Design of a framework for combating human trafficking and kidnapping using smart objects and Internet-of-things
Bibliographic record
Abstract
The security problems arising from activities of terrorists, kidnappers and human traffickers in the world, at large, can be tackled using Information and Communication Technology (ICT) related approaches. This paper presents the framework of smart objects and Internet of Things (IoT) based system for achieving cost effective and time saving combat of human trafficking and kidnapping. The major components of the system are Sensor Processing Station (SPS), Media Server Station (MSS), Smart Engine Server (SES) and Digital Situation Room (DSR). The SPS is for signal sensing via a number of workstations equipped with video camera sensors, Radio Frequency Identification Card (RFID) tags to the Global Positioning System (GPS) receivers and body worn sensors while MSS will capture and store data from the sensor processing units on the cloud server. The SES will perform logical reasoning of the system such as motion detection, face recognition, position tracking and activities recognition, DSR will be used to monitor events in real-time and on-demand modes based on Internet Protocol version 6 (IPv6)-enabled communications. The work presents an integration of different technologies towards combating human trafficking and kidnapping with a view to enhance existing piecewise development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".