Debugging web apps MIT researchers reported that they've developed a system that can quickly comb through tens of thousands of lines of application code to find security flaws by exploiting some peculiarities of the Ruby on Rails web programming framework. The team said that in tests on 50 popular web applications written using Ruby on Rails, the system found 23 previously undiagnosed security flaws, and it took no more than 64 seconds to analyze any given program. Daniel Jackson, professor in the Department of Electrical Engineering and Computer Science, said the system uses static analysis, which seeks to describe, in a very general way, how data flows through a program. "The classic example of this is if you wanted to do an abstract analysis of a program that manipulates integers, you might divide the integers into the positive integers, the negative integers, and zero." The static analysis would then evaluate every operation in the program according to its effect on integers' signs.

The purpose of this article is to clarify a few misconceptions about data and statistical science. I will start with a controversial statement: data science barely uses statistical science and techniques. The truth is actually more nuanced, as explained below. But the new statistical science in question is not regarded as statistics, by many statisticians. I don't know how to call it, "new statistical science" is a misnomer, because it is not all that novel.

Embedding-based Knowledge Base Completion models have so far mostly combined distributed representations of individual entities or relations to compute truth scores of missing links. Facts can however also be represented using pairwise embeddings, i.e. embeddings for pairs of entities and relations. In this paper we explore such bigram embeddings with a flexible Factorization Machine model and several ablations from it. We investigate the relevance of various bigram types on the fb15k237 dataset and find relative improvements compared to a compositional model.

In this paper we propose an approach to preference elicitation that is suitable to large configuration spaces beyond the reach of existing state-of-the-art approaches. Our setwise max-margin method can be viewed as a generalization of max-margin learning to sets, and can produce a set of "diverse" items that can be used to ask informative queries to the user. Moreover, the approach can encourage sparsity in the parameter space, in order to favor the assessment of utility towards combinations of weights that concentrate on just few features. We present a mixed integer linear programming formulation and show how our approach compares favourably with Bayesian preference elicitation alternatives and easily scales to realistic datasets.

With the next series of Game of Thrones set to hit our screens this month anxious fans may be wondering which one of their favourite characters is next on the hit list. The first five of the series have not been an easy watch, with the writers killing off key characters just when their luck starts to change and as audiences warmed to them. But researchers have developed a computer algorithm aimed at predicting the next character to die in the hit series. Students in Germany have developed a GoT-related computer algorithm which uses available data from the internet to predict the next character to die. By trawling the internet for data and clues, a team of computer scientists have created a model which crunches the numbers to work out which characters are most likely to die in the upcoming sixth series.

April 24 can't come soon enough for Game of Thrones fans eagerly awaiting the premiere of the hit show's sixth season. Naturally, most of us have been speculating wildly about the fate of our favorite characters for the past year, but now there's a clever app to help you withthat. The project, A Song of Ice and Data, was developed by a group of students of a JavaScript course at the Technical University of Munich. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May. It looks at 24 features of each character, the list of which includes attributes like their age, the House they belong to, whether they're married and how popular they are based on how many wiki pages link to them.

If you suspect that your significant other is bringing others into your bedroom, you could have an adult conversation about it or seek couple's counseling. Alternatively, you can buy a 1,700 smart mattress called the Smarttress that will tell you when your partner is having sex with someone that isn't you. Smarttress is the invention of Durmet, a Spanish mattress company that was inspired by the fact that Madrid has the highest number of cheating spouses in Europe. It features 24 sensors within the springs, which the company calls the "Lover Detection System." These sensors know which areas of the mattress are receiving pressure and make a 3D map of the bed.

It's a good time to be Alphabet Inc. (GOOG), the parent company of Google. The holding company that owns Google, YouTube and Android -- as well as so-called moonshots like self-driving cars, the home-networking division Nest and Google Fiber -- is expected to turn in healthy first-quarter results on Thursday, driven by its dominant position in online search and display advertising. On Wednesday, the European Commission is expected to formally charge Google for favoring its own apps and services on its Android mobile operating system, which powers more than 80 percent of the world's smartphones. That will be the latest in a decade of entanglements with regulators on both sides of the Atlantic; Google also got some bad press in Britain earlier this year for having paid just 185 million in taxes over the past decade. Also confronting Google -- and the rest of the tech industry -- is how to manage government and law enforcement requests for information.

Malware has proven increasingly difficult to detect via signature or heuristic-based methods, which means most Antivirus (AV) programs are woefully ineffective against mutating malware, and especially ineffective against APT attacks (Advanced Persistent Threats). Typical malware consists of about 10,000 lines of code. Five to six years ago marked the beginning of the use of machine learning to solve non-linear problems such as facial recognition or understanding malware, and what features one needs to extract to uniquely identify such programs. Other techniques, such as sandboxing and machine-based techniques, are not as fast nor as accurate as Deep Learning. Deep Instinct, founded by Guy Caspi and Eli David, Israeli Defense Force Cybersecurity veterans, applies artificial intelligence Deep Learning algorithms to detect structures and program functions that are indicative of malware.

High performance DAQMAG2A Rugged Display Computer: High performance DAQMAG2A Rugged Display Computer from GE's Intelligent Platforms business are designed to minimize cost, risk and time-to-market for prime contractors, systems integrators and OEMs developing sophisticated video capture, processing and transmission applications with multiple inputs and outputs, it is qualified to the DO-160G Environmental Conditions and Test Procedures for Airborne Equipment standard. The DAQMAG2A Line Replaceable Unit (LRU) has already been successfully deployed by AgustaWestland on the AW189 and AW101 helicopters and by FLIR Systems Inc.GE's Intelligent Platforms business (NYSE: GE) is headquartered in Charlottesville, VA and part of GE Energy Management. The company's work in the military/aerospace segment, headquartered in Huntsville, AL, and Towcester, England, provides one of the industry's broadest ranges of high performance, rugged, SWaP-optimized embedded computing platforms. Backed by programs that provide responsive customer support and minimize long term cost of ownership for multi-year programs, GE's solutions are designed to help customers minimize program risk and cost, and to speed time-to-market. The high TRL (Technology Readiness Level 9) of the DAQMAG2A means systems integrators can select it with confidence to concentrate on solving more important challenges such as integration into the higher level system, application development/port and so on.