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Law Enforcement & Public Safety


Using Data Science to Catch Criminals

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The power of data science is not limited to solving technical or business issues. Its usage is not limited to data analytics to create new technologies, target ads to consumers, and maximize profits and sales in business. The concept of open science has led organizations to use data to handle social problems. It can offer a statistical and data-driven solution to hidden human behavior and cultural patterns. We will be using data from the San Francisco crime department to understand the relation between civilian-reported incidents of crime and police-reported incidents of crime. To store and readily access a large amount of data, we will be using GridDB as our database platform.


The case for placing AI at the heart of digitally robust financial regulation

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"Data is the new oil." Originally coined in 2006 by the British mathematician Clive Humby, this phrase is arguably more apt today than it was then, as smartphones rival automobiles for relevance and the technology giants know more about us than we would like to admit. Just as it does for the financial services industry, the hyper-digitization of the economy presents both opportunity and potential peril for financial regulators. On the upside, reams of information are newly within their reach, filled with signals about financial system risks that regulators spend their days trying to understand. The explosion of data sheds light on global money movement, economic trends, customer onboarding decisions, quality of loan underwriting, noncompliance with regulations, financial institutions' efforts to reach the underserved, and much more. Importantly, it also contains the answers to regulators' questions about the risks of new technology itself. Digitization of finance generates novel kinds of hazards and accelerates their development. Problems can flare up between scheduled regulatory examinations and can accumulate imperceptibly beneath the surface of information reflected in traditional reports. Thanks to digitization, regulators today have a chance to gather and analyze much more data and to see much of it in something close to real time. The potential for peril arises from the concern that the regulators' current technology framework lacks the capacity to synthesize the data. The irony is that this flood of information is too much for them to handle.


Feitian unveils portfolio of handheld Android biometric devices

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Feitian Technologies showed off its newest portfolio of four Android handheld devices, three of which include fingerprint biometrics, and which support an assortment of applications from law enforcement to voting. The Handheld Biometric Identification Terminal (V11) is a wireless, five-inch terminal with fingerprint, iris, and face biometric verification. Customers can choose between fingerprint sensors certified for single flat fingers at FAP30, FAP20, or FAP10, from Integrated Biometrics, Suprema, Idemia, Futronic, Aratek and SecuGen, according to the product page. The device also supports scanning of digital identity documents through NFC, MRZ Passport reading, and optical character recognition (OCR). The Multifunction Handheld Terminal (V12) terminal is intended for law enforcement that sports a fingerprint sensor with live finger detection, breathalyzer, and narcotics detector, and can issue tickets.


How AI Can Supercharge Your Ransomware Defense In 2022?

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Human-operated ransomware attacks have threat actors using certain methods to get into your devices. They depend on hands-on-keyboard activities to get into your network. AI can protect you in the event of these and other attacks. Since the decisions are data-driven, you have a lower likelihood of falling victim to attacks. The decisions are based on extensive experimentation and research to improve effectiveness without altering customer experience.


Deepfake attacks can easily trick facial recognition

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In brief Miscreants can easily steal someone else's identity by tricking live facial recognition software using deepfakes, according to a new report. Sensity AI, a startup focused on tackling identity fraud, carried out a series of pretend attacks. Engineers scanned the image of someone from an ID card, and mapped their likeness onto another person's face. Sensity then tested whether they could breach live facial recognition systems by tricking them into believing the pretend attacker is a real user. So-called "liveness tests" try to authenticate identities in real-time, relying on images or video streams from cameras like face recognition used to unlock mobile phones, for example.


AI may be searching you for guns the next time you go out in public

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When Peter George saw news of the racially motivated mass-shooting at the Tops supermarket in Buffalo last weekend, he had a thought he's often had after such tragedies. "Could our system have stopped it?" he said. But I think we could democratize security so that someone planning on hurting people can't easily go into an unsuspecting place." George is chief executive of Evolv Technology, an AI-based system meant to flag weapons, "democratizing security" so that weapons can be kept out of public places without elaborate checkpoints. As U.S. gun violence like the kind seen in Buffalo increases -- firearms sales reached record heights in 2020 and 2021 while the Gun Violence Archive reports 198 mass shootings since January -- Evolv has become increasingly popular, used at schools, stadiums, stores and other gathering spots. To its supporters, the system is a more effective and less obtrusive alternative to the age-old metal detector, making events both safer and more pleasant to attend. To its critics, however, Evolv's effectiveness has hardly been proved. And it opens up a Pandora's box of ethical issues in which convenience is paid for with RoboCop surveillance. "The idea of a kinder, gentler metal detector is a nice solution in theory to these terrible shootings," said Jay Stanley, senior policy analyst for the American Civil Liberties Union's project on speech, privacy, and technology. "But do we really want to create more ways for security to invade our privacy?


A Brief Overview of Machine Learning

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As we randomly search terms on the internet, we often encounter "machine learning" and "deep learning" and how they are revolutionizing the way in which we live our lives. At present, machine learning is almost used everywhere from self-driving cars, email spam detection, recommender systems that we see in Netflix and Amazon, credit card fraud detection used by banks and so on. The list goes on and on with potential new applications being created. Therefore, it is very important to stay updated with the latest trends and understand what machine learning actually is and get a good broader understanding of some of the types of machine learning. In this article, I would explain machine learning and the different categories of machine learning.


Democratic lawmakers want FTC to investigate controversial identity firm ID.me

Engadget

A group of Democratic lawmakers led by Senator Ron Wyden of Oregon is calling on the Federal Trade Commission to investigate ID.me, the controversial identification company best known for its work with the Internal Revenue Service. In a letter addressed to FTC Chair Lina Khan, the group suggests the firm misled the American public about the capabilities of its facial recognition technology. Specifically, lawmakers point to a statement ID.me made at the start of the year. After CEO Blake Hall said the company did not use one-to-many facial recognition, an approach that involves matching images against those in a database, ID.me backtracked on those claims. It clarified it uses a "specific" one-to-many check during user enrollment to prevent identity theft.


The Download: The grim spread of the Buffalo shooting video, and crypto's tough test

MIT Technology Review

Although Twitch took down the livestream within two minutes from the start of the attack, a recording of the video was swiftly posted on a site called Streamable. That video was viewed more than 3 million times before it was taken down, according to the New York Times. Links to the recording were shared across Facebook and Twitter, and another clip that purported to show the gunman firing at people in the supermarket was visible on Twitter more than four hours after being uploaded. Additionally, TikTok users shared search terms that would take viewers to the full video on Twitter, according to Washington Post reporter Taylor Lorenz. Although Twitch removed the livestream in less time than the 17 minutes it took Facebook to take down the live broadcast of the 2019 mosque shooting.


Church where shooting took place was home away from home for Taiwanese immigrants

Los Angeles Times

The Irvine Taiwanese Presbyterian Church has never had a home. It started in 1994 in borrowed space in another church in its namesake city. It eventually moved to another borrowed space in a Tustin church before settling at Geneva Presbyterian Church in Laguna Woods in 2012. On Sundays, the Taiwanese group worships at 10 a.m., while the Geneva group gathers separately at 10:30. The 100 or so church members, most of whom are senior citizens, worship in their native language -- not Mandarin but Taiwanese, a dialect that was once suppressed by the Kuomintang regime.