The sparse representation classifier (SRC) has been utilized in various classification problems, which makes use of L1 minimization and is shown to work well for image recognition problems that satisfy a subspace assumption. In this paper we propose a new implementation of SRC via screening, establish its equivalence to the original SRC under regularity conditions, and prove its classification consistency under a latent subspace model. The results are demonstrated via simulations and real data experiments, where the new algorithm achieves comparable numerical performance but significantly faster.

The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris domain, taking steps in the creation of an orbital debris ontology (ODO). The purpose of this ontological system is to (I) represent general orbital debris and SSA domain knowledge, (II) structure, and standardize where needed, orbital data and terminology, and (III) foster semantic interoperability and data-sharing. In doing so I hope to (IV) contribute to solving the orbital debris problem, improving peaceful global SSA, and ensuring safe space travel for future generations.

Yesterday I was honored to be one of the featured speakers at the TEDxReset Conference in Istanbul, Turkey where I predicted that over 2 billion jobs will disappear by 2030. Since my 18-minute talk was about the rapidly shifting nature of colleges and higher education, I didn't have time to explain how and why so many jobs would be going away. Because of all of the questions I received afterwards, I will do that here. If you haven't been to a TEDx event, it is hard to confer the life-changing nature of something like this. Ali Ustundag and his team pulled off a wonderful event.

Two years after World War II, billionaire Howard Hughes personally piloted his "Spruce Goose" troop transport aircraft on the first and only flight of the largest seaplane ever built. It lasted barely a minute. Now, more than 70 years later, a U.S. startup is testing a new seaplane concept--one that could evolve into huge cargo drones that fly 109 metric tons of freight across the Pacific, touch down autonomously over water, and unload at ports around the world. The startup Natilus was founded in 2014 with a dream of building large cargo drones to deliver international freight for about half the price of piloted aircraft, and much faster than ships. In December, Natilus planned to test the water-taxiing capabilities of a small prototype drone with a 9-meter wingspan in San Francisco Bay.

Machine learning (ML) is taking cybersecurity by storm nowadays as well as other tech fields. In the past year, there has been ample information on the use of machine learning in both defense and attacks. While the defense was covered in most articles (I recommend reading "The Truth about Machine Learning in Cybersecurity"), Machine Learning for Cybercriminals seems to be overshadowed and not unanimous. The recent findings show how cybercriminals can deploy machine learning to make attacks better, faster, and much cheaper to perform. The objective of this article is systemizing information on possible or real-life methods of machine learning deployment in malicious cyberspace. It is intended to help members of the Information Security teams to prepare for imminent threats.

David Decary-Hetu, assistant professor of criminology at the University of Montreal, says defenders have an edge right now in using artificial intelligence. "But who knows what's going to happen in a few years from now," he said in an interview. "The main issue is that if you're defending a system you have to be good 100 per cent of the time, but when you're attacking the system you only have to be successful once to get in." Decary-Hetu said a growing list of corporate and government officials who say infiltrations are their top worry, including Bank of Canada governor Stephen Poloz, have a very good reason to fear. The Bank of Canada warned in its semi-annual review released this month that the high degree of interconnectedness among Canadian financial institutions means any successful cyberattack could spread widely throughout the financial system.

Two years after World War II, billionaire Howard Hughes personally piloted his "Spruce Goose" troop transport aircraft on the first and only flight of the largest seaplane ever built. It lasted barely a minute. Now, more than 70 years later, a U.S. startup is testing a new seaplane concept--one that could evolve into huge cargo drones that fly 109 metric tons of freight across the Pacific, touch down autonomously over water, and unload at ports around the world. The startup Natilus was founded in 2014 with a dream of building large cargo drones to deliver international freight for about half the price of piloted aircraft, and much faster than ships. In December, Natilus planned to test the water-taxiing capabilities of a small prototype drone with a 9-meter wingspan in San Francisco Bay.

As a lawyer on the staff of the FBI Cyber Division, Andrew Burt spent a good deal of time looking at the intersection of national security and technology. That meant looking at policy in an organization charged to look at massive amounts of sensitive data. Now, as chief privacy officer and legal engineer at startup Immuta Inc., he is one among a new cadre working to bring more data governance to machine learning, the artificial intelligence-style technology that is moving from laboratories into mainstream computing. Machine learning algorithms are something of a black box for governance, as the technology does not necessarily disclose how it reached its decisions. To cast some light on this black box and what it means to data governance, we recently connected with Burt to discuss sensitive data processing at scale.

Abraham Lincoln once said, "The best thing about the future is that it comes one day at a time." Winston Churchill once said, "If you're going through hell, keep going." And, "Never, never, never give up." As we look back at top cyber stories and security trends in 2017, these wise words from fearless leaders who have gone before us certainly apply to cybersecurity and the new 21st-century challenges confronting our world in 2018. Last year we started with, "You ain't seen nothing yet!"

LogRhythm says AI lets organisations minimise false positives and be more productive. Software inefficiencies play a major role in slowing an organisation's ability to detect and respond to cyberthreats, says security intelligence company LogRhythm. One in three IT decision-makers say their teams spend at least three hours a day on tasks that could be handled by software. Most of them think that the average cybersecurity professional wastes close to 10 hours a week because of inadequate software. The study, commissioned by LogRhythm and conducted by Widmeyer, surveyed 751 IT decision-makers in the US, Britain and Asia-Pacific and found that an overwhelming majority (88%) of respondents view insider threats as a dangerous and growing concern in defending their organisations.