The autonomous vehicle division of Google's parent company will start hauling cargo using self-driving trucks, capping a busy week for next-generation shipping technology. Waymo, the driverless vehicle unit of Alphabet, announced a pilot programme that will have self-driving big rigs transport cargo to the company's data centres in Georgia. Several companies are vying to dominate the nascent self-driving vehicle industry, believing the technology will reshape how humans and goods travel. Waymo has already extensively tested autonomous cars intended to ferry people around. "Now we're turning our attention to things as well", the company said in a blog post, noting that driverless trucks pose unique tech challenges.

Technology and automotive companies touting self-driving cars as the future of transportation may have some work to convince San Franciscans, who keep attacking the vehicles. A third of traffic collisions involving autonomous vehicles in 2018 so far featured humans physically confronting the cars, according to data released by California. In one case, a taxi driver exited his cab and slapped the front passenger window of a General Motors Cruise parked behind him. No one was hurt, though the car sustained a scratch. In another case, a pedestrian hurtled across an intersection despite a "do not walk" sign, shouting as he went, and rammed his body into a different Cruise's rear bumper.

Michelle Obama took an apparent swipe at Donald Trump's social media habits, saying she uses social media "like a grown-up". "How many kids do you know that the first thing that comes off the top of their head is the first thing they should express? It's like, 'Take a minute. Talk to your crew before you put that [out there] and then spell check and check the grammar,'" the former First Lady said during a panel in New York, according to People. While Ms Obama did not mention the President by name, Mr Trump is known for stream-of-consciousness bursts of tweets that periodically contain grammatical and spelling errors.

This paper deals with the binary classification task when the target class has the lower probability of occurrence. In such situation, it is not possible to build a powerful classifier by using standard methods such as logistic regression, classification tree, discriminant analysis, etc. To overcome this short-coming of these methods which yield classifiers with low sensibility, we tackled the classification problem here through an approach based on the association rules learning. This approach has the advantage of allowing the identification of the patterns that are well correlated with the target class. Association rules learning is a well known method in the area of data-mining. It is used when dealing with large database for unsupervised discovery of local patterns that expresses hidden relationships between input variables. In considering association rules from a supervised learning point of view, a relevant set of weak classifiers is obtained from which one derives a classifier that performs well.