Bibliographic record
Abstract
Purpose To survey ambient intelligence research in Europe, the USA and Japan and, in particular, in the context of the issues of privacy, identity, security and trust and the safeguards proposed to protect them. Design/methodology/approach This paper is based on research being conducted by the SWAMI consortium under the EC's Sixth Framework Programme. SWAMI stands for Safeguards in a World of Ambient Intelligence. The consortium comprises five partners: the Fraunhofer Institute (Germany), the Technical Research Center of Finland (VTT Electronics), Vrije Universiteit Brussel (Belgium), the Institute for Prospective Technological Studies (IPTS) (Spain) and Trilateral Research & Consulting (UK). The 18‐month SWAMI project began in February 2005. Findings Most AmI projects do not take into account privacy, security and related issues. However, a reasonable number do (perhaps a quarter of those in Europe) to a greater or lesser extent and some have proposed safeguards. Research limitations/implications This paper references only a limited set of the research projects being carried out in Europe, the USA and Japan. More detailed information can be found on the SWAMI web site ( http://swami.jrc.es ). Practical implications A mix of different safeguards will be needed to adequately protect privacy, etc. in the new world of AmI. Originality/value Until now, there has been no reasonably comprehensive survey of AmI research projects in Europe, the USA and Canada focused on privacy, security, identity and trust issues. None has considered the range of safeguards needed to protect privacy, etc.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".