This paper presents the Speech Technology Center (STC) replay attack detection systems proposed for Automatic Speaker Verification Spoofing and Countermeasures Challenge 2017. In this study we focused on comparison of different spoofing detection approaches. These were GMM based methods, high level features extraction with simple classifier and deep learning frameworks. Experiments performed on the development and evaluation parts of the challenge dataset demonstrated stable efficiency of deep learning approaches in case of changing acoustic conditions. At the same time SVM classifier with high level features provided a substantial input in the efficiency of the resulting STC systems according to the fusion systems results.

Machine learning techniques are increasingly demonstrating success in image-based diagnosis, disease detection and disease prognosis. To reduce operator dependency and get better diagnostic accuracy, a computer aided diagnositic (CAD) system is a valuable and beneficial means for breast tumor detection and classification, fetal development and growth, Brain functioning, skin lesions and Lungs diseases [1]. Image denoising using machine learning techniques plays important role in the various application area of medical imaging such as pre-processing (noise removal from Ultrasound (US) images, segmentation (MRI of brain tumors and lung infections using X-rays), Computer aided diagnosis (CAD) for breast cancer, fetus development and many more). Further, denoising of medical images using data mining methods are analyzed. This paper focuses on the review of various denoising methods along with machine learning approaches to develop a systematic decision for diagnosing and prediction for medical images. The representation of the machine learning i.e. based on various numbers of methods which focuses on prediction, based on known properties learned from the training data has been considered.

The 21st International Cloud Expo[17] has announced that its Call for Papers is open[18]. Cloud Expo, to be held October 31 - November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA, brings together Cloud Computing, Big Data, Internet of Things, DevOps, Digital Transformation, Machine Learning and WebRTC to one location. All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo[28] @ThingsExpo[29], June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA.

Are brands ready for mixed reality? The answer is a resounding no! Fortunately, there's still time left to prepare for one of the biggest digital transformations of all time. Reports suggest that a collection of the world's leading tech companies, including Microsoft, Apple, Samsung, Huawei, LG, Acer, Intel, Meta, Magic Leap, and ODG are all planning to double down on mixed reality (MR) this year by bringing affordable devices to the masses. In particular, excitement continues to grow over speculation that Apple may be ready to unveil its MR device in the coming months to coincide with the 10-year anniversary of the iPhone. Tim Cook has recently made bold statements about augmented reality (AR), indicating his company will have a major horse in the race.

Check out Part I of this article for background information, and to discover the first 12 uses of statistical modeling. Here we list another 12 popular uses of statistical, data science, machine learning, optimization, graph theory, mathematical and operations research techniques. Monte-Carlo simulations are used in many contexts: to produce high quality pseudo-random numbers, in complex settings such as multi-layer spatio-temporal hierarchical Bayesian models, to estimate parameters (see picture below), to compute statistics associated with very rare events, or even to generate large amount of data (for instance cross and auto-correlated time series) to test and compare various algorithms, especially for stock trading or in engineering. Customer churn analysis helps you identify and focus on higher value customers, determine what actions typically precede a lost customer or sale, and better understand what factors influence customer retention. Statistical techniques involved include survival analysis (see Part I of this article) as well as Markov chains with four states: brand new customer, returning customer, inactive (lost) customer, and re-acquired customer, along with path analysis (including root cause analysis) to understand how customers move from one state to another, to maximize profit.

It's going to be a long, long time before we have amphibious robots that are anywhere near as capable as the mighty and majestic turtle. While many roboticists are working diligently on TurtleBots of all kinds, researchers at the Korea Advanced Institute of Science and Technology (KAIST) have taken things much more literally with the development of a robot-turtle hybrid: a "parasitic robot," as they call it, that lives on the back of a real turtle, guiding the animal from place to place with the aid of an array of LEDs coupled with positive reinforcement from tasty turtle snacks. Animals have the benefit of millions of years of evolution, which has carefully optimized their design towards versatile and efficient locomotion. Robots have been around for not quite that long, and they suffer from all kinds of specific limitations as well as a sort of general comic ineptitude that keeps them from being useful most of the time, especially when try to do basic, simple tasks that animals excel at. In a recent paper in the Journal of Bionic Engineering, the KAIST researchers argue that animal-robot hybrids "could provide an alternative solution to some of the limitations of conventional mobile robot systems in various fields."

What is a Time Series? Many data sets are cross-sectional and represent a single slice of time. However, we also have data collected over many periods - weekly sales data, for instance. This is an example of time series data. Time series analysis is a specialized branch of statistics used extensively in fields such as Econometrics and Operations Research.

On January 22nd, two days after President Trump was inaugurated, he received a memo from his new Secretary of Defense, James Mattis, recommending that the United States launch a military strike in Yemen. In a forty-year career, Mattis, a retired Marine Corps general and a veteran of the wars in Afghanistan and Iraq, had cultivated a reputation for being both deeply thoughtful and extremely aggressive. By law and by custom, the position of Defense Secretary is reserved for civilians, but Mattis was still a marine at heart. He had been out of the military for only three years (the rule is seven), and his appointment required Congress to pass a waiver. For the first time in his professional life, he was going to the Pentagon in a suit and tie. Mattis urged Trump to launch the raid swiftly: the operation, which was aimed at one of the leaders of Al Qaeda in Yemen, required a moonless night, and the window for action was approaching. Under previous Administrations, such attacks entailed ...

The objective of this course is to give you a wholistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved.

Despite all the innovations around us, kids still need as much encouragement as possible to become the next mad genius to push us into the future, which is what Amazon CEO Jeff Bezos did during a recent talk at The Museum of Flight in Seattle. SEE ALSO: Forget about Mars for a minute: Let's talk about these rad moon missions Staying true to the theme of the facility, Bezos launched into an almost Elon Musk-like talk on his private space travel company Blue Origin and why humans should go to space and what challenges we face. "I want to see millions of people living and working in space," said Bezos to a large group of children who asked some pretty damn smart questions about space travel. "I think we should build a permanent human settlement on one of the poles of the moon," said Bezos, explaining how robots could land supplies on the moon before humans arrive. "It's time to go back to the moon, but this time to stay." "I want to see millions of people living and working in space."