Strict laws, lack of shops and pandemic-related delays are making it harder for Americans to purchase guns in crime-ridden cities; attorney and gun rights activist Colion Noir weighs in. Authorities said a man from Boston had a stun gun pulled on him Tuesday morning, as he was being robbed by a woman he met through an online dating app. The unidentified man rendezvoused with the young woman at a local hotel, the Associated Press reported. He told police the two talked for about 30 minutes before she pointed a Taser stun gun at him and began rifling through his pockets. She allegedly stole $100 in cash before law enforcement was called in.

You can choose from a variety of customizable clock faces to match your home's décor. Amazon's Echo Show 5 may be small, but it's loaded with many of the same features as bigger and more expensive models like the 2nd-generation Echo Show and Echo Show 8. The Echo Show 5 is great for tight spaces like desks, nightstands, and other books of the home. It comes with a front-facing camera so you can video chat with friends and family. The screen is bright and the resolution, while lower than other small smart displays, is pretty sharp given its petite size.

Fox News Flash top headlines are here. Check out what's clicking on FoxNews.com. Flaws in Amazon's Alexa were serious enough that a user "in just one-click" could have handed over their voice history, home address and control of their Amazon account, cybersecurity firm Check Point said in a recent report. An attacker could have also silently installed, viewed and removed Alexa skills, Check Point said, referring to voice-driven Alexa apps. A hacker could have also accessed a victim's personal information, such as banking data history and usernames.

Smart-assistant devices have had their share of privacy missteps, but they're generally considered safe enough for most people. New research into vulnerabilities in Amazon's Alexa platform, though, highlights the importance of thinking about the personal data your smart assistant stores about you--and minimizing it as much as you can. Findings published on Thursday by the security firm Check Point reveal that Alexa's web services had bugs that a hacker could have exploited to grab a target's entire voice history, meaning their recorded audio interactions with Alexa. Amazon has patched the flaws, but the vulnerability could have also yielded profile information, including home address, as well as all of the "skills," or apps, the user had added for Alexa. An attacker could have even deleted an existing skill and installed a malicious one to grab more data after the initial attack.

The company is calling for 10 billion yuan ($1.4 billion) in damages and demands that Apple cease "manufacturing, using, promising to sell, selling, and importing" products that infringe on the patent, it said in a social media post. In the lawsuit filed in a local Chinese court, Xiao-i argued that Apple's voice-recognition technology Siri infringes on a patent that it applied for in 2004 and was granted in 2009. In a statement, Apple said that its Siri does not contain features included in the Xiao-i patent, which the iPhone maker argues relates to games and instant messaging. The company also said that independent appraisers certified by the Supreme People's Court have concluded that Apple does not infringe Xiao-i Robot's technology. "We are disappointed Xiao-i Robot has filed another lawsuit," Apple said in a statement.

And then there are really bad dates. A Washington state woman with a pink taser was arrested on Tuesday after she was accused of trying to hold up a man she had met online, Boston police said. Selena Rivera-Apodaca, 24, of Kent, Wash., is expected to be arraigned in Boston Municipal Court on Wednesday on a charge of armed robbery. Shortly before noon on Tuesday, officers went to the Hyatt Regency Hotel at 1 Avenue De LaFayette in response to a radio call about an armed robbery. When they arrived, police said, officers were met by a man who said he had met a woman on an online dating app.

Bitcoin mining and dating apps are now banned in government-issued mobile devices in the United States Marine Corps. Citing privacy and security concerns, marines are also not allowed to install gambling apps or jailbreak their phones, according to a memo signed July 20. The marines are advised to check the list of prohibited apps and delete them. Additionally, they are advised to watch out for any application that records video or voice, monitors GPS, or access the user's contacts or calendars. These are risks, the memo said, that could disclose location or any important security information that should always be private.

Follow up on my previous post discussing the key technologies around the conversational AI solution, I will be dive into the typical challenges the AI Engineer team would encounter when building a virtual agent or a chatbot solution for your clients or customers. Let firstly define the scope and goal of the conversational application. The conversational agents can be categorized into two main streams. The typical agents for Open Domain Conversation are Siri, Google Assistant, BlenderBot from Facebook, Meena from Google. Users can start a conversation without a clear goal, and the topics are unrestricted.

When it comes to dimensionality reduction, the Singular Value Decomposition (SVD) is a popular method in linear algebra for matrix factorization in machine learning. Such a method shrinks the space dimension from N-dimension to K-dimension (where K N) and reduces the number of features. SVD constructs a matrix with the row of users and columns of items and the elements are given by the users' ratings.

Modeling user preferences (long-term history) and user dynamics (short-term history) is of greatest importance to build efficient sequential recommender systems. The challenge lies in the successful combination of the whole user's history and his recent actions (sequential dynamics) to provide personalized recommendations. Existing methods capture the sequential dynamics of a user using fixed-order Markov chains (usually first order chains) regardless of the user, which limits both the impact of the past of the user on the recommendation and the ability to adapt its length to the user profile. In this article, we propose to use frequent sequences to identify the most relevant part of the user history for the recommendation. The most salient items are then used in a unified metric model that embeds items based on user preferences and sequential dynamics. Extensive experiments demonstrate that our method outperforms state-of-the-art, especially on sparse datasets. We show that considering sequences of varying lengths improves the recommendations and we also emphasize that these sequences provide explanations on the recommendation.