The overall goal is to target treatment specifically to each individual so that clinical outcomes for that individual are optimized. One direction of attack is to use patient data to discover decision rules which specify the treatment to use as a function of a vector of features from the patient. Regression and classification are important statistical tools for estimating such rules based on either observational data or data from a randomized trial, and machine learning can help with this because of its ability to artfully handle high dimensional feature spaces with potentially complex interactions.

Social media and interplanetary mission -- what do they have in common? Well, they have in common the Mars Orbiter Mission, also known as'Mangalyaan'. It was launched on 5th November by the Indian Space Research Organization (ISRO). It generated a lot of interest across the globe among millions of people on Social Media networks. In this blog, we analyze how Twitterati reacted to this news. Asia was by far the most interested in the subject, covering 74% of all the tweets on'Mangalyaan'.

An Australian professor is developing a robot to monitor the health of grazing livestock, a development that could bring big changes to a profession that's relied largely on a low-tech approach for decades but is facing a labor shortage. Salah Sukkarieh, a robotics professor at the University of Sydney, sees robots as necessary given how cattlemen are aging. The average age of a farmer in Australia is 52, according to the Australian Farm Institute. Sukkarieh is building a four-wheeled robot that will run on solar and electric power. It will roam pastures alongside livestock and monitor the animals using cameras, thermal sensors and infrared.

Jobs have become a core Brexit issue. Would leaving the EU be good or bad for job creation? Why are migrants taking so many of the new jobs being created in the UK, and what are the implications for the freedom of movement for labour? But the changing nature of work is an issue that runs throughout the developed world, far beyond the UK and Europe. We see this through the prism of our relationship with Europe, for the UK and Germany have become the two strongest job markets in this time zone and have accordingly been sucking in labour from elsewhere.

Companies everywhere are looking for ways to improve customer service. For example, companies with call-in support centers might track how long agents take to answer calls, or how long customers stay on hold. While many companies are looking at every possible method to gain customer insights, many don't go far enough. How many businesses take facial recognition into account as a way to track the quality of their customer service efforts? Big data could make facial recognition for brick-and-mortar businesses just as ubiquitous as call tracking is for call centers. The growth of VoIP and cloud computing has sparked a revolution in customer service.

If the battle between rival digital assistants can be summed up by the NBA championships, then Google's take would be the Golden State Warriors? That's assuming, of course, the record-setting Warriors beat the Cleveland Cavaliers to defend their NBA title. It's the analogy used by Google CEO Sundar Pichai, who characterized the competition as more friendly than bloody. "This is not like'Game of Thrones,'" he said Wednesday at Recode's Code conference in Ranchos Palos Verdes, California. Artificial intelligence is already a hot topic at the conference, and it's a big part of Google's future.

Children as young as four can learn to program robots, according to an expert at Queensland University of Technology. Queensland University of Technology's (QUT) Christina Chalmers, who specializes in the teaching and application of robotics in classrooms, says robotic coding is a growth area in a range of industries, a trend that increases the demands on educators to promote student education in robotics. "Preliminary findings from a current study have shown even pre-school students have gone beyond simply playing games with a NAO robot," Chalmers says. Coding and robotics were implemented into Queensland's state primary schools this year. "Research tells us that if kids don't form positive attitudes towards science, maths, and technology early in life they can find it difficult to engage later on," she says, adding robotics provides an engaging way for both students and teachers to work together.

The following publication was presented at the 2014 IEEE International Workshop on Cyber Crime and received the Best Paper Award on 5/18/2014. The original IEEE LaTeX formatted PDF publication can also be downloaded from here: IWCC Combined Clustering. To be successful, cybercriminals must figure out how to scale their scams. They duplicate content on new websites, often staying one step ahead of defenders that shut down past schemes. For some scams, such as phishing and counterfeitgoods shops, the duplicated content remains nearly identical. In others, such as advanced-fee fraud and online Ponzi schemes, the criminal must alter content so that it appears different in order to evade detection by victims and law enforcement. Nevertheless, similarities often remain, in terms of the website structure or content, since making truly unique copies does not scale well. In this paper, we present a novel combined clustering method that links together replicated scam websites, even when the criminal has taken steps to hide connections. We evaluate its performance against two collected datasets of scam websites: fake-escrow services and high-yield investment programs (HYIPs). We find that our method more accurately groups similar websites together than does existing general-purpose consensus clustering methods.

Global consulting firm Accenture has released a report looking at the potential benefits of autonomous vehicles in Australia and what connected industries may be affected by the advent of self-driving.

It's a weird feeling, cruising around Silicon Valley in a car driven by no one. I am in the back seat of one of Google's self-driving cars – a converted Lexus SUV with lasers, radar and low-res cameras strapped to the roof and fenders – as it manoeuvres the streets of Mountain View, California, not far from Google's headquarters. I grew up about eight kilometres from here and remember riding around on these same streets on a Schwinn Sting-Ray. Now, I am riding an algorithm, you might say – a mathematical equation, which, written as computer code, controls the Lexus. The car does not feel dangerous, nor does it feel like it is being driven by a human. It rolls to a full stop at stop signs, veers too far away from a delivery van, taps the brakes for no apparent reason as we pass a line of parked cars. I wonder if the flaw is in me, not the car: Is it reacting to something I can't see? The car is capable of detecting the motion of a cat, or a car crossing the street hundreds of metres away in any direction, day or night (snow and fog can be another matter). "It sees much better than a human being," Dmitri Dolgov, the lead software engineer for Google's self-driving-car project, says proudly. He is sitting behind the wheel, his hands on his lap. As we stop at the intersection, waiting for a left turn, I glance over at a laptop in the passenger seat that provides a real-time look at how the car interprets its surroundings. On it, I see a gridlike world of colourful objects – cars, trucks, bicyclists, pedestrians – drifting by in a video-game-like tableau. Each sensor offers a different view – the lasers provide three-dimensional depth, the cameras identify road signs, turn signals, colours and lights. The computer in the back processes all this information in real time, gauging the speed of oncoming traffic, making a judgment about when it is OK to make a left turn. Waiting for the car to make that decision is a spooky moment. I am betting my life that one of the coders who worked on the algorithm for when it's safe to make a left-hand turn in traffic had not had a fight with his girlfriend (or boyfriend) the night before and screwed up the code.