MétaCan
Menu
Back to cohort
Record W4375953126 · doi:10.22214/ijraset.2023.51487

Used Car Price Prediction Using Machine Learning

2023· article· en· W4375953126 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal for Research in Applied Science and Engineering Technology · 2023
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMachine learningArtificial intelligence

Abstract

fetched live from OpenAlex

The Stingray or IMSI-catcher is a surveillance device for cellular phones that was initially developed by the Harris Corporation for military use. Nowadays, various local and state law enforcement agencies across countries such as Canada, the United States, and the United Kingdom use similar devices widely. The term Stingray has also become a general term for this type of device. The IMSI catcher has two modes of operation-active and passive. In the active mode, the device pretends to be a cell tower, tricking all nearby mobile phones and cellular devices to connect to it. It can be mounted on vehicles, low flying airplanes and helicopters, UAVs, etc. It broadcasts signals that seem stronger than the cell tower, and thus, it forces each compatible cellular device to disconnect from its service provider (e.g., Jio, BSNL, etc.) and establish a new connection with the device. Cellular communications protocols require mobile phones and cellular devices to connect to the strongest signal. We have used a Software Defined Radio (SDR) to replicate the Stingray device manufactured by the Harris Corporation. Although this device has a shorter range, it can still track the IMSI of all cellular devices around it. This project also demonstrates how fragile our privacy is concerning our deviI. INTRODUCTION Cyber-Surveillance has been increasingly relied on by governments to carry out certain administrative tasks in the health, welfare, education and civil security sectors.Businesses keen to protect certain information or to monitor the behavior of their employees or clients have also engaged in "cyber-surveillance" and corporate surveillance.Civil society and citizens' organizations may also use information technologies to monitor the words and deeds of authorities or businesses as part of strategies to publicly denounce conduct they deem to be unacceptable.Finally, delinquents and criminal groups may turn to cyber-surveillance in the pursuit of their objectives.The stingray device can be extremely beneficial to the government if used for the intended purpose, i.e. to hunt for criminals and national threats.If an approximate location of the threat is known, a stingray can be deployed near the region.The stingray will provide the phone numbers present in a particular radius around it.An even more advanced version can intercept the calls and messages being sent through the target's device.The motivation for this project was taken from the highly regarded Netflix documentary, "Web of Make Believe."

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.356
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it