Creating a superior customer experience means stc's customers become stickier, are willing to upgrade, accept a cross-sell, or upsell more frequently which drives stc loyalty. However, before stc can accelerate growth in their traditional business through upsell and cross-selling opportunities, they must ensure that customer experience is simplified to minimize customer disruptions, making stc easy to do business with. 'Quality of Service' starts with optimizing calls through reducing call setup times, preventing unsuccessful dropped calls, maximizing speech connection quality, and maximizing internet connectivity and bandwidth speeds. However, optimizing phone and internet services quality is just the start. Should an issue arise, stc looks across its digital and call center channels to minimize disruption and inconvenience to its customers. Response times to call centers and disruption resolution are key indicators where'Quality of Experience' may be monitored and improved upon.

Like the prior Cortex-A77, the Cortex-A78 will consist of what ARM calls its big.LITTLE octacore architecture, with four high-performance A78 cores and four A55 cores optimized for long battery life. ARM said that a Cortex-A78 core running at 3GHz would deliver 20 percent more sustained, single-core performance than the Cortex-A77 core running at 2.6GHz, assuming 1 watt per core. The performance is based on simulated estimates. Alternatively, a phone maker could clock the A78 to consume half the power at the same performance as the A77, Williamson said. ARM believes that the octacore Cortex-A78 layout will require 15 percent less die space than the Cortex-A77, leading to smaller phones.

In this paper, the design of an optimal trajectory for an energy-constrained drone operating in dynamic network environments is studied. In the considered model, a drone base station (DBS) is dispatched to provide uplink connectivity to ground users whose demand is dynamic and unpredictable. In this case, the DBS's trajectory must be adaptively adjusted to satisfy the dynamic user access requests. To this end, a meta-learning algorithm is proposed in order to adapt the DBS's trajectory when it encounters novel environments, by tuning a reinforcement learning (RL) solution. The meta-learning algorithm provides a solution that adapts the DBS in novel environments quickly based on limited former experiences. The meta-tuned RL is shown to yield a faster convergence to the optimal coverage in unseen environments with a considerably low computation complexity, compared to the baseline policy gradient algorithm. Simulation results show that, the proposed meta-learning solution yields a 25% improvement in the convergence speed, and about 10% improvement in the DBS' communication performance, compared to a baseline policy gradient algorithm. Meanwhile, the probability that the DBS serves over 50% of user requests increases about 27%, compared to the baseline policy gradient algorithm.

The best algorithm for a computational problem generally depends on the "relevant inputs," a concept that depends on the application domain and often defies formal articulation. Although there is a large literature on empirical approaches to selecting the best algorithm for a given application domain, there has been surprisingly little theoretical analysis of the problem. Our framework captures several state-of-the-art empirical and theoretical approaches to the problem, and our results identify conditions under which these approaches are guaranteed to perform well. We interpret our results in the contexts of learning greedy heuristics, instance feature-based algorithm selection, and parameter tuning in machine learning. Rigorously comparing algorithms is hard. Two different algorithms for a computational problem generally have incomparable performance: one algorithm is better on some inputs but worse on the others. The simplest and most common solution in the theoretical analysis of algorithms is to summarize the performance of an algorithm using a single number, such as its worst-case performance or its average-case performance with respect to an input distribution. This approach effectively advocates using the algorithm with the best summarizing value (e.g., the smallest worst-case running time). Solving a problem "in practice" generally means identifying an algorithm that works well for most or all instances of interest. When the "instances of interest" are easy to specify formally in advance--say, planar graphs, the traditional analysis approaches often give accurate performance predictions and identify useful algorithms.

The commercialization of AI is playing a positive role in accelerating business digitalization, improving industry chain structures and enhancing information use efficiency. AI has entered an age of machine learning, and the future of AI development will depend on the integration of key technologies and industries. AI investment is returning to reason, with underlying technologies and easy-to-deploy applications more favored by AI leading institutions. The Internet giant has also begun to strategically deploy in the artificial intelligence business related industry. As a new industry in the future, AI enterprises show the characteristics of high growth rate.

