Real-life mobile phone data may contain noisy instances, which is a fundamental issue for building a prediction model with many potential negative consequences. The complexity of the inferred model may increase, may arise overfitting problem, and thereby the overall prediction accuracy of the model may decrease. In this paper, we address these issues and present a robust prediction model for real-life mobile phone data of individual users, in order to improve the prediction accuracy of the model. In our robust model, we first effectively identify and eliminate the noisy instances from the training dataset by determining a dynamic noise threshold using naive Bayes classifier and laplace estimator, which may differ from user-to-user according to their unique behavioral patterns. After that, we employ the most popular rule-based machine learning classification technique, i.e., decision tree, on the noise-free quality dataset to build the prediction model. Experimental results on the real-life mobile phone datasets (e.g., phone call log) of individual mobile phone users, show the effectiveness of our robust model in terms of precision, recall and f-measure.

Franz Schubert composed his Symphony No.8 in 1822, but never completed it, making only two movements along with an outline of a third. Nearly 200 years later, Huawei, Emmy-awarding composer Lucas Cantor, and artificial intelligence (A.I.) inside the Mate 20 Pro smartphone have done what the renowned composer didn't. They've finished the unfinished symphony. The project is a continued illustration of not only the power of Huawei's Kirin 980 processor and Dual-Neural Processing Unit (NPU) artificial intelligence accelerator, but also the potential for using A.I. in varied creative projects. We're familiar with A.I. modes on smartphone cameras, and Huawei has previously demonstrated the power and speed of its A.I. in a self-driving car, where a phone was used to identify and help avoid obstacles.

Intelligent connectivity is the combination of high-speed, low-latency 5G networks, cutting-edge artificial intelligence (AI) and the linking of billions of devices through the Internet of Things (IoT). As these three revolutionary technologies combine they will enable transformational new capabilities in transport, entertainment, industry and public services, and much more beyond. As operators expand beyond provision largely of network access to facilitation of holistic services, they are rapidly bringing into view a world of technological ease and sophistication which not long ago still seemed a long way off. The GSMA estimates that, by 2025, there will be 25 billion connected devices. This hyperconnectivity will be enabled by undisturbed mobile broadband, which will make the number of connected devices communicating with one other will be virtually limitless.

While the objective in traditional multi-armed bandit problems is to find the arm with the highest mean, in many settings, finding an arm that best captures information about other arms is of interest. This objective, however, requires learning the underlying correlation structure and not just the means. Sensors placement for industrial surveillance and cellular network monitoring are a few applications, where the underlying correlation structure plays an important role. Motivated by such applications, we formulate the correlated bandit problem, where the objective is to find the arm with the lowest mean-squared error (MSE) in estimating all the arms. To this end, we derive first an MSE estimator based on sample variances/covariances and show that our estimator exponentially concentrates around the true MSE. Under a best-arm identification framework, we propose a successive rejects type algorithm and provide bounds on the probability of error in identifying the best arm. Using minimax theory, we also derive fundamental performance limits for the correlated bandit problem.

Tech giant Huawei's president has denied the firm has any links to Chinese spying operations. In a letter to the House of Commons Science and Technology Committee, the firm's president Ryan Ding insisted the firm was not involved with such practices. But a 2012 US House Intelligence Committee report outlined Huawei's links to the Chinese state, has since been picked up by other western governments, including Australia, Germany and the UK. FBI Director Christopher Wray has also suggested that the company's smartphones could be used to "maliciously modify or steal information." But Mr Ding insisted that Huawei had never and would never assist any country in gathering intelligence on other countries.

Erbium-doped fiber amplifier (EDFA) is an optical amplifier/repeater device used to boost the intensity of optical signals being carried through a fiber optic communication system. A highly accurate EDFA model is important because of its crucial role in optical network management and optimization. The input channels of an EDFA device are treated as either on or off, hence the input features are binary. Labeled training data is very expensive to collect for EDFA devices, therefore we devise an active learning strategy suitable for binary variables to overcome this issue. We propose to take advantage of sparse linear models to simplify the predictive model. This approach simultaneously improves prediction and accelerates active learning query generation. We show the performance of our proposed active learning strategies on simulated data and real EDFA data.

Dr. Graham Allison -- a specialist in national security at Harvard, where he has taught for five decades -- tells me: "The story beneath the story is the Great Rivalry between a meteorically rising China and a ruling U.S." The lead story of last Sunday's New York Times ("In 5G Race With China, U.S. Pushes Allies to Fight Huawei") trumpets the contest over 5G cellular networks, which exponentially accelerate online speed and ubiquity. Be smart, from Axios chief tech correspondent Ina Fried: Often forgotten is how much China and the U.S. still need one another.

This work develops robust diffusion recursive least squares algorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate update at each node. A recursive strategy for computing the constraint is proposed using side information from the neighboring nodes to further improve the robustness. We also analyze the mean-square convergence behavior of the proposed algorithm. The second proposed algorithm is a modification of the first one based on the dichotomous coordinate descent iterations. It has a performance similar to that of the former, however its complexity is significantly lower especially when input regressors of agents have a shift structure and it is well suited to practical implementation. Simulations show the superiority of the proposed algorithms over previously reported techniques in various impulsive noise scenarios.

We present DANTE, a novel method for training neural networks using the alternating minimization principle. DANTE provides an alternate perspective to traditional gradient-based backpropagation techniques commonly used to train deep networks. It utilizes an adaptation of quasi-convexity to cast training a neural network as a bi-quasi-convex optimization problem. We show that for neural network configurations with both differentiable (e.g. sigmoid) and non-differentiable (e.g. ReLU) activation functions, we can perform the alternations very effectively. DANTE can also be extended to networks with multiple hidden layers. In experiments on standard datasets, neural networks trained using the proposed method were found to be very promising and competitive to traditional backpropagation techniques, both in terms of quality of the solution, as well as training speed.

Integrate.ai has raised undisclosed financing from Telus Ventures, the strategic investment arm of Canadian telecommunications company Telus Corp (TSX: T, NYSE: TU). The funds raised will support development of the Toronto-based company's artificial intelligence (AI) enterprise software platform. Launched in 2017 by CEO Steve Irvine, Integrate.ai in September closed a $39.5 million Series A financing. The round was led by Portag3 Ventures and joined by Georgian Partners and Real Ventures. Deal to accelerate development of ethical AI-powered software platform; multi-year commercial deal will deploy solution to optimize TELUS' customers' experience TORONTO, Jan. 31, 2019 /CNW/ – Integrate.ai