Following the 2017 Royal Rumble, the WrestleMania 33 match card looks to be falling into place. Brock Lesnar vs. Goldberg has already been made official for the pay-per-view, and a few of the other top matches aren't difficult to predict. There seems to be some uncertainty, however, surrounding the future plans for WWE's biggest full-time performer. With WrestleMania 33 still nearly two months away, Cena's opponent remains unclear. As the current WWE Champion, Cena is in line to face Royal Rumble winner Randy Orton on April 2 in Orlando. That could change at Sunday's PPV when Cena puts his belt on the line against five other wrestlers in the Elimination Chamber.

Sophos (LSE: SOPH), a global leader in network and endpoint security, today announced it has entered into an agreement to acquire Invincea, a visionary provider of next-generation malware protection. Invincea's endpoint security portfolio is designed to detect and prevent unknown malware and sophisticated attacks via its patented deep learning neural-network algorithms. It has been consistently ranked as among the best performing machine learning, signature-less next-generation endpoint technologies in third-party testing and rated highly both for high detection and low false-positive rates. Headquartered in Fairfax, Va., Invincea was founded by chief executive officer Anup Ghosh to address the rapidly growing zero-day security threat from nation states, cyber criminals and rogue actors. Invincea's flagship product X by Invincea uses deep learning neural networks and behavioral monitoring to detect previously unseen malware and stops attacks before damage occurs.

The road to WrestleMania 33 is in full swing, but there are still two pay-per-views on the schedule before the biggest WWE show of 2017. The first one is Elimination Chamber Sunday night, featuring the "SmackDown Live" roster. Elimination Chamber will likely establish multiple WrestleMania title matches, starting with the WWE World Championship match. Bray Wyatt, Baron Corbin, The Miz, AJ Styles and Dean Ambrose will all compete against John Cena for the No. 1 belt on "SmackDown Live." The SmackDown Women's Championship and the SmackDown Tag Team Championships will also be on the line at the PPV in Phoenix.

This paper proposes a generic classification system designed to detect security threats based on the behavior of malware samples. The system relies on statistical features computed from proxy log fields to train detectors using a database of malware samples. The behavior detectors serve as basic reusable building blocks of the multi-level detection architecture. The detectors identify malicious communication exploiting encrypted URL strings and domains generated by a Domain Generation Algorithm (DGA) which are frequently used in Command and Control (C&C), phishing, and click fraud. Surprisingly, very precise detectors can be built given only a limited amount of information extracted from a single proxy log. This way, the computational requirements of the detectors are kept low which allows for deployment on a wide range of security devices and without depending on traffic context such as DNS logs, Whois records, webpage content, etc. Results on several weeks of live traffic from 100+ companies having 350k+ hosts show correct detection with a precision exceeding 95% of malicious flows, 95% of malicious URLs and 90% of infected hosts. In addition, a comparison with a signature and rule-based solution shows that our system is able to detect significant amount of new threats.

Microsoft has a mission, "To empower every person and every organization on the planet to achieve more" and they are great words, but action speaks louder than words. You will also hear the phrase'Democratizing AI" being used increasingly over the following months, in other words making Artificial Intelligence available to everyone. I have had the pleasure of working with some of Microsoft's finest over the last two days at a Hackathon in London, covering a number of aspects of Machine Learning, Cognitive Computing and Data Analytics - this post is making some of the learning accessible and available to you. Whether you are just curious or want to start a Data Science journey, this will get you started on your first data science experiment! You need nothing to get started, no previous knowledge, no credit card, just curiosity and a little patience.

Activists protest against the gang-rape of a 16-year-old girl in Brazil last year – videos and images of the assault were posted to social media and circulated widely, revictimizing the woman. As Facebook defended itself in a German court against claims that it does too little to counter abusive content on its platform, one of its lawyers made the intriguing claim "There are billions of postings each day. You want us to employ a sort of wonder machine to detect each misuse. Such a machine doesn't exist." For a technology company with a heavy investment in deep learning and filtering technologies that has repeatedly run afoul of free speech advocates for its aggressive stance on content removal this is certainly a curious claim to make.

There is a need for affordable, widely deployable maternal-fetal ECG monitors to improve maternal and fetal health during pregnancy and delivery. Based on the diffusion-based channel selection, here we present the mathematical formalism and clinical validation of an algorithm capable of accurate separation of maternal and fetal ECG from a two channel signal acquired over maternal abdomen.

One of the most typical tasks in machine learning is classification tasks. It may seem that evaluating the effectiveness of such a model is easy. Let's assume that we have a model which, based on historical data, calculates if a client will pay back credit obligations. We evaluate 100 bank customers and our model correctly guesses in 93 instances. That may appear to be a good result – but is it really?

Missingness was an important issue with this dataset. I ignored certain missing items, such as a house not being available on Zillow, under the assumption that they item got dropped for technical reasons in the web scraping process. But others I was less confident about.

Regularized discriminant analysis (RDA), proposed by Friedman (1989), is a widely popular classifier that lacks interpretability and is impractical for high-dimensional data sets. Here, we present an interpretable and computationally efficient classifier called high-dimensional RDA (HDRDA), designed for the small-sample, high-dimensional setting. For HDRDA, we show that each training observation, regardless of class, contributes to the class covariance matrix, resulting in an interpretable estimator that borrows from the pooled sample covariance matrix. Moreover, we show that HDRDA is equivalent to a classifier in a reduced-feature space with dimension approximately equal to the training sample size. As a result, the matrix operations employed by HDRDA are computationally linear in the number of features, making the classifier well-suited for high-dimensional classification in practice. We demonstrate that HDRDA is often superior to several sparse and regularized classifiers in terms of classification accuracy with three artificial and six real high-dimensional data sets. Also, timing comparisons between our HDRDA implementation in the sparsediscrim R package and the standard RDA formulation in the klaR R package demonstrate that as the number of features increases, the computational runtime of HDRDA is drastically smaller than that of RDA.