Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received online and in chunks. Many of the current multi-label classification methods require a lot of time and memory, which make them infeasible for practical real-world applications. In this paper, we propose a fast linear label space dimension reduction method that transforms the labels into a reduced encoded space and trains models on the obtained pseudo labels. Additionally, it provides an analytical method to update the decoding matrix which maps the labels into the original space and is used during the test phase. Experimental results show the effectiveness of this approach in terms of running times and the prediction performance over different measures. Keywords: data stream classification, multi-label data, label compression 1. Introduction Standard classification is the task of assigning the correct class to previously unknown test instances based on training instances. Training data consist of a set of features and an associated target class or class label. Many modern data mining applications, however, need to deal with more than one label per instance.

Mining frequent sequential patterns consists in extracting recurrent behaviors, modeled as patterns, in a big sequence dataset. Such patterns inform about which events are frequently observed in sequences, i.e. what does really happen. Sometimes, knowing that some specific event does not happen is more informative than extracting a lot of observed events. Negative sequential patterns (NSP) formulate recurrent behaviors by patterns containing both observed events and absent events. Few approaches have been proposed to mine such NSPs. In addition, the syntax and semantics of NSPs differ in the different methods which makes it difficult to compare them. This article provides a unified framework for the formulation of the syntax and the semantics of NSPs. Then, we introduce a new algorithm, NegPSpan, that extracts NSPs using a PrefixSpan depth-first scheme and enabling maxgap constraints that other approaches do not take into account. The formal framework allows for highlighting the differences between the proposed approach wrt to the methods from the literature, especially wrt the state of the art approach eNSP. Intensive experiments on synthetic and real datasets show that NegPSpan can extract meaningful NSPs and that it can process bigger datasets than eNSP thanks to significantly lower memory requirements and better computation times.

Speaking verbally and performing sign language require the same parts of the brain, according to a new study. Researchers at New York University found that the neural skills needed to perform sign language are the similar to those required for speaking out loud. Their report is the first of its kind to prove the association between the two communication forms. Sign language communicators and verbal English speakers rely on the same neural skills, a new report says. The new research was published in the journal Scientific Reports.

The space race was in full swing. For the first time, a space probe had recently landed on another planet (Venus). And I was eagerly studying everything I could to do with space. Then on April 2, 1968 (May 15 in the UK), the movie 2001: A Space Odyssey was released--and I was keen to see it. So in the early summer of 1968 there I was, the first time I'd ever been in an actual cinema (yes, it was called that in the UK). I'd been dropped off for a matinee, and was pretty much the only person in the theater. And to this day, I remember sitting in a plush seat and eagerly waiting for the curtain to go up, and the movie to begin. It started with an impressive extraterrestrial sunrise. But then what was going on? Those were landscapes, and animals. I was confused, and frankly a little bored. But just when I was getting concerned, there was a bone thrown in the air that morphed into a spacecraft, and pretty soon there was a rousing waltz--and a big space station turning majestically on the screen. The next two hours had a big effect on me. It wasn't really the spacecraft (I'd seen plenty of them in books by then, and in fact made many of my own concept designs). But what was new and exciting for me in the movie was the whole atmosphere of a world full of technology--and the notion of what might be possible there, with all those bright screens doing things, and, yes, computers driving it all. It would be another year before I saw my first actual computer in real life. But those two hours in 1968 watching 2001 defined an image of what the computational future could be like, that I carried around for years. I think it was during the intermission to the movie that some seller of refreshments--perhaps charmed by a solitary kid so earnestly pondering the movie--gave me a "cinema program" about the movie. Half a century later I still have that program, complete with a food stain, and faded writing from my 8-year-old self, recording (with some misspelling) where and when I saw the movie. A lot has happened in the past 50 years, particularly in technology, and it's an interesting experience for me to watch 2001 again--and compare what it predicted with what's actually happened. Of course, some of what's actually been built over the past 50 years has been done by people like me, who were influenced in larger or smaller ways by 2001. When Wolfram Alpha was launched in 2009--showing some distinctly HAL-like characteristics--we paid a little homage to 2001 in our failure message (needless to say, one piece of notable feedback we got at the beginning was someone asking: "How did you know my name was Dave?!"). One very obvious prediction of 2001 that hasn't panned out, at least yet, is routine, luxurious space travel. But like many other things in the movie, it doesn't feel like what was predicted was off track; it's just that--50 years later--we still haven't got there yet. Well, they have lots of flat-screen displays, just like real computers today.

