The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

Recent research on dense captioning based on the recurrent neural network and the convolutional neural network has made a great progress. However, mapping from an image feature space to a description space is a nonlinear and multimodel task, which makes it difficult for the current methods to get accurate results. In this paper, we put forward a novel approach for dense captioning based on hourglass-structured residual learning. Discriminant feature maps are obtained by incorporating dense connected networks and residual learning in our model. Finally, the performance of the approach on the Visual Genome V1.0 dataset and the region labelled MS-COCO (Microsoft Common Objects in Context) dataset are demonstrated. The experimental results have shown that our approach outperforms most current methods.

Looking back, we can see the value these emerging innovations offered, but in the moment, their promise seemed less clear. It is, therefore, remarkable how quickly organizations across sectors and regions navigated through the so what and into the now what for these trends and went on to successfully traverse the new digital landscape. This journey from uncertainty to digital transformation informs our latest offering, Tech Trends 2019: Beyond the digital frontier. A persistent theme of every Tech Trends report has been the increasing, often mind-bending velocity of change. A decade ago many companies could achieve competitive advantage by embracing innovations and trends that were already underway. Today, this kind of reactive approach is no longer enough. To stay ahead of the game, companies must work methodically to sense new innovations and possibilities, make sense of their ambitions for tomorrow, and find the confidence to boldly go beyond the digital frontier. So here's to the next decade of opportunity, whatever it may be. Along the way, embrace that queasy feeling of uncertainty.

In my previous post, I suggested that it was possible to provide a road map that would help with the introduction of artificial intelligence, advanced analytics and machine learning into insurance companies. This post outlines the process. The first area to address is applications where the adoption of analytics will have an immediate impact on cost reduction and efficiency. The obvious point is process automation. In many insurance companies, the first projects involving advanced analytics and machine learning models are the digitisation and optimisation of processes.

Decentralized Online Learning (online learning in decentralized networks) attracts more and more attention, since it is believed that Decentralized Online Learning can help the data providers cooperatively better solve their online problems without sharing their private data to a third party or other providers. Typically, the cooperation is achieved by letting the data providers exchange their models between neighbors, e.g., recommendation model. However, the best regret bound for a decentralized online learning algorithm is $\Ocal{n\sqrt{T}}$, where $n$ is the number of nodes (or users) and $T$ is the number of iterations. This is clearly insignificant since this bound can be achieved \emph{without} any communication in the networks. This reminds us to ask a fundamental question: \emph{Can people really get benefit from the decentralized online learning by exchanging information?} In this paper, we studied when and why the communication can help the decentralized online learning to reduce the regret. Specifically, each loss function is characterized by two components: the adversarial component and the stochastic component. Under this characterization, we show that decentralized online gradient (DOG) enjoys a regret bound $\Ocal{n\sqrt{T}G + \sqrt{nT}\sigma}$, where $G$ measures the magnitude of the adversarial component in the private data (or equivalently the local loss function) and $\sigma$ measures the randomness within the private data. This regret suggests that people can get benefits from the randomness in the private data by exchanging private information. Another important contribution of this paper is to consider the dynamic regret -- a more practical regret to track users' interest dynamics. Empirical studies are also conducted to validate our analysis.

Spectral clustering refers to a family of unsupervised learning algorithms that compute a spectral embedding of the original data based on the eigenvectors of a similarity graph. This non-linear transformation of the data is both the key of these algorithms' success and their Achilles heel: forming a graph and computing its dominant eigenvectors can indeed be computationally prohibitive when dealing with more that a few tens of thousands of points. In this paper, we review the principal research efforts aiming to reduce this computational cost. We focus on methods that come with a theoretical control on the clustering performance and incorporate some form of sampling in their operation. Such methods abound in the machine learning, numerical linear algebra, and graph signal processing literature and, amongst others, include Nystr\"om-approximation, landmarks, coarsening, coresets, and compressive spectral clustering. We present the approximation guarantees available for each and discuss practical merits and limitations. Surprisingly, despite the breadth of the literature explored, we conclude that there is still a gap between theory and practice: the most scalable methods are only intuitively motivated or loosely controlled, whereas those that come with end-to-end guarantees rely on strong assumptions or enable a limited gain of computation time.

We've been sharing a lot of information about why we're really excited about HiveManager Shortcuts with Amazon Alexa. While we don't think IT Managers are going to replace 100 percent of browser-based management with voice, we do think it can be a great addition to help simplify a number of tasks. Let's watch a video to see some of the ways HiveManager Shortcuts with Amazon Alexa can simplify a project for IT departments. One of the aspects of IT management that HiveManager Shortcuts with Amazon Alexa integration simplifies is onboarding of new access points. Instead of manually typing in serial numbers of new access points, you can use an Alexa-enabled device to add it verbally.

This post was co-written with Joseph Rocca. Suppose that you are working in a given company and you are asked to create a model that, based on various measurements at your disposal, predicts whether a product is defective or not. You decide to use your favourite classifier, train it on the data and voila: you get a 96.2% accuracy! Your boss is astonished and decides to use your model without any further tests. A few weeks later he enters your office and underlines the uselessness of your model.

Black-box explanation is the problem of explaining how a machine learning model -- whose internal logic is hidden to the auditor and generally complex -- produces its outcomes. Current approaches for solving this problem include model explanation, outcome explanation as well as model inspection. While these techniques can be beneficial by providing interpretability, they can be used in a negative manner to perform fairwashing, which we define as promoting the perception that a machine learning model respects some ethical values while it might not be the case. In particular, we demonstrate that it is possible to systematically rationalize decisions taken by an unfair black-box model using the model explanation as well as the outcome explanation approaches with a given fairness metric. Our solution, LaundryML, is based on a regularized rule list enumeration algorithm whose objective is to search for fair rule lists approximating an unfair black-box model. We empirically evaluate our rationalization technique on black-box models trained on real-world datasets and show that one can obtain rule lists with high fidelity to the black-box model while being considerably less unfair at the same time.

Artificial intelligence is opening the best opportunities for semiconductor companies in decades. How can they capture this value? Software has been the star of high tech over the past few decades, and it's easy to understand why. With PCs and mobile phones, the game-changing innovations that defined this era, the architecture and software layers of the technology stack enabled several important advances. In this environment, semiconductor companies were in a difficult position. Although their innovations in chip design and fabrication enabled next-generation devices, they received only a small share of the value coming from the technology stack--about 20 to 30 percent with PCs and 10 to 20 percent with mobile. But the story for semiconductor companies could be different with the growth of artificial intelligence (AI)--typically defined as the ability of a machine to perform cognitive functions associated with human minds, such as perceiving, reasoning, and learning.