Ambient and Context-Aware Services for the Future Web
Bibliographic record
Abstract
Context awareness refers to the system capability of both sensing and reacting to situational changes. It is one of the most exciting trends in computing today that holds the potential to make our daily life more productive, convenient and enjoyable. Over the years, advances in Web and mobile technologies are gradually bringing the full potential of desktop computers to potable mobile devices. As a matter of the fact that the number of mobile phone users is already much higher than desktop users, most Internet and Web-based services such as search engine querying, news reading, multimedia downloading, instant messaging, online shopping and also social networking can be accessed mainly through a large variety of mobile devices instead of desktop computers. Indeed, context-aware mobile services are emerging as an important technology to underpin the new breed of user-centric smart applications on the future ubiquitous Web. Although mobile devices naturally have the capability to capture both the physical context, such as location, and the social context, such as presence and relationships, of users, there are many hurdles to cross in order to realize the full potential of context-aware ambient services. This theme issue looks at the new development in ambient and context-aware Web information systems such as location-based applications, mobile payment systems, mobile context-aware applications and mobile Web data search engines.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".