Mobile spatial interaction in the Future Internet of Things
Bibliographic record
Abstract
Research and development of mobile information systems in the Future Internet of Things is about delivering technologies built around management and access to real-time heterogeneous datasets. Analyzing these enormous volumes of disparate data on mobile devices requires context-aware smart applications and services. 3DQ (Three Dimensional Query) is our novel mobile spatial interaction (MSI) prototype for data mining and analysis on today's location and orientation aware “smartphones” within such 3D sensor web environments. Our application tailors a military style threat dome query calculation using MSI with “hidden query removal” functionality to reduce information overload and heighten situation awareness on these commercial off-the-shelf (COTS) devices. Allied MSI research into the information overload problem is ongoing, where map personalisation and other semantic based filtering mechanisms are essential to de-clutter and adapt the exploration of the real world to the processing/display limitations of mobile devices. We propose that another way to filter this information is to intelligently refine the search space. The combined effect gives a more accurate and expected query (search) result for Location-Based Services (LBS) applications by returning information on only those objects/sensor enabled “things” visible within a user's 3D field-of-view (FOV) as they move through a built environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".