Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series'Possession' review: A chilling, powerful horror series with a lot to say Colonial violence haunts this powerful Gugu Mbatha-Raw-led series set across Britain and Jamaica. Shannon Connellan is Mashable's Senior Editor, General Assignments, based in London. She has been Mashable's UK Editor (and still manages the illustrious UK team) and Australia Editor, but emotionally, she lives searching for . A Tomatometer-approved critic, Shannon writes about entertainment, tech, social good, science, culture, and Australian horror, and loves to nerd out with movie stars, filmmakers, and TV creators . All products featured here are independently selected by our editors and writers.

As if airline flights haven't gotten stressful enough, here comes the latest twist. The Department of Homeland Security is considering banning computers aboard flights from Europe to the United States, because it believes terrorists could conceivably convert a laptop into a bomb, according to news reports. . Tell me it's not so. Snap's rough start as a public company, a watch from Microsoft that can calm Parkinson's tremors, and a new touch screen Amazon Echo all grabbed headlines this week. But the one that rocked this reporter was a proposed laptop ban.

In this excerpt from the #TalkingTech Live podcast, host Jefferson Graham and panelists Tov Arnerson and Dawn Chmielewski weigh over the possible laptop and tablet ban on flights from Europe--will we survive? As if airline flights haven't gotten stressful enough, here comes the latest twist. The Department of Homeland Security is reportedly considering a laptop ban on flights from Europe to the U.S. (Photo: Hero Images, Getty Images) The Department of Homeland Security is considering banning computers aboard flights from Europe to the United States, because it believes terrorists could conceivably convert a laptop into a bomb, according to news reports. . Tell me it's not so. Snap's rough start as a public company, a watch from Microsoft that can calm Parkinson's tremors, and a new touch screen Amazon Echo all grabbed headlines this week.

In his biting, much-cheered defense of the work of local newspapers on his show "Last Week Tonight," the red-hot HBO satirist John Oliver had much fun at the expense of the role of "artificial intelligence" in modern journalism. Oliver's highly entertaining piece -- which quickly garnered well in excess of 4 million views -- contrasted the current enthusiasm of some publishing executives, including the ones who currently pay my salary, for various automated manifestations of reporting, editing and news distribution with what you might call the old-fashioned, sentimental view of the profession: the notepad-wielding reporter at the quotidian school board meeting, fighting corrupt politicians and delivering the truth to your stoop. There is a lot to unpack in Oliver's 19-minute segment and various levels of irony at work. For starters, there's this: In decrying the tendency of panicked newspapers to veer toward populist click-bait, Oliver cleverly created, well, his own populist click-bait. Oliver humbly and openly acknowledged how much his show depends on newspapers for its material -- thank you very much on behalf of my hard-working colleagues.

We describe a real-time computer vision and machine learning system for modeling and recognizing human behaviors in two different scenarios: (1) complex, twohanded actionrecognition in the martial art of Tai Chi and (2) detection and recognition of individual human behaviors and multiple-person interactions in a visual surveillance task. In the latter case, the system is particularly concerned with detecting when interactions between people occur, and classifying them. Graphical models, such as Hidden Markov Models (HMMs) [6] and Coupled Hidden MarkovModels (CHMMs) [3, 2], seem appropriate for modeling and, classifying human behaviors because they offer dynamic time warping, a well-understood training algorithm, and a clear Bayesian semantics for both individual (HMMs) and interacting or coupled (CHMMs) generative processes. A major problem with this data-driven statistical approach, especially when modeling rare or anomalous behaviors, is the limited number of training examples. A major emphasis of our work, therefore, is on efficient Bayesian integration of both prior knowledge with evidence from data.