Most real-world datasets, and particularly those collected from physical systems, are full of noise, packet loss, and other imperfections. However, most specification mining, anomaly detection and other such algorithms assume, or even require, perfect data quality to function properly. Such algorithms may work in lab conditions when given clean, controlled data, but will fail in the field when given imperfect data. We propose a method for accurately reconstructing discrete temporal or sequential system traces affected by data loss, using Long Short-Term Memory Networks (LSTMs). The model works by learning to predict the next event in a sequence of events, and uses its own output as an input to continue predicting future events. As a result, this method can be used for data restoration even with streamed data. Such a method can reconstruct even long sequence of missing events, and can also help validate and improve data quality for noisy data. The output of the model will be a close reconstruction of the true data, and can be fed to algorithms that rely on clean data. We demonstrate our method by reconstructing automotive CAN traces consisting of long sequences of discrete events. We show that given even small parts of a CAN trace, our LSTM model can predict future events with an accuracy of almost 90%, and can successfully reconstruct large portions of the original trace, greatly outperforming a Markov Model benchmark. We separately feed the original, lossy, and reconstructed traces into a specification mining framework to perform downstream analysis of the effect of our method on state-of-the-art models that use these traces for understanding the behavior of complex systems.

In my last article, I covered how 5G with Cloud technologies will transform computing by shifting the computing power from the device to the Cloud through edge computing, making high-end experiences, such as high-quality wireless VR, accessible to more people at lower cost, without the need for constant upgrades and with a lighter form factor. This transformation is pervasive, as the enhanced mobile broadband represented by 5G enables high bandwidth and low latency across all devices and sensors. The miniaturization of 5G chipsets and their integration in UHD video and depth cameras makes those truly mobile. As 5G networks are rolled out either through Fixed Wireless Access (WTTx) and mobile networks, mobile video capture, broadcasting, and consumption are made possible. WTTx has considerable impact on very high resolution capture thanks to a larger data bandwidth pipe, making not just multicast possible, but 3D capture also a reality.

With conventional anti-jamming solutions like frequency hopping or spread spectrum, legitimate transceivers often tend to "escape" or "hide" themselves from jammers. These reactive anti-jamming approaches are constrained by the lack of timely knowledge of jamming attacks. Bringing together the latest advances in neural network architectures and ambient backscattering communications, this work allows wireless nodes to effectively "face" the jammer by first learning its jamming strategy, then adapting the rate or transmitting information right on the jamming signal. Specifically, to deal with unknown jamming attacks, existing work often relies on reinforcement learning algorithms, e.g., Q-learning. However, the Q-learning algorithm is notorious for its slow convergence to the optimal policy, especially when the system state and action spaces are large. This makes the Q-learning algorithm pragmatically inapplicable. To overcome this problem, we design a novel deep reinforcement learning algorithm using the recent dueling neural network architecture. Our proposed algorithm allows the transmitter to effectively learn about the jammer and attain the optimal countermeasures thousand times faster than that of the conventional Q-learning algorithm. Through extensive simulation results, we show that our design (using ambient backscattering and the deep dueling neural network architecture) can improve the average throughput by up to 426% and reduce the packet loss by 24%. By augmenting the ambient backscattering capability on devices and using our algorithm, it is interesting to observe that the (successful) transmission rate increases with the jamming power. Our proposed solution can find its applications in both civil (e.g., ultra-reliable and low-latency communications or URLLC) and military scenarios (to combat both inadvertent and deliberate jamming).

Verizon said Wednesday it had turned on its ultrafast 5G wireless network in parts of Chicago and Minneapolis, though it will be available only to certain subscribers who pay a fee and own a compatible smartphone. The move makes Verizon the first wireless carrier in the United States to flip the switch on speedy, smartphone-ready 5G service in select urban areas, the company said in a statement, though other U.S. carriers including AT&T, Sprint and T-Mobile have pledged to do the same in the coming months. The service Verizon is offering -- 5G, the fifth generation of wireless data networks -- could provide consumers Internet speeds that are up to 100 times faster than 4G networks, according to an industry trade association. Through the placement of small boxes that serve as conduits for invisible, data-transmitting radio waves, 5G networks could power a wide range of consumer devices, from smartphones that can stream Netflix videos more quickly to enabling the arrival of self-driving cars. The promise of faster speeds and more reliable connections has generated a full-on race between AT&T, Verizon, Sprint and T-Mobile, the country's four largest carriers, to see who can offer service first (and capture new consumers and their cash in the process). The U.S. government also has taken notice, with lawmakers and the Trump administration looking to supercharge research, investment and development in the telecom sector, believing that better wireless networks will grant the country a more competitive business edge -- particularly against China, which is racing to deploy 5G as well.

Amazon's Echo speakers have a broadcast feature that will help you send a message to family members that might be scattered around the house. It's time for homework or to summon the troops for dinner. Sure, you could holler upstairs, but that seems so last century. You save your voice and text them instead. It's not just the kids we are texting – we're also texting each other, at home. So is texting under one roof becoming the modern-day equivalent of the dinner bell or intercom?

Broadband customers who are having internet problems are about to start getting refunds – without even having to ask. At the moment, only about one in seven people who have internet or landline problems such as repairs, installations or missed engineer appointments are given any kind of compensation from the companies responsible, according to regulator Ofcom. Even if they do, the amounts are usually small. But now customers will find themselves being given those refunds automatically, for any kind of broadband problems, Ofcom said. We'll tell you what's true.

Customer churn is a major problem and one of the most important concerns for large companies. Due to the direct effect on the revenues of the companies, especially in the telecom field, companies are seeking to develop means to predict potential customer to churn. Therefore, finding factors that increase customer churn is important to take necessary actions to reduce this churn. The main contribution of our work is to develop a churn prediction model which assists telecom operators to predict customers who are most likely subject to churn. The model developed in this work uses machine learning techniques on big data platform and builds a new way of features' engineering and selection. In order to measure the performance of the model, the Area Under Curve (AUC) standard measure is adopted, and the AUC value obtained is 93.3%. Another main contribution is to use customer social network in the prediction model by extracting Social Network Analysis (SNA) features. The use of SNA enhanced the performance of the model from 84 to 93.3% against AUC standard. The model was prepared and tested through Spark environment by working on a large dataset created by transforming big raw data provided by SyriaTel telecom company. The dataset contained all customers' information over 9 months, and was used to train, test, and evaluate the system at SyriaTel. The model experimented four algorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree "GBM" and Extreme Gradient Boosting "XGBOOST". However, the best results were obtained by applying XGBOOST algorithm. This algorithm was used for classification in this churn predictive model.

LG has announced that the G8 will arrive on April 11th, with pre-orders starting March 29th at major carriers including AT&T, Sprint, T-Mobile and Verizon (Engadget's parent company). It'll undercut its South Korean rival's price by a fair margin -- pricing starts at $820 up front versus Samsung's $900, and that's before the usual promos that knock as much as $150 off the price. Whether or not it's worth the savings over the S10 will likely depend on just how much you like LG's rather unusual priorities. You don't get a telephoto lens or 8GB of RAM ('just' 6GB) for the money, but you do get party tricks like in-the-air hand gestures, an OLED display that doubles as the speaker and a more secure, depth-based face unlock. That's not including more familiar staples like the quad DAC and a dedicated Google Assistant button.

The intelligent Processing technique is more and more attractive to researchers due to its ability to deal with key problems in Vehicular Ad hoc networks. However, several problems in applying intelligent processing technologies in VANETs remain open. The existing applications are comprehensively reviewed and discussed, and classified into different categories in this paper. Their strategies, advantages/disadvantages, and performances are elaborated. By generalizing different tactics in various applications related to different scenarios of VANETs and evaluating their performances, several promising directions for future research have been suggested.

The world's second biggest smartphone maker is about to unveil its latest range of flagship phones, but an inundation of leaks mean there is little to reveal that is not already known. Huawei will show off the P30, P30 Pro and P30 Lite - as they are expected to be called - in what the Chinese manufacturer hopes will offer a trio of rivals to Apple's iPhones and Samsung's Galaxy range of smartphones. The latest leak of the new phones, which comes just hours before the 26 March unveiling event in Paris, shows complete front and rear images of the three Huawei devices. We'll tell you what's true. You can form your own view.