Bibliographic record
Abstract
Commercial drones have become much popular in recent years.Their low cost and high capabilities have rendered drones feasible for different applications.These include weather observations, traffic monitoring, inspection of buildings, fire detection, delivery of goods, agriculture and security.However, the abuse of the advancements of drone capabilities can lead to security and public safety issues.Such abuses include the transfer of illegal or dangerous goods, assaults and terrorizing actions, espionage or spying.In such a context, can the behavior of a quadcopter be determined from observations?Can those observed behaviours be utilized for training a machine learning model for classifying future comportment?In this work, we look at three pieces of information that we can predict about a quadcopter or a group of quadcopters, leveraging behavior observations.First, we try to predict the formation a group of drones intend to make while in transition by training a machine learning model, based on Softmax regression, on navigational data collected for this purpose.Second, we train an Long Short-Term Memory (LSTM) neural network on Mel Frequncy Cepstral Coefficients (MFCCs), which are audio features extracted from an acoustic signal, to predict the weight of the payload of a quadcopter.Furthermore, in our third task, we identify whether a drone pilot is a human or an autopilot using a deep neural network trained on features processed from collected navigational data.In each of the three tasks, we evaluate features extracted from the data, then build and evaluate different models.The best-performing models in each of the three tasks are then compared to three different dummy classifiers.This comparison is made by performing statistical iii iv
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".