OVERLAND PARK, Kan.--(BUSINESS WIRE)--Ready, set, INNOVATE! T-Mobile US, Inc. (NASDAQ: TMUS) today unveiled six exciting companies handpicked to participate in this year's T-Mobile Accelerator. These companies will work directly with T-Mobile leaders and other industry experts and mentors to develop and commercialize the next disruptive emerging products, applications and solutions made possible by T-Mobile's nationwide 5G network today and in the future. Formerly the Sprint Accelerator, the immersive program runs through July 30 and will culminate in Demo Day where participants showcase their accomplishments. "We are committed to using our broad and deep nationwide 5G network to accelerate innovation and spur the development of new, transformative applications. Mentoring, collaborating with, and providing resources to these six promising companies is an important part of that mission," said Neville Ray, President of Technology at T-Mobile.

Internet speeds are still running largely as normal despite the increased pressure of lockdown, according to research from regulator Ofcom. Download speeds have only dropped by an average of 2 per cent, according to the research, even with the extra load. That is despite some high-profile outages, including Virgin Media problems that took the internet offline for users across the country. Networks have been under increased strain with more people across the country working from home, children using online platforms to carry on school work, and greater gaming and streaming as a source of entertainment. The communications regulator measured broadband performance for 3,481 users at the beginning and end of March, to compare results before and after lockdown started.

The Trump administration has extended the executive order banning American businesses from working with companies that pose a national security risk, extending the muddled relationship between US enterprise and Chinese conglomerates such as smartphone maker Huawei and telecom equipment manufacturer ZTE. The order, called the International Emergency Economic Powers Act which gives the president the authority to regulate commerce during a national emergency, was implemented in May 2019. The result of the ban, which has now been extended until May 2021, means that Google cannot provide Huawei with access to Google Mobile Services and therefore popular apps such as Maps and YouTube are not available on Huawei phones. Google Mobile Services are the commercial aspects to the Android platform that all major smartphones - apart from Apple's iPhones - use. It includes Google's apps and back-end services which powers other apps including Netflix and Citymapper.

Verdict lists the top five terms tweeted on IoT in Q1 2020, based on data from GlobalData's Influencer Platform. The top tweeted terms are the trending industry discussions happening on Twitter by key individuals (influencers) as tracked by the platform. Tech innovations in the form of robotics, automated parking, and more, the use cases of artificial intelligence across industries such as retail, automotive, and smart cities, were popularly discussed in Q1 2020. According to a video shared by Evan Kirstel, a top B2B influencer, buyers can shop for they wanted at stores in just 14 seconds, much like the Amazon Go stores that allows cashierless shopping by infusing sensors all over the stores that tracks what customers maybe buying after scanning the app, after which buyers just walk away with their products. Mike Quindazzi, a digital alliances sales leader, further shared a video on new automated parking that saves both time and space.

Appropriate allocation of system resources is essential for meeting the increased user-traffic demands in the next generation wireless technologies. Traditionally, the system relies on channel state information (CSI) of the users for optimizing the resource allocation, which becomes costly for fast-varying channel conditions. Considering that future wireless technologies will be based on dense network deployment, where the mobile terminals are in line-of-sight of the transmitters, the position information of terminals provides an alternative to estimate the channel condition. In this work, we propose a coordinates-based resource allocation scheme using supervised machine learning techniques, and investigate how efficiently this scheme performs in comparison to the traditional approach under various propagation conditions. We consider a simplistic system set up as a first step, where a single transmitter serves a single mobile user. The performance results show that the coordinates-based resource allocation scheme achieves a performance very close to the CSI-based scheme, even when the available coordinates of terminals are erroneous. The proposed scheme performs consistently well with realistic-system simulation, requiring only 4 s of training time, and the appropriate resource allocation is predicted in less than 90 microseconds with a learnt model of size less than 1 kB.