The "Ok Google" command has expanded since the debut of Android KitKat, allowing devices to respond in various smart ways to those two words. Now, Google Maps on Android has this ability too, whether you're in the middle of a road trip or doing something else. Once it's enabled in settings, you can choose whether it works only while Maps is open or from any screen. Say the magic phrase then follow it up with something like "find a gas station" or "How's traffic ahead?" and it will let you know. Existing Google Maps voice commands like "What's my ETA?" work too, plus new addition like the ability to turn traffic display on or off.
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Discovering motion activities in videos is a key problem in computer vision, with applications in scene analysis, video categorization, and video indexing. In this paper, we propose a method that uses probabilistic topic modeling for discovering patterns of motion that occur in a given activity. Our method also identifies how the discovered patterns of motion relate to one another in space and time. The topic-modeling approach used by our method is the relational topic model. Our experiments show that our method is able to discover relevant spatio-temporal motion patterns in videos.
Researchers have found a simple way to prevent ISPs from spying on you through your Internet of Things (IoT) devices. IoT and smart home products are becoming more common in households worldwide. Whether it be Amazon's Alexa voice assistant, Google Home, or smart lighting systems, IoT has become an established industry in itself with countless products now on the market. The majority of connected devices require Internet access to function, and when this avenue is carved into your home, there is also the risk of compromise, spying, and data theft. Shodan is a prime example of IoT security gone wrong.
In this paper we describe a qualitative approach for natural language communication about vehicle traffic. It is an intuitive and simple model that can be used as the basis for defining more detailed position descriptions and transitions. It can also function as a framework for relating different aggregation levels. We apply a diagrammatic abstraction of traffic that mirrors the different possible interpretations of it and with this the different mental abstractions that humans might make. The abstractions are kept in parallel and according to the communicative context it will be switched to the corresponding interpretation.