Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents' experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contains valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.

As machine learning becomes more pervasive, there is an urgent need for interpretable explanations of predictive models. Prior work has developed effective methods for visualizing global model behavior, as well as generating local (instance-specific) explanations. However, relatively little work has addressed regional explanations - how groups of similar instances behave in a complex model, and the related issue of visualizing statistical feature interactions. The lack of utilities available for these analytical needs hinders the development of models that are mission-critical, transparent, and align with social goals. We present VINE (Visual INteraction Effects), a novel algorithm to extract and visualize statistical interaction effects in black box models. We also present a novel evaluation metric for visualizations in the interpretable ML space.

Industry Research is an upscale platform to help key personnel in the business world in strategizing and taking visionary decisions based on facts and figures derived from in depth market research. We are one of the top report resellers in the market, dedicated towards bringing you an ingenious concoction of data parameters.

The sports industry has observed a paradigm shift over the decades. Thanks to the technology that had taken the sports from a dirt field to the high-tech arenas, from field training to the virtual training with stats, from the manual scoreboard updation to the electronic scoreboard and from cheering up the team from the stadium to the sports enjoyment from the comfort of living room. It has really come a long way. A lot has changed with the technology advancements that one can easily witness by looking at the differences between the first Olympic games played in 776 BC and the Olympic games organized now. Take a look at the technology trends that will continue to impact sports industry and are here to stay: Online streaming A decade back, to view the matches or the highlights at home, the viewers have to wait for a couple of hours due to unavailability of any technology in place and time zone difference.

Density Estimation is one of the central areas of statistics whose purpose is to estimate the probability density function underlying the observed data. It serves as a building block for many tasks in statistical inference, visualization, and machine learning. Density Estimation is widely adopted in the domain of unsupervised learning especially for the application of clustering. As big data become pervasive in almost every area of data sciences, analyzing high-dimensional data that have many features and variables appears to be a major focus in both academia and industry. High-dimensional data pose challenges not only from the theoretical aspects of statistical inference, but also from the algorithmic/computational considerations of machine learning and data analytics. This paper reviews a collection of selected nonparametric density estimation algorithms for high-dimensional data, some of them are recently published and provide interesting mathematical insights. The important application domain of nonparametric density estimation, such as { modal clustering}, are also included in this paper. Several research directions related to density estimation and high-dimensional data analysis are suggested by the authors.

Despite the great successes of machine learning, it can have its limits when dealing with insufficient training data.A potential solution is to incorporate additional knowledge into the training process which leads to the idea of informed machine learning. We present a research survey and structured overview of various approaches in this field. We aim to establish a taxonomy which can serve as a classification framework that considers the kind of additional knowledge, its representation,and its integration into the machine learning pipeline. The evaluation of numerous papers on the bases of the taxonomy uncovers key methods in this field.

Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice. In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner's guide to kernel-based graph classification.

As we finalize this article November 11, 2018, and consider current and future directions for computing in Europe and across the globe, we remember the end of World War I exactly 100 years ago: the end to a war of atrocities at a scale previously unseen and the culmination of a series of events that European nations had allowed themselves to'sleepwalk' into, with little thought for the consequences.10 When this article appears in spring 2019, we will remember the first proposal for a new global information sharing system written by Tim Berners-Lee 30 years ago at CERN,4 the European organization for nuclear research. This proposal marked the beginning of the World Wide Web, which now pervades every facet of modern life for over four billion users. However, the Web 30 years on, is not the land of free information and discussion, or an egalitarian space that supports the interests of all, as originally imagined.4 Rather, egotisms, nationalisms, and fundamentalisms freewheel on a landscape that is increasingly dominated by powerful corporate actors, often silencing other voices, including democratically elected representatives. For seven decades Europe has been a political and social project, seeking to integrate what has been divisive historically and to make citizens more equal. While the proponents of the Web were driven by similar values, there is now increasing concern in Europe--and beyond--that the Web has become a vehicle of disintegration, polarization, and exploitation.

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-art results in various domains such as image recognition and natural language processing. One of the reasons for this success is the increasing size of DL models and the proliferation of vast amounts of training data being available. To keep on improving the performance of DL, increasing the scalability of DL systems is necessary. In this survey, we perform a broad and thorough investigation on challenges, techniques and tools for scalable DL on distributed infrastructures. This incorporates infrastructures for DL, methods for parallel DL training, multi-tenant resource scheduling and the management of training and model data. Further, we analyze and compare 11 current open-source DL frameworks and tools and investigate which of the techniques are commonly implemented in practice. Finally, we highlight future research trends in DL systems that deserve further research.

Sparse principal component analysis (PCA) and sparse canonical correlation analysis (CCA) are two essential techniques from high-dimensional statistics and machine learning for analyzing large-scale data. Both problems can be formulated as an optimization problem with nonsmooth objective and nonconvex constraints. Since non-smoothness and nonconvexity bring numerical difficulties, most algorithms suggested in the literature either solve some relaxations or are heuristic and lack convergence guarantees. In this paper, we propose a new alternating manifold proximal gradient method to solve these two high-dimensional problems and provide a unified convergence analysis. Numerical experiment results are reported to demonstrate the advantages of our algorithm.