Customizing and delivering mobile services using software agents and CC/PP
Bibliographic record
Abstract
A downside brought about by the explosive growth of online information and the shear amount of data traffic moving through our networks is the modern day headaches of information overload. The growth of handheld wireless devices can only mean that the end-users will continually be bombarded by emails and real-time information feeds, but now, even as they are on the go. Software agents provide mechanisms that have good potentials to lower the amount of information that an end- user has to deal with. Agent-enabled applications allow the end- user to personalize the way online resources are presented and can also filter out irrelevant or unwanted information. In this paper, we present our experience in using software agents and the Composite Capabilities/Preference Profiles (CC/PP) for customizing and delivering mobile services for Java 2 Micro Edition (J2ME) enabled and Wireless Application Protocol (WAP) enabled devices. We use software agents because such autonomous software entities have characteristics that can benefit mobile devices and the wireless environment, and the CC/PP is a standard for defining profiles for user preferences and device capabilities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".