Editorial Message - Special Track on Embedded Systems: Applications, Solutions, and Techniques
Bibliographic record
Abstract
Spatial and Image based information systems are increasingly at the heart of novel applications, raising new challenges in complex spatial data modelling, spatial data and image sharing, and emergent spatial data semantics.The ACM SAC track on Advances in Spatial and Image-based Information Systems (ASIIS) aims to foster interdisciplinary discussions and research in complementary aspects of these systems.The track includes original research contribution and practical design solutions and brings together different points of view when addressing spatial and image based information systems.This year 20 papers were submitted from 15 countries, showing the establishment of the event as a truly international competition.After a rigorous blind peer review procees, only 5 papers were accepted for inclusion in the conference proceedings.Due to space limitations many good papers were rejected.In the first paper, entitled Structural similarity in geographical queries to improve query answering, Arianna D'Ulizia, Fernando Ferri, Anna Formica, Patrizia Grifoni, and Maurizio Rafanelli address query approximation problem with imprecise or missing query data in Geographic Information Systems, and proposes an approach for query constraints relaxing.The authors use the concept similarity in an ontology management system mixed with the maximum weighted matching problem in bipartite graphs.In the second paper entitled "Distortion-constrained compression of vector maps", Alexander Kolesnikov and Alexander Akimov provide an algorithm for lossy compression of vector maps for given error tolerance.The algorithm is based on optimal polygonal approximation and dynamic quantization of vector data.A near optimal distortion-constrained quantizer with step defined by the tolerance level was constructed.
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 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.036 | 0.024 |
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".