Online Social Networks(OSNs) have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, the concept of social trust has attracted the attention of information processors/data scientists and information consumers/business firms. One of the main reasons for acquiring the value of Social Big Data (SBD) is to provide frameworks and methodologies using which the credibility of OSNs users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring the credibility of SBD. Given the exposed environment's settings and fewer limitations related to OSNs, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. Hence, this paper presents an approach incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.

At CES 2020 in Las Vegas, TechRepublic's Bill Detwiler spoke with CNET and CBS News' Dan Patterson about the various technologies featured at the electronics show. The following is an edited transcript of the interview. Now with CNET and CBS News, to talk a little bit about the tech we've seen this week. Dan, you cover a lot of privacy and security, and it's always an important issue, especially at CES, as we talk about technology blending into everyday objects. What have you seen here at the show that brings to light those issues of data privacy, data security? Dan Patterson: Well, what's really interesting to watch is this evolution of CES itself.

Current chart sensations Lizzo and Billie Ellish don't stand on stage with guitars around their neck like Eric Clapton, Slash from Guns N' Roses or Bruce Springsteen did (and still do.) So what are guitar makers to do to keep their factories humming? Turn to streaming, classic rock and YouTube to reach tomorrow's guitar player. The NAMM show, a collection of music store operators, music professionals and tens of thousands of fans is concluding this weekend here, where guitars of every color and imaginable shape were on display. The goal for many guitar makers: to either get older folks to spring out more money to add even more guitars to the collection, or better yet, get tomorrow's generation excited to start playing with new shapes.

Explainable recommendation is far from being well solved partly due to three challenges. The first is the personalization of preference learning, which requires that different items/users have different contributions to the learning of user preference or item quality. The second one is dynamic explanation, which is crucial for the timeliness of recommendation explanations. The last one is the granularity of explanations. In practice, aspect-level explanations are more persuasive than item-level or user-level ones. In this paper, to address these challenges simultaneously, we propose a novel model called Hybrid Deep Embedding (HDE) for aspect-based explainable recommendations, which can make recommendations with dynamic aspect-level explanations. The main idea of HDE is to learn the dynamic embeddings of users and items for rating prediction and the dynamic latent aspect preference/quality vectors for the generation of aspect-level explanations, through fusion of the dynamic implicit feedbacks extracted from reviews and the attentive user-item interactions. Particularly, as the aspect preference/quality of users/items is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item. The extensive experiments conducted on real datasets verify the recommending performance and explainability of HDE. The source code of our work is available at \url{https://github.com/lola63/HDE-Python}

Software engineers have significant expertise to offer when building intelligent systems, drawing on decades of experience and methods for building systems that are scalable, responsive and robust, even when built on unreliable components. Systems with artificial-intelligence or machine-learning (ML) components raise new challenges and require careful engineering. We designed a new course to teach software-engineering skills to students with a background in ML. We specifically go beyond traditional ML courses that teach modeling techniques under artificial conditions and focus, in lecture and assignments, on realism with large and changing datasets, robust and evolvable infrastructure, and purposeful requirements engineering that considers ethics and fairness as well. We describe the course and our infrastructure and share experience and all material from teaching the course for the first time.

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. There is a silent epidemic. Globally, in increasing numbers, young people are facing mental-health issues.

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Felsenthal is the Executive Chairman and former Editor in Chief of TIME. My 11-year-old daughter often asks me, tauntingly, what things were like "in the 20th century."

Smart assistants can make life so much easier--and Google Assistant is no exception. If you're just dipping your toes into the world of smart home, a smart speaker like the Nest Mini from Google is a fantastic place to start. The Nest Mini is a much-improved version of the Google Home Mini, but it's been given a new name and a few software upgrades. The powerful little device has 360-degree sound, three far-field microphones, and Voice Match technology that can differentiate your voice from other family members. But before you get to using that, here's how to set up your new Nest Mini.

Answering questions and managing schedules are some tasks people ask voice assistants to work for. The traditional process is slow and clunky; however, with better technology, you can eliminate difficult manual tasks. Updating and checking spreadsheets is a lot of manual work. Voice technology can replace manual work by simply asking the device to tell about the upcoming work schedule. With advances in artificial intelligence and natural language processing, it's no longer a risk to understand requests from voices and styles of speech.

Google's owner Alphabet has become a trillion-dollar company for the first time, making it only the fourth US firm to reach the bumper valuation. Alphabet's value, based on the price of its Wall Street-listed shares, passed $1tn (£776bn) in the final minutes of trading on Thursday night, with shares closing at a record high of $1,450.16 It marks a stellar rise for Alphabet, which floated as Google for $85 a share in 2004. After its initial public offering, the Silicon Valley firm was worth $23bn. It has followed its tech rivals Microsoft, Apple and Amazon over the $1tn mark, amid a long rally in so-called Faang stocks. Google's value has steadily surged as it has tightened its grip on the search market, boosted its advertising revenues from web searches and YouTube, created and grown its Android mobile operating system, and launched a series of smart-tech products including Google Home and Google Assistant.