Identification of Performance Requirements for Design of Smartphones Based on Analysis of the Collected Operating Data
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
In order to overcome the problems due to subjective judgments in the traditional product requirement acquisition techniques based on the “users’ voices,” a new data-based approach is developed in this research to identify the performance requirements for design of smartphones. The operating data are collected from smartphones and curve fitting method is used to obtain the performance distributions. The sigmoidlike function is employed to construct nonlinear customer satisfaction function (CSF) based on the performance distributions. From the CSF, customer required performance with a target satisfaction degree can be obtained. The cost-effective point for satisfaction improvement is determined to get a reasonable degree of satisfaction. A case study is conducted to identify the customer requirements on CPU performance based on the collected CPU utilization data.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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