Context-Aware Data Analytics Variability in IoT Neural Network-Based Systems
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
Emergent software applications are increasingly becoming (self-)adaptive and autonomous. Further, Internet of Things (IoT) applications increasingly involve data analytics. The introduction of neural networks in IoT systems has enabled a new generation of applications capable of performing complex sensing and actuation analysis tasks that were not previously possible with other approaches. A key component in the development of these systems is the ability to represent data analytics variability, which captures the ways in which the system can adapt in terms of the data analysis at design and run times. Although variability has been explored in the domain of software product lines (SPLs), data analytics variability in IoT neural network-based systems still seems to be poorly understood and needs to be investigated appropriately. In this paper, we introduce an approach to capture data analytics variability in IoT neural network-based systems (IoTNNSs). The approach represents several types of variability inherent in the development of these analytics systems, including those related to the application context, behavior, quality attributes, IoT devices, and neural networks.
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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 | 0.004 | 0.001 |
| 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 | 0.001 | 0.002 |
| Open science | 0.015 | 0.006 |
| 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