The scandal that has erupted around Cambridge Analytica's alleged use of 50 million Facebook profiles in the 2016 US election campaign is worrying for legitimate researchers who use social media data. Cambridge Analytica has denied any wrongdoing and said that the business tactics it used are widespread among other firms. But a day after the scandal hit, Facebook's shares plummeted on Wall Street amid a privacy backlash. The worry is the incident could also affect legitimate academic research. Social media data is a rich source of information for many areas of research in psychology, technology, business and humanities.

Many travel industry executives are optimistic that new technologies such as artificial intelligence can make their businesses smarter and more efficient while plenty of travel industry employees wonder if they'll eventually be replaced by robots and self-driving cars. Well, these are indeed early days, but emerging technologies do not appear to be slowing travel industry job growth -- not yet. Gloria Guevara, president and CEO of the World Travel & Tourism Council, said travel jobs globally grew last year even as new technologies and platforms made their debuts, albeit relatively modestly compared with what may be in the offing. One in five jobs that were created globally in 2017 were in the travel industry, according to WTTC's data. Some 119 million jobs worldwide last year were directly attributed to tourism, and the industry created two million new jobs.

The world's second-largest economy, China, is en route to achieving great things in the next decade and a half. Projections suggest that by 2032, the Chinese Republic will overtake the United States and become the largest economy in the world. This is a far cry from the China of the '70s before which it was a largely agrarian society. After the introduction of economic reforms in 1978 by Deng Xiaoping and the reopening of Shanghai Stock Market in 1990, China evolved into an industrial powerhouse and its economy started expanding at a brisk pace, averaging growth rates of nearly 10 per cent for almost three decades. Though the benefits of growth in GDP did trickle down to the public as wages and subsequently living standards received a considerable bump, it was largely the Communist Party-controlled state machinery and the People's Liberation Army (PLA) that enjoyed the fruits of China's meteoric growth.

The world's second-largest economy, China, is en route to achieving great things in the next decade and a half. Projections suggest that by 2032, the Chinese Republic will overtake the United States and become the largest economy in the world. This is a far cry from the China of the '70s before which it was a largely agrarian society. After the introduction of economic reforms in 1978 by Deng Xiaoping and the reopening of Shanghai Stock Market in 1990, China evolved into an industrial powerhouse and its economy started expanding at a brisk pace, averaging growth rates of nearly 10 per cent for almost three decades. Though the benefits of growth in GDP did trickle down to the public as wages and subsequently living standards received a considerable bump, it was largely the Communist Party-controlled state machinery and the People's Liberation Army (PLA) that enjoyed the fruits of China's meteoric growth.

In a bid to modernize battlefield resources, the Chinese Army has started trialing unmanned tanks, according to a new report from state-run publication Global Times. The upgraded military vehicles are currently being tested with a distant manned console, much like remotely operated drones. However, the People's Liberation Army Ground Force, aka PLAGF, also plans to integrate them with artificial intelligence, in order to make them nearly self-operable. A short video from CCTV, a prominent state television broadcaster in the People's Republic of China, recently appeared on the internet showcasing one of the unmanned vehicles being tested. The clip features a modified version of a dated Type 59 tank moving forward and backward like a remote-controlled car and a distant Chinese Army official manning its control-box a few meters away.

SHANGHAI – U.S. President Donald Trump often tweets from his iPhone about pressuring China to address its $375 billion trade surplus with the United States. But a closer look at the Apple smartphone reveals how the headline figure is distorted. The big trade imbalance -- at the heart of a potential trade war, with Trump expected to impose tariffs on Chinese imports this week -- exists in large part because of electrical goods and tech, the biggest U.S. import item from China. Apple Inc.'s iPhone, however, illustrates how a big portion of that imbalance is due to imports of American-branded products -- many of which use global suppliers for parts but are put together in China and shipped around the world. Take a look at the iPhone X. IHS Markit estimates its components cost a total of $370.25.

Aleksandr Kogan, the Russian-American academic at the center of the Cambridge Analytica controversy, has been relatively vocal yet strikingly calm in public since news broke that the company had acquired personal information from more than 50 million Facebook accounts and later used it to boost Donald Trump's presidential campaign. Cambridge Analytica and Facebook have both blamed Kogan for the imbroglio, though he's denied any wrongdoing. In Facebook's first statement disclosing Cambridge Analytica's activities on Friday, the company accused Kogan of lying and breaking its policies. According to its narrative, Kogan gave data he had gathered from users and their friends through his personality prediction Facebook app to the political consulting firm. Developers are not allowed to give such data to third parties.

Though Convolutional Neural Networks (CNNs) have surpassed human-level performance on tasks such as object classification and face verification, they can easily be fooled by adversarial attacks. These attacks add a small perturbation to the input image that causes the network to mis-classify the sample. In this paper, we focus on neutralizing adversarial attacks by compact feature learning. In particular, we show that learning features in a closed and bounded space improves the robustness of the network. We explore the effect of L2-Softmax Loss, that enforces compactness in the learned features, thus resulting in enhanced robustness to adversarial perturbations. Additionally, we propose compact convolution, a novel method of convolution that when incorporated in conventional CNNs improves their robustness. Compact convolution ensures feature compactness at every layer such that they are bounded and close to each other. Extensive experiments show that Compact Convolutional Networks (CCNs) neutralize multiple types of attacks, and perform better than existing methods in defending adversarial attacks, without incurring any additional training overhead compared to CNNs.

Recent successes in word embedding and document embedding have motivated researchers to explore similar representations for networks and to use such representations for tasks such as edge prediction, node label prediction, and community detection. Such network embedding methods are largely focused on finding distributed representations for unsigned networks and are unable to discover embeddings that respect polarities inherent in edges. We propose SIGNet, a fast scalable embedding method suitable for signed networks. Our proposed objective function aims to carefully model the social structure implicit in signed networks by reinforcing the principles of social balance theory. Our method builds upon the traditional word2vec family of embedding approaches and adds a new targeted node sampling strategy to maintain structural balance in higher-order neighborhoods. We demonstrate the superiority of SIGNet over state-of-the-art methods proposed for both signed and unsigned networks on several real world datasets from different domains. In particular, SIGNet offers an approach to generate a richer vocabulary of features of signed networks to support representation and reasoning.

National Transportation Safety Board (NTSB) investigators examine a self-driving Uber vehicle involved in a fatal accident in Tempe, Arizona, U.S., March 20, 2018. A women was struck and killed by the vehicle on March 18, 2018.