Ontology alignment is widely-used to find the correspondences between different ontologies in diverse fields.After discovering the alignments,several performance scores are available to evaluate them.The scores typically require the identified alignment and a reference containing the underlying actual correspondences of the given ontologies.The current trend in the alignment evaluation is to put forward a new score(e.g., precision, weighted precision, etc.)and to compare various alignments by juxtaposing the obtained scores. However,it is substantially provocative to select one measure among others for comparison.On top of that, claiming if one system has a better performance than one another cannot be substantiated solely by comparing two scalars.In this paper,we propose the statistical procedures which enable us to theoretically favor one system over one another.The McNemar's test is the statistical means by which the comparison of two ontology alignment systems over one matching task is drawn.The test applies to a 2x2 contingency table which can be constructed in two different ways based on the alignments,each of which has their own merits/pitfalls.The ways of the contingency table construction and various apposite statistics from the McNemar's test are elaborated in minute detail.In the case of having more than two alignment systems for comparison, the family-wise error rate is expected to happen. Thus, the ways of preventing such an error are also discussed.A directed graph visualizes the outcome of the McNemar's test in the presence of multiple alignment systems.From this graph, it is readily understood if one system is better than one another or if their differences are imperceptible.The proposed statistical methodologies are applied to the systems participated in the OAEI 2016 anatomy track, and also compares several well-known similarity metrics for the same matching problem.

High above the Hudson River in downtown Jersey City, a former U.S. Secret Service agent named Peter Cavicchia III ran special ops for JPMorgan Chase & Co. His insider threat group--most large financial institutions have one--used computer algorithms to monitor the bank's employees, ostensibly to protect against perfidious traders and other miscreants. Aided by as many as 120 "forward-deployed engineers" from the data mining company Palantir Technologies Inc., which JPMorgan engaged in 2009, Cavicchia's group vacuumed up emails and browser histories, GPS locations from company-issued smartphones, printer and download activity, and transcripts of digitally recorded phone conversations. Palantir's software aggregated, searched, sorted, and analyzed these records, surfacing keywords and patterns of behavior that Cavicchia's team had flagged for potential abuse of corporate assets. Palantir's algorithm, for example, alerted the insider threat team when an employee started badging into work later than usual, a sign of potential disgruntlement. That would trigger further scrutiny and possibly physical surveillance after hours by bank security personnel. Over time, however, Cavicchia himself went rogue. Former JPMorgan colleagues describe the environment as Wall Street meets Apocalypse Now, with Cavicchia as Colonel Kurtz, ensconced upriver in his office suite eight floors above the rest of the bank's security team. People in the department were shocked that no one from the bank or Palantir set any real limits.

The 526th Intelligence Squadron at Nellis Air Force Base, Nevada, hosted an Artificial Intelligence and Design Thinking seminar at AFWERX Vegas. Ian, superintendent of the 9th Intelligence Squadron at Beale AFB, California, introduced more than 100 Airmen, contractors and Department of Defense employees to the fundamentals of design thinking, artificial intelligence and cutting-edge computer technology. "I want to expose you to the way we do business in (intelligence, surveillance and reconnaissance) and empower you to be part of the conversation," said event coordinator Senior Master Sgt. Amy, superintendent of the 526th IS, to the group of attendees. "Hopefully, as the concepts becomes less intimidating, they will stimulate a culture of curiosity within you that makes you want to learn more and dig deeper."

The question of whether you should let Facebook save your face is gaining in urgency as Facebook makes moves to expand its deployment of facial recognition. It faces a lawsuit by Illinois residents over the technology. SAN FRANCISCO -- Of all the information Facebook collects about you, nothing is more personal than your face. With 2.2 billion users uploading hundreds of millions of photos a day, the giant social network has developed one of the single-largest databases of faces and -- with so many images to train its facial recognition software -- one of the most accurate. The question of whether you should let Facebook save your face is gaining in urgency as it moves to expand its deployment of facial recognition, rolling it out in Europe, where it was scrapped in 2012 over privacy concerns and scanning and identifying more people in photos. At the same time, the giant social network is attempting to quash efforts to restrict the use of facial recognitionin the U.S., from legislation to litigation.

