Automatic sentiment analysis play vital role in decision making. Many organizations spend a lot of budget to understand their customer satisfaction by manually going over their feedback/comments or tweets. Automatic sentiment analysis can give overall picture of the comments received against any event, product, or activity. Usually, the comments/tweets are classified into two main classes that are negative or positive. However, the negative comments are too abstract to understand the basic reason or the context. organizations are interested to identify the exact reason for the negativity. In this research study, we hierarchically goes down into negative comments, and link them with more classes. Tweets are extracted from social media sites such as Twitter and Facebook. If the sentiment analysis classifies any tweet into negative class, then we further try to associates that negative comments with more possible negative classes. Based on expert opinions, the negative comments/tweets are further classified into 8 classes. Different machine learning algorithms are evaluated and their accuracy are reported.

Four years ago, organizers created the international AI City Challenge to spur the development of artificial intelligence for real-world scenarios like counting cars traveling through intersections or spotting accidents on freeways. Last week, Chinese tech giants Alibaba and Baidu swept the AI City Challenge, beating competitors from nearly 40 nations. Hundreds of Chinese cities have pilot programs, and by some estimates, China has half of the world's smart cities. One of the competitions in the AI City Challenge asked participants to identify cars in videofeeds; for the first time this year, the descriptions were in ordinary language, such as "a blue Jeep goes straight down a winding road behind a red pickup truck." He says AI researchers in the US can also compete for government grants like the National Science Foundation's Civic Innovation Challenge or the Department of Transportation's Smart City Challenge.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Israel reportedly used a swarm of drones to locate and attack Hamas targets during the 11 day conflict that broke out in May. The Israeli Defense Forces employed artificial intelligence to identify and strike targets in the Gaza Strip, according to a report from the New Scientist, which alleged it may be the first time a drone swarm has been used in combat. Drone swarms have been characterized as the next phase of war fighting, whereby "hundreds of drones that integrate their actions using emergent behavior."

Does it strike you that the cybercriminals are outgunning you? You must be right 100% of the time, but the cybercriminals only need to be right once to penetrate your network. So, we need some help, as in Artificial Intelligence. Manufacturing, Supply Chain, Logistics industries as well as Cybersecurity -- Artificial Intelligence (AI) is everywhere. Wondering what is so special about Artificial Intelligence?

Voice assistants changed the way we interact with technology. Why set an alarm manually when Alexa can do it for you? Want to get more out of your Amazon Echo? Tap or click here for new tricks such as using your Echo as a speakerphone to finding the right wine to pair with a particular dish. Tap or click here for my favorite Siri shortcuts.

Scott Petry, co-founder and CEO of Authentic8, maker of Silo, a platform for secure and controlled access to the web. Security misconfiguration and broken authentication. It plays out time and again: A bad person invents a way to attack a computer or a network. A good person discovers the attack and figures out how to detect future attacks. More good people build on that work and learn how to block them.

Two astronauts on Sunday made the first spacewalk outside China's new orbital station to set up cameras and other equipment using a 50-foot-long robotic arm. Liu Boming and Tang Hongbo were shown by state TV climbing out of the airlock as Earth rolled past below them. The third crew member, commander Nie Haisheng, stayed inside. Liu and Tang spent nearly seven hours outside the station, the Chinese space agency said. The astronauts arrived June 17 for a three-month mission aboard China's third orbital station, part of an ambitious space program that landed a robot rover on Mars in May.

SARS-CoV-2, the causative virus of COVID-19 continues to cause an ongoing global pandemic. Therapeutics are still needed to treat mild and severe COVID-19. Drug repurposing provides an opportunity to deploy drugs for COVID-19 more rapidly than developing novel therapeutics. Some existing drugs have shown promise for treating COVID-19 in clinical trials. This in silico study uses structural similarity to clinical trial drugs to identify two drugs with potential applications to treat early COVID-19. We apply in silico validation to suggest a possible mechanism of action for both. Triamcinolone is a corticosteroid structurally similar to Dexamethasone. Gallopamil is a calcium channel blocker structurally similar to Verapamil. We propose that both these drugs could be useful to treat early COVID-19 infection due to the proximity of their targets within a SARS-CoV-2-induced protein-protein interaction network to kinases active in early infection, and the APOA1 protein which is linked to the spread of COVID-19.

The increasing digitalization of the manufacturing domain requires adequate knowledge modeling to capture relevant information. Ontologies and Knowledge Graphs provide means to model and relate a wide range of concepts, problems, and configurations. Both can be used to generate new knowledge through deductive inference and identify missing knowledge. While digitalization increases the amount of data available, much data is not labeled and cannot be directly used to train supervised machine learning models. Active learning can be used to identify the most informative data instances for which to obtain users' feedback, reduce friction, and maximize knowledge acquisition. By combining semantic technologies and active learning, multiple use cases in the manufacturing domain can be addressed taking advantage of the available knowledge and data.

Research on open-domain question answering (QA) has a long tradition. A challenge in this domain is answering complex questions (CQA) that require complex inference methods and large amounts of knowledge. In low resource languages, such as Persian, there are not many datasets for open-domain complex questions and also the language processing toolkits are not very accurate. In this paper, we propose a knowledge-based approach for answering Persian complex questions using Farsbase; the Persian knowledge graph, exploiting PeCoQ; the newly created complex Persian question dataset. In this work, we handle multi-constraint and multi-hop questions by building their set of possible corresponding logical forms. Then Multilingual-BERT is used to select the logical form that best describes the input complex question syntactically and semantically. The answer to the question is built from the answer to the logical form, extracted from the knowledge graph. Experiments show that our approach outperforms other approaches in Persian CQA.