In recent years, an increasing number of robots have relied on screens rather than physical mechanisms to generate expressive faces. Screens are cheap, they're easy to work with, and they allow for nearly unlimited creativity. Consequently, there's an enormous variety of robot faces, with a spectrum of similarities and differences both obvious and subtle. However, there hasn't been a comprehensive study of the entire design space, possibly because of how large it is, and this is bad, because there's a lot to learn. At the ACM/IEEE International Conference on Human Robot Interaction (HRI) last month, roboticists from the University of Washington in Seattle presented a paper entitled "Characterizing the Design Space of Rendered Robot Faces."

Alibaba A.I. Labs, Baidu DuerOS and iFLYTEK, among others, will exhibit the latest in big data analytics, speech recognition and predictive technology. With China setting the pace to become a global innovation leader in AI, CES Asia will bring together the biggest players shaping the future of artificial intelligence. "Artificial intelligence is one of those exciting technologies that will become ubiquitous in the next decade as it becomes more deeply embedded in the products that we use day in and day out to make our lives better, " said John T. Kelley, senior director, international programs and show director, CES Asia. "AI is already being incorporated in everyday consumer technology products that provide practical benefits, including cars, smart homes, robotics, health and wellness devices and home security. China is leading the way in AI, embracing innovation and setting global standards and it will be front and center during CES Asia 2018!" More, ARM Accelerator and the Hong Kong University of Science and Technology will bring several cutting-edge companies to CES Asia.

One of China's biggest technology companies is using its expertise to improve the country's healthcare system. China has a rapidly aging population and hospitals are becoming burdened with too many patients and not enough doctors. Chinese leaders have called on tech companies to help modernize and automate medical tasks as a way to improve patients' access to healthcare. E-commerce leader Alibaba has committed to using AI and machine learning to help hospitals and doctors. Alibaba's cloud computing division has created a suite of AI medical solutions that can assist with drug development, medical imaging and diagnostics.

AI and data science has Innumerable Applications. Let's look at 100 use-cases of AI, ML and data science. We, as humans, are shaped by our experiences. Touch a hot stove element as a child, and you learn quickly never to do it again. Spin and spin with your friends on the front lawn until you fall down, to experience the feeling that they sky is spinning around you. Keep doing it over and over until you throw up, and you have learned a limitation. You may do it again, but you've learned to stop doing it sooner because of the consequences. Artificial Intelligence (AI) does not possess the amazing feedback system that has evolved on this planet for everything with a brain and sophisticated nervous system weighing more than a few grams. Instead, we have to define what constitutes success or failure for our increasingly clever AIs, and they need to assess everything they learn through a "Is this what humans would want?" filter. They fit in every industry, whether manufacturing or service, and so there is not much point in specifying how we will use it in "Real Estate" or "Biotechnology". Instead there will be many instantiations that fit multiple industries. Collecting data encompassing the accumulated wisdom of thousands of experts, an AI System could access these details to address any problem that arose on that subject. Combined Expert Systems will have a vast interdisciplinary knowledge capable of solving some of humanity's toughest questions. During an Ebola outbreak, instead of the typical two to three years of development time, one Pharma company set its AI loose on its drug molecular database and within a day it had come up with two candidates suitable for Clinical Trials. AIs discover relationships that humans beings miss, which means they will aid us in discovering essential new drugs (such as a new antibiotic, which we desperately need) or drug combination therapies to solve unique problems that arise. Britain and other countries already have AI Physician Expert Systems available to the public for a cost of about £60 per year. You can check symptoms anytime, 24 hours per day, and get practical medical advice as often as you wish, without limitation.

Deep Learning, one of the subfields of Machine Learning and Statistical Learning has been advancing in impressive levels in the past years. Cloud computing, robust open source tools and vast amounts of available data have been some of the levers for these impressive breakthroughs. The criteria used to select the 20 top papers is by using citation counts from academic.microsoft.com. It is important to mention that these metrics are changing rapidly so the citations valued must be considered as the numbers when this article was published. In this list of papers more than 75% refer to deep learning and neural networks, specifically Convolutional Neural Networks (CNN).

Chinese phone makers are in a tough spot if they want to use voice assistants. Google is largely a no-show in the country, Siri is limited to Apple devices and services like Alexa or Cortana don't have nearly as much influence as they do elsewhere. Xiaomi has a straightforward solution to that problem: create its own AI companion. The company has posted a video showing off Xiao Ai, an assistant designed with China in mind. The functionality is familiar: you can play music, check the weather, control smart home devices and translate foreign languages.