Blend two or three metals together and you get an alloy that usually looks and acts like a metal, with its atoms arranged in rigid geometric patterns. But once in a while, under just the right conditions, you get something entirely new: a futuristic alloy called metallic glass that's amorphous, with its atoms arranged every which way, much like the atoms of the glass in a window. Its glassy nature makes it stronger and lighter than today's best steel, plus it stands up better to corrosion and wear. Even though metallic glass shows a lot of promise as a protective coating and alternative to steel, only a few thousand of the millions of possible combinations of ingredients have been evaluated over the past 50 years, and only a handful developed to the point that they may become useful. Now a group led by scientists at the Department of Energy's SLAC National Accelerator Laboratory, the National Institute of Standards and Technology (NIST) and Northwestern University has reported a shortcut for discovering and improving metallic glass – and, by extension, other elusive materials - at a fraction of the time and cost.

On Tuesday, for example, 34 companies including Microsoft, Oracle and Facebook signed the Cybersecurity Tech Accord, publicly committing to protect internet users, work together and improve resilience in the space. But outside of large-scale initiatives, the basics, such as malware detection, have a long road ahead as the cyberattacks keep rolling in. Advancements in AI and ML on the enterprise side are important to counter hackers also utilizing the technology to automate attacks. The most effective kind of malware is a strain that hits without a business ever knowing, but advanced detection capabilities harnessing AI and ML are steadily helping cybersecurity teams overcome the odds. But without good data, good defensive and detection measures are hard to build. In the same vein as cybersecurity, other companies are working to build out massive data sets for image recognition ML models.

The risks posed by intelligent devices will soon surpass the magnitude of those associated with natural disasters. Tens of billions of connected sensors are being embedded in everything ranging from industrial robots and safety systems to self-driving cars and refrigerators. At the same time, the capabilities of artificial intelligence (AI) algorithms are evolving rapidly. Our growing reliance on so many intelligent, connected devices is opening up the possibility of global-scale shutdowns. The good news is that natural disasters themselves, which Munich Re says caused $330 billion in economic losses globally in 2017, provide a template for how to mitigate the growing and catastrophic risk posed by AI.

Digital recruitment – think Monster and LinkedIn – is routine today. But what if a voice-powered AI assistant like Amazon's Alexa could find the best candidate for a critical role just by asking it? By leveraging the power of social networks and data and analytics, enterprising employers already are sourcing, screening and retaining talent more efficiently and effectively, so this scenario is not that far away. Networked talent sourcing: Savvy sourcing recruiters find the right talent faster by leveraging social networks, web 2.0, newsgroups, blogs and online data sources to scan "passive" talent pools. Oracle's subsidiary Opower employs such talent analytics to hire about 200 employees annually.

"When they order military equipment from us, we will get it taken care of and they will get their equipment rapidly," Trump told reporters at a joint news conference with Japanese Prime Minister Shinzo Abe in Florida on Wednesday. "It would be, in some cases, years before orders would take place because of bureaucracy with Department of Defense, State Department. It's now going to be a matter of days. If they're our allies, we are going to help them get this very important, great military equipment. And nobody, nobody, makes it like the United States.

In science news around the world, the second annual March for Science features rallies in cities around the world to support the use of scientific analysis and evidence in public policy. The U.S. National Institutes of Health (NIH) in Bethesda, Maryland, broadens the scope of its investigation into ties between the alcohol industry and NIH's alcoholism institute to include funding decisions by the institute's director. Members of the United Nations's International Maritime Organization in London agree to cut greenhouse gas emissions from international shipping to 50% of 2008 levels by 2050. The U.S. National Science Foundation awards its prestigious prize for young scientists to Kristina Olson of the University of Washington in Seattle, the first psychologist ever to receive it and the first woman so honored since 2004. The U.S. Food and Drug Administration announces the first approval of artificial intelligence–based disease screening software that doesn't need a clinician to interpret the results--a computer program that analyzes digital images of a patient's retina to detect diabetic retinopathy, a common complication of diabetes that causes blindness.