Network slicing promises to provision diversified services with distinct requirements in one infrastructure. Deep reinforcement learning (e.g., deep $\mathcal{Q}$-learning, DQL) is assumed to be an appropriate algorithm to solve the demand-aware inter-slice resource management issue in network slicing by regarding the varying demands and the allocated bandwidth as the environment state and the action, respectively. However, allocating bandwidth in a finer resolution usually implies larger action space, and unfortunately DQL fails to quickly converge in this case. In this paper, we introduce discrete normalized advantage functions (DNAF) into DQL, by separating the $\mathcal{Q}$-value function as a state-value function term and an advantage term and exploiting a deterministic policy gradient descent (DPGD) algorithm to avoid the unnecessary calculation of $\mathcal{Q}$-value for every state-action pair. Furthermore, as DPGD only works in continuous action space, we embed a k-nearest neighbor algorithm into DQL to quickly find a valid action in the discrete space nearest to the DPGD output. Finally, we verify the faster convergence of the DNAF-based DQL through extensive simulations.

In their work to unearth evidence of fraudulent activities, forensic accounting investigators dig through diverse data looking for anomalies that suggest something is just not right. But as the massive volumes of data collected by companies balloon, this task has become increasingly arduous, time-consuming and humanly impossible. Instead of investigators manually reviewing spreadsheet rows and columns, looking for three or four data elements that together indicate a suspicious transaction, ML can peruse thousands of data elements -- instantly. The regrettable consequence is the greater chance of a well-thought-out scam slipping through the cracks. A case in point is healthcare fraud, which has been estimated to cost the United States tens of billions of dollars annually.

Lately it seems that AI is our knight in shining armor, the missing link between our past and our future. AI is now being used to steer the direction of hedge funds and drive much needed efficiency upgrades to our supply chains, for example. The machine learning society introduced a face recognition algorithm that is able to distinguish gender with an accuracy of up to 91%. AI is becoming so commonplace, in fact, that our electronics are often using it in the background to improve our experience, from taking a picture or securing our devices, without us even knowing it. Smaller AI startups are introducing AI services that can help sales teams surface talking points that closes deals. According to Element AI, a specialty lab in Montreal, "in the entire world, fewer than 10,000 people have the skills necessary to tackle serious artificial intelligence research."

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Network providers agree that they need to develop effective mechanisms for collecting, structuring and analyzing the huge volumes of data that AI is capable of amassing. A key takeaway from the report is that the early adopters of AI who find solutions to the challenges of today and tomorrow will have a clear first-mover advantage. It is our belief that AI will open up exciting opportunities for the mobile communications sector, as it can be utilized to create a more personal approach for customers, while helping to manage the costs of deploying and maintaining networks.

In this paper, we study how to solve resource allocation problems in ultra-reliable and low-latency communications by unsupervised deep learning, which often yield functional optimization problems with quality-of-service (QoS) constraints. We take a joint power and bandwidth allocation problem as an example, which minimizes the total bandwidth required to guarantee the QoS of each user in terms of the delay bound and overall packet loss probability. The global optimal solution is found in a symmetric scenario. A neural network was introduced to find an approximated optimal solution in general scenarios, where the QoS is ensured by using the property that the optimal solution should satisfy as the "supervision signal". Simulation results show that the learning-based solution performs the same as the optimal solution in the symmetric scenario, and can save around 40% bandwidth with respect to the state-of-the-art policy.

AMSTERDAM / LONDON June 3, 2019 -- Contexta360, a leading speech analytics and conversational computing company, today announced it has signed a strategic partnership agreement with KPN, a leading Dutch Telecommunications provider. The agreement delivers class-leading speech-to-text and call analytics solutions to KPN's API Store as well as advanced speech analytics, compliance and real-time capabilities to their call centre and CX team. This wider portfolio of capabilities will allow KPN's customers gain greater insight into call context, but also improve clients' compliance, quality and revenue opportunities. The agreement will extend to Contexta360: Speech-to-text, Contexta360: Analyzer, C360: Qualitymonitor, C360: Connect and C360: Assist and will allow KPN's clients to analyse tens of millions of call conversations for sentiment, compliance, key selling or service opportunities and dramatically improve agent training and quality performance metrics both in real-time and post-call analysis. The relationship will enhance KPN's portfolio and enable customers using Genesys, Avaya, Mitel, NICE, Verint to add significant capabilities without the need to replace any of their current technology.

THE HAGUE, Netherlands – Artificial intelligence will be a requirement for securing carrier 5G networks – which is shaping up to be a technology juggernaut that presents unique challenges unlike any ever seen in the world of telecom until now. That was the assessment at the GSMA Mobile 360 Security for 5G conference, taking place here this week. To understand the challenges and the drivers for artificial intelligence (AI), it's important to understand that existing telecom networks, even today's 4G LTE networks, are built from a hardware-centric perspective, using the vertical-stack Open Systems Interconnection (OSI) model. Features include a heavy reliance on hardware big routers and switches with device-specific software to run them. Functions are hard-coded and largely siloed.

Prediction of user traffic in cellular networks has attracted profound attention for improving resource utilization. In this paper, we study the problem of network traffic traffic prediction and classification by employing standard machine learning and statistical learning time series prediction methods, including long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA), respectively. We present an extensive experimental evaluation of the designed tools over a real network traffic dataset. Within this analysis, we explore the impact of different parameters to the effectiveness of the predictions. We further extend our analysis to the problem of network traffic classification and prediction of traffic bursts. The results, on the one hand, demonstrate superior performance of LSTM over ARIMA in general, especially when the length of the training time series is high enough, and it is augmented by a wisely-selected set of features. On the other hand, the results shed light on the circumstances in which, ARIMA performs close to the optimal with lower complexity.

We consider estimating the edge-probability matrix of a network generated from a graphon model when the full network is not observed---only some overlapping subgraphs are. We extend the neighbourhood smoothing (NBS) algorithm of Zhang et al. (2017) to this missing-data set-up and show experimentally that, for a wide range of graphons, the extended NBS algorithm achieves significantly smaller error rates than standard graphon estimation algorithms such as vanilla neighbourhood smoothing (NBS), universal singular value thresholding (USVT), blockmodel approximation, matrix completion, etc. We also show that the extended NBS algorithm is much more robust to missing data.