Internet of unmanned aerial vehicle (I-UAV) networks promise to accomplish sensing and transmission tasks quickly, robustly, and cost-efficiently via effective cooperation among UAVs. To achieve the promising benefits, the crucial I-UAV networking issue should be tackled. This article argues that I-UAV networking can be classified into three categories, quality-of-service (QoS) driven networking, quality-of-experience (QoE) driven networking, and situation aware networking. Each category of networking poses emerging challenges which have severe effects on the safe and efficient accomplishment of I-UAV missions. This article elaborately analyzes these challenges and expounds on the corresponding intelligent approaches to tackle the I-UAV networking issue. Besides, considering the uplifting effect of extending the scalability of I-UAV networks through cooperating with high altitude platforms (HAPs), this article gives an overview of the integrated HAP and I-UAV networks and presents the corresponding networking challenges and intelligent approaches.

To further explore the intersection of 5G and manufacturing, register for the 5G Manufacturing Forum. Global manufactuers are starting to adopt 5G to improve manufacturing processes. Low latency and high reliability are needed to support critical applications in the manufacturing field. Several top manufacturers are already taking advantage of 5G implementation to improve operations in different industrial environments. Here we briefly describe some implementations by large manufacturers globally.

Protecting users from accessing malicious web sites is one of the important management tasks for network operators. There are many open-source and commercial products to control web sites users can access. The most traditional approach is blacklist-based filtering. This mechanism is simple but not scalable, though there are some enhanced approaches utilizing fuzzy matching technologies. Other approaches try to use machine learning (ML) techniques by extracting features from URL strings. This approach can cover a wider area of Internet web sites, but finding good features requires deep knowledge of trends of web site design. Recently, another approach using deep learning (DL) has appeared. The DL approach will help to extract features automatically by investigating a lot of existing sample data. Using this technique, we can build a flexible filtering decision module by keep teaching the neural network module about recent trends, without any specific expert knowledge of the URL domain. In this paper, we apply a mechanical approach to generate feature vectors from URL strings. We implemented our approach and tested with realistic URL access history data taken from a research organization and data from the famous archive site of phishing site information, PhishTank.com. Our approach achieved 2~3% better accuracy compared to the existing DL-based approach.

The application of Artificial Intelligence in the telecommunication industry has gained quite a much traction in the recent past and for the right reasons. The role of the telecommunications industry in today's world has expanded beyond the provision of simple phone and internet interaction services for individuals and corporates. In the current era of the Internet of Things (IoT), telecommunication companies have leveraged mobile and broadband services to take center stage in technological growth and innovation. That is not all; educated prospects point to a future commercial world where Artificial intelligence is vital. For example, Technavio, a leading market research, and advisory firm globally, expects growth in technology to continue for the foreseeable future and record a Compounded Annual Growth Rate (CAGR) of above 42% next year.

We present a novel approach to maximize the communication-aware coverage for robots operating over large-scale geographical regions of interest (ROIs). Our approach complements the underlying network topology in neighborhood selection and control, rendering it highly robust in dynamic environments. We formulate the coverage as a multi-stage, cooperative graphical game and employ Variational Inference (VI) to reach the equilibrium. We experimentally validate our approach in an mobile ad-hoc wireless network scenario using Unmanned Aerial Vehicles (UAV) and User Equipment (UE) robots. We show that it can cater to ROIs defined by stationary and moving User Equipment (UE) robots under realistic network conditions.

INTRACOM TELECOM is a global telecommunication systems and solutions vendor operating for over 40 years in the market. The company innovates in the wireless access and transmission field, offers a competitive telco software solutions portfolio and combines its offerings with a complete range of professional services. Our mission is to shape future through technology and we recognize that human capital is the key factor to achieve this in today's business environment. Our company's highly specialized and experienced personnel are pivotal to achieving demanding objectives and advancing the capabilities of the company to better serve its customers. Within this framework, we are looking for an agile and highly-motivated "Data Scientist" to join a team of future shapers.

As it has become difficult for Huawei to focus on the smartphone business because of the sanctions imposed by the United States, the Chinese giant has been focusing on other products. In line with that, the company has today launched the Huawei AI Speaker 2e, which is now available for purchase for 199 yuan (approximately $31). The speaker comes in a pill-shaped design and woven mesh look. It comes with LED dot matrix lights that can show several different information on the speaker itself, including times, weather, temperature, and more. The Huawei AI Speaker 2e has a row of buttons at the top with a yellow-colored button in the center for one-click communication while the other buttons on the speaker are for controlling the playback.

Mobile network traffic forecasting is one of the key functions in daily network operation. A commercial mobile network is large, heterogeneous, complex and dynamic. These intrinsic features make mobile network traffic forecasting far from being solved even with recent advanced algorithms such as graph convolutional network-based prediction approaches and various attention mechanisms, which have been proved successful in vehicle traffic forecasting. In this paper, we cast the problem as a spatial-temporal sequence prediction task. We propose a novel deep learning network architecture, Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Networks (AMF-STGCN), to model the traffic dynamics of mobile base stations. AMF-STGCN extends GCN by (1) jointly modeling the complex spatial-temporal dependencies in mobile networks, (2) applying attention mechanisms to capture various Receptive Fields of heterogeneous base stations, and (3) introducing an extra decoder based on a fully connected deep network to conquer the error propagation challenge with multi-step forecasting. Experiments on four real-world datasets from two different domains consistently show AMF-STGCN outperforms the state-of-the-art methods.

It's been estimated that 96% of companies consider Account Based Marketing (ABM) as the key driver to increase revenue in B2B sales. ABM enables organizations, such as Reuters, to modify their marketing budgets by focusing on the customers' likelihood of purchase in order to optimize their marketing strategies and target customers who are more prone to convert. Account-based marketing is a data-driven customized B2B marketing approach. It focuses on high-value accounts and delivers messages based on particular traits and needs of these accounts. The sales, marketing, and executive teams sustain and improve their relations with these customers to direct them to buy more expensive or additional products.

AI adoption and maturity levels are significantly lower at other types of technology companies, with many companies insisting on seeing sector-specific use cases and proven results before scaling up their AI programs and investments. Many existing AI efforts in the sector are limited to scattered experiments and small-scale pilots, without an overarching strategy for harnessing the full power of AI and digital data. As more organizations shift their AI workloads to a cloud environment, data integration challenges are intensifying. Some of the most common barriers to access third-party data sources include dealing with disparate data that exists on different systems and merging data from diverse sources. For all these efforts, the right talent and expertise can be critical.