Proceedings of the 1st ACM SIGMOBILE international workshop on Systems and networking support for healthcare and assisted living environments
Bibliographic record
Abstract
It is our greatest pleasure to welcome you to the First ACM SIGMOBILE International Workshop on Systems and Networking Support for Healthcare and Assisted Living Environments (HealthNet'07), co-located with ACM MobiSys 2007. We have put together an excellent technical program that we hope you will enjoy and that you take advantage of the excellent group of people that have come together for HealthNet 07. As the world's population grows older, healthcare and high-tech companies are joining forces. New technologies are being used to provide improved support for elderly and home bound people in their homes and in assisted living environments. The overall goal of these initiatives is to improve quality of life by providing customized support to people in need of assistance. The ultimate goal is a system that can adapt to the users' needs, helping them get through their daily routine in a way that is effective in providing support where needed without making them feel humiliated by excessive attention. To provide such support, it will be necessary to combine efforts from many areas of computer science, including networking, distributed systems, security, data management, HCI and middleware. The mission of the HealthNet workshop is to bring together researchers from both industry and academia from these different areas to provide a forum for discussing the cross-area interactions that will be necessary for successful systems and applications. HealthNet, therefore, gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of healthcare systems. After a very successful publicity phase, the response to the call for papers was extremely positive. We received a total of 45 submissions from 11 countries with papers from Asia, Canada, Europe, and the United States. These papers cover diverse topics from architectures and protocols to case-studies and real-world experiences. The selection of papers for this year's program was carried out by a 17 member international technical program committee. As a result of our review and discussion phase, a total of 13 full papers were accepted for presentation at the conference, which corresponds to a competitive acceptance rate of 28%. Additionally, we accepted 9 short papers to be presented as posters during the workshop.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".