Proceedings of the 2008 Ambi-Sys workshop on Software Organisation and MonIToring of Ambient Systems
Bibliographic record
Abstract
Weiser's dream of an environment enhanced with a set of invisible computing devices is slowly becoming a reality. While most technological requirements can be fulfilled with the current technology, there are still many open questions regarding how to design, build and deploy this kind of systems. It is a quite remarkable fact that the world of software engineering has been in a sense surprised by the world of electronics in such a way that we now have sensors and multimodal interactors and no rigourous methodology to create context-aware programs. Moreover, existing monitoring systems for networked computerized systems are not obviously able to adapt to these new systems. We can think of a future where humans interact with a seamlessly integrated cloud of processes and a partly invisible set of devices. What are suitable system architectures for dynamic multi-device appliances? Which programming languages cope best with the needs? How should we devise our applications in order to adapt to hardware evolution? How can our systems interact with previously unknown sensor networks? How flexible can applications adapt to new situations and what level of anticipation is unavoidable? How should systems be presented to end users with such a variety of computing devices? How shall we monitor the different devices and the global architecture ?
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".