In one Pennsylvania high school, more than 15 languages are spoken in a student body of nearly 4,000. WASHINGTON -- In middle school, Junior Alvarado often struggled with multiplication and earned poor grades in math, so when he started his freshman year at Washington Leadership Academy, a charter high school in the nation's capital, he fretted that he would lag behind. But his teachers used technology to identify his weak spots, customize a learning plan just for him and coach him through it. This past week, as Alvarado started sophomore geometry, he was more confident in his skills. "For me personalized learning is having classes set at your level," Alvarado, 15, said in between lessons.

Gaming is an addiction but for cyber criminals, it is a lucrative business. IT security researchers at ESET have discovered a new malware targeting gamers around the world. Dubbed "Joao" by researchers; the malware exists in third party websites offering malicious setups for Aeria games. The malware works in such a way that once executed it can install other malicious codes on a targeted device. Furthermore, Joao takes advantage of "Massively multiplayer online role-playing games (MMORPGs)," a platform for role-playing video games and massively multiplayer online games where a large number of gamers get together to interact. The attackers behind Joao have developed the malware in such a way that when a victim executes the game launcher, it silently launches itself in the background and sends device information to the attackers including its operating system, name and what privileges a user has on that device.

Nuance Communications, Inc. said latest artificial intelligence (AI) framework could serve as a game changer for the UAE and GCC telecom industry. The Middle East region is now witnessing an accelerating technology migration to higher speed networks and smartphones, facilitated by operator investments to extend network coverage. Telecom operators need to transform their revenue opportunity thro-ugh data and voice services that are high quality, while managing capital allocation, and investing in new technologies and innovations. "By addressing customer needs in real time, innovative solutions will open new possibilities and increase value. Nuance Loop is specially designed to fit with the framework of UAE and GCC telecom operators as it engages mobile subscribers at virtually any touch point from voice to text to browser," said Rajesh Razdan, VP and GM, APAC, CSP Business, Nuance.

Kernel mean embeddings provide a versatile and powerful nonparametric representation of probability distributions with several fundamental applications in machine learning. Key to the success of the technique is whether the embedding is injective. This characteristic property of the underlying kernel ensures that probability distributions can be discriminated via their representations. In this paper, we consider kernels of tensor product type and various notions of characteristic property (including the one that captures joint independence of random variables) and provide a complete characterization for the corresponding embedding to be injective. This has applications, for example in independence measures such as Hilbert-Schmidt independence criterion (HSIC) to characterize the joint independence of multiple random variables.

Distributed machine learning algorithms enable processing of datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfunctional indeed happen in the real world. This paper focuses on the problem of Byzantine failures, which are the hardest to safeguard against in distributed algorithms. While Byzantine fault tolerance has a rich history, existing work does not translate into efficient and practical algorithms for high-dimensional distributed learning tasks. In this paper, two variants of an algorithm termed Byzantine-resilient distributed coordinate descent (ByRDiE) are developed and analyzed that solve distributed learning problems in the presence of Byzantine failures. Theoretical analysis as well as numerical experiments presented in the paper highlight the usefulness of ByRDiE for high-dimensional distributed learning in the presence of Byzantine failures.

We consider the well-studied cake cutting problem in which the goal is to find an envy-free allocation based on queries from $n$ agents. The problem has received attention in computer science, mathematics, and economics. It has been a major open problem whether there exists a discrete and bounded envy-free protocol. We resolve the problem by proposing a discrete and bounded envy-free protocol for any number of agents. The maximum number of queries required by the protocol is $n^{n^{n^{n^{n^n}}}}$. We additionally show that even if we do not run our protocol to completion, it can find in at most $n^3{(n^2)}^n$ queries a partial allocation of the cake that achieves proportionality (each agent gets at least $1/n$ of the value of the whole cake) and envy-freeness. Finally we show that an envy-free partial allocation can be computed in at most $n^3{(n^2)}^n$ queries such that each agent gets a connected piece that gives the agent at least $1/(3n)$ of the value of the whole cake.

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Artificial Intelligence, Machine Learning and Deep Learning are all the rage in the press these days, and if you want to be a good Data Scientist you're going to need more than just a passing understanding of what they are and what you can do with them. There are loads of different methodologies, but for me I would always suggest Artificial Neural Networks as the first AI to learn - but then I've always had a soft spot for ANNs since I did my PhD on them. They've been around since the 1970s, and until recently have only really been used as research tools in medicine and engineering. Google, Facebook and a few others, though, have realised that there are commercial uses for ANNs, and so everyone is interested in them again. When it comes to algorithms used in AI, Machine Learning and Deep Learning, there are 3 types of learning process (aka'training').

Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I'll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn't. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon.

During the London 2012 Olympics the Transport for London (TfL) network needed to manage 18 million local journeys made by spectators. One can only imagine the volume of data generated during this time; the data and analytics, mostly from the games, was utilized by TfL to predict the number of people who were likely to use public transport during that time, in order to ensure that the system was running effectively. With the evolution of technology changing the way we live and work, it is only a matter of time before governments around the world upgrade their infrastructure to offer citizens efficient services through smart cities, where enormous amounts of data moves within complex information supply chains. Yet, smart cities are not about constantly introducing new technologies. Data sources are everywhere around us, ranging from smart phones and computers, to Global Positioning System (GPS) and social media sites.