The government plans to use a facial recognition system at a ceremony later this month to mark the 30th anniversary of Emperor Akihito's accession to the throne, officials said. The use of facial recognition technology, a first for a government-sponsored event in Japan, is designed to reduce the time required for participant identification and help prevent terrorism. Using images of the faces of participants registered in advance, the system authenticates recognized faces in some 10 seconds per person with an accuracy rate of more than 99 percent, the officials said. More than 1,000 people are expected to attend the ceremony set to take place at Tokyo's National Theatre on Feb. 24. The facial recognition system will be used for hundreds of people including government officials and journalists.

President TrumpDonald John TrumpGillibrand backs federal classification of third gender: report Former Carter pollster, Bannon ally Patrick Caddell dies at 68 Heather Nauert withdraws her name from consideration for UN Ambassador job MORE issued an executive order this week directing federal agencies to support the development of artificial intelligence. It couldn't have come at a better time. That's because the U.S. is in a race against China to develop cutting-edge artificial intelligence technology, and it's a race we can't afford to lose. "Artificial Intelligence" (AI) may bring to mind any number of futuristic pop culture references, from "Star Wars" to "Westworld", and it may seem like something that's decades or even centuries away. The reality is that AI is already here – it's in the apps we use to navigate through traffic, it protects us from spam emails and more nefarious online security threats, and it's what responds when we say "OK Google..." and "Alexa?"

AND THEN there were three. Amazon has joined Microsoft and Google in supporting regulation of facial recognition technology, and it is easy to guess why: Research on bias in the software has amplified public skepticism, and legislators are starting to take note by proposing restrictions and even bans. Facial recognition technology could have many beneficial effects. The software could help stop human trafficking, reunify refugee families and make everyday services -- from banking to paying for groceries -- safer and faster. But it could come with costs, too, which is why regulators are right to pay attention.

AI technology has become widespread and accessible to hundreds of thousands of IT security professionals worldwide. Human researchers are no longer behind their computers crunching the data and numbers, nor should they be when AI technology is available. The increase in computing power, especially through economical cloud solutions and easy-to-use tools, has allowed a much wider range of users to apply sophisticated machine learning and artificial intelligence algorithms to solve their problems. At the same time, companies and security vendors have realized how difficult it is to fight cyber criminals who are constantly evolving to find new ways to infiltrate corporate networks without being spotted. For IT teams, updating and maintaining security solutions and policies to keep up with this volatile threat landscape is extremely costly and an unsustainable solution to protecting against incoming threats.

V alidation is one of the most important aspects of clustering, but most approaches have been batch methods. Recently, interest has grown in providing incremental alternatives. This paper extends the incremental cluster validity index (iCVI) family to include incremental versions of Calinski-Harabasz (iCH), I index and Pakhira-Bandyopadhyay-Maulik (iI and iPBM), Silhouette (iSIL), Negentropy Increment (iNI), Representative Cross Information Potential (irCIP) and Representative Cross Entropy (irH), and Conn Index (iConn Index). Additionally, the effect of under-and over-partitioning on the behavior of these six iCVIs, the Partition Separation (PS) index, as well as two other recently developed iCVIs (incremental Xie-Beni (iXB) and incremental Davies-Bouldin (iDB)) was examined through a comparative study. Experimental results using fuzzy adaptive resonance theory (ART)-based clustering methods showed that while evidence of most under-partitioning cases could be inferred from the behaviors of all these iCVIs, over-partitioning was found to be a more challenging scenario indicated only by the iConn Index. The expansion of incremental validity indices provides significant novel opportunities for assessing and interpreting the results of unsupervised learning. L. E. Brito da Silva is with the Applied Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409 USA, and also with the CAPES Foundation, Ministry of Education of Brazil, Bras ılia, DF 70040-020, Brazil (email: leonardoenzo@ieee.org). N. M. Melton is with the Applied Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409 USA (email: niklasmelton@ieee.org). D. C. Wunsch II is with the Applied Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409 USA (email: wunsch@ieee.org). I NTRODUCTION Cluster validation [1] is a critical topic in cluster analysis.

Many societal decision problems lie in high-dimensional continuous spaces not amenable to the voting techniques common for their discrete or single-dimensional counterparts. These problems are typically discretized before running an election or decided upon through negotiation by representatives. We propose a algorithm called Iterative Local Voting for collective decision-making in this setting. In this algorithm, voters are sequentially sampled and asked to modify a candidate solution within some local neighborhood of its current value, as defined by a ball in some chosen norm, with the size of the ball shrinking at a specified rate. We first prove the convergence of this algorithm under appropriate choices of neighborhoods to Pareto optimal solutions with desirable fairness properties in certain natural settings: when the voters' utilities can be expressed in terms of some form of distance from their ideal solution, and when these utilities are additively decomposable across dimensions. In many of these cases, we obtain convergence to the societal welfare maximizing solution.We then describe an experiment in which we test our algorithm for the decision of the U.S. Federal Budget on Mechanical Turk with over 2,000 workers, employing neighborhoods defined by various L-Norm balls. We make several observations that inform future implementations of such a procedure.

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On an isolated stretch of industrial flatland outside Knoxville, Tenn., a minibus is taking shape in a car factory unlike any other. The space is small, the size of a supermarket, and all but tool-free. Instead, perched in the center is the world's largest 3D printer, a gangly 10-by-40-foot behemoth with a steel-gray exterior, thick columnar footings, and derrick-like roof beams to true its frame. When the print heads are in motion, the equipment emits little more than a whisper, dexterously cutting sharp angles and rounded edges. Programmers on laptops and quality-control experts with tablets mill around, inputting design changes and fine-tuning the minibus's sensor instructions. Beyond the assembly room lies a kind of alchemist's playground, where young staffers with advanced degrees in materials science and mechanical engineering synthesize nanopolymers or test exotic particles for strength or thermal and electrical conductivity. The minibus, named Olli, is the latest offbeat product from Local Motors Inc., an 11-year-old startup.

The 52 Week Low Stocks Package is designed for investors and analysts who need predictions for stocks currently at their 52-week low price level, offering the best market opportunities based on algo-trading. Package Name: 52 Week Low Stocks Recommended Positions: Long Forecast Length: 3 Months (11/13/2018 – 02/13/2019) I Know First Average: 18.61% In this 3 Months forecast for the 52 Week Low Stocks Package, there were many high performing trades and the algorithm correctly predicted 9 out 10 trades. The top-performing prediction in this forecast was FNMA, which registered a return of 102.29%. Other notable stocks were LRCX and CGNX with a return of 26.66% and 17.63%. The package had an overall average return of 18.61%, providing investors with a premium of 17.63% over the S&P 500's return of 0.98% during the same period.

Recently, the Victorian Government brought in new rules stating Victorian state schools will be banned from using facial recognition technology in classrooms unless they have the approval of parents, students and the Department of Education. Students may be justifiably horrified at the thought of being monitored as they move throughout the school during the day. But a roll marking system could be as simple as looking at a tablet or iPad once a day instead of being signed off on a paper roll. It simply depends on the implementation. Trials have already begun in independent schools in NSW and up to 100 campuses across Australia.