AQUARIA: Assistive Querying in Augmented Reality for Interactive Analytics
Bibliographic record
Abstract
This study introduces a novel system designed for the exploration and visualization of maritime data, aimed at enhancing decision-making, particularly in identifying unlawful maritime activities. The system fosters collaboration across diverse expertise and data requirements by merging augmented reality (AR) head-worn display with both individual and communal touchscreen interfaces. Through an iterative process, we developed capabilities for visual languages querying and visualizing maritime data over time and space, employing both a node-link visual query language and hands-on geospatial interactions for selections, filters, and queries. Evaluations through cognitive walkthroughs by professionals in interaction design and maritime fields revealed the effectiveness and intuitiveness of our direct manipulation approach, though some discrepancies were noted between user expectations and the node-link query process. We suggest advanced AR techniques to enhance the intuitive use of direct manipulation in data queries and to more closely integrate map visuals with query components. Further refinement of our system was achieved through detailed cognitive walkthroughs with experts in interface design and maritime data. This comprehensive assessment, involving 8 key tasks and 38 actions over two phases, highlighted the vital role of accurate spatial and temporal data filtering in maximizing the efficiency and user experience of AR-enhanced maritime tools. Effective data management is crucial for the system's performance and ensuring smooth, engaging interactions for users.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".