O2 has revealed the compensation package it will offer to customers hit by its data outage. Both pay monthly and pay as you go customers will receive free time after they were left entirely without data for a whole day. Pay monthly customers will be given two free days on their contract. And pay as you go customers will get 10 per cent of their credit for free. "We're very sorry about yesterday's data issue," an O2 spokesperson said. "We understand how important it is to stay connected, especially at this time of year."

Customers frustrated by the O2 hack are now being tricked into scams as they search for ways to get their money back. Police say that scammers are taking advantage of angry users in an attempt to dupe them into getting themselves in trouble. It comes as false rumours spread across the web about the vast sums of money people might be able to claim. All the same, O2 really is offering compensation. But it is vastly less than the scams suggest.

The Apple Watch's long promised heart features have finally arrived on people's watches. When the Series 4 was announced in September, the company said that it would let people take ECGs and see if their heartbeat is irregular. But it would not be available straight away because it had to be approved by regulators, it said at the time. Now the features have been given clearance by the US Food and Drug Administration and so will finally be available, arriving through a software update. The ECG and atrial fibrillation features are not yet available outside the US, including in the UK.

The president of Microsoft has called for greater government regulation of AI facial recognition technology, because of the risk of it discriminating against women and people of colour. In a rare incident of a tech giant calling for greater government scrutiny, Brad Smith said such regulation would help avoid "a commercial race to the bottom, with tech companies forced to choose between social responsibility and market success". The comments of Mr Smith, 59, which were released at the same time as a report by a research group consisting of both Microsoft and Google employees also calling for more regulation, are especially noteworthy because of the controversy the company triggered earlier this year over its AI work. In June, the company's general manager Tom Keane, wrote how proud Microsoft was to be working with the US Immigration and Customs Enforcement agency (ICE) to use facial recognition technology to help identify immigrants and process applications. In a blog post about Azure Government, a programme designed to allow government agencies upload information to the computing cloud, he said: "The agency is currently implementing transformative technologies for homeland security and public safety, and we're proud to support this work with our mission-critical cloud." The comments were made as the Trump administration and ICE were facing intense criticism from human rights advocates and others for the way migrant families were being broken up and separated at the US-Mexico border.

It demonstrates Beijing's growing ambitions as a space power to rival Russia, the European Union and the US. With its Chang'e 4 mission, China hopes to be the first country to make a soft landing, which is a landing of a spacecraft during which no serious damage is incurred. The moon's far side is also known as the dark side because it faces away from Earth and remains comparatively unknown. It has a different composition than sites on the near side, where previous missions have landed. If successful, the mission would propel the Chinese space program to a leading position in one of the most important areas of lunar exploration.

Machine learning relies on the availability of a vast amount of data for training. However, in reality, most data are scattered across different organizations and cannot be easily integrated under many legal and practical constraints. In this paper, we introduce a new technique and framework, known as federated transfer learning (FTL), to improve statistical models under a data federation. The federation allows knowledge to be shared without compromising user privacy, and enables complimentary knowledge to be transferred in the network. As a result, a target-domain party can build more flexible and powerful models by leveraging rich labels from a source-domain party. A secure transfer cross validation approach is also proposed to guard the FTL performance under the federation. The framework requires minimal modifications to the existing model structure and provides the same level of accuracy as the non-privacy-preserving approach. This framework is very flexible and can be effectively adapted to various secure multi-party machine learning tasks.

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions.

We present a novel method that can learn a graph representation from multivariate data. In our representation, each node represents a cluster of data points and each edge represents the subset-superset relationship between clusters, which can be mutually overlapped. The key to our method is to use formal concept analysis (FCA), which can extract hierarchical relationships between clusters based on the algebraic closedness property. We empirically show that our method can effectively extract hierarchical structures of clusters compared to the baseline method.

Faces play a magnificent role in human robot interaction, as they do in our daily life. The inherent ability of the human mind facilitates us to recognize a person by exploiting various challenges such as bad illumination, occlusions, pose variation etc. which are involved in face recognition. But it is a very complex task in nature to identify a human face by humanoid robots. The recent literatures on face biometric recognition are extremely rich in its application on structured environment for solving human identification problem. But the application of face biometric on mobile robotics is limited for its inability to produce accurate identification in uneven circumstances. The existing face recognition problem has been tackled with our proposed component based fragmented face recognition framework. The proposed framework uses only a subset of the full face such as eyes, nose and mouth to recognize a person. It's less searching cost, encouraging accuracy and ability to handle various challenges of face recognition offers its applicability on humanoid robots. The second problem in face recognition is the face spoofing, in which a face recognition system is not able to distinguish between a person and an imposter (photo/video of the genuine user). The problem will become more detrimental when robots are used as an authenticator. A depth analysis method has been investigated in our research work to test the liveness of imposters to discriminate them from the legitimate users. The implication of the previous earned techniques has been used with respect to criminal identification with NAO robot. An eyewitness can interact with NAO through a user interface. NAO asks several questions about the suspect, such as age, height, her/his facial shape and size etc., and then making a guess about her/his face.

O2's 4G is finally working again, after people were left for almost a full day without internet. Plenty of people are using their newly restored internet connections to vent about the outage, and to try and secure some compensation for the day spent without any data at all. The outage caused issues not only for people rushing to catch up on social networks. Many rely on their data connections for important healthcare or other uses, and the lack of data even meant that bus timetables and other important infrastructure stopped working properly. Amid that frustration, many have asked whether it will be possible to claim compensation or at least some form of redress for the problems, to make up for the frustration.