An Agent Enabled System for Personalizing Wireless Mobile Services
Bibliographic record
Abstract
Handheld wireless devices such as cellular phones and personal digital assistants (PDAs) have limited memory, storage, and processing power. In addition, small screens and limited input facilities make entering information tedious. It is therefore important that wireless mobile applications optimize resource usage and minimize input effort imposed on the user. One way is to download to the client only the information most relevant to the user, then present that information effectively, taking into account the user's preferences and history as well as the task at hand. This personalization approach minimizes the information to be displayed. In this paper, we present a mobile agent-based system for personalizing mobile services; we use mobile agents simply because such autonomous software entities have characteristics that can benefit mobile devices and the wireless environment. We introduce the tiered architecture of the proposed system and the functions of the different components; then we discuss how we use the Composite/Capabilities Preferences/Profile (CC/PP) in personalizing wireless mobile services. A proof of concept implementation has been developed using Java technologies.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".