First announced at a University Senate Meeting in the spring, Georgia State welcomed a group of researchers from both the University of New Mexico and the Mind Research Network, a non-profit imaging center. This fall, the two seperate research groups came together to work at Georgia State.

These new researchers now combined to work at the Center for Translational Research in Neuroimaging and Data Science (TReNDS), which is now located on the 18th floor of 55 Park Place.

All of the researchers were brought over by one person: Vince Calhoun, the founding director and visionary of TReNDS, who also made the trip across the states to come to Georgia State.

But why did the researchers travel across the country to come here?

According to Calhoun, the university was interested in making a mark and expanding their brain imaging portfolio.

The TReNDS center researches the brain in a more general manner, meaning that the center is looking at healthy and unhealthy brains, normal and disordered brains and everything else in between.

With the analysis of brain imaging comes complicated data, especially for the unhealthy and disordered brains. What the center has developed and continues to develop are the techniques for making sense of the complex brain imaging data.

On a deeper level, there are several other, more specific projects going on. One of them is a research project focusing on using tools to analyze brain-imaging data in order to better understand and find features relating to abnormal human behavior, specifically psychiatric disorders like dementia and schizophrenia.

Another project is international, involving the study and research of the effects of city lights and the greenness of the environment on brain patterns using satellite imaging data and brain imaging data.

Sergei Plis, associate professor of computer science and working member of TReNDS, compared the tools involved, to simplify, as being similar to how google translate functions.

Although the center is located at Georgia State, TReNDS is a tri-institutional center shared between Georgia State, Georgia Tech and Emory University. This means that each institution contributes resources in some way to the TReNDS center, such as faculty or support for postdoctorales. Emory, specifically, contributes to the patient population and clinical expertise within psychiatry and neurology.

As work transferred to the state of Georgia, so did ten of the graduate research students involved with the center. But Georgia State lacked one thing: a graduate engineering program.

Because of this, the ten students involved transferred to Georgia Tech. Since the move, there are some postdoctoral research assistants who have joined the team from Georgia State.

Reliable and accurate data needs large and diverse sample sizes, according to Plis. With help from the growing neuroimaging community, TReNDS is able to receive data from across the nation and around the world. Some collaborators are in India, China, and England.

How is this possible? Doesnt the Health Insurance Portability and Accountability Act, better known as HIPAA, protect patient data? What about ethical and legal issues? And why would researchers share data that they have worked years to develop?

As Plis explains it, TReNDS built a system that allows for the data to stay where it is and never leave the data center no matter the location.

We can connect online using algorithms we developed to run around and collect certain data here and there in different data sets. We are sharing minimal information, and we still get the results we need as if all the data were still together, Plis said. We are kind of sharing without sharing and solving this problem of data sharing.

Along with the data gathered around the world, the TReNDS center utilizes the Center for Advanced Brain Imaging, located near Georgia Tech. Here, you will find a 3-Tesla Siemens Prisma-Fit MRI system, which according to Calhoun is the most modern, cutting-edge scanner for research available.

The TReNDS center also developed some collaborative tools that are put into CABI so that other people can more easily get access to their data, share it, collaborate and anonymize it.

The data will all get archived and analyzed in standard pipeline, Calhoun said. We speed up the process and eventually were planning to have that enable us to compute scores for the different brain imaging markers that were developing.

Whats unique about the center, according to Jean Liu, an associate professor of computer science at Georgia State, is the strength of the team.

We have a group of extremely trained engineers that can use very sophisticated algorithms to study brain imaging, which is not very common within other brain centers, Liu said.

Other centers, Liu said, will have people with various backgrounds like neuroscience and psychology. Although this may be helpful in some respects, there are obstacles that present themselves in finding specific features when big data is presented, according to Liu.

This is where the trained engineers with sophisticated algorithms that the team relies on come in. Their job is to develop tools using the algorithms to help people in different professional backgrounds better understand and find the specific data they are looking for.

Read more here:
The reachers who travelled across the country: Why they came to Georgia State to study the brain - The Signal

In August, trolls took to Twitter to spout racist slurs at Paul Pogba of Manchester United for committing the unforgivable offense of missing a kick from 12 yards. They felt comfortable doing it because it's easy to be anonymous on social media since all it takes to set up a fake account is an email address, password and name. People can hide heinous actions behind these accounts, shielding themselves from taking responsibility for their actions and possible litigation.

