Context-aware reconfiguration of large-scale surveillance systems: argumentative approach
Bibliographic record
Abstract
The Metis research project aims at supporting maritime safety and security by facilitating continuous monitoring of vessels in national coastal waters and prevention of phenomena, such as vessel collisions, environmental hazard, or detection of malicious intents, such as smuggling.Surveillance systems such as Metis typically comprise a number of heterogeneous information sources and information aggregators.Among the main problems of their deployment lies their scalability with respect to a potentially large number of monitored entities.One of the solutions to the problem is continuous and timely adaptation and reconfiguration of the system according to the changing environment it operates in.At any given timepoint, the system should use only a minimal set of information sources and aggregators needed to facilitate effective and early detection of indicators of interest.Here, we describe the Metis system prototype and introduce a theoretical framework for modelling scalable information-aggregation systems.We model information-aggregation systems as networks of inter-dependent reasoning agents, each representing a mechanism for justification/refutation of a conclusion derived by the agent.The proposed continuous reconfiguration algorithm relies on standard results from abstract argumentation and corresponds to computation of a grounded extension of the argumentation framework associated with the system.Finally, we demonstrate the flexibility of the presented framework by extending the proposed algorithm to adapt to context-dependent changes in information sources availability, as well as shifts in system's focus according to its context.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".