receive the award and is invited to give a presentation to DSN attendees. His/her conference registration is borne by the conference. The winner of the 2020 Rising Star in Dependability Award is: Karthik Pattabiraman (University of British Columbia, Canada).
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.118 | 0.095 |
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".