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　　However, only hours later, she deleted all her posts and reactivated her account, adding a single video of a happy-looking Coco playing on a green lawn.The video footage which had been posted online saw her dog Coco inside the China high speed flat embroidery machines Factory washing machine, with her face pushed up against the door. The Instagram user has been labelled ‘evil’ after posting the video..A shocking animal abuse scenario saw an instagram user posting a video of her dog in a washing machine, all in an apparent bid to gain more followers.Having come under fire for her outrageous video, Rotamn had to delete her account.She also posted a lengthy justification about her actions, claiming she had been "misinterpreted" adding that the pooch was only in the washing machine for a few seconds, which she insisted had not been switched on at any point.Rotman added a survey with the question "are you going to be really traumatised?" next to the video.Instagram users were quick to condemn the woman’s ‘stupidity’ with many calling her out for animal abuse.The disturbing video had been posted by Tamara Rotman, a popular social media star from Argentina.The West Highland White Terrier, who seemed traumatised at being trapped inside the washing machine struggles to get out.

　　Researchers at the University of Sussexs Wearable Technologies Lab believe that the machine learning techniques developed in a global research competition they initiated could also lead to smartphones being able to predict upcoming road conditions and traffic levels, offer route or parking recommendations and even detect the food and drink consumed by a phone user while on the move.The winning team, JSI-Deep of the Jozef Stefan Institute in Slovenia, achieved the highest score of 93."Previous studies generally collected only GPS and motion data.9 per cent through the use of a combination of deep and classical machine learning models."By organising a machine learning competition with this dataset we can share experiences in the scientific community and set a baseline for future work.. Our study is much wider in scope: we collected all sensor modalities of smartphones, and we collected the data with phones placed simultaneously at four locations where people typically carry their phones such as the hand, backpack, handbag and pocket," said study author Daniel Roggen.

　　Roggen and his team collected the equivalent of more than 117 days worth of data monitoring aspects of commuters journeys in the UK using a variety of transport methods to create the largest publicly available data set of its kind.According to a new research, apps can soon detect what mode of transport commuters are using and automatically offer relevant advice."This is extremely important to design robust machine learning algorithms. The study appeared in the Journal of the ACM. Automatically recognising modes of transportation is important to improve several mobile services - for example to ensure video streaming quality despite entering in tunnels or subways, or to proactively display information about connection schedules or traffic conditions," said Roggen.It is now hoped that the data set will be used for a wide range of studies into electronic logging devices exploring transportation mode recognition, mobility pattern mining, localisation, tracking, and sensor fusion.