Honor Magic 2 comes with Yoyo, its own digital assistant, that can be commanded to fly a drone. Honor Magic 2 has been launched in China, and features an in-display fingerprint scanner as well as a sliding design that houses the front camera. The phone offers AI-based features, like its own digital assistant, Yoyo, and connectivity across a range of devices. Honor Magic 2 has been priced from 3799 yuan and is available in three colour variants: Gradient Red, Gradient Blue and Gradient Black. Featuring a 6.39-inch AMOLED display, the Honor phone offers a resolution of 2140 1080 pixels, and a screen-to-body ratio of 91.5 per cent.

As mentioned in our Industry 4.0 overview there are significant similarities regarding digital transformation maturity and Industry 4.0 maturity. Manufacturers and other companies in the broader scope of the Industrial Internet, digital transformation of manufacturing and anything related with it share several common challenges and issues as other organizations going through digital transformation. True innovation in manufacturing based upon interoperable and open ecosystems with an end-to-end data approach and a focus on the end customer and consumer is rare if existing at all. In digital transformation in the broader sense there is more focus on customer-facing goals in some areas and in some sectors for all the obvious reasons such as the type of process, the ways to achieve a specific goal, the degree of contact/interaction with the (end) customer and/or the role of the consumer in the digital innovations and business as such (think automotive and the role of technology in the driver experience and in the branding of car manufacturers where'tech' is omnipresent). In some industrial markets such as oil and gas or mining the focus is of course less on consumers.

According to IDC, global spending on Artificial Intelligence (AI) and cognitive systems will reach $19 billion by 2018. This is an increase by approximately 54% over the total amount consumed in 2017. Mergers and acquisitions are constantly taking place. We all know that AI is creating new opportunities in every sector be it healthcare or travel. So, companies all around the world are enchasing on such opportunities to offer much-improved products or services to consumers through mergers and acquisitions.

The International Conference on Advances in Computer Entertainment Technology, a relatively niche academic symposium in its 15th year, is embroiled in a white-hot controversy over its keynote speaker: Steve Bannon, the former White House Chief Strategist and founding member of the right-wing publication Breitbart. According to the conference organizer, Bannon--who has no academic background in computer science or interactive design but whose policy ideas have been embraced by white nationalists--will give a speech about how he believes "economic nationalism" will allow for a higher number of minorities to get jobs in sectors like computer science and gaming. The conference, also known as ACE, is scheduled to take place at the University of Montana in December. Since Bannon was added to the conference's roster last week, academics, scholarly associations, and university departments around the world have called for boycotting the conference, including the Milieux Institute for Arts, Culture and Technology at Concordia University, the Canadian Game Studies Association, and the Australian Digital Games Research Association. "Nothing of what Bannon can say represents the ACE community, or the games research community at large. His is a marginal discourse that should stay where it is, marginalized. And that's why we ask our community to #boycottACE," Miguel Angel Sicart, a games, art and interactive design researcher at the IT University of Copenhagen, told WIRED in an email.

More than 200 defence and intelligence companies from 24 countries have displayed technological solutions to security threats at a Qatar event, aiming to sign contracts with the Gulf state. With Qatar making logistical and security preparations for the 2022 World Cup, a delegation from Russia - the host nation of this year's football tournament - had the most recent logistical and security lessons to share during the three-day Milipol Qatar 2018 event, which started on Monday. The advice included hiring and training tens of thousands of stewards who are not military or security forces, but another layer of security specialised in managing large crowds. Other issues included securing and safeguarding the crowds and the provision of food to cater for an influx of diverse people. Major General Nasser bin Fahad Al Thani, the president of Milipol Qatar, said his country has signed several contracts with international contractors worth tens of millions of dollars to enhance the country's technological infrastructure and its computer systems.

China has released a new propaganda video of its deadly unmanned fighter jet, which shows the aircraft striking still and moving targets. CH-5, also known as Rainbow-5, was unveiled in 2016 and is said to be China's largest and most powerful drone bomber. The aircraft can carry 16 missiles and strike targets while flying at an altitude of 6,000 metres (19,685 feet), Chinese media have claimed. It can fly up to 60 hours without refuelling with a maximum flight altitude of 8,000 metres (26,246 feet) and a maximum range of 10,000 kilometres (6,213 miles). CH-5, also known as Rainbow-5, is on display during the 11th China International Aviation and Aerospace Exhibition in 2016.

Comparing with other biometrics, gait has advantages of being unobtrusive and difficult to conceal. Inertial sensors such as accelerometer and gyroscope are often used to capture gait dynamics. Nowadays, these inertial sensors have commonly been integrated in smartphones and widely used by average person, which makes it very convenient and inexpensive to collect gait data. In this paper, we study gait recognition using smartphones in the wild. Unlike traditional methods that often require the person to walk along a specified road and/or at a normal walking speed, the proposed method collects inertial gait data under a condition of unconstraint without knowing when, where, and how the user walks. To obtain a high performance of person identification and authentication, deep-learning techniques are presented to learn and model the gait biometrics from the walking data. Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space domain and in the time domain are successively abstracted by a convolutional neural network and a recurrent neural network. In the experiments, two datasets collected by smartphones on a total of 118 subjects are used for evaluations. Experiments show that the proposed method achieves over 93.5% and 93.7% accuracy in person identification and authentication, respectively.

Question answering (QA) has achieved promising progress recently. However, answering a question in real-world scenarios like the medical domain is still challenging, due to the requirement of external knowledge and the insufficient of high-quality training data. In the light of these challenges, we study the task of generating medical QA pairs in this paper. With the insight that each medical question can be considered as a sample from the latent distribution conditioned on the corresponding answer, we propose an automated medical QA pair generation framework, consisting of an unsupervised key phrase detector that explores unstructured material for validity, and a generator that involves multi-pass decoder to integrate with structural knowledge for diversity. Series of experiments have been conducted on a real-world dataset collected from the National Medical Licensing Examination of China. Both automatic evaluation and human annotation demonstrate the effectiveness of the proposed method. Further investigation shows that, by incorporating the generated QA pairs for training, significant improvement in terms of accuracy can be achieved for the examination QA system.

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.

A newly introduced method called Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting method is applied and extended in this study to forecast numerical values. Unlike traditional forecasting techniques which forecast only future values, our proposed method provides a new extension to correct the predicted values which is done by forecasting the estimated error. Experimental results demonstrated that the proposed method has a high accuracy both in training and testing data and outperform the state-of-the-art RNN models on Mackey-Glass, NARMA, Lorenz and Henon map datasets.