Utility-Aware Legitimacy Detection of Mobile Crowdsensing Tasks via Knowledge-Based Self Organizing Feature Map
Bibliographic record
Abstract
In Mobile Crowdsensing (MCS), fake tasks can drain significant amount of resources. This paper proposes a new methodology to determine a proper time window for the training dataset and the impact of the accuracy of task legitimacy detection on the MCS campaign performance. To reach the desired performance, the task legitimacy detection is utilized in such a way that while legitimate tasks are kept, the fake tasks are eliminated as much as possible in the MCS platform through machine learning (ML) prediction. The proposed methodology is evaluated for legitimacy detection under multiple ML methods. Moreover, a knowledge-based fake task detection technique with effective feature selection is formulated to ensure fake tasks are filtered at the MCS servers. Detection accuracy is improved by using shorter time frame in training and longer time frame in prediction. The overall performance improvement based on profit, cost, legitimate tasks loss ratio, and fake tasks elimination ratio has been achieved under three different sizes of training datasets to verify the efficiency of the proposed methodology. Moreover, Prior Knowledge Input with Self-Organizing Feature Map outperforms the conventional legitimacy detection by 5.48%, 12.11% and 58.05% in terms of test accuracy, profit and cost under the small dataset, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".