Author name disambiguation in bibliographic databases is the problem of grouping together scientific publications written by the same person, accounting for potential homonyms and/or synonyms. Among solutions to this problem, digital libraries are increasingly offering tools for authors to manually curate their publications and claim those that are theirs. Indirectly, these tools allow for the inexpensive collection of large annotated training data, which can be further leveraged to build a complementary automated disambiguation system capable of inferring patterns for identifying publications written by the same person. Building on more than 1 million publicly released crowdsourced annotations, we propose an automated author disambiguation solution exploiting this data (i) to learn an accurate classifier for identifying coreferring authors and (ii) to guide the clustering of scientific publications by distinct authors in a semi-supervised way. To the best of our knowledge, our analysis is the first to be carried out on data of this size and coverage. With respect to the state of the art, we validate the general pipeline used in most existing solutions, and improve by: (i) proposing phonetic-based blocking strategies, thereby increasing recall; and (ii) adding strong ethnicity-sensitive features for learning a linkage function, thereby tailoring disambiguation to non-Western author names whenever necessary.

Partitioning a dataset into classes based on a similarity between data points, known as cluster analysis, is one of the most basic and practically important problems in data analysis and machine learning. It has a vast array of applications from speech recognition to image analysis to bioinformatics and to data compression. There is an extensive literature on the subject, including a number of different methodologies as well as their various practical and theoretical aspects [11]. In recent years spectral clustering--a class of methods based on the eigenvectors of a certain matrix, typically the graph Laplacian constructed from data--has become a widely used method for cluster analysis. This is due to the simplicity of the algorithm, a number of desirable properties it exhibits and its amenability to theoretical analysis. In its simplest form, spectral bi-partitioning is an attractively straightforward algorithm based on thresholding the second bottom eigenvector of the Laplacian matrix of a graph. However, the more practically significant problem of multiway spectral clustering is considerably more complex. While hierarchical methods based on a sequence of binary splits have been used, the most common approaches use k-means or weighted k-means clustering in the spectral space or related iterative procedures [17, 15, 2, 25].

These are the voyages of the starship Call of Duty. Its 14-year mission: to boldly blow up more stuff than any game has blown up before. Now the series, which began in world war two, is finally braking its bonds with Earth. The solar system awaits and it is heavily armed. In a live Twitch session, held on Monday evening, the Infinity Ward narrative director, Taylor Kurosaki, and the design director, Jacob Minkoff, revealed some fresh information about the latest game in this billion-dollar series.

It's not only drones and driverless cars that may become the norm someday -- ocean-faring ships might also run without captains or crews. The Pentagon on Monday showed off the world's largest unmanned surface vessel, a self-driving 132-foot ship able to travel up to 10,000 nautical miles on its own to hunt for stealthy submarines and underwater mines. The military's research arm, the Defense Advanced Research Projects Agency, or DARPA, in conjunction with the Navy will be testing the ship off the San Diego coast over the next two years to observe how it interacts with other vessels and avoids collisions. Unlike smaller, remote-controlled craft launched from ships, the so-called "Sea Hunter" is built to operate on its own. "It's not a joy-stick ship," said DARPA spokesman Jared B. Adams, standing in front of the sleek, futuristic-looking steel-gray vessel docked at a maritime terminal in the heart of San Diego's shipbuilding district, where TV crews filmed the robotic craft.

The company is researching ways to get rid of its surge pricing, a feature that drivers like but that can make costs unpredictable for consumers. The company is researching ways to get rid of its surge pricing, a feature that drivers like but that can make costs unpredictable for consumers. Sometimes you call an Uber, and what you thought would be an 8 ride is going to be two, three, even four times more -- the result of greater demand brought on by a blizzard, or a baseball game. Whatever the reason, surge pricing is not fun. It turns out Uber is working to fix it -- or, should we say, end it.

RE•WORK will host it's annual East Coast events on Deep Learning and the Internet of Things in Boston on 12 & 13 May. Over 300 machine learning and IoT enthusiasts and experts will come together to hear keynote presentations, panel discussions, fireside chats and to explore the startup showcase area. The Deep Learning Summit brings together leaders from industry, academia and startups to explore advances in deep learning methods and techniques, as well as their business applications in areas including finance, manufacturing, healthcare & transportation. The Connected Home Summit is the fifth installment in RE•WORK's Internet of Things series, following the Connected City Summit held in London earlier in 2016 and previous IoT Summits in San Francisco, London and Boston as well as dinners and meetups in 2015. The Summits are a unique opportunity to meet and interact with CTOs, founders, data scientists, engineers, designers and industry experts leading the connected home and deep learning revolutions.

It provides an agnostic introduction to operational ML with open source and cloud platforms. It is the first ML workshop to go all the way from data preparation to the integration of predictive models in real-world applications and their deployment in production. Participants will learn to use Python open source libraries scikit-learn, Pandas and SKLL, and cloud platforms Microsoft Azure ML, Amazon ML, BigML and Indico (along with their APIs).

With more than 8 year experience in Information Security, Competitive Intelligence and Data Sciences, for Testing, securing the business & infrastructure, designing and developing the solutions for the said line of business, he created and worked at "Voice of Green Hats"; LiFi Research & Development; Competitive Intelligence, Testing environment Robotics software and automation (Virtualization). He has been working as under: Information Communication Technology (Cloud computing, Virtualization, Networking) Information Security (Ethical Hacking & Digital Forensic Investigation) International Business (Trade supporting IT Engineering) Competitive Intelligence (Digital branding, business success axis, upgrading expertise and businesses) Business Intelligence (Data Sciences) Artificial Intelligence (IoT, Robotics) With vast business professional networking of chambers of commerce, business council and professional associations in especially in Malaysia, Canada, Australia, New Zealand, EU and Middle East.

In warehouses, hospitals and retail stores, and on city streets, industrial parks and the footpaths of college campuses, the first representatives of this new invading force are starting to become apparent. "The robots are among us," says Steve Jurvetson, a Silicon Valley investor and a director at Elon Musk's Tesla and SpaceX companies, which have relied heavily on robotics. A multitude of machines will follow, he says: "A lot of people are going to come in contact with robots in the next two to five years." The arrival of the robots -- and their potentially devastating effect on human employment -- has been widely predicted. Now, the machines are starting to roll or walk out of the labs.

A new app that uses a neural network combined with facial recognition software to help put names to faces in random photographs by scanning social network data is being used in Russia to identify and harass young women who have previously appeared in pornographic films. Trinity Digital, an app developer in Russia, released a free iOS and Android app called FindFace in February that enables people to identify people by taking a random photograph and using its neural network to figure out the person's name, location, occupation and other details. A neural network is a huge network of computers that are trained using computer algorithms to solve complex problems quickly, as well as improving artificial intelligence by gaining a deeper recognition and understanding of art and the world around us. On 9 April, members of a disreputable imageboard called Dvach, which is like the Russian cousin of 4chan, launched a campaign to deliberately try to locate and identify actresses who appear in pornography, as well as women listed on Intimcity, a Russian website advertising prostitution and escort services. Using FindFace, the Dvach users not only identified the women, but they also shared archived copies of their profiles on Vkontakte (the Russian version of Facebook) publicly online and repeatedly spammed the families of the women letting them know that they had been outed as porn stars and prostitutes.