--Noise and inconsistency commonly exist in real-world information networks, due to inherent error-prone nature of human or user privacy concerns. T o date, tremendous efforts have been made to advance feature learning from networks, including the most recent Graph Convolutional Networks (GCN) or attention GCN, by integrating node content and topology structures. However, all existing methods consider networks as error-free sources and treat feature content in each node as independent and equally important to model node relations. The erroneous node content, combined with sparse features, provide essential challenges for existing methods to be used on real-world noisy networks. In this paper, we propose F A-GCN, a feature-attention graph convolution learning framework, to handle networks with noisy and sparse node content. T o tackle noise and sparse content in each node, F A-GCN first employs a long short-term memory (LSTM) network to learn dense representation for each feature. T o model interactions between neighboring nodes, a feature-attention mechanism is introduced to allow neighboring nodes learn and vary feature importance, with respect to their connections. By using spectral-based graph convolution aggregation process, each node is allowed to concentrate more on the most determining neighborhood features aligned with the corresponding learning task. I NTRODUCTION M ANY real-world applications involve knowledge mining and analysis from network or graph-based data such as citation networks, social networks, telecommunication networks, and biological networks, etc, where data are often collected from noisy channels with erroneous/inconsistent labels or features [1]. In order to carry out pattern mining from networks, such as community detection [2], node classification [3], link prediction [4], etc., network representation learning (or embedding learning) [5] is commonly used to construct features to represent nodes for learning.

Dreams of a future built on 5G networks and powered by IoT dominated the conversation at conferences in 2019. But for all these grand visions there's a problem: How do you manage networks with millions of cell sites connecting billions of IoT devices? According to some the answer is better visibility enabled by artificial intelligence (AI). While most of these technologies are still years from reaching maturity, that's not stopping companies in the performance analytics space like EXFO and Vitria from investing big in machine learning (ML) and AI. According to Ken Gold, director of test, monitoring, and analytics solutions at EXFO, the implicit complexity associated with massive 5G IoT deployments is only going to make identifying and resolving network anomalies all the more challenging.

To study users' travel behaviour and travel time between origin and destination, researchers employ travel surveys. Although there is consensus in the field about the potential, after over ten years of research and field experimentation, Smartphone-based travel surveys still did not take off to a large scale. Here, computer intelligence algorithms take the role that operators have in Traditional Travel Surveys; since we train each algorithm on data, performances rest on the data quality, thus on the ground truth. Inaccurate validations affect negatively: labels, algorithms' training, travel diaries precision, and therefore data validation, within a very critical loop. Interestingly, boundaries are proven burdensome to push even for Machine Learning methods. To support optimal investment decisions for practitioners, we expose the drivers they should consider when assessing what they need against what they get. This paper highlights and examines the critical aspects of the underlying research and provides some recommendations: (i) from the device perspective, on the main physical limitations; (ii) from the application perspective, the methodological framework deployed for the automatic generation of travel diaries; (iii)from the ground truth perspective, the relationship between user interaction, methods, and data.

With the growth of using cell phones and the increase in diversity of smart mobile devices, a massive volume of data is generated continuously in the process of using these devices. Among these data, Call Detail Records, CDR, is highly remarkable. Since CDR contains both temporal and spatial labels, mobility analysis of CDR is one of the favorite subjects of study among the researchers. The user next location prediction is one of the main problems in the field of human mobility analysis. In this paper, we propose a data processing framework to predict user next location. We propose domain-specific data processing strategies and design a deep neural network model which is based on recurrent neurons and perform regression tasks. Using this prediction framework, the error of the prediction decreases from 74% to 55% in comparison to the worst and best performing traditional models. Methods, strategies, the framework and the results of this paper can be helpful in many applications such as urban planning and digital marketing.

The history of Convolutional neural networks have a remote origin. It is actually in 1979, when Professor Kunihiko Fukushima proposed a hierarchical, multilayered artificial neural network called The neocognitron. The neocognitron has been used for solving the problem of handwritten character recognition and some other pattern recognition tasks, and served as the inspiration for convolutional neural networks. But, if you asked about the history of the neocognitron, we simply can tell you that it was inspired by the model proposed by Hubel & Wiesel in 1959. They found two types of cells in the visual primary cortex called simple cells and complex cells, and also proposed a cascading model of these two types of cells for use in pattern recognition tasks.

The business communications landscape has changed dramatically in the last twenty years, thanks to Voice-Over-Internet-Protocol (VoIP) and the rise of the internet. Telephone networks are no longer tied to a wired landline network. Instead, your VoIP phone system can be operated from various IP addresses, providing flexibility for workers and devices. Machine learning is further transforming that landscape. Gartner, in their report, Top 10 Strategic Technology Trends for 2020, state that AI and machine learning are increasingly used to make decisions in place of humans.

Tue 17 Dec 2019 02.00 EST Last modified on Tue 17 Dec 2019 02.02 EST Need a new smartphone but don't know which one is the very best? Here's a guide comparing the current top-end smartphones from Apple, Samsung, Huawei, OnePlus and others to help you pick the best handset for you. There has never been a better time to buy a new flagship smartphone with many quality handsets available at a wider range of prices than ever before. Whether your priority is two-day battery life, fantastic camera performance or a spectacular screen, there's plenty to choose from. This Guardian buyer's guide to top-end smartphones was last updated on 17 December 2019, and represents the best available models at the time. As new models are released and tested, this guide will be updated to help you choose the right flagship phone for you. Welcome to one of the Guardian's new buyer's guides.

Focus is vital to thrive in any career, and data science is no different. Since being a proficient data scientist requires various skills, developers get perplexed and fail to concentrate on the core of the data science. To understand effective ways for flourishing in data science landscape, we interviewed Arihant Jain for our weekly column My Journey In Data Science. Jain is a Staff Data Scientist at General Electric. He has 5 years of experience in the data domain while working at Genpact, RBL Bank, Vodafone, and GE. Jain is a mechanical engineer-turned-data scientist by choice.

Qualcomm Ventures said Thursday that it has invested $8 million in a New York-based Internet of Things startup that helps companies predict when their machines will fail. Augury, founded in 2011, collects data from equipment via advanced sensors and then applies artificial intelligence algorithms to anticipate when they will break down. It saves customers money by flagging the need for maintenance ahead of a problem. Qualcomm Ventures believes the investment will help jumpstart the emergence of wireless connected factories, shipyards and other industrial operations -- all of which are expected to accelerate with the rollout of new 5G networks. In industrial settings, every machine generates data.

The call center trends for 2019 saw the rise of call center analytics, social media engagement, and the importance of AI. Businesses are already changing at a relatively faster pace than it was expected. Domains like call center technologies lay enough emphasis on customer satisfaction, improving customer experience, enhancing service levels, all this while simultaneously increasing the sales of the organization. Artificial intelligence, big data, business analytics technologies are driving incredible innovations and bringing tremendous advancements that are making it possible to do stuff with user data that people only dreamed of a while ago. In this blog post, we will look at some most dominant trends for the year 2020 that will make a considerable impact on the way call center industry works.