Context-aware reconfiguration of large-scale surveillance systems: argumentative approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 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 it