Research models use data from Toronto's Don River and Calgary's Bow River TORONTO, November 11, 2019 – Using complex models based on artificial intelligence (AI) and data from the Don River in Toronto and Bow River in Calgary, researchers at the Lassonde School of Engineering can now predict the water levels in rivers days in advance of floods. "We've created methods to predict real-time flood risk," says Usman T. Khan, professor in the Department of Civil Engineering at York's Lassonde School of Engineering. "These results outline an approach that can be used to create models with higher accuracy and lower data requirements, which translates to improved flood early warning systems. Early warning systems are considered the most effective way to mitigate flood induced hazards." The study, led by Khan, was published today in the Journal of Hydrology.
In the busy weeks leading up to RSA this year, I was taking a rare break to drive my daughter to the airport. She was flying back to school to continue her 2nd year at University of Toronto (shout out to all of my Canadian peeps!). Btw, if you've not seen "Stronger Beer" highly recommended. Anyway, my daughter asked me an intriguing question on the ride to LAX. She said, "Last time I got caught in a random search… do you think the TSA finds anything doing that…" Great question, and my answer was "No" it's a horrible way to search people.
Two prison inmates in eastern Quebec were arrested Wednesday after they took a female security guard hostage, prompting the evacuation of a courthouse and an hours-long standoff with police. Authorities said the unidentified guard was uninjured in the altercation. CBC News reported that the incident began when police were called to the courthouse in Sept-Iles at around 3 p.m. local time. The facility, which houses the jail in the basement and the town's courts on the main floors, was evacuated and a security perimeter was established. Radio-Canada reported a man in handcuffs was seen being taken out of the jail at around 6:30 p.m local time.
The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML- and DL-based IoT security.
AI and Machines Learning Innovations from H2O.ai Drive Personalized and Better Experiences for Jewelers and Consumers H2O.ai, the open source leader in artificial intelligence (AI) and machine learning (ML), announced Jewelers Mutual, one of the United States' and Canada's most established and trusted providers of affordable and comprehensive insurance for jewelers and consumers, has chosen its award winning AI platforms to provide AI and machine learning capabilities to better serve its customers. As a leader in driving customer-focused innovation and providing the latest technology to a long-standing industry, Jewelers Mutual is using H2O-3 open source and H2O Driverless AI to deliver exceptional customer experiences, prevent losses, and provide better protection and policies for both jewelers and customers. "We have been in the jewelry insurance business for over 100 years, and our leadership team has been looking to raise the bar for technology-driven innovation in the industry. After two years of experimentation with AI and machine learning, we came to place a high value on model transparency and explainability. Our business end-users demanded it. The initial AI platform we used was lacking in this area so we began searching for a new platform," said Andrew Langsner, Senior Manager, Embedded Analytics at Jewelers Mutual.