Automatic Reuse of User Inputs to Services among End-Users in Service Composition
Why this work is in the frame
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Bibliographic record
Abstract
End-users conduct various on-line activities. Quite often, they re-visit websites and use services to perform re-occurring activities, such as on-line shopping. The end-users are required to enter the same information into various web services to accomplish such re-occurring tasks. It can negatively impact user experience when a user needs to type the re-occurring information repetitively into such web services. In this paper, we propose an approach to prevent end-users from performing such repetitive tasks. Our approach propagates user inputs across services by linking similar input and output parameters. Our approach pre-fills values to the input parameters for an end-user using his or her previous inputs. To increase the chance of identifying a proper value for an input parameter performed by one end-user, our approach also leverages the inputs from other end-users. We identify and link similar end-users to enable the propagation of user inputs among end-users. We have designed and developed a prototype. We also conduct an empirical study to evaluate our approach using the real world services. The empirical results show that our approach using an end-user's previous inputs can reduce on average 41 percent of repetitive typing for the execution of composed services. Furthermore, the previous inputs from the similar end-users can improve our approach in reducing the repetitive typing for an end-user.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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