Harmon Leon
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How Internet Mob Justice Can Easily Destroy Innocent Lives

How Internet Mob Justice Can Easily Destroy Innocent Lives

Truly innocent people have experienced the internet mob grab their metaphorical pitchforks and chase after them.This isn't KYLE QUINN .:. Who is he ??? ???? .:.— Ferris Bueller (@YoungWaxGod)We have verified that the man in the photo is not assistant professor Quinn.— University of Arkansas (@UArkansas)The selfie that sparked a social media firestorm., , , , , , , , , ,We get it: you like to have control of your own internet experience. But advertising revenue helps support our journalism. To read our full stories, please turn off your ad blocker.We'd really appreciate it.Below are steps...

May 31, 2019
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How a Hacked Light Bulb Could Lead to Your Bank Account Being Drained

How a Hacked Light Bulb Could Lead to Your Bank Account Being Drained

Any app that doesn’t protect its code—small, big, stupid, pointless—is vulnerable. Unprotected code doesn’t play favorites; it could be a banking app or an app that allows you to draw mustaches on cats. If it contains code, it has the potential to be hacked. Every device and system is hackable—it’s just a matter of time and hacker motivation. Observer recently connected with Asaf Ashkenazi, chief strategy officer at Verimatrix, who warned that the security of every connected device matters. And Ashkenazi knows; he’s been a cybersecurity industry veteran for more than 15 years and is a...

September 11, 2019
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Whole Foods Secretly Upgrades Tech to Target and Squash Unionizing Efforts

Whole Foods Secretly Upgrades Tech to Target and Squash Unionizing Efforts

Whole Foods’ heat map data suggest that stores with low racial diversity have a high likelihood of unionizing.SEE ALSO:unsafe working conditions amid the pandemic.The researchers accumulated “potential turnover indicators” for more than 500,000 workers in the U.S. and based their analytics on “personal factors tied to embeddedness that were in the public domain, such as the number of past jobs, employment anniversary and tenure, skills, education, gender and geography.”“We used machine learning to classify each individual as unlikely, less likely, more likely, or most likely to be receptive...

April 24, 2020
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