Perhaps unbeknownst to Pogba, his home country of France has already been taking action to try to prevent things like this happening on social media, as Facebook founder Mark Zuckerberg met with French President Emmanuel Macron in May to discuss the platform's role in spreading hate speech and misinformation. Facebook has recognized that it needs to do more to stop extremist content on its platform and, as a result, has granted the French courts access to IP addresses to help them identify its proponents.

But even if Facebook and Twitter allowed this for all countries, does it go far enough? While social media provides a valuable service to billions of people worldwide by connecting the world and giving a voice to those who typically don't have one, such as with #MeToo movement, it also has its issues. It can potentially be used as a platform for misleading people on important topics such as the U.S. elections, European elections, Brexit and the Cambridge Analytica scandal.

There is increasing pressure on social media companies to protect their users from this content. In 2017, Germany passed a law that gives social media companies 24 hours to remove any "obviously illegal" posts or face stiff financial penalties. Now, France is looking to go a step further by gaining access to these IP addresses.

While knowing someone's IP address will certainly help, it's no panacea given the proliferation and consumerization of VPN software that can mask a computer's IP address. A better way would be to know what legal identity is connected with the social media account in question. This would make investigating and holding people accountable much easier and help the public regain trust in social media platforms. I believe more identity proofing will only elevate and enhance a social media platform's brand and reputation and make perpetrators think twice about committing such atrocities in the first place.

At Onfido, we're actively working with some social media companies to help them provide a more secure and trusted route to authenticating their users. For bots, which are essentially computer algorithms that mimic human behavior in online social networks to spread misinformation, we can periodically introduce "liveness" tests. These tests act as a CAPTCHA, where the account holder would need to carry out a selfie video in order to proceed to post an article to their account.

Facebook recently showed some signs of moving in this direction with its selfie CAPTCHA, but it doesn't take into account higher-value or riskier transactions. Although there are fears that Facebook may misuse face data, it has confirmed that this service is dedicated to motion only in order to stop bots and "does not use facial recognition."

We're not the only ones looking at this problem. There are startups like Digital Shadows, a cybersecurity firm that uses AI to identify fake websites, phony social media profiles and "counterfeit" company domains set up to spoof a brand's online identity.

LinkedIn also announced it is proactively taking a stance by using a mixture of machine learning and human moderation "to detect groups of accounts that look or act similarly, which implies they were created or controlled by the same bad actor."

Using methods like these can help social media networks stop the spread of misinformation via bots like the recent accusations surrounding the Hong Kong riots while making hate speech spreaders accountable for their actions and helping businesses create a foundation of trust.

Social media companies need only look at the sharing economy space to see how digital identity verification is taking off. Many scooter, car and apartment rental companies are using it to help build trust between service providers and consumers. It has become a necessity in the financial services industry, where fraudulent accounts have the potential for massive financial loses and large fines from regulators if they don't follow AML or KYC policies. Some new online banks have welcomed the arrival of online identity verification, which has helped make banks such as Revolut and Monzo successful at onboarding users quickly but safely.

From my experience, the leaders in these companies needed to figure out the extent to which identity verification had a strategic role within their organization whether it was more a tick-box or core to the integrity of their offerings. They also had to consider where their customers are and how common fraud is in those places before deciding their comfort level with respect to fraud, answering whether they wanted to prioritize the speed of onboarding or the ability to catch all bad actors.

According to research published by DataReportal, there are roughly 3.5 billion global social media users. I believe we have a responsibility to take action now before hate speech becomes the new norm and we find it so hard to distinguish real news from fake news that we no longer accept or consume information.

Here is the original post:
Identity Verification Is Critical To Combating Misinformation And Extremist Content - Forbes

At the beginning of this millennium, just two months after 9/11, scientists in the Peruvian Amazon made one of the most unsettling discoveries ever to be reported in the pages of the prestigious journal Mammalian Biology.

It was a sloth, and it was hanging out inside of a toilet, and it was absolutely gorging itself on a potent liquid slurry of human waste by the handful. It apparently didn't want to be seen engaging in whatever esoteric sloth ritual this was.

"It was scooping with one hand from the semi-liquid manure composed of faeces, urine and toilet paper and then eating from the hand," the researchers reported in a 2011 research paper that made the rounds last week for some unknown reason, but which I absolutely could not resist clicking on because I am broken.

"When more persons gathered around the latrine to watch this bizarre behavior, the sloth emerged from the latrine and climbed into the nearest tree," the researchers wrote.

Over the next few years, until the latrine was fenced in in 2007, the researchers observed 25 more sloths heading to the poo pit for a midnight feast. As for why this is a thing, the researchers speculated that the sloths could be trying to glean some nutrients from human waste or possibly eating worms.

And yet, I have questions that demand answers:

1. Did we bother you?

I have seen this face before, in the mirror. This is how I look when I am forced to admit I ate an entire pizza after the box is discovered. I am weak, and I am ashamed, and I am fundamentally seen. This is Lenny from The Simpsons feebly pleading, "Please don't tell anyone how I live" after his wretched existence is exposed by a wall in his house comically falling down. I empathize deeply, and I want to apologize if we humans interrupted or embarrassed you, the poo sloth.

2. Where are you now?

It's been a long time since we found you eating crap. Are you OK? Have you moved on to trading cryptocurrencies or investing in cannabis? How long do sloths even live?

3. What is sloths' whole deal with poo?

It seems like poo is a whole thing for sloths. These creatures have one gigantic, probably-painful poo once a week. It is risky because they have to do it on the ground, exposing them to predators, and honestly does not sound all that fun. And yet, sloths desire to consume this same substance with unmatched ferocity, even desperation. What gives?

All I know is that, somehow, this sloth set the tone for our current shitworld which was just ramping up when we found the long-armed toilet hamster eating its first handful of poo, like it was pulling some huge cosmic lever.

Visit link:
We Have Questions for This Sloth Found Gorging on Human Poo in a Toilet - Free

Marrying up the law of entropy and the fact that the number of digital payments being made across the globe is increasing dramatically, by default means that fraud management processes need to be constantly refined.

Entropy as a scientific principle concerns the loss of energy from a system and describes how an ordered system moves towards disorder. The key point in understanding entropy is that it cannot be stopped, and to maintain a desired level of order, energy or performance, more of the same must be added into the system. A simple example is when you wear your coat on a cold day. When you take your coat off, entropy is the process that explains the loss of warmth, which can only be countered by putting it on again.

In the payment industry this is fully supported by the fact that fraud has reached the highest levels on record, affecting more organizations than ever. The scale of the problem was revealed in last years PWC Global Economic Crime and Fraud Survey. Nearly half (49%) of the 7,228 businesses across 123 territories that were interviewed reported that they had experienced fraud and economic crime over a two-year period.

Today, fraud management consists of several manual processes models and rules performance monitoring, fraud pattern discovery and fraud alert management, to name a few. While these manual processes may be manageable at first, as the number of payment types and channels increase, it can rapidly become untenable to add more and more staff to manage and monitor all processes. Managing fraud can become very expensive, which is why efficient management processes are so important.

The fact that entropy exists, and remains a factor that cannot be stopped, means that all aspects of the business need to be monitored. You may be most interested in product development or working with clients, but if you do not watch the other parts of the business, such as accounts payable or accounting, entropy will eventually cause problems. Management regarding entropy aims at small corrections to keep projects or departments on track, rather than letting those areas run in isolation, until there is a much larger breakdown or problem.

There is a lot of information on how machine learning is helping to understand human behavior and more specifically, false/positive detection. However, there is little available research on how this relates to the end-to-end process within fraud management.

This draws you back to the fact that the industry is focused, and rightly so, on detecting fraud, but is not focused on evaluating the impact to the whole end-to-end process. Clearly the interdependencies on these two activity streams are significant, so the question is why both factors arent being considered by the fraud prevention suppliers.

While things naturally move to disorder over time, we can position ourselves to create stability. There are two types of stability: active and passive. Consider an airplane, which, if designed well, should be able to fly without intervention this is passive stability. Conversely, a fighter jet requires active stability. With active stability, you are applying energy to a system, in order to bring about some advantage (keeping the plane from crashing). The plane cant fly for more than a few seconds, without having to adjust its wings and these adjustments happen so quickly that its controlled by software in modern airplanes.

Autopilot ML, in this analogy, is the fighter pilot for fraud defense. Reacting quickly to fraud pattern changes by creating new machine learning models to stop the threats; while continually optimizing the fraud detection strategy, so that it is equipped to counter the newest and most damaging threats, while maintaining high acceptance and low insult rates.

In summary, machine learning is having a huge impact on the entropy of fraud detection, it is helping to maintain order, providing the system with passive stability. However, as stated above, without constant refinement and active stability, effectiveness is likely to decay. This rate of entropic decay needs to be measured, understood and more importantly learned from and acted upon. In terms of the latter, it is the efficiency of the changes that are critical, essentially providing stability optimization.

Are the fraud prevention products and services you are currently deploying maintaining stability?

For reprint and licensing requests for this article, click here.

Link:
Business entropy is making the fraud problem worse - PaymentsSource