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Independent candidate for U.S. Senate from Massachusetts Shiva Ayyadurai protests being excluded from a debate between Sen. Elizabeth Warren and her Republican opponent Geoff Diehl in Boston, Friday, Oct. 19, 2018. (AP Photo/Michael Dwyer)

Opinion

Are Conservatives 3/10 of a Person? Testimony and Math Shows Votes were Weighed Differently

President Donald J. Trump tweeted out a graph on Tuesday that showed what one Mathematician says shows the discrepancies in value between how votes were tabulated or weighed, between Trump and former Vice President Joe Biden, in the 2020 Presidential election.

Dr. Shiva Ayyadurai presents his data at the #ArizonaHearing, showing the only way for Biden to have statistically caught up with @realDonaldTrumpwas if Democrat votes came in at 130% for Biden and -30% for Trump,” one person reported as they watched the Voter Integrity hearing in Arizona on Monday with AZ state legislators, experts in statistics and Trump’s legal team. 

On Tuesday Trump retweeted more about the presentation by Shiva, who said:

Just testified in AZ hearing on #ElectionFraud. Presented implausibility of official result. Showed 30% of @realDonaldTrump votes electronically siphoned to@JoeBiden The REAL CRIME is “Weighted Race” algorithms certified by Sec.’s of State. We DON’T have “One Person, One Vote!”

https://twitter.com/realDonaldTrump/status/1333771923214491648?s=20

At the hearing, Shiva gave a presentation of his findings. Shiva was one of two eyewitness experts who testified that votes for Trump are showed being stored with a decimal mark.

Shiva made numerous references to “The Three-fifths Compromise”, which according to WIKI “was a compromise reached among state delegates during the … Southern states a third more seats in Congress and a third more electoral votes than if slaves had been ignored, … be determined through the apportionment of “one Member for every thousand Inhabitants 3/5 of Blacks included.”

To Shiva what he found shows the same mindset in the Dominion Voting Systems algorithms.

https://twitter.com/BobbyB0ucherJr/status/1333527427553882117?s=20

Shiva said at the hearing while presenting computer modeling, that in order for the Trump/Biden total vote accumulation to occur in Arizona, using known party affiliation, Biden would need 130% Democrat registered voter turnout with Trump-Republican turnout of -30%.

Shiva outlined his professional experiences with pattern recognition, including multiple advanced degrees from MIT and who says he created EMAIL at 14 years old, exposed how these voting machines calculate fake votes to manipulate the tallies. In the following video clip Shiva names his degrees and experiences:

https://twitter.com/michaelbeatty3/status/1333529310930485249?s=20

Shiva testified that votes are stored in fractions & questions why the machine would even have this function in the first place. He said that just the very presence of this feature on a machine should have disqualified the machine from being used by the Government, because cheating was the only purpose to store votes in that way.

Shiva said that “votes for Trump were being eaten and given to Biden”, that is how Biden ended up with 130% of the tabulation.

Shiva tweeted out on Tuesday his long claim about Voter Integrity, “anything the GOP says about “fighting” against “election fraud” has nothing to do with the actual fight for election integrity, rather an occasion to raise more $$$ for their coffers. Both parties have been using the Weighted Race feature for decades! Time to get smart.”

I covered a majority of the hearing that went on for 11 hours of eyewitness testimony about irregularities and what sounded like outright “crimes” as asserted by one lawmaker who was there, who said he is concerned about crimes and he would call a special session for lawmakers to hear more about the claims.

Opponents of Trump are working diligently to shut down all opportunities for Trump’s legal team to present their concerns and evidence which they feel should cause concerns for lawmakers, the media and the public.

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