OSU Researchers Look Into How Individual Players Affect Football Program Revenue

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In the first study of its kind data suggests that the most elite college football players might bring in an average revenue of $650,000 a year. According to the Ohio State University, who relied heavily on data from Rivals (a recruiting news service), those ranked highest by the Rivals publication are the ones bringing in this kind of money.

Four-star Rivals recruits generated $350,000 year and three-star Rivals recruits brought in about $150,000 a year. Meanwhile the study found that 2-star recruits may actually reduce revenue slightly by $13,000.

While one study never tells the whole story during the continued debate about how college athletes should be compensated this study begins to tell the story of how players impact the income of the highest earning college sport.

Researchers at the Ohio State University hope that this first look into the effect players have on college football program bottom lines will open up the dialog to new possibilities.

New Study Finds Screen Time Doesn’t Negatively Affect Social Skills

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Despite the “common wisdom” in our society that says young people are not socially skilled because of their time spent on smartphones and social media a new study suggests otherwise.

Scientists analyzed and compared evaluations made by parents and teachers on students who started school in 1998 and those who started 2010. In 1998 Facebook didn’t exist and wouldn’t for another six years. In 2010 the first iPad was released.

Both groups, according to the data gathered from their evaluations, were rated about the same when it came to interpersonal skills like forming and maintaining friendships or get along with people who are different. They were also rated about the same when it came to self-control, meaning, for example, the ability to control their temper.

Researchers reported that in every comparison made the two groups were rated about the same and in some cases the scores of the children born later even went up. These researchers say there is little evidence screen time affects children’s social skills.

Researchers believe older folk’s views on social media and smartphones are shaped by “moral panic” which is an older generations tendency to worry that the younger generation are doing something wrong. It is a narrative that has played out through the generations and with all kinds of technology.

 

Secret Doors in Your Mobile Apps: an OSU Study

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The study found that many mobile apps might have hidden, programmed behaviors that the average user would be totally unaware of. Usually apps work on the premise of interacting with users via the data they input. This input can vary from word data, swipes or button presses.

In this particular study 150,000 apps were examined. Of those 100,000 were chosen based on their popularity in the Google Play store, another 20K were chosen for their popularity in an alternative market, along with 30K pre-installed apps that appear on Android systems.

8.5% of those apps, 12, 706 apps, contained some kind of programming labeled by the research team as “backdoor secrets.” These are hidden commands in the app which trigger background behaviors unknown to the user.

Other apps had programmed master passwords that would allow anyone with the master password to potentially private data. Other apps had secret keys that could trigger hidden options like bypassing a pay-to-play screen.

Another 4.028 apps (about 2.7%) were found to block content when it contained specified key words that were meant to be censored, or if it was cyber bullying or discrimination.

 

Legitimate News or Not? Scientists Find Out Why People Can’t Tell the Difference Between Real News and Satire on Social Media

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Researchers at the Ohio State University have found there may be clear downsides to getting news from social media. And not for the reasons you might think.

Researchers found that when people view a blend of news and entertainment through a single portal, through a single social media app they pay less attention to the source of content they consumed. Meaning there is a higher risk for mistaking satire for news or vice versa.

When consuming content that is separated into clearly defined categories (a news section, entertainment section, health and wellness etc.) they didn’t have the same problems deciding on the credibility of the content.

The scientists involved in this research believe they have found a legitimate danger when it comes to people blending news and entertainment viewing on apps like Facebook and Twitter. Researchers stated that while people like that one-stop-shop idea for media content, that jumbling of content makes everything seem the same or equal to us.

The issues is that there is no visual difference on Facebook, for example, between something like the New York Times and a random blog. Everything is the same, color scheme, font, frames etc. So one obvious solution would be for social media companies to develop ways to distinguish content.

Until something like this happens researchers believe that using social media as a one stop shop for content could be reducing positive media literacy behaviors.

 

OSU Scientists Use Machine Learning to Find Unexploded Ordinance

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Researchers at the Ohio State University have found a unique use for artificial intelligence; they’ve been using AI to look at satellite images of Cambodia looking for unexploded bombs from the Vietnam War era. This new approach has already drastically increased crater decetion by more than 160%.

The model created by AI combined with declassified military records from the U.S. suggest that as many as 44 to 50% of bombs in the area remain unexploded. Most attempts to find and safely remove unexploded ordinance like bombs and landmines has been much less effective than what is needed in Cambodia.

Researchers found that efforts on the part of Cambodia and their national clearance agency have been concentrating on low risk areas and that there are other areas that present a much greater risk that they should be focusing on.

Researchers stated that until efforts to clear mines has been less effective because no one was able to accurately pinpoint the areas that needed demining the most.

Researchers used machine learning to analyze satellite images for evidence of bomb craters. Between the researchers knowing how many bombs were dropped in the area and the general location where they fell and the AI finding the craters researchers are able to determine with how many bombs exploded and where. They can then determine with more accuracy how many bombs are left unexploded and where they might be found.