An Agent Enabled System for Personalizing Wireless Mobile Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 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 | 0.001 | 0.004 |
| Open science | 0.001 | 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 it