Ahmedabad, April 8, 2017: Indian engineers have developed a real-time automatic obstacle detection and alert system to help cars avoid colliding with cows on the road, a common sight in this part of the world.
The system uses a dashboard camera and an algorithm that can determine whether an object near the vehicle is an on-road cow and whether or not its movements represent a risk to the vehicle.
A timely audio or visual indicator can then be triggered to nudge the driver to apply the brakes whether or not they have seen the animal.
“The proposed system has achieved an overall efficiency of 80 per cent in terms of cow detection,” the researchers said in a study published in the Indonesian Journal of Electrical Engineering and Computer Science.
According to researchers Sachin Sharma and Dharmesh Shah of the Department of Electronics & Communication, at Gujarat Technological University in Ahmedabad, the proposed system is a low-cost, highly reliable system which can easily be implemented in automobiles for detection of cow or any other animal after proper training and testing on the highway.
The algorithm requires optimisation and the issue of night-time driving is yet to be addressed, the team said in an article in International Journal of Vehicle Autonomous Systems. (IANS)
About 1.5 million people in the United States are victims of human trafficking
The new research uses an algorithm that analyzes writing styles to identify authors and could be applied to online trafficking ads
A second algorithm can use time stamps to trace ad payments to accounts known as wallets at Bitcoin
New York, USA, August 23, 2017:A U.S. researcher Rebecca Portnoff says that she has developed automated ways to identify links between online sex trafficking ads and the digital currency Bitcoin, techniques that may help locate children being sold for sex.
Law enforcement and anti-trafficking groups could use the methods to investigate Backpage.com, an online classified advertising site where sex ads can be found, according to a statement by the University of California Berkeley, where the research was based.
About 1.5 million people in the United States are victims of human trafficking, mostly for sexual exploitation, according to anti-trafficking groups.
Most sex trafficking victims are children, and most are advertised or sold online, according to a U.S. Senate subcommittee report released this year.
Algorithms do the digging
The new research uses an algorithm that analyzes writing styles to identify authors and could be applied to online trafficking ads, Rebecca Portnoff, its lead author, said Thursday.
A second algorithm can use time stamps to trace ad payments to accounts, known as wallets, at Bitcoin, a web-based digital currency that allows money to move quickly and anonymously.
Comparing time stamps of ad purchases on Bitcoin and time stamps and information on Backpage ads could help identify who is paying for them, said Rebecca Portnoff, a UC Berkeley doctoral candidate in computer science who developed the techniques as part of her dissertation.
“Where previously you might have five different phone numbers that you had no idea were connected when you can see that they all came from the same wallets, that the same person paid for them, that’s a concrete sign that these five phone numbers are all related to each other,” Rebecca Portnoff said.
Rebecca Portnoff added, “I knew this was an issue that law enforcement was especially interested in.”
Having automated style and time stamp analyses to identify sex ads by authors and Bitcoin owners is significant, said Damon McCoy, a New York University Tandon School of Engineering assistant professor of computer science and engineering and a co-author of the research.
“Any technique that can surface commonalities between ads and potentially shed light on the owners is a big boost for those working to curb exploitation,” McCoy said in a statement.
The National Center for Missing and Exploited Children has said more than 70 percent of the reports it gets of trafficked children involve Backpage, based in Dallas, Texas.
Backpage did not respond to a request for comment.
The findings will be published by the Association for Computing Machinery’s Conference on Knowledge Discovery and Data Mining, UC Berkeley said.
It said the work was funded by the Amazon Web Services Cloud Credits for Research Program, the technology, and security firm Giant Oak, Google, the National Science Foundation and the U.S. Department of Education. (VOA)