The Security Assignment Problem and Its Solution
Bibliographic record
Abstract
In this paper we derived a new method for finding the optimal solution to security assignment problems using projection onto a convex set. This study will help communities find the optimal number of assigned and reserved personnels in designating security officers to an area. This study is applicable as well to CCTV assignment problems. The main goal of this study is to give a new method that can be applied in solving security assignment problems. We used some of the known properties of the convex optimization in proving the properties of the optimal solution, such as the concept of proximity operator, projection onto the convex set, and primal and dual problem. In addition to that, we used some basic knowledge in graph theory to answer our real-life application of this study. The main results of this paper showed that we found instances when the optimal solution to a security problem exists and when the solutions exist, we can determine the answer to the problem explicitly. Also, we proved that there always exists a Pseudo-solution in security assignment problems, and if the solution exists, then the Pseudo-solution will coincide with it. The most important aspect of this paper is the introduction of the application of convex optimization in the security problem.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".