Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty

With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks: people are related in myriad ways, but most graph learning models treat relations as binary. In this paper, we develop a graph-based convolutional network for learning on multi-view networks. We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries. We also show that, while designed specifically for use in poverty research, the algorithm also outperforms existing benchmarks on a broader set of learning tasks on multi-view networks, including node labelling in citation networks

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Deep Quaternion Networks

The field of deep learning has seen significant advancement in recent years. However, much of the existing work has been focused on real-valued numbers. Recent work has shown that a deep learning system using the complex numbers can be deeper for a fixed parameter budget compared to its real-valued counterpart. In this work, we explore the benefits of generalizing one step further into the hyper-complex numbers, quaternions specifically, and provide the architecture components needed to build deep quaternion networks. We develop the theoretical basis by reviewing quaternion convolutions, developing a novel quaternion weight initialization scheme, and developing novel algorithms for quaternion batch-normalization. These pieces are tested in a classification model by end-to-end training on the CIFAR-10 and CIFAR-100 data sets and a segmentation model by end-to-end training on the KITTI Road Segmentation data set. These quaternion networks show improved convergence compared to real-valued and complex-valued networks, especially on the segmentation task, while having fewer parametersComment: IJCNN 2018, 8 pages, 1 figur

Developing online communities to support distance learning in secure environments

The UK Open University (OU) has successfully provided Higher Education (HE), through distance learning to students in prison, almost since its inception. This paper uses results from a recent qualitative study to discuss the learning process in a secure environment. It argues that the soft skills developed during the self-directed distance learning process are identity forming and discusses the social elements which support the learning. It highlights the major barriers to learning including the lack of access to the internet and explains two possible solutions which are being trialed to bridge the digital divide. It then introduces many of support networks and online learning communities which are developing within and around secure learning environments

Didactic Networks and exemplification

After a general overview in a previous paper [AMJ10b], in which we proposed Didactic Networks (DN) as a new way for developing and exploiting web-learning content, we offer here a deeper study showing how to use them for web-learning design and content generation based on Instructional Theory with the coherence guaranty of the RST [MT99]. By using a set of expressivity patterns, it is possible to obtain different final ¿products¿ from the DNs such as different level or different aspect web-learning lessons, depending on the target, documents or evaluation tests. In parallel we are defining the Fundamental Cognitive Networks (FCN), in which we deal with the most common patterns human being uses to think and communicate ideas. This FCN set reuses the representation of Concepts, Procedures and Principles defined here, and it is the main topic of a paper we are working on for the very near future