Attribute-Driven Design of Incremental Learning Component of a Ubiquitous Multimodal Multimedia Computing System
Bibliographic record
Abstract
System design using attribute-driven design (ADD) means that system requirements, including functional and quality requirements and constraints, are considered as drivers in the design process that yields the system's conceptual software architecture. The output architecture satisfies not only that the functional requirements but also the important qualities the system must possess. In ADD, the secondary qualities are satisfied within the constraints of achieving the most important ones. In this paper, we detail the design of our system's machine learning (ML) component using ADD. Tactics and primitives to achieve system qualities (i.e. performance, security, availability, modifiability, and usability) are essayed in this paper. The ML component of our system is responsible for (1) determining the appropriate media and modalities based on user context, (2) finding the replacement to a failed/missing device or modality, and (3) providing the context suitability of newly-added media or modality. The ML component's knowledge acquisition is incremental; it keeps its previously-earned knowledge in its knowledge database (KD) and appends newly-acquired ones onto it. The ML component makes the system intelligent, adaptive and fault-tolerant. This work on ML-based media and modality selection is our original contribution to the domain of intelligent pervasive human-machine interface
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".