Recognition of fuzzy contexts from temporal data under uncertainty case study: Activity recognition in smart homes
Bibliographic record
Abstract
Intelligence within a system causes non-linearly behavior of system to achieve its goals, which is defined as desired world states of the intelligent system. To achieve the intended goals, an intelligent system actuates the world by realizing of strategies, scenarios, actions, activities and operations. To assist an intelligent system, we need information and knowledge from world to reason in normality of the world states and to evaluate how much the intelligent system succeeds. The non-linear behavior of intelligent systems makes it rather difficult to reason in normality of the world state, because there is no clear border or frontier to isolate the normal and abnormal world states. To do this judgment two criteria are considered. One criterion is the possible context that an activity can be accomplished in it and the other criterion is verification of the correct realization of activities. In this paper, we propose a temporal data-driven artificial intelligence technique to recognize contexts of scenarios and verify if a scenario is done in an appropriate context.
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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 | 0.002 | 0.000 |
| 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 | 0.005 |
| Open science | 0.001 | 0.001 |
| 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".