Eccws 2016 - Proceedings of the 15th European Conference on Cyber Warfare and Security
Bibliographic record
Abstract
These proceedings represent the work of researchers participating in the 15th European Conference on Cyber Warfare and Security (ECCWS 2016) which is being hosted this year by the Universitat der Bundeswehr, Munich, Germany on the 7-8 July 2016. ECCWS is a recognised event on the International research conferences calendar and provides a valuable plat-form for individuals to present their research findings, display their work in progress and discuss conceptual and empirical advances in the area of Cyberwar and Cyber Security. It provides an important opportunity for researchers and managers to come together with peers to share their experiences of using the varied and ex-panding range of Cyberwar and Cyber Security research available to them. With an initial submission of 110 abstracts, after the double blind, peer review process there are 37 Academic research papers and 11 PhD research papers, 1 Master's research paper, 2 Work In Progress papers and 2 non-academic papers published in these Conference Proceedings. These papers come from many different coun-tries including Austria, Belgium, Canada, Czech Republic, Finland, France, Germany, Greece, Hungary, Ireland, Kenya, Luxembourg, Netherlands, Norway, Portugal, Romania, Russia, Slovenia, South Africa, Sweden, Turkey, UK and USA. This is not only highlighting the international character of the conference, but is also promising very interesting discussions based on the broad treasure trove of experience of our community and partici-pants.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.135 | 0.073 |
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".