Nowadays the increasing and rapid progress of technology and the availability of electronic documents from a variety of sources have made a huge amount of textual data available. Hence, one of the prominent research topics of statistical andmachine learning communities is to provide suitable and feasible methods to extract high-quality information from unstructured textual data (Lata and Loar, 2018) for the different purposes of clustering, classification and document retrieval (Khan et al., 2010). This work originates from an empirical problem of classification of the content ofcalls made to the customer service of an important mobile phone company inItaly. The received calls are written down by an operator and classified into relevant classes (e.g.

In the wake of recent customer data breaches, companies are recognizing the need for more protections and transparency around the collection and use of customer data. But few have paid equal attention to the issues arising from the collection and mining of workplace data. Companies have vast amounts of valuable data on work and their workforce, and executives recognize the opportunity to use this data to improve productivity and to motivate and engage people. We surveyed more than 10,000 workers, across all skill levels and generations, and 1400 C-level executives, in 13 countries and 13 industries. We found that more than 90% of the employees are willing to let their employers collect and use data on them and their work, but only if they benefit in some way.

"Machine learning is the big one," Duncan Stewart, the director of technology, media, and telecommunications research at Deloitte Canada, told CNBC. The dominant tech trend in places like Southeast Asia will not be artificial intelligence as a whole, but rather it will be the more specific AI field of machine learning, according to a researcher from Deloitte. "Machine learning is the big one, not AI. AI's a very broad field, we are talking about the very narrow field of machine learning," Duncan Stewart, the director of technology, media, and telecommunications research at Deloitte Canada, told CNBC. Deloitte predicts that the next 12 months will see significant progress in augmented reality, mobile device usage, and increasingly sophisticated chips.

Apple is selling older iPhones in Germany again, amid an ongoing dispute with chipmaker Qualcomm. For now, all of the iPhone 7 and 8 models that are returning to sale will use the chipmaker's components to get around a patent dispute and allow them to be bought again in Germany, Apple said. But it remained committed to fighting the argument that led to the ban, it said, and continued to attack Qualcomm over the patent dispute that has blighted both companies. "Qualcomm is attempting to use injunctions against our products to try to get Apple to succumb to their extortionist demands. In many cases they are using patents they purchased or that have nothing to do with their cellular technology to harass Apple and other industry players," an Apple spokesperson said.

China is moving forward in the global "race to 5G," as state-owned carrier China Mobile has announced (via Xinhua) that it's already building the first 5G smart highway -- a city-scale system of roads capable of supporting cellular network-coordinated transportation services. The infrastructure is currently under construction in Wuhan, the capital of China's centrally located Hubei Province. As the country's largest telecommunications company, China Mobile plans to roll out a collection of 5G services on the highway, beginning with "smart toll stations" that could do away with current toll transponders and human operators. The carrier also plans to gather real-time traffic information and make AI-assisted predictions using the data, as well as supporting autonomous cars. While China Mobile isn't the world's first carrier to either announce 5G highway plans or begin limited deployments, it may wind up being the first to offer actual commercial and coordinated transportation services on live highways -- depending on progress made by rivals in other countries.

At HUAWEI CONNECT 2018, we unveiled our AI strategy and portfolio. At HUAWEI EC0-CONNECT EUROPE 2018, we explored how we can work with our partners and customers to create an open industry ecosystem and help build pervasive intelligence in Europe. We also spoke to Europe's industry leaders and prominent experts about artificial intelligence, including Marco Menichelli, CTO of XSENSE. The full interview transcript is below. Marco Menichelli: What I call Artificial Intuition.

KEY FINDINGS The automated chatbots, personalized offers, and efficiently streamlined customer service processes can be managed to provide enhanced customer service by the telecommunication services if the Artificial Intelligence gets integrated with the former. By assimilating advanced technologies like Artificial Intelligence, machine learning, etc. and 5G system, the telecommunication operators, can enhance and implement realization of high levels of self-organization, intelligent management and fault-free networks that are much more reliable as compared to the earlier networks. Advantages like detection of flaws in the network, network security, network optimization & offer virtual assistance are influencing the global market for Artificial Intelligence in telecommunication to propel vigorously at a CAGR of 42.16% from 2019-2027, as estimated by Inkwood Research. Furthermore, the incorporation of artificial intelligence technologies with upcoming wireless networks is appraised to increase the demand & adoption of such artificial intelligence tools & services in the telecommunication sector. MARKET INSIGHTS The upsurge in mobile data traffic & smartphone users across the world and the integration of AI with newer wireless networks will necessarily drive the global AI in the telecom market.Concerns related to incompatibility, the unreliability of artificial intelligence algorithms, lack of skilled personnel & difficulties in the protection of confidential & private data are the primary challenges faced by the market players.

The cloud radio access network (CRAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, wireless traffic prediction is a key enabler for C-RANs to improve both the spectrum efficiency and energy efficiency through load-aware network managements. This paper proposes a scalable Gaussian process (GP) framework as a promising solution to achieve large-scale wireless traffic prediction in a cost-efficient manner. First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions. Second, in the prediction phase, we fuse local predictions obtained from the BBUs via a cross-validation based optimal strategy, which demonstrates itself to be reliable and robust for general regression tasks. Moreover, such a cross-validation based optimal fusion strategy is built upon a well acknowledged probabilistic model to retain the valuable closed-form GP inference properties. Third, we propose a CRAN based scalable wireless prediction architecture, where the prediction accuracy and the time consumption can be balanced by tuning the number of the BBUs according to the real-time system demands. Experimental results show that our proposed scalable GP model can outperform the state-of-the-art approaches considerably, in terms of wireless traffic prediction performance. I. INTRODUCTION The fifth generation (5G) system is expected to provide approximately 1000 times higher wireless capacity and reduce up to 90 percent of energy consumption compared with the current 4G system [1]. A CRAN is composed of two parts: the distributed remote radio heads (RRHs) with basic radio functionalities to provide coverage over a large area, and the centralized baseband units (BBUs) pool with parallel BBUs to support joint processing and cooperative network management. The BBUs can perform dynamic resource allocation in accordance with realtime networkdemands based on the virtualized resources in cloud computing. One major feature for the C-RANs to enable high energy-efficient services is the fast adaptability to nonuniform traffic variations [1]-[4], e.g., the tidal effects. Consequently, wireless traffic prediction techniques stand out as the key enabler to realize such loadaware managementand proactive control in C-RANs, e.g., the load-aware RRH on/off operation [4].

Abstract--In this paper we consider the task of detecting abnormal communication volume occurring at node-level in communication networks. The signal of the communication activity is modeled by means of a clique stream: each occurring communication event is instantaneous and activates an undirected subgraph spanning over a set of equally participating nodes. We present a probabilistic framework to model and assess the communication volume observed at any single node. Specifically, we employ nonparametric regression to learn the probability that a node takes part in a certain event knowing the set of other nodes that are involved. On the top of that, we present a concentration inequality around the estimated volume of events in which a node could participate, which in turn allows us to build an efficient and interpretable anomaly scoring function. Finally, the superior performance of the proposed approach is empirically demonstrated in real-world sensor network data, as well as using synthetic communication activity that is in accordance with that latter setting. I. INTRODUCTION Monitoring the activity in communication networks has become a popular area of research and particular attention has been paid to detection tasks such as spotting events or anomalies. Aneffective way to represent the communication activity is via a dynamic graph where the entities are considered to be nodes, and each communication event (or more simply event) to be represented by a set of connecting edges that appear at a specific time interval.