Apt slogan for Modi govt would be sabka saath sabka vinash, says Yashwant Sinha:Former Union Minister Yashwant Sinha came down heavily on Modi government saying that this was the first government after Independence which has been playing "mischief" with the developmental statistics. But from their side, this name (Narendra Modi) has disappointed the country, who is your another name?" Yadav said. 

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Inspired by the Chausat Yogini Temple, the 64 tantric female deities of Odisha, designer Gunjun Jain’s Yogini collection celebrates womanhood representing varied expressions of modern-day Yoginis through handwoven sarees styled in experimental drapes teamed with workwear blouses.From rural West Bengal, Eka by Rina Singh is presenting hand spun and woven textile using the technique of ‘Kata Makur Kaj’, as called in rural Bengal or ‘Cut Shuttle’ in layman language. 

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Our machines would have been sold to local garment factories and though there are a few of them still around, most of the market has been taken over by international brands, so the demand for industrial machines have lessened," he laments. Sewing machines ranging from those used to sew linings on shirts to those that sew shut industrial gunny bags can be found scattered around on every available surface.

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Not that we haven’t seen such false and distorted representation of facts earlier in Karan Johar’s or other Mumbai filmmakers’ cinema. They seem to be stuck in a time warp.And there are many areas where the director-producer duo seems to be trying to relive the earlier grand success of Baazigar and rehash it into Abbas’ son Mustafa Burmawala’s debut film.Before long, cracks begin to be seen in Ransh’s character. Instead, it was the turn of the "anti-hero", which then became a mainstream reality. Johnny Lever acts as a policeman here, and other characters also appear and disappear as the writing hinges on the so-called star charisma of Mustafa.After falling in love Ransh’s (Mustafa) first job at hand seems to get two of his rivals out of the way.

If any of us has ever received a modicum of education in any institution, one may infer that schooling — as is it actually shown here — appears to be a travesty of education. Abbas-Mustan haven’t grown with time.Rating: Cast: Kiara Advani, Ronit Roy, Mustafa Burmawala, Carla Dennis, Eshan ShankerDirector: Abbas-MustanVeteran filmmakers are most revered and looked up to, more so, if they have given us suspense-filled edge-of-the-seat thrillers in the past..Soon a swanky college Woodstock in North India, seemingly near Shimla, exposes some of the brats (students).And that’s not all.

There is an unmistakable humour that runs through Machine; it begins right from the opening scene. And that is the major drawback that makes Abbas-Mustan’s Machine a trite overwritten drama.He also must win over Sarah’s father (Ronit Roy) and prove that he is the best son-in-law that the family could have ever had. As their bond becomes stronger, love blossoms and soon they tie the knot.From the way the students dress up to the way their classrooms are adorned with upscale furniture in the classrooms to the activities they all indulge in, it all looks fake and artificial. Soon, as the two (Ransh) and Sarah (Kiara Advani) get married, they don’t take long to allow their passion to overpower them. Thereafter, many scenes from the director duos past films are reused to a pathetic finale. In the midst of love and betrayal, many unpredicted twists are thrown in, that neither take you by surprise nor help sustain your interest.

After a frolicsome outing on their honeymoon, Ransh throws Sarah off the cliff and thus begins a sordid tale of suspicion, distrust, obsession, greed and treachery. Imagine an opening frame that shows a camera aiming at an eardrum all along taking us through the maze of its insides.At its core, Machine is the story of racing enthusiasts, who meet each other under mysterious circumstances.What unfolds before you are many scenes from Baazigar that give you a sense of déjà vu, and from then on the film is on its China plastic blow moulding machine manufacturer downhill. What worked in their favour nearly 25 years ago was the freshness that Indian audiences lapped it up because of its hero getting depicted as a villain. The duo Abbas-Mustan made us sit up and take note of their ability to spring surprises like Khiladi, Daraar, Humraaz and Soldier to name a few. SRK’s character in the film turned the notion of protagonist on its head. But their grandest contribution has to be Baazigar — a film that catapulted Shah Rukh Khan to the unparalleled big lead

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First, the team gauged the participants preconceived beliefs about and attitudes toward machines - what is called a "machine heuristic".The researchers recruited participants from the online workforce, Amazon Mechanical Turk, to gain a better understanding of user psychology behind the acceptance of automation in clinics.The study was presented at the ACM Conference on Human Factors in Computing Systems in Glasgow, Scotland.

"Doctors are limited by their human bandwidth, by their experience, knowledge and even state of mind from minute to minute," he said."There is increasing use of automated systems in the medical field, where intake is now often conducted through a kiosk instead of by a receptionist," said S Shyam Sundar, one of the researchers, Penn State University. They initiated online chat interactions with the various types of avatars to test the participants acceptance of those healthcare providers and their intentions to use those providers in the future."In contrast, machines can be programmed to think of all the possible conditions that a patients symptoms could point to, and they never get tired.."A machine heuristic involves stereotypes people have about machines, including their beliefs in machines infallibility, objectivity, and efficiency," said Sundar.Researchers at Penn State University have claimed that people who have high confidence in machines and in their own technological capabilities are more likely to use and accept digital healthcare services.

Some level of automation is clearly needed," he added.According to Sundar, the healthcare industry can benefit from increased reliance on automated systems.Next, they exposed participants to various combinations of the healthcare provider, such as receptionist, nurse, and doctor; and agent types, such as human, avatar, and machine."We also found that power usage predicted acceptance of digital healthcare providers."We found that if you are high on machine heuristic and you are high on power usage, you have the most positive attitude toward automated healthcare providers. This combination seems to make people more accepting of these technologies," said Sundar.The team also noticed a double dose effect of machine heuristic and power usage.

The researchers also asked participants a variety of questions to rate their "power usage," or level of expertise and comfort in using machines."We found that the higher peoples beliefs were in the machine heuristic, the more positive their attitude was toward the agent and the greater their intention was to China 72 cavity-Preform Mould Manufacturers use the service in the future," said Sundar.The team measured participants adherence to the machine heuristic by asking them to indicate their level of agreement with statements. A power user (a person with advanced computer skills) is more likely to accept a robot doctor, for example, than a non-power user," he added

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The winning team, JSI-Deep of the Jozef Stefan Institute in Slovenia, achieved the highest score of 93.The team launched a global competition challenging teams to develop the most accurate algorithms to recognise eight modes of transport (sitting still, walking, running, cycling or taking the bus, car, train or subway) from the data collected from 15 sensors measuring everything from movement to ambient pressure..According to a new research, apps can soon detect what mode of transport commuters are using and automatically offer relevant advice.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. In general, deep learning techniques tended to outperform traditional machine learning approaches, although not to any significant degree.9 per cent through the use of a combination of deep and classical machine learning models. The study appeared in the Journal of the ACM.

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.The project saw 17 teams take part with two entries achieving results with more than 90 per cent accuracy, eight with between 80 and 90 per cent, and nine between 50 and 80 per cent. Our study is much wider in scope: we collected all sensor modalities of smartphones, China water bottling machine Manufacturers 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.

"This is extremely important to design robust machine learning algorithms.The project gathered data from four mobile phones carried by researchers as they went about their daily commute over seven months.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."Previous studies generally collected only GPS and motion data."By organising a machine learning competition with this dataset we can share experiences in the scientific community and set a baseline for future work. 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. The variety of transport modes, the range of conditions measured and the sheer number of sensors and hours of data recorded is unprecedented," he added

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