REVISOR
Your Unblinking Poll Watcher
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Description:
REVISOR is a neural network-based software package for counting the number of actual voters and monitoring compliance with some electoral procedures. This AI-enabled monitoring is fast, reliable, inexpensive, and doesn’t know the limits of the scale. For a fraction of the cost, you can deploy an election observation mission with thousands of poll watchers covering 100% of polling stations in the target constituency. And those poll watchers don’t even blink!
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Features
- Detect ballot boxes on video records
- Detect a ballot box’s outline and its top
- Identify the type of the ballot box
- Identify parameters of box location
- Detect some types of violations
- Count the number of voters who cast their ballots
- Identify polling stations with falsified turnout comparing the real turnout to the officially published results
- Draft a formal complaint
- Speed up manual recount
Advantages
- Fast and reliable
- Inexpensive
- Can be deployed on a large scale
- Can be used to monitor different types of voting procedures, elections, and electoral systems
- Can be trained to work with different types of video recordings
Disadvantages
- May not be able to detect all types of voting irregularities
- Requires a lot of video data to train
- Can be biased if the training data is biased
Frequently Asked Questions
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Q:What is REVISOR?
A:REVISOR is a neural network-based software package for counting the number of actual voters and monitoring compliance with some electoral procedures. -
Q:How does REVISOR work?
A:REVISOR uses neural networks to learn to recognize physical objects, track their relative movements, detect voting events, and distinguish them from other activities at a polling station. -
Q:What are the benefits of using REVISOR?
A:REVISOR is fast, reliable, inexpensive, and can be deployed on a large scale. -
Q:What are the limitations of REVISOR?
A:REVISOR may not be able to detect all types of voting irregularities, requires a lot of video data to train, and can be biased if the training data is biased.
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