When Pham Quang Hung started studying Japanese at Foreign Trade University in Hanoi in 1994, he never imagined that Vietnamese children would one day be able to learn the language in elementary school. Now the first secretary for educational affairs at the Vietnamese Embassy in Tokyo can hardly wait to see the launch in September of a pilot project to offer Japanese lessons at three elementary schools in Hanoi. It will be the first time that Japanese language education has been offered at the publicly run primary school level in Southeast Asia, according to Japanese officials. The project follows the development of a Japanese program that the Vietnamese government introduced for middle and high school students in 2003. At present, English and French are the only foreign languages Vietnamese students can learn in elementary school.

Up to 1.6 million patients have had their private medical files passed on to Google without their permission, it was revealed today The internet giant was given the data by one of the largest NHS trusts to develop an app to monitor possible kidney failure. The encrypted information includes the names and medical histories of every patient who had stayed in hospital overnight or attended A&E in the past five years. None of the patients was told beforehand and the revelation has exposed the ease with which private companies can obtain highly sensitive medical information without consent. Privacy campaigners told MailOnline that the revelations exposed the public's lack of power and control over their own personal details. Experts say the deal between Google and the Royal Free NHS Trust could set a precedent for patient data to be routinely passed on to private firms.

During the past nine months, an Nvidia engineering team built a self-driving car with one camera, one Drive-PX embedded computer and only 72 hours of training data. Nvidia published an academic preprint of the results of the DAVE2 project entitled End to End Learning for Self-Driving Cars on arXiv.org The Nvidia project called DAVE2 is named after a 10-year-old Defense Advanced Research Projects Agency (DARPA) project known as DARPA Autonomous Vehicle (DAVE). Although neural networks and autonomous vehicles seem like a just-invented-now technology, researchers such as Google's Geoffrey Hinton, Facebook's Yann Lecune and the University of Montreal's Yoshua Bengio have collaboratively researched this branch of artificial intelligence for more than two decades. And the DARPA DAVE project application of neural network-based autonomous vehicles was preceded by the ALVINN project developed at Carnegie Mellon in 1989.

At the core of the project is the idea of using machines to identify launcher-shaped objects buried within the staggering amount of digital imagery collected by US spy satellites, manned and unmanned aircraft. A senior official in the Department of Defense explained that it is this vast amount of data that makes manual research inefficient. "What was largely a manual process for intelligence analysts has to become an automated one," he said, cited by Defense One. The ultimate goal is to train computers spot what are called transporter-erector-launchers (TELs). North Korea used these kinds of launchers during missile tests conducted over the last few months.

We examine the question of whether we can automatically classify the sentiment of individual tweets in Farsi, to determine their changing sentiments over time toward a number of trending political topics. Examining tweets in Farsi adds challenges such as the lack of a sentiment lexicon and part-of-speech taggers, frequent use of colloquial words, and unique orthography and morphology characteristics. We have collected over 1 million Tweets on political topics in the Farsi language, with an annotated data set of over 3,000 tweets. We find that an SVM classifier with Brown clustering for feature selection yields a median accuracy of 56% and accuracy as high as 70%. We use this classifier to track dynamic sentiment during a key period of Irans negotiations over its nuclear program.

Discourse on social media platforms is often plagued by acute polarization, with different camps promoting different perspectives on the issue at hand—compare, for example, the differences in the liberal and conservative discourse on the U.S. immigration debate. A large body of research has studied this phenomenon by focusing on the affiliation of groups and individuals. We propose a new finer-grained perspective: studying the impartiality of individual messages. While the notion of message impartiality is quite intuitive, the lack of an objective definition and of a way to measure it directly has largely obstructed scientific examination. In this work we operationalize message impartiality in terms of how discernible the affiliation of its author is, and introduce a methodology for quantifying it automatically. Unlike a supervised machine learning approach, our method can be used in the context of emerging events where impartiality labels are not immediately available. Our framework enables us to study the effects of (im)partiality on social media discussions at scale. We show that this phenomenon is highly consequential, with partial messages being twice more likely to spread than impartial ones, even after controlling for author and topic. By taking this fine-grained approach to polarization, we also provide new insights into the temporal evolution of online discussions centered around major political and sporting events.

Consider the problem of estimating the expected number of distinct eligible voters among the authors of a set of electronic signatures gathered for a petition (or citizen initiative) that has to pass legally required thresholds. We formalize this problem and propose an extension to the Pretty Good Privacy Web Of Trust, a mechanism for reciprocally certifying identities between peers. The extension (a) enables agents to certify additional relevant statements about others, and (b) gives agents opportunities for negative authentication statements (e.g., on ineligibility of an identity). A Bayesian Network model enables inferences on the data provided by the proposed PGP extension. Simulations and an agent-based platform are used to validate the concepts.

Planning air missions for a team flying in hostile environments is a complex task, since multiple interrelated goals need to be considered, e.g., performing the mission tasks and avoiding enemy fire. The target assignment and route planning for the team should therefore be performed in a coordinated way. The mission planner suggested in this work combines genetic algorithms and particle swarm optimization in order to solve these two problems in an interconnected manner. Simulations are used for testing and analyzing the approach. It is concluded that the mission planner is able to suggest suitable plans in complex scenarios with three interrelated objectives: low risk exposure, high mission effectiveness and short route length.

Wikipedia is one of the most popular sources of free data on the Internet and subject to extensive use in numerous areas of research. Wikidata on the other hand, the knowledge base behind Wikipedia, is less popular as a source of data, despite having the "data" already in its name, and despite the fact that many applications in Natural Language Processing in general and Information Extraction in particular benefit immensely from the integration of knowledge bases. In part, this imbalance is owed to the younger age of Wikidata, which launched over a decade after Wikipedia. However, this is also owed to challenges posed by the still evolving properties of Wikidata that make its content more difficult to consume for third parties than is desirable. In this article, we analzye the causes of these challenges from the viewpoint of a data consumer and discuss possible avenues of research and advancement that both the scientific and the Wikidata community can collaborate on to turn the knowledge base into the invaluable asset that it is uniquely positioned to become.

The White House is doing a lot more thinking about the arrival of automated decision-making -- super-intelligent or otherwise. No one in government is yet screaming "Skynet," but in two actions this week the concerns about our artificial intelligence future were sketched out. The big risks of A.I. are well-known (a robot takeover), but the more immediate worries are about the subtle, or not-so-subtle, decisions made by badly coded and designed algorithms. President Barack Obama's administration released a report this week that examines the problem associated with poorly designed systems that, increasingly, are being used in automated decision making. Algorithmic systems can affect employment, education, access to credit -- anything that relies on computer-assisted